Indigenous Communities Food Receipts Crowdsourcing with Optical Character Recognition

By: Shannon Lo, Joanne Yoon, Kimberley Flak, Statistics Canada

Everyone deserves access to healthy and affordable food, no matter where they live. However, many Canadians living in northern and isolated communities face increased costs related to shipping rates and supply chain challenges. In response to food security concerns in the North, the Government of Canada established the Nutrition North Canada (NNC) subsidy program. Administered by Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC), this program helps make nutritious foods like meat, milk, cereals, fruit, and vegetables more affordable and accessible. To better understand the challenges impacting food security, improved price data is needed.

On behalf of CIRNAC, and in collaboration with the Centre for Special Business Projects (CSBP), Statistics Canada’s Data Science Division conducted a proof-of-concept project to investigate crowdsourcing as a potential solution to the data gap. This project evaluated the feasibility of using optical character recognition (OCR) and natural language processing (NLP) to extract and tabulate pricing information from images of grocery receipts as well as developing a web application for uploading and processing receipt images. This article focuses on the text identification and extraction algorithm, while the web application component is not covered.

Data

The input data for the project consisted of images of grocery receipts from purchases made in isolated Indigenous regions, including photos taken with a camera and scanned images. The layout and content of the receipts varied across retailers. From these receipts, we aimed to extract both product-level pricing information as well as receipt-level information, such as the date and location of purchase, which provide important context for downstream analysis. The extracted data were compiled into a database to support validation, analysis, and search functions.

High level design

Figure 1 illustrates the data flow, from receipt submission to digitization, storage, and display. This article focuses on the digitization process.

Figure 1: Data flow

Figure 1: Data flow

This is a process diagram depicting the flow of data between the various processes in the project. It highlights the three digitalization processes that this article will focus on: Extract text using OCR, Correct spelling & Classify text, and Package data.

  1. Receipt: Take photo of receipt
    1. Forward to Web App: Upload using a web application
  2. Web App: Upload using a web application
    1. Forward to Text: Extract text using OCR
  3. Text: Extract text using OCR
    1. Forward to Classified Text: Correct spelling & Classify text
  4. Classified Text: Correct spelling & Classify text
    1. Forward to Record: Package data
  5. Record: Package data
    1. Forward to Protected Database: Save data
  6. Protected Database: Save data
    1. Forward to Web Dashboard: Display data in web dashboard
  7. Web Dashboard: Display data in web dashboard

Text extraction using OCR

We extracted text from receipts by first detecting text regions using Character-Region Awareness For Text detection (CRAFT) and then recognizing characters using Google's OCR engine, Tesseract. CRAFT was chosen over other text detection models because it effectively detected text even in blurred, low-resolution areas or those with missing ink spots. For more information on CRAFT and Tesseract, refer to the Data Science Network’s article, Comparing Optical Character Recognition Tools for Text-Dense Documents vs. Scene Text .

Tesseract recognized text from detected text boxes. Generally, Tesseract looked for English and French alphabets, digits and punctuation. However, for text boxes that started on the far right (i.e., those with a left x coordinate at least three-quarters of the way toward the maximum x coordinate in the current block), Tesseract only looked for digits, punctuation, and certain single characters used to indicate product tax type, assuming the text box contained price information. By limiting the characters to recognize, we prevented cases such as a zero from being recognized as the character “O.”

If Tesseract did not recognize any text in the text box or if the confidence of the recognition was less than 50%, we first tried cleaning the image. Texts with uneven darkness or missing ink areas were patched using Contrast Limited Adaptive Histogram Equalization (CLAHE). This method improved an image’s overall contrast by calculating the histogram of pixel intensities and distributing these pixels into buckets with fewer pixels. The image’s brightness and contrast were adjusted to make the black text stand out more. These cleaning methods allowed Tesseract to better recognize the text.  However, applying this image preprocessing method to all text boxes was not recommended because it hindered Tesseract in some images taken under different conditions. Text recognition after this image preprocessing method was only used when the text recognition probability increased. When Tesseract failed even after image preprocessing, the program used EasyOCR’s Scene Text Recognition (STR) model instead. This alternative text recognition model performed better on noisier images where the text was printed with spotty amounts of ink or where the image was blurry.

Spell check

SymSpell was trained using individual product names from the 2019 Survey of Household Spending (SHS) database. To improve the quality of the correction, the spell corrector selected the most common word based on nearby word information. For example, if the recognized line was “suo dried tomatoes,” the spelling corrector could correct the first word to “sub,” “sun,” and “sum,” but it would choose “sun” since it recognizes the bigram “sun dried,” but not “sub dried.” On the other hand, if the OCR predicted the line to be “sub dried tomatoes,” no words were corrected since each word was a valid entry in the database. We aimed to avoid false corrections as much as possible. If a character was not detected due to vertical lines of no ink, the missing character was also recovered using spell correction. For example, if the recognized line was “sun dri d tomatoes,” the spelling corrector corrected the line to “sun dried tomatoes.”

A separate spell checker corrected the spelling of store names and community names.

Text classification

To identify what each line of extracted text was describing, a receipt-level and a product-level entity classifier was built. The following sections describe the relevant entities, sources of training data, explored models, and their performances.  

Entities

Each extracted row of text was classified into one of the 11 groups shown in Table 1. This step enables sensitive information to be redacted, and the remainder of the information to be used meaningfully.

Table 1: Entities extracted from receipts
Receipt-level entities Price
(% correct)		 NNC Subsidy
(% correct)

Date

Store name

Store location

Sale summary

Product

Price per quantity

Subsidy

Discount

Deposit

Sensitive information (includes customer’s address, phone number, and name)

Other

Entity classifier training data

Training data was gathered from labelled receipts, the SHS database, as well as public sources such as data available on GitHub. Refer to Table 2 for details about each training data source.

Table 2: Training data sources

Data

 Records

Source

 Additional Details
Labelled receipts 1,803 CIRNAC Used OCR to extract information from receipt images which were then labelled by analysts.
Products 76,392 SHS database 2 or more occurrences.
Store names 8,804 SHS database 2 or more occurrences.
Canadian cities 3,427 GitHub  
Canadian provinces 26 GitHub Full names and the abbreviated forms of 13 provinces and territories.
Communities 131 Nutrition North Canada Communities eligible for the NNC program.
Last Names 87,960 GitHub This is categorized as sensitive information.

Model selection and hyperparameter tuning

Two multiclass classifiers were used, one to classify receipt-level entities (i.e., store name and location) and the other to classify product-level entities (i.e., product description, subsidy, price per quantity, discount, and deposit). Table 3 describes the various models used in the experiment to classify receipt-level and product-level entities. The corresponding F1 macro scores for the two different classifiers are also displayed.

Table 3: Various models experimented with for the receipts and products classifier.
Experimented Models Description Receipts Classifier F1 Macro Score Products Classifier F1 Macro Score
Multinomial Naïve Bayes (MNB) The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). [1] 0.602 0.188
Linear Support Vector Machine with SGD training This estimator implements regularized linear models (SVM, logistic regression, etc.) with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). [2] 0.828 0.899
Linear Support Vector Classification Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples. This class supports both dense and sparse input and the multiclass support is handled according to a one-vs-the-rest scheme. [3] 0.834 0.900
Decision Tree Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. [4] 0.634 0.398
Random Forest A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. [5] 0.269 0.206
XGBoost XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. [6] 0.812 0.841

Before selecting the best models, hyperparameter tuning was conducted using grid search. Stratified K-Folds cross-validation was then used to train and test the models, addressing challenges with class imbalance in the training dataset which was mostly comprised of sensitive information (49%) and product names and/or prices (44%). The remaining 7% of the dataset included information such as store name, location, subsidy, discount, date, and price per quantity. After testing and training, the best performing models for receipt-level and product-level entities were selected based on the F1 macro score. The F1 macro score was used as a determinant of performance because it weighs the importance of each class equally, meaning even if a class has very few examples in the training data, the quality of predictions for that class is just as important as a class that has many examples. This is often the case in a project where the training dataset is imbalanced where some classes have few examples while other have many examples.

A rule-based approach was used to identify dates because the standard formats for dates make this a more robust method.

The Linear Support Vector Classification (SVC) classifier was chosen as the best model for both the receipt and product classifiers based on its F1 macro score of 0.834 (receipts) and 0.900 (products), which was higher than all the other models that were tested. Despite being the top performing model, it is worth noting that SVC classifiers generally take more time to train compared to MNB classifiers.

Packaging OCR’d text to a receipt record

The trained receipt-level and product-level entity classifiers were used on different parts of the receipt. Assuming the receipt was laid out as seen in figure 2, the receipt-level entity classifier predicted the class of all extracted receipt lines except for section 3: Products, and the product-level entity classifier was only used on section 3: Products. This layout worked for all receipts in our dataset. If a component, such as store names, was cropped out of the photo, that field was left empty in the final output.

Figure 2: Receipt component layout

Figure 2: Receipt component layout

This is an image of a receipt which shows an example of the different sections on a receipt.

  • Store name and address
  • Transaction record
  • Products (description, SKU, price, discount, subsidy and price per quantity)
  • Subtotal, taxes and total
  • Transaction record

The beginning of the receipt, including 1) Store name and address and 2) Transaction record, consisted of text lines found before the line that the products classifier predicted to be a product and that had a dollar value. No store name and location were returned if this part was empty, and if the first line directly described a product. Of all text recognized in this section, the text the receipt classifier predicted as the store name with the highest prediction probability was assigned as the store name. A valid community name was extracted from lines predicted as a location. Lines that the receipt classifier predicted to be sensitive information in this section were redacted.

The main body of a receipt included 3) Products list. Each line that the products classifier predicted as a Product and had a dollar value considered as a new product. Any lines of text following this product that were predicted to be a subsidy, discount, deposit, or price per quantity were added as auxiliary information for the product. Subsidies were further broken down into Nutrition North Canada (NNC) subsidy and Nunavik cost of living subsidy depending on its text description.

The end of the receipt included 4) Subtotal, taxes and total and 5) Transaction record. Nothing needed to be extracted from these two sections but lines that the receipt classifier predicted to be sensitive information in this section were redacted.

The date of purchase appeared either at the beginning or the end of the receipt. Dates were thus parsed by searching for known date format regular expression patterns in those sections of the receipt.

Results

The algorithm was evaluated using photos of grocery receipts from northern food retailers in remote Indigenous communities. Analysts from Statistics Canada’s Centre for Special Business Projects labeled products and sales information in each image.

Extracted texts, including store names, community names, product descriptions and dates, were evaluated as a similarity score. The similarity between the two texts was calculated as two times the total number of matching characters divided by the total number of characters in both descriptions. Extracted numbers, such as the product price, subsidy, discount and deposit, were each evaluated to be a match (1) or not (0).

For singular fields such as store names, it was easy to compare the predicted value with the actual value. Nevertheless, a simple one-to-one comparison was not possible for comparing the multiple items captured manually with the multiple items predicted by the OCR algorithm. Consequently, each manually captured item was first matched to the most similar item extracted by the OCR algorithm. The matched items from two sources needed to be at least 50% similar to each other. Items captured manually but not by the algorithm were called missed items. Items captured by the algorithm but not manually were called extra items. Once the common items were matched together, the similarity scores for all pairs were averaged to produce an overall similarity score for all common items found on the receipts.

The OCR algorithm excelled at identifying products from the receipts. Of the 1,637 total products listed on the receipts, 1,633 (99.76%) were captured (Table 4), with an average product description similarity of 96.85% (Table 5). The algorithm failed when text in the image was cut off, blurred, creased, or had areas with no ink. As a result, we recommended that OCR extractions be followed by human verification via the web interface. For the products in common, prices were correctly extracted 95.47% of the time, NNC subsidies were correct 99.14% of the time, Nunavik COL subsidies were correct 99.76% of the time, discounts were correct 100.0% of the time, deposits were correct 99.76% of the time, price per quantities were correct 95.71% of the time, and SKUs were correct 95.22% of the time (Table 5).

Even though product descriptions and prices were always present, other fields such as NNC subsidy were only present when applicable. For this reason, Table 5 also reports accuracies restricted to non-missing fields, in order to evaluate the OCR performance exclusively. No discount entries were included in this batch of receipts, so another batch was used to evaluate that discounts were correctly extracted 98.53% of the time. The text similarity score for fields observed and OCR’d was 87.1%.

Table 5: Accuracy of OCR on product-level information
Number of receipts Number of items Number of items extracted Number of items in common Percentage of items missed Percentage of extra items
182 1,637 1,634 1,633 0.24% (4/1,637) 0.06% (1/1,630)

Table 4. Products extracted from receipts from CIRNAC

Table 3: Training data sources
 

Product description
(Average text similarity score)

Price
(% correct)

NNC Subsidy
(% correct)

Nunavik COL Subsidy
(% correct)

Discount
(% correct)

Deposit
(% correct)

Price per quantity
(% correct)

SKU
(% correct)
Accuracy on items in common 96.85% 95.47% (1,559/1,633) 99.14% (1,619/1,633) 99.76% (1,629/1,633) 100.0% (1,633/1633) 99.76% (1,629/1,633) 95.71% (1,563/1,633) 95.22% (1,555/1,633)
Accuracy on items when fields were present 96.85% 95.47% (1,559/1,633) 99.08% (647/653) 100.0% (282/282) Not available. No actual occurrence 97.56% (160/164) 72.97% (81/111) 95.52% (1,555/1,628)

Receipt information was extracted effectively with no communities, store names, or dates being completely missed or falsely identified. The average text similarity score was consistently high: 99.12% for community, 98.23% for store names, and 99.89% for dates. Using the OCR algorithm and the receipt entity classifier to process receipts appears promising.

Additionally, 88.00% of sensitive texts were correctly redacted. Of the texts that failed to be redacted, most were cashier IDs. These were not redacted because the entity classifier had not seen this type of sensitive information before. Retraining the entity classifier with examples of cashier IDs will improve the results, much like how the classifier recognizes cashier names to be sensitive because of examples like "Your cashier today was <cashier name>" in its training data.

Table 6: Accuracy of OCR on receipt-level information
  Number of receipts

Store name
(Average text similarity score)

Community
(Average text similarity score

Date
(Average text similarity score)

 Sensitive info (Recall %)
Evaluation 182 98.23% 99.12% 99.89% 88.80
Evaluation when fields were present 164 99.02% 99.88% 98.03% Not applicable

Conclusion

This project demonstrated that an entity classification and OCR algorithm can accurately capture various components of grocery receipts from northern retailers. Automating this process makes it easier to collect data on the cost of living in the North. If and when this solution goes into production, the automation should be followed with a human-in-the-loop validation process through a web interface to ensure that the receipt is correctly digitized, and corrections are iteratively used for retraining. This validation feature has been implemented but is not discussed in this article.

Aggregate anonymized data collected through crowdsourcing has the potential to provide better insight into issues associated with the high cost of food in isolated Indigenous communities and could improve the transparency and accountability of Nutrition North Canada subsidy recipients to residents in these communities. If you are interested in learning more about the web application component, please reach out to datascience@statcan.gc.ca.

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References

  1. sklearn.naive_bayes.MultinomialNB — scikit-learn 1.2.0 documentation
  2. sklearn.linear_model.SGDClassifier — scikit-learn 1.2.0 documentation
  3. sklearn.svm.LinearSVC — scikit-learn 1.2.2 documentation
  4. 1.10. Decision Trees — scikit-learn 1.2.0 documentation
  5. sklearn.ensemble.RandomForestClassifier — scikit-learn 1.2.0 documentation
  6. XGBoost Documentation — xgboost 1.7.2 documentation
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Supplement to Statistics Canada's Generic Privacy Impact Assessment related to Profiles of Victims of Gang Violence: Analysis of a BC Cohort

Date: December 2022

Program manager: Director, Canadian Centre for Justice and Community Safety Statistics
Director General, Heath Justice and Special Surveys

Reference to Personal Information Bank (PIB)

Personal information collected through the Profiles of Victims of Gang Violence: Analysis of a BC Cohort project is described in Statistics Canada's "Statistical Infrastructure Services" Class of Personal Information. The Class of Personal Information refers to records related to statistical infrastructure services (e.g., development of concepts, support for data collection and dissemination activities, provision of advice and technical assistance) offered on a cost-recovery basis to meet the needs of external clients such as other federal government departments.

"Statistical Infrastructure Services" Class of Personal Information (Record number: StatCan SOP 610) is published on the Statistics Canada website under the latest Information about programs and Information Holdings chapter.

Description of statistical activity

Under the authority of the Statistics ActFootnote1, Statistics Canada has entered into an agreement with British Columbia's integrated anti-gang police force, Combined Forces Special Enforcement Unit of British Columbia (CFSEU-BC) to study the effects of gang-related violence within the province of British Columbia. This study will require the submission of personal information from CFSEU-BC that will be used in conjunction with existing Statistics Canada databases, to produce a de-identified and aggregated report, as well as other possible de-identified and aggregated publications. No personal identifiable information from Statistics Canada's databases will be shared with CFSEU-BC or included in any products resulting from the study. The personal information received by CFSEU-BC will be used strictly for this activity. All identifiers will be destroyed as soon as the study is complete, and no identifiable personal information received from CFSEU BC will be retained by Statistics Canada as a result of this study.

This project has been requested by the CFSEU-BC who maintain a gang-related victim repository that includes data for individuals across the province who are the victims of a suspected or confirmed gang-related homicide or attempt homicide. The CFSEU-BC seeks to further understand the profiles of the victims and their trajectories through the justice and other social systems, which will require linkage of the CFSEU-BC cohort to other social data sources held by Statistics Canada.

The CFSEU-BC will be providing Statistics Canada with an extract of Police Records Information Management Environment (PRIME) and Canadian Police Information Centre (CPIC) data via Statistics Canada's secure data acquisition process, a process that includes but is not limited to the use of the encrypted Electronic File Transfer (EFT) service to place the data on secure Statistics Canada servers which are monitored by active security groups.  The PRIME data will detail information concerning the victims of gang-related homicide and attempted homicide in British Columbia between 2006 and 2020, specifically the events surrounding the incidents of homicide or attempted homicide. This data will contain names; dates of birth; race; and last known address or minimum postal code. The CPIC data will detail the criminal convictions, criminal charges, outstanding warrants and charges, and records of discharge of the victims. The PRIME and CPIC data may contain data on minors.

In order for Statistics Canada to conduct the required analysis, data will be linked to other Statistics Canada datasets through the Social Data Linkage Environment (SDLE)Footnote2, including the following datasets:

  • the Canadian Correctional Services Survey (CCSS)
  • the Census of Population 1991, 1996, 2001, 2006, 2011, and 2016;
  • the Discharge Abstract Database (DAD)
  • the Employment Insurance Status Vector (EISV)
  • the Integrated Criminal Court (ICCS)
  • the Longitudinal Immigration Database (IMDB)
  • the National Ambulatory Care Reporting System (NACRS)
  • the Postsecondary Student Information System (PSIS)
  • the Registered Apprenticeship System (RAIS)
  • the T1 Personal Master File (T1FF)
  • the T4 Summary and T4 Supplementary files (T4Summ and T4Supp)

This will enable the determination of contact between the victim and the criminal justice system, health care system, and employment and social assistance services. A linkage to the Vital Statistics (Deaths) file in the SDLE will be used to validate the linkage of victims of homicide.

Statistics Canada's microdata linkageFootnote3 and related statistical activities were assessed for privacy in the Social Data Linkage Environment (SDLE) Privacy Impact AssessmentFootnote4 and Statistics Canada's Generic Privacy Impact AssessmentFootnote5.

Statistics Canada will share the subsequent analysis with the CFSEU-BC in the form of aggregated results and any conclusions drawn compiled into a report, which may be further published in a volume of JuristatFootnote6. Any analysis released outside of Statistics Canada will be fully anonymized and non-confidential, without any direct personal identifiers, which prevents the possibility of identifying individuals. Data used for this project will not be made available in the Research Data Centres nor anywhere outside of Statistics Canada and will be destroyed following the conclusion of the project. 

The personal information elements, method of collection, legal authority for collection, relevant PIB, storage location/system, and purpose of collection are listed in Appendix 1 - Personal Information Elements Table.

Reason for supplement

While the Generic Privacy Impact Assessment (PIA) addresses most of the privacy and security risks related to statistical activities conducted by Statistics Canada, this supplement is being carried out due to the sensitive nature of data surrounding victimization. The supplement also emphasizes that none of the data or resulting analysis will be used for administrative, investigative, or operational uses. As is the case with all PIAs, Statistics Canada's privacy framework ensures that elements of privacy protection and privacy controls are documented and applied.

Necessity and Proportionality

The use of personal information for the activity can be justified against Statistics Canada's Necessity and Proportionality Framework:

  1. Necessity: Research has demonstrated that individuals involved in gangs engage in antisocialFootnote7 and often violent behavioursFootnote8. In addition to individual adverse health risks for those involved in gangs, including substance abuse, gun carrying, arrest, illicit drug sales, familial hardship, high-risk sexual behaviour, nonfatal intentional injury, and homicide, gangs and their criminal behaviours have a detrimental effect on communities and public safetyFootnote9. British Columbia has observed negative impacts that gangs and gang activity have on the community, such as violence related to drugs, firearms, illegal gaming, and other criminal activity. In fact, the CFSEU-BC data details the more than 500 homicides and attempted homicides with a gang nexus across British Columbia between January 2006 and December 2020, demonstrating the frequency and severity of gang-related violence across the province. These occurrences have impacted more than 30 jurisdictions across the province, and have even occurred within institutions, such as correctional facilities.

    Through this analysis, Statistics Canada aims to provide information that will allow an in-depth understanding of victims of gang-related violence (homicide and attempted homicide) and explore longitudinal trajectories and life histories that have led to individuals becoming victims of gang-related violence. For this reason, the data originating from the CFSEU-BC and, in particular, personal indicators, are needed for linking to administrative data within Statistics Canada's Social Data Linkage Environment (SDLE). Microdata linkage works best using personal indicators such as names, date of birth, and addresses as these most accurately identify the same individuals from different data sources. Without the personal identifiers, an accurate linkage of this administrative data, and therefore this analysis, would not be possible.

    The variables to be included in the linkage and analysis, as well as the rationale for including the various databases/variables, can be found in Appendix 1.

    While data on race is particularly sensitive and could potentially lead to stereotyping and result in harm to certain vulnerable groups, it is being included as a variable to ensure that the life courses of individuals of differing racial backgrounds are captured as accurately as possible, which is in line with Statistics Canada's efforts to perform analysis using disaggregated dataFootnote10. Statistics Canada's Canadian Centre for Justice and Community Safety Statistics (CCJCSS) has years of experience with sensitively and carefully producing reports on gang-related individuals, including Indigenous and other racialized groups, and will ensure that the information is presented in a manner which does not stigmatize particular groups.

    The ability to link information on the same individual through their income, education, immigration, health, and other data across various human services and across the individual's lifespan, prior to becoming a gang victim, is integral to the ability to identify risk factors for gang involvement and victimization.

    This project will help Statistics Canada meet its statistical mandate by providing statistical information and analysis about Canada's social structure to develop and evaluate public policies and programs and to improve public and private decision-making for the benefit of all Canadians. Specifically, this data-driven effort will enhance CFSEU-BC's ability to develop an informed, community-based program that could be funded through Public Safety's Youth Gang Prevention Fund (YGPF) or Crime Prevention Action Fund (CPAF), as well as provincial funding streams. These programs, if successful, could then be applied across the country.

    Further, this project promotes sound statistical standards and practices by improving statistical methods and systems through joint research studies and projects.

  2. Effectiveness - Working assumptions: With the personal identifiers from the CFSEU-BC datasets, Statistics Canada will be in a unique position to be able to map the life events of the victims of gang-related homicide and gang-related assisted homicide through the administrative data holdings contained within the SDLE. High quality personal indicators, such as names and dates of birth, are required to ensure that the linkage can be done as accurately as possible.

    The linked data will provide an in-depth understanding of the victims of gang-related violence, including their trajectories and life history to the point of becoming a victim of violence. Integrating these additional administrative data with the victim records will allow Statistics Canada researchers to examine how victims have interacted with different government entities such as the criminal justice system, health care system, and employment and social assistance services, etc., along their life course. More specifically, cluster analysisFootnote11 of the linked data will allow for the identification of key risk factors associated with the likelihood of joining gangs, coming into contact with police, courts, and corrections, as well as becoming a victim of gang-related violence. Survival analysisFootnote12 will then be used to estimate the strength of the relationships between risk factors and victimization, which would then allow for an exploration of which interventions would have the most impact. Examples of the linked variables to be analyzed can be found in the appendix.

    With this better understanding stemming from the aggregated analysis, the CFSEU-BC hope to inform better allocation of resources for gang prevention initiatives. The analysis performed for this project will provide policy- and program-relevant results by helping to show which background factors are most strongly associated with gang involvement and gang violence, and this could help shape programs designed to address the identified risk factors.

  3. Proportionality: The sample size to be used for this project is relatively small and it is not expected for every member of the sample to be found in each data source, meaning that the pool of linked observations will be even smaller. Any data, despite the sensitivity, which might lead to a linkage is therefore crucial to achieve the goal of this project. All personal information being collected about each affected individual via the CFSEU-BC, despite the sensitivity of any of the elements of personal information, is crucial to this collection to ensure the highest possible quality of linkage and analysis can be performed, resulting in strong and actionable conclusions. The data to be used in this project have already been collected, thus eliminating the need of any additional burden on the victims. The files and variables used represent only those needed to produce high quality aggregated analytical information.

    In the preparation of this project, consultations have been held with the Canadian Centre for Justice and Community Safety Statistics (CCJCSS), the Data Ethics Committee (DEC), the Office of Privacy Management and Information Coordination (OPMIC), and SDLE at Statistics Canada, as well as the CFSEU-BC, to ensure that only that data which is necessary is included in this project.

    While the data being used for this project are sensitive, their analysis will allow the CFSEU-BC to enhance prevention and intervention efforts for at-risk individuals by informing policy and helping programs aimed at preventing British Columbians from entering into a criminal lifestyle. As mentioned above, these efforts aim to protect the lives and wellbeing of those at risk.

  4. Alternatives: The PRIME and CPIC data being provided by the CFSEU-BC are records that are only available from the Royal Canadian Mounted Police (RCMP) in British Columbia. There is no alternative source for this data currently available to Statistics Canada other than contacting each of the regional RCMP detachments individually. Collecting this information directly from the victims would create an increased and undue burden on the victims and is not possible in all cases.

Mitigation factors

Information that will be provided by the CFSEU-BC for this analysis includes:

  • names
  • date of birth
  • race
  • last known address or minimum postal code
  • criminal convictions
  • criminal charges
  • outstanding warrants and charges
  • records of discharge

These data are sensitive as they concern the victims of gang-related homicide and gang-related assisted homicide, and may include data on minors.

The overall risk of harm to the survey respondents has been deemed manageable with existing Statistics Canada safeguards that are described in Statistics Canada's Generic Privacy Impact Assessment, of which the following is of particular importance for this project: upon receipt of the files, personal identifiers will be separated from the other data and replaced with an anonymized linkage key. The personal identifiers will be stored securely and separately from the analytical file and will be available only to the project team at Statistics Canada on a need-to-know basis.

It should be noted that no investigative file information will be disclosed by the CFSEU-BC to Statistics Canada, and Statistics Canada will not be providing any identifiers to the CFSEU-BC. The data will only be used for research purposes (i.e., analysis and the development of aggregate reports for publication), and will not be used for administrative, investigative, or operational purposes.

Conclusion

This assessment concludes that, with the existing Statistics Canada safeguards, any remaining risks are such that Statistics Canada is prepared to accept and manage the risk.

References

  • Adams Jennifer J, Pizarro Jesenia M. "Patterns of Specialization and Escalation in the Criminal Careers of Gang and Non-Gang Homicide Offenders." Criminal Justice and Behaviour 41, no. 2 (2013): 237-255.
  • Babchishin, K.M., Keown, L-A., & Mularczyk, K.P. Economic outcomes of Canadian federal offenders. Public Safety Canada, 2021.
  • Boyce Jillian, Te Sun, Brennan Shannon. Economic profiles of offenders in Saskatchewan. Juristat, Statistics Canada, 2018.
  • C., Pyrooz David. "From Colors and Guns to Caps and Gowns? The Effects of Gang Membership on Educational Attainment." Journal of Research in Crime and Delinquency 51, no. 1 (2014): 56-87.
  • Felson RB, Staff J. "Committing Economic Crime for Drug Money." Crime and Delinquency 63, no. 4 (2017): 375-390.
  • Ibrahim, Dyna. Violent victimization, discrimination and perceptions of safety: An immigrant perspective, Canada, 2014. Juristat, Statistics Canada, 2018.
  • May JP, Hemenway D, Hall A. "Do criminals go to the hospital when they are shot?" Injury Prevention 8 (2002): 236-238.

Acronyms used:

CCJCSS
Canadian Centre for Justice and Community Safety Statistics
CISWS
Centre for Income and Socioeconomics Well-being Statistics
CLMI
Centre for Labour Market Information
CPHD
Centre for Population Health Data
CCES
Canadian Centre for Education Statistics
DSS
Diversity and Sociocultural Statistics
COD
Census Operations Division

Retail Trade Survey (Monthly): CVs for total sales by geography - March 2023

CVs for total sales by geography-March 2023
Geography Month
202303
%
Canada 0.7
Newfoundland and Labrador 1.6
Prince Edward Island 2.5
Nova Scotia 2.2
New Brunswick 1.5
Quebec 1.2
Ontario 1.4
Manitoba 1.3
Saskatchewan 2.8
Alberta 1.3
British Columbia 1.5
Yukon Territory 0.9
Northwest Territories 2.0
Nunavut 1.2

Tackling Information Overload: How Global Affairs Canada’s “Document Cracker” AI Application Streamlines Crisis Response Efforts

Prepared by the data science team at Global Affairs Canada

Introduction

When a global crisis hits, government officials often face the challenge of sifting through a flood of new information to find key insights that will help them to manage Canada’s response effectively. For example, following the Russian invasion of Ukraine in February of 2022, a substantial proportion of Canada’s diplomatic missions began filing situation reports (or SitReps) on local developments related to the conflict. With the sheer number of these SitReps, along with related meeting readouts, statements from international meetings and news media reports, it quickly became infeasible for individual decision makers to manually read all the relevant information made available to them.

To help address this challenge, the data science team at Global Affairs Canada (GAC) developed a document search and analysis tool called Document Cracker (hereafter “DocCracker”) that helps officials quickly find the information they need. At its core, DocCracker provides two key features: (1) the ability to search across a large volume of documents using a sophisticated indexing platform; and (2) the ability to automatically monitor new documents for specific topics, emerging trends, and mentions of key people, locations, or organizations. In the context of the Russian invasion, these features of the application are intended to allow Canadian officials to quickly identify pressing issues, formulate a preferred stance on these issues, and track the evolving stances of other countries. By providing such insights, the application can play a key role in helping officials to both design and measure the ongoing impacts of Canada’s response to the crisis.

Importantly, while DocCracker was developed specifically in response to events in Ukraine, it was also designed as a multi-tenant application that can provide separate search and monitoring interfaces for numerous global issues at the same time. For example, the application is currently being extended to support the analysis of geopolitical developments in the Middle East.

Application Overview

From a user’s perspective, the DocCracker interface is comprised of a landing page with a search bar and a variety of content cards that track recent updates involving specific geographical regions and persons of interest. The user can either drill down on these recent updates or submit a search query, which returns a ranked list of documents. Selecting a document provides access to the underlying transcript, along with the set of links to related documents. Users can also access the metadata associated with each document, which includes automatically extracted lists of topics, organizations, persons, locations, and key phrases. At all times, a banner at the top of the application page allows users to access a series of dashboards that highlight global and mission-specific trends concerning a predefined list of ten important topics (e.g., food security, war crimes, energy crisis, etc.).

To enable these user experiences, DocCracker implements a software pipeline that (a) loads newly available documents from a range of internal and external data sources, (b) “cracks” these documents by applying a variety of natural language processing tools to extract structured data, and (c) uses this structured data to create a search index that supports querying and dashboard creation. Figure 1 below provides a visual overview of the pipeline.

Figure 1: DocCracker processing pipeline

Figure 1: DocCracker processing pipeline

During the “load” stage of the pipeline, internal and external data sources are ingested and preprocessed to extract basic forms of metadata such as report type, report date, source location, title, and web URL. During the “crack” stage of the pipeline, the loaded documents are run through a suite of natural language processing tools to provide topic labels, identify named entities, extract summaries, and translate any non-English text to English. During the final “index” stage of the pipeline, the cracked documents are used to create a search index that support flexible document queries and the creation of dashboards that provide aggregated snapshots of the document attributes used to populate this search index.

Implementation Details

DocCracker is hosted as a web application in Microsoft Azure’s cloud computing environment, and makes use of Azure services to support each stage of processing.

Data Ingestion

During the “load” stage, documents are collected into an Azure storage container either via automated pulls from external sources (e.g., the Factiva news feed, non-protected GAC databases) or manual uploads. Next, a series of Python scripts are executed to eliminate duplicate or erroneous documents and perform some preliminary text cleaning and metadata extraction. Because the documents span a variety of file formats (e.g., .pdf, .txt, .msg, .docx, etc.), different cleaning and extraction methods are applied to different document types. In all cases, however, Python’s regular expression library is used to strip out irrelevant text (e.g., email signatures, BCC lists) and extract relevant metadata (e.g., title or email subject line, date of submission).

Regular expressions provide a powerful syntax for specifying sets of strings to search for within a body of text. In formal terms, a given regular expression defines a set of strings that can all be recognized by a finite state machine that undergoes state transitions upon receiving each character in span of input text; if these state transitions result in the machine entering an “acceptance” state, then the input span is a member of the set of strings being searched for. Upon detection, such a string can either be deleted (i.e., to perform data cleaning) or extracted (i.e., to collect metadata). Almost all programming languages provide support for regular expressions, and they are often a tool of first resort in data cleaning and data engineering projects.

Natural Language Processing

Once the documents have been preprocessed, they are split into chunks of text with at most 5120 characters to satisfy the input length requirements of many of Azure’s natural language processing services. Each text chunk is processed to remove non-linguistic information such as web URLs, empty white space, and bullet points. The chunks are then moved to a new storage container to undergo further processing using a variety of machine learning models.

To identify mentions of persons, organizations, and locations, each text chunk is processed using an Azure service that performs named entity recognition (NER). This service functions by mapping spans of text onto a predefined set of entity types. Next, similar services are used to extract key phrases and a few summary sentences from each document, while also performing inline translations of non-English text. Finally, a sentiment analysis service is used to provide sentiment ratings on specific organizations for display in the application’s landing page. The outputs of each Azure service are saved back to a SQL database as metadata attributes associated with the underlying documents that have been processed.

To augment these results obtained with Azure, GAC’s data science team also developed a customized topic labelling model that identifies the presence of any of ten specific topics of interest in each text chunk. This model uses a technique called “bidirectional encoder representations from transformers” BERT to analyze chunks of text and determine which of the predefined topics are present in the text. The model provides a list of topics found, which can range from zero to ten topic labels.

As shown in Figure 2 below, the model was developed iteratively with increasing amounts of labelled training data. By the third round of model training, highly accurate classification results were obtained for eight of the ten topics, while moderately accurate results were obtained for two of the ten topics. Model testing was carried out using 30% of the labelled data samples, while model training was performed using the other 70% of samples. In total, roughly 2000 labelled samples were used to develop the model.

While this is a small amount of data in context of typical approaches to developing supervised machine learning systems, one of the key advantages of using a BERT architecture is that the model is first pre-trained on a large amount of unlabelled text before being fine-tuned to perform some task of interest. During pre-training, the model simply learns to predict the identities of missing words that have been randomly blanked out of a corpus of text. By performing this task, the model develops highly accurate internal representations of the statistical properties of human language. These representations can then be efficiently repurposed during the fine-tuning stage to learn effective classification decisions from a small number of labelled examples.

Figure 2 - DocCracker AI Model Training Results

Figure 2: DocCracker AI Model Training Results

Evaluation results are shown following three rounds of training for a customized topic identification model that performs multi-label classification to identify up to ten predefined topics in a chunk of input text. With progressive increases in the amount of training data, the transformer-based neural network model is shownto achieve strong accuracy results for almost all the topics. 

Finally, the outputs of the topic model get saved back to the SQL database as additional metadata attributes for each underlying document. This database now contains all the documents that have been ingested along with a rich collection of metadata derived using the natural language processing techniques just described. With this combination of documents and metadata, it is possible to create a search index that allows users to perform flexible document searches and create informative dashboard visualizations.

Indexing

In its simplest form, a search index is a collection of one or more tables that provide links between search terms and sets of documents that match these terms. When a user provides a search query, the query is broken apart into a collection of terms that are used to look up documents in the index. A ranking algorithm is then used to prioritize the documents that are matched by each term so as to return an ordered list of the documents that are most relevant to the search query.

In DocCracker, Azure’s cognitive search service is used to automatically create an index from the SQL database produced during earlier stages of the processing pipeline. Once this index is created, it is straightforward to create a landing page that allows users to enter search queries and obtain relevant documents. The metadata used to create the index can also be exported to CSV files to create dashboards that track a range of time-varying measures of how the situation in Ukraine is unfolding. For example, by selecting the metadata fields for topic labels and dates, it is possible to display the frequency with which different topics have been mentioned over time. Similarly, by selecting on named entities, it is possible to visualize which persons or organizations have been mentioned most often over a given time range. The volume of reporting coming out of different missions can also be easily tracked using a similar method of selection.

Overall, the search index provides a structured representation of the many unstructured SitReps, reports, and news articles that have been ingested into DocCracker. With this structured representation in hand, it becomes possible to enable search and monitoring capabilities that aid the important analytical work being done by the officials at Global Affairs tasked with managing Canada’s response to the Russian invasion.

Next Steps

Given the ever-increasing speed with which international crises are being reported on, it is essential to develop tools like DocCracker that help analysts draw insights from large volumes of text data. To build on the current version of this tool, the GAC data science team is working on several enhancements simultaneously. First, Latent Dirichlet Allocation (LDA) is being assessed to automatically identify novel topics as they emerge amongst incoming documents, thereby alerting analysts to new issues that might require their attention. Second, generative pre-trained transformer (GPT) models are being used to automatically summarize multiple documents, thereby helping analysts produce briefing notes more quickly for senior decision makers. Finally, stance detection models are being developed to automatically identify the positions that individual countries are adopting with respect to specific diplomatic issues (e.g., the issue of providing advanced weapons systems to Ukraine). With such models in hand, analysts should be able to track how countries are adapting their positions on a given issue in response to both diplomatic inducements and changing geopolitical conditions.

Overall, as tools like DocCracker become more widely used, we expect to see a range of new applications for the underlying technology to emerge. To discuss such applications or to learn more about the GAC data science team’s ongoing efforts in this area, please contact datascience.sciencedesdonnees@international.gc.ca.

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Summary of the Evaluation of Statistics Canada's Centre for Gender, Diversity and Inclusion Statistics

In line with the Government of Canada's priority to make more detailed data accessible on gender and diversity characteristics to support and inform the development of equitable policies and programs, Budget 2018 committed funds for Statistics Canada to create the Centre for Gender, Diversity and Inclusion Statistics (CGDIS). While the original focus of the CGDIS was mainly on Gender-based Analysis Plus (GBA Plus), the scope and mandate broadened over time and now include other groups such as 2SLGBTQI+ and racialized groups.

Through its work, the CGDIS aims to:

  • Address important gaps in available data and analysis to make detailed data accessible for increased analysis on gender, diversity and inclusion (GDI) in Canada;
  • Support data development and capacity-building strategies to measure GDI outcomes; and
  • Increase knowledge and capacity on GDI.

These measures extend to conducting intersectional analyses and contributing to training initiatives that will help build an understanding of the barriers that different groups face and how best to support them with evidence-based policies.

The CGDIS strives to achieve its mandate through the following three objectives:

  • Reporting to Canadians about the state and progress of GDI in Canada;
  • Generating new information to increase knowledge and develop new concepts and measures to address intersecting identity characteristics; and
  • Building statistical capacity to increase knowledge of GDI and GBA Plus.

Moreover, as Statistics Canada's centre of excellence for GDI-related data, the CGDIS has also been assigned a key role in supporting the Disaggregated Data Action Plan, which aims to fill data and knowledge gaps around GDI.

This evaluation was conducted by Statistics Canada in accordance with the Treasury Board Policy on Results and Statistics Canada's Integrated Risk-Based Audit and Evaluation Plan (2021/2022 to 2025/2026). The objective of the evaluation is to assess the relevance of the CGDIS, its effectiveness in achieving intended results and its readiness to move forward. Effectiveness was examined in terms of its progress towards its objectives.

The CGDIS made progress on all three key objectives and has met the key deliverables outlined in its strategic proposal. The Gender, Diversity and Inclusion Statistics (GDIS) Hub was launched in September 2018, and the Centre delivered several products over the period. In addition, GBA Plus training material was developed in collaboration with Women and Gender Equality Canada and the Canada School of Public Service, and hub tours were delivered.

The CGDIS' engagement strategy promoted the awareness and use of the GDIS Hub and related products but mainly among federal government organizations (FGOs). Overall, the CGDIS met the needs of most FGOs, particularly those who were regular clients of the Centre. While the needs of non-FGOs were met to a lesser extent, most of them still supported the work of the CGDIS and underscored the importance of the GDIS Hub. Data needs varied across user groups, but they all expressed a desire to have more disaggregated data to better understand the intersectionality as opposed to binary interactions. Many opportunities for improvements were also noted for the presentation of data, overall functionality of the Hub and outreach efforts to non-FGOs.

The CGDIS has strategically transformed its organization to adapt and evolve. However, gaps in its overall program management, oversight and capacity are risks. Having robust planning and prioritization processes, relevant performance measurement, clearly defined roles and responsibilities, and proper governance are key considerations as the CGDIS moves forward. A more strategic targeted approach is also required to improve outreach and engagement with non-FGOs.

The evaluation proposed the following three recommendations:

Recommendation 1

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes a recurring activity prioritization and decision-making process at the CGDIS level which would facilitate the ongoing review and rebalancing of activities; and
  • Develops and implements key performance measures that will support the monitoring of activities in view of the objectives set forth by the Centre.
Recommendation 2

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes or leverages a senior-level governance body to provide guidance; and
  • Formalizes roles and responsibilities with internal partners such as other program areas.
Recommendation 3

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS takes steps to broaden and enhance its outreach, consultation and engagement efforts to include external non-FGO stakeholders.

Why are we conducting this survey?

This survey collects information on scientific activities of Canadian businesses. The research and development expenditures and personnel information is used by federal, provincial and territorial governments and agencies, academics, trade associations and international organizations for statistical analyses and policy purposes. These data also contribute to national totals of research and development activities. The payments and receipts information is used by these agencies to monitor knowledge flows across international borders and between Canadian businesses.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Your participation in this survey is required under the authority of the Statistics Act.

Other important information

Authorization to collect this information

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Confidentiality

By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.

Record linkages

To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.

Data-sharing agreements

To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

Provincial and territorial statistical agencies

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia and the Yukon. The shared data will be limited to information on in-house research and development expenditures (Question 14) and in-house research and development personnel (Question 72) pertaining to business establishments located within the jurisdiction of the respective province or territory.

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

You may also contact us by email at statcan.esdhelpdesk-dsebureaudedepannage.statcan@statcan.gc.ca or by fax at 613-951-6583.

Other data-sharing agreement

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut. The shared data will be limited to information on in-house research and development expenditures (Question 14) and in-house research and development personnel (Question 72) pertaining to business establishments located within the jurisdiction of the respective province or territory.

Innovation, Science and Economic Development Canada

For this survey, Statistics Canada will share survey data with Innovation, Science and Economic Development Canada. The shared data will be limited to information on research and development expenditures (Questions 4 to 21) and in-house research and development personnel (Questions 70 to 72).

Natural Resources Canada

For respondents with expenditures on energy-related research and development in technology (fossil fuels, renewable energy resources, nuclear fission and fusion, electric power, hydrogen and fuel cells, energy efficiency, other energy-related technologies), Statistics Canada will also share survey data with the Office of Energy Research and Development (OERD) of Natural Resources Canada. The shared data will be limited to information on Energy Research and Development Expenditures by Area of Technology (Questions 22 to 69).

Sustainable Development Technology Canada

For this survey, Statistics Canada will share survey data with Sustainable Development Technology Canada.

Business or organization and contact information

1. Verify or provide the business or organization's legal and operating name and correct where needed.

Note: Legal name modifications should only be done to correct a spelling error or typo.

Legal Name

The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

  • Legal name
  • Operating name (if applicable)

2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.

Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.

  • First name
  • Last name
  • Title
  • Preferred language of communication
    • English
    • French
  • Mailing address (number and street)
  • City
  • Province, territory or state
  • Postal code or ZIP code
  • Country
    • Canada
    • United States
  • Email address
  • Telephone number (including area code)
  • Extension number (if applicable)
    The maximum number of characters is 10.
  • Fax number (including area code)

3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.

  • Operational
  • Not currently operational
    Why is this business or organization not currently operational?
    • Seasonal operations
      • When did this business or organization close for the season?
        • Date
      • When does this business or organization expect to resume operations?
        • Date
    • Ceased operations
      • When did this business or organization cease operations?
        • Date
      • Why did this business or organization cease operations?
        • Bankruptcy
        • Liquidation
        • Dissolution
        • Other - Specify the other reasons for ceased operations
    • Sold operations
      • When was this business or organization sold?
        • Date
      • What is the legal name of the buyer?
    • Amalgamated with other businesses or organizations
      • When did this business or organization amalgamate?
        • Date
      • What is the legal name of the resulting or continuing business or organization?
      • What are the legal names of the other amalgamated businesses or organizations?
    • Temporarily inactive but will re-open
      • When did this business or organization become temporarily inactive?
        • Date
      • When does this business or organization expect to resume operations?
        • Date
      • Why is this business or organization temporarily inactive?
    • No longer operating due to other reasons
      • When did this business or organization cease operations?
        • Date
      • Why did this business or organization cease operations?

4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.

Note: The described activity was assigned using the North American Industry Classification System (NAICS).

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

The following is the detailed description including any applicable examples or exclusions for the classification currently associated with this business or organization.

Description and examples

  • This is the current main activity
    • Provide a brief but precise description of this business or organization's main activity
    • e.g., breakfast cereal manufacturing, shoe store, software development
  • This is not the current main activity

Main activity

5. You indicated that is not the current main activity.

Was this business or organization's main activity ever classified as:?

  • Yes
    • When did the main activity change?
      Date
  • No

6. Search and select the industry classification code that best corresponds to this business or organization's main activity.

How to search:

  • if desired, you can filter the search results by first selecting this business or organization's activity sector
  • enter keywords or a brief description that best describes this business or organization main activity
  • press the Search button to search the database for an activity that best matches the keywords or description you provided
  • then select an activity from the list.

Select this business or organization's activity sector (optional)

  • Farming or logging operation
  • Construction company or general contractor
  • Manufacturer
  • Wholesaler
  • Retailer
  • Provider of passenger or freight transportation
  • Provider of investment, savings or insurance products
  • Real estate agency, real estate brokerage or leasing company
  • Provider of professional, scientific or technical services
  • Provider of health care or social services
  • Restaurant, bar, hotel, motel or other lodging establishment
  • Other sector

Enter keywords or a brief description, then press the Search button

Additional reporting instructions

1. Throughout this questionnaire, please report financial information in thousands of Canadian dollars.

For example, an amount of $763,880.25 should be reported as: 764, CAN$ '000

I will report in the format above

Reporting period

1. What is the end date of this business's fiscal year?

Note: For this survey, this business's fiscal year end date should fall on or before March 31, 2023.

Here are some examples of fiscal periods that fall within the targeted dates:

  • May 1, 2021 to April 30, 2022
  • July 1, 2021 to June 30, 2022
  • October 1, 2021 to September 30, 2022
  • January 1, 2022 to December 31, 2022
  • February 1, 2022 to January 31, 2023
  • April 1, 2022 to March 31, 2023

Fiscal Year-End date

This fiscal year will be referred to as 2022 throughout the questionnaire

Business status

2. What is this business's GST number (9-digit business number)?

GST number (9-digit business number)

In-house research and development (R&D) expenditures

Before you begin, differences between Scientific Research and Experimental Development (SR&ED) tax incentive program and this survey

Include the following expenditures in this survey:

  • capital R&D expenditures
  • R&D expenditures in the social sciences and humanities
  • payments for R&D performed by other organizations outside Canada.

Definitions for this survey

'In-house R&D ' refers to
Expenditures within Canada for R&D performed within this business by:

  • employees (permanent, temporary or casual)
  • self-employed individuals or contractors who are working on-site on this business's R&D projects.

'Outsourced R&D ' refers to
Payments made within or outside Canada to other businesses, organizations or individuals to fund R&D performance:

  • grants
  • fellowships
  • contracts.

In-house research and development ( R&D ) expenditures

3. In 2022, did this business have expenditures for R&D performed in-house within Canada?

Exclude payments for outsourced (contracted out or granted) R&D, which should be reported in question 9.

In-house refers to R&D which is performed on-site or within the business's establishment. Exclude R&D expenses performed by other companies or organizations. A later question will collect these data.

Research and experimental development ( R&D ) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

R&D is performed in the natural sciences, engineering, social sciences and humanities. There are three types of R&D activities: basic research, applied research and experimental development.

Research work in the social sciences

Include if projects are employing new or significantly different modelling techniques or developing new formulae, analyzing data not previously available or applying new research techniques, development of community strategies for disease prevention, or health education.

Exclude:

  • routine analytical projects using standard techniques and existing data
  • routine market research
  • routine statistical analysis intended for on-going monitoring of an activity.
    • Yes
    • No

4. In 2022, what were this business's expenditures for R&D performed in-house within Canada?

Exclude payments for outsourced (contracted out or granted) R&D, which should be reported in question 9.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In-house R&D expenditures are composed of current in-house R&D expenditures and capital in-house R&D expenditures.

Current in-house R&D expenditures

Include:

  • wages, salaries, benefits and fringe benefits, materials and supplies
  • services to support R&D, including on-site R&D consultants and contractors
  • necessary background literature
  • minor scientific equipment
  • associated administrative overhead costs.

a. Wages, salaries of permanent, temporary and casual R&D employees

Include benefits and fringe benefits of employees engaged in R&D activities. Benefits and fringe benefits include bonus payments, holiday or vacation pay, pension fund contributions, other social security payments, payroll taxes, etc.

b. Services to support R&D

Include:

  • payments to on-site R&D consultants and contractors working under the direct control of your business
  • other services including indirect services purchased to support in-house R&D such as security, storage, repair, maintenance and use of buildings and equipment
  • computer services, software licensing fees and dissemination of R&D findings.

c. R&D materials

Include:

  • water, fuel, gas and electricity
  • materials for creation of prototypes
  • reference materials (books, journals, etc.)
  • subscriptions to libraries and data bases, memberships to scientific societies, etc.
  • cost of outsourced (contracted out or granted) small R&D prototypes or R&D models
  • materials for laboratories (chemicals, animal, etc.)
  • all other R&D -related materials.

d. All other current R&D costs including overhead

Include administrative and overhead costs (e.g., office, lease/rent, post and telecommunications, internet, legal expenditures, insurance), prorated if necessary to allow for non- R&D activities within the business.

Exclude:

  • interest charges
  • value-added taxes (goods and services tax (GST) or harmonized sales tax (HST)).

Capital in-house expenditures are the annual gross amount paid for the acquisition of fixed assets that are used repeatedly, or continuously in the performance of R&D for more than one year. Report capital in-house expenditures in full for the period when they occurred.

Include costs for software, land, buildings and structures, equipment, machinery and other capital costs.

Exclude capital depreciation.

e. Software

Include applications and systems software (original, customized and off-the-shelf software), supporting documentation and other software-related acquisitions.

f. Land acquired for R&D including testing grounds, sites for laboratories and pilot plants.

g. Buildings and structures that are constructed or purchased for R&D activities or that have undergone major improvements, modifications, renovations and repairs for R&D activities.

h. Equipment, machinery and all other capital

Include major equipment, machinery and instruments, including embedded software, acquired for R&D activities.

In 2022, what were this business's expenditures for R&D performed in-house within Canada?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
2022 - Current in-house R&D expenditures within Canada  
Wages, salaries of permanent, temporary and casual R&D employees
Include fringe benefits.		  
Services to support R&D
Include services of self-employed individuals or contractors who are working on-site on this business's R&D projects.
Exclude contracted out or granted expenditures to other organizations to perform R&D (report in question 9).		  
R&D materials  
All other current R&D costs
Include overhead costs.		  
2022 - Total current in-house R&D expenditures within Canada  
2022 - Capital in-house R&D expenditures within Canada  
Software
Exclude capital depreciation.		  
Land
Exclude capital depreciation.		  
Buildings and structures
Exclude capital depreciation.		  
Equipment, machinery and all other capital
Exclude capital depreciation.		  
2022 - Total capital in-house R&D expenditures within Canada  
2022 - Total in-house R&D expenditures within Canada  

5. In 2023 and 2024, does this business plan to have expenditures for R&D performed in-house within Canada?

Exclude payments for outsourced (contracted out or granted) R&D, which should be reported in question 11.

Select all that apply

In-house R&D expenditures are composed of current in-house R&D expenditures and capital in-house R&D expenditures.

Research and experimental development ( R&D ) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge

Inclusions

Prototypes
Include design, construction and operation of prototypes, provided that the primary objective is to make further improvements or to undertake technical testing.
Exclude if the prototype is for commercial purposes.

Clinical Trials
Include clinical trial phases 1, 2, and 3. Include clinical trial phase 4 only if it brings about a further scientific or technological advance.

Pilot plants
Include construction and operation of pilot plants, provided that the primary objective is to make further improvements or to undertake technical testing.
Exclude if the pilot plant is intended to be operated for commercial purposes.

New computer software or significant improvements/modifications to existing computer software
Includes technological or scientific advances in theoretical computer sciences; operating systems e.g., improvement in interface management, developing new operating system of converting an existing operating system to a significantly different hardware environment; programming languages; and applications if a significant technological change occurs.

Contracts
Include all contracts which require R&D. For contracts which include other work, report only the R&D costs.

Research work in the social sciences
Include if projects are employing new or significantly different modelling techniques or developing new formulae, analyzing data not previously available or applying new research techniques, development of community strategies for disease prevention, analysis of the effectiveness of health interventions, or health education.

Exclusions

Routine analysis in the social sciences including policy-related studies, management studies and efficiency studies
Exclude analytical projects of a routine nature, with established methodologies, principles and models of the related social sciences to bear on a particular problem (e.g., commentary on the probable economic effects of a change in the tax structure, using existing economic data; use of standard techniques in applied psychology to select and classify industrial and military personnel, students, etc., and to test children with reading or other disabilities).

Consumer surveys, advertising, market research
Exclude projects of a routine nature, with established methodologies intended for commercialization of the results of R&D.

Routine quality control and testing
Exclude projects of a routine nature, with established methodologies not intended to create new knowledge, even if carried out by personnel normally engaged in R&D.

Pre-production activities such as demonstration of commercial viability, tooling up, trial production, trouble shooting
Although R&D may be required as a result of these steps, these activities are excluded.

Prospecting, exploratory drilling, development of mines, oil or gas wells
Include only if for R&D projects concerned with new equipment or techniques in these activities, such as in-situ and tertiary recovery research.

Engineering
Exclude engineering unless it is in direct support of R&D.

Design and drawing
Exclude design and drawing unless it is in direct support of R&D.

Patent and licence work
Exclude all administrative and legal work connected with patents and licences.

Cosmetic modifications or style changes to existing products
Exclude if no significant technical improvement or modification to the existing products has occurred.

General purpose or routine data collection
Exclude projects of a routine nature, with established methodologies intended for on-going monitoring of an activity.

Routine computer programming, systems maintenance or software application
Exclude projects of a routine nature, with established methodologies intended to support on-going operations.

Routine mathematical or statistical analysis or operations analysis
Exclude projects of a routine nature, with established methodologies intended for on-going monitoring of an activity.

Activities associated with standards compliance
Exclude projects of a routine nature, with established methodologies intended to support standards compliance.

Specialized routine medical care such as routine pathology services
Exclude projects of a routine nature, with established methodologies intended for on-going monitoring of an activity where results do not further scientific, technological advance, or understanding of the effectiveness of a technology.

  • In 2023
  • In 2024
  • No planned in-house R&D expenditures

6. In 2023, what are this business's planned expenditures for R&D performed in-house within Canada?

Exclude payments for outsourced (contracted out or granted) R&D, which should be reported in question 11.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2023, what are this business's planned expenditures for R&D performed in-house within Canada?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
2023 - Total current in-house R&D expenditures within Canada  
2023 - Total capital in-house R&D expenditures within Canada  

7. In 2024, what are this business's planned expenditures for R&D performed in-house within Canada?

Exclude payments for outsourced (contracted out or granted) R&D, which should be reported in question 11.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2024, what are this business's planned expenditures for R&D performed in-house within Canada?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
2024 - Total current in-house R&D expenditures within Canada  
2024 - Total capital in-house R&D expenditures within Canada  

Outsourced (contracted out or granted) R&D expenditures

8. In 2022, did this business have outsourced (contracted out or granted) R&D expenditures within Canada or outside Canada?

Include:

  • funding or grants provided to other organizations to perform R&D
  • contracted out expenditures for R&D.

Exclude services of self-employed individuals or contractors who are working on-site on this business's R&D projects, which should be reported in question 4.

Select all that apply.

Outsourced (contracted out or granted) R&D expenditures are payments made through contracts, grants and fellowships to another company, organization or individual to purchase R&D activities.

  • Within Canada
  • Outside Canada
  • No payment made to others to perform R&D

9. In 2022, what were this business's outsourced (contracted out or granted) R&D expenditures within Canada or outside Canada?

Include:

  • funding or grants provided to other organizations to perform R&D
  • contracted out expenditures for R&D.

Exclude services of self-employed individuals or contractors who are working on-site on this business's R&D projects, which should be reported in question 4.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Include payments made through contracts, grants, donations and fellowships to another company, organization or individual to purchase or fund R&D activities.

Exclude expenditures for on-site R&D contractors.

Parent and subsidiary companies are companies connected to each other through majority ownership of the subsidiary company by the parent company. Affiliated companies are companies connected to a parent through minority ownership of the affiliated companies by the parent.

Companies include all incorporated for-profit businesses and government business enterprises providing products in the market at market rates.

Private non-profit organizations include voluntary health organizations, private philanthropic foundations, associations, consortia, accelerators, and societies and research institutes. They are not-for-profit organizations that serve the public interest by supporting activities related to public welfare (such as health, education, the environment).

Industrial research institutes or associations include all non-profit organizations that serve the business sector, with industrial associations frequently consisting of their membership.

Universities include hospitals and clinics when they are affiliated with a university and provide education services or when R&D activity is under the direct control of a university.

Federal government includes all federal government departments and agencies. It excludes federal government business enterprises providing products in the market.

Provincial or territorial governments include all provincial or territorial government ministries, departments and agencies. It excludes provincial or territorial government business enterprises providing products in the market.

Provincial or territorial research organizations are organizations created under provincial or territorial law which conduct or facilitate research on behalf of the province or territory.

Other organizations - individuals, non-university educational institutions, for profit accelerators and incubators, foreign governments including ministries, departments and agencies of foreign governments.

In 2022, what were this business's outsourced (contracted out or granted) R&D expenditures within Canada or outside Canada?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Within Canada
CAN$ '000		 Outside Canada
CAN$ '000
Parent, affiliated and subsidiary companies    
Other companies    
Private non-profit organizations    
Industrial research institutes or associations    
Hospitals    
Universities    
Federal government departments and agencies    
Provincial or territorial government departments, ministries and agencies    
Provincial or territorial research organizations    
Other organizations e.g., individuals, non-university educational institutions, foreign governments    
2022 - Total outsourced (contracted out or granted) R&D expenditures    

10. In 2023 and 2024, does this business plan to outsource (contract out or grant) R&D expenditures within Canada or outside Canada?

Include:

  • funding or grants provided to other organizations to perform R&D
  • contracted out expenditures for R&D.

Exclude services of self-employed individuals or contractors who are working on-site on this business's R&D projects, which should be reported in questions 6 and 7.

Select all that apply.

Outsourced (contracted out or granted) R&D expenditures are payments made through contracts, grants and fellowships to another company, organization or individual to purchase R&D activities.

  • In 2023
  • In 2024
  • No planned payments to others to perform R&D

11. In 2023 and 2024, what are this business's planned outsourced (contracted out or granted) R&D expenditures within Canada or outside Canada?

Include:

  • funding or grants provided to other organizations to perform R&D
  • contracted out expenditures for R&D.

Exclude services of self-employed individuals or contractors who are working on-site on this business's R&D projects, which should be reported in questions 6 and 7.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Include payments made through contracts, licenses, grants, donations, endowments and fellowships to another company, university, hospital, consortia, organization or individual to purchase or fund R&D activities.

Exclude expenditures for on-site R&D contractors.

In 2023 and 2024, what are this business's planned outsourced (contracted out or granted) R&D expenditures within Canada or outside Canada?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Within Canada
CAN$ '000		 Outside Canada
CAN$ '000
a. 2023    
b. 2024    

In-house and Outsourced (contracted out or granted) R&D expenditures from 2022 to 2024

12. Summary of total R&D expenditures from 2022 to 2024

12. Summary of total R&D expenditures from 2022 to 2024
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  2022
CAN$ '000		 2023
CAN$ '000		 2024
CAN$ '000
Total current in-house R&D expenditures within Canada      
Total capital in-house R&D expenditures within Canada      
Total in-house R&D expenditures within Canada      
Total outsourced (contracted out or granted) R&D expenditures      
Total R&D expenditures      

Geographic distribution of in-house R&D expenditures within Canada in 2022

13. In 2022, in which provinces or territories did this business have expenditures for R&D performed in-house?

Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9
  • capital depreciation.

Select all that apply.

  • Newfoundland and Labrador
  • Prince Edward Island
  • Nova Scotia
  • New Brunswick
  • Quebec
  • Ontario
  • Manitoba
  • Saskatchewan
  • Alberta
  • British Columbia
  • Yukon
  • Northwest Territories
  • Nunavut

14. In 2022, how were this business's total expenditures for R&D performed in-house distributed by province or territory?

Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9
  • capital depreciation.

Please report all amounts in thousands of Canadian dollars.

For in-house R&D activities on federal lands, please include in the closest province or territory.

In 2022, how were this business's total expenditures for R&D performed in-house distributed by province or territory?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Current in-house R&D expenditures
CAN$ '000		 Capital in-house R&D expenditures
CAN$ '000
Newfoundland and Labrador    
Prince Edward Island    
Nova Scotia    
New Brunswick    
Quebec    
Ontario    
Manitoba    
Saskatchewan    
Alberta    
British Columbia    
Yukon    
Northwest Territories    
Nunavut    
2022 - Total current and capital in-house R&D expenditures    
2022 - Total current and capital in-house R&D expenditures previously reported from question 4    

Sources of funds for in-house R&D expenditures in 2022

15. In 2022, what were the sources of funds for this business's total expenditures for R&D performed in-house?

Include Canadian and foreign sources.

Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9.
  • capital depreciation.

Select all that apply.

Funds from this business
Amount contributed by this business to R&D performed within Canada (include amounts eligible for income tax purposes, e.g., Scientific Research and Experimental Development (SR-ED) program, other amounts spent for projects not claimed through SR-ED, and funds for land, buildings, machinery and equipment (capital expenditures) purchased for R&D).

Funds from parent, affiliated and subsidiary companies
Amount received from parent, affiliated and subsidiary companies used to perform R&D within Canada (include amounts eligible for income tax purposes, e.g., Scientific Research and Experimental Development (SR-ED) program, other amounts spent for projects not claimed through SR-ED, and funds for land, buildings, machinery and equipment (capital expenditures) purchased for R&D).

R&D contract work for other companies
Funds received from other companies to perform R&D on their behalf.

Federal government grants or funding
Grants or funds received from the federal government in support of R&D activities not connected to a specific contractual deliverable.

Federal government contracts
Funds received from the federal government in support of R&D activities connected to a specific contractual deliverable.

Provincial or territorial government grants or funding
Grants or funds received from the provincial or territorial government in support of R&D activities not connected to a specific contractual deliverable.

Provincial or territorial government contracts
Funds received from the provincial or territorial government in support of R&D activities connected to a specific contractual deliverable.

R&D contract work for private non-profit organizations
Funds received from non-profit organizations to perform R&D on their behalf.

Other sources
Funds received from all other sources not previously classified.

  • Funds from this business
    Include interest payments, other income and funding or tax credits from tax incentives.
  • Funds from parent, affiliated and subsidiary companies
  • Federal government grants or funding
    Include R&D grants or funding or R&D portion only of other grants or funding.
  • Federal government contracts
    Include R&D contracts or R&D portion only of other contracts.
  • R&D contract work for other companies
  • Provincial or territorial government grants or funding
    Include R&D grants or funding or R&D portion only of other grants or funding.
  • From which province or territory did this business receive provincial or territorial government R&D grants or funding?
    Select all that apply.
    • Newfoundland and Labrador
    • Prince Edward Island
    • Nova Scotia
    • New Brunswick
    • Quebec
    • Ontario
    • Manitoba
    • Saskatchewan
    • Alberta
    • British Columbia
    • Yukon
    • Northwest Territories
    • Nunavut
  • Provincial or territorial government contracts
    Include R&D contracts or R&D portion only of other contracts.
  • From which province or territory did this business receive provincial or territorial government R&D contracts?
    Select all that apply.
    • Newfoundland and Labrador
    • Prince Edward Island
    • Nova Scotia
    • New Brunswick
    • Quebec
    • Ontario
    • Manitoba
    • Saskatchewan
    • Alberta
    • British Columbia
    • Yukon
    • Northwest Territories
    • Nunavut
  • R&D contract work for private non-profit organizations
  • Other sources -e.g., universities, foreign governments, individuals

16. In 2022, what were the sources of funds for this business's total expenditures of $ [Amount] for R&D performed in-house?

Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9
  • capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Funds from this business
Amount contributed by this business to R&D performed within Canada (include amounts eligible for income tax purposes, e.g., Scientific Research and Experimental Development ( SR-ED ) program, other amounts spent for projects not claimed through SR-ED, and funds for land, buildings, machinery and equipment (capital expenditures) purchased for R&D ).

Funds from parent, affiliated and subsidiary companies
Amount received from parent, affiliated and subsidiary companies used to perform R&D within Canada (include amounts eligible for income tax purposes, e.g., Scientific Research and Experimental Development ( SR-ED ) program, other amounts spent for projects not claimed through SR-ED, and funds for land, buildings, machinery and equipment (capital expenditures) purchased for R&D ).

R&D contract work for other companies
Funds received from other companies to perform R&D on their behalf.

Federal government grants or funding
Grants or funds received from the federal government in support of R&D activities not connected to a specific contractual deliverable.

Federal government contracts
Funds received from the federal government in support of R&D activities connected to a specific contractual deliverable.

Provincial or territorial government grants or funding
Grants or funds received from the provincial or territorial government in support of R&D activities not connected to a specific contractual deliverable.

Provincial or territorial government contracts
Funds received from the provincial or territorial government in support of R&D activities connected to a specific contractual deliverable.

R&D contract work for private non-profit organizations
Funds received from non-profit organizations to perform R&D on their behalf.

Other sources
Funds received from all other sources not previously classified.

In 2022, what were the sources of funds for this business's total expenditures of $ [Amount] for R&D performed in-house?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  From within Canada
CAN$ '000		 From outside Canada
CAN$ '000
Funds from this business
Include interest payments, other income and funding or tax credits from tax incentives.		    
Funds from parent, affiliated and subsidiary companies    
Federal government grants or funding
Include R&D grants or funding or R&D portion only of other grants or funding.		    
Federal government contracts
Include R&D contracts or R&D portion only of other contracts.		    
R&D contract work for other companies
Business 1
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
Business 2
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
Business 3
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
Business 4
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
Other contracts not listed above    
Provincial or territorial government grants or funding
Include R&D grants or funding or R&D portion only of other grants or funding.
Newfoundland and Labrador    
Prince Edward Island    
Nova Scotia    
New Brunswick    
Quebec    
Ontario    
Manitoba    
Saskatchewan    
Alberta    
British Columbia    
Yukon    
Northwest Territories    
Nunavut    
Provincial or territorial government contracts
Include R&D contracts or R&D portion only of other contracts.
Newfoundland and Labrador    
Prince Edward Island    
Nova Scotia    
New Brunswick    
Quebec    
ab. Ontario    
ac. Manitoba    
ad. Saskatchewan    
ae. Alberta    
af. British Columbia    
ag. Yukon    
ah. Northwest Territories    
ai. Nunavut    
R&D contract work for private non-profit organizations 
aj. Organization 1
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
ak. Organization 2
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
al. Organization 3
GST number (9-digit business number (BN) or charitable registration number)
Legal name		    
am. Other sources
e.g., universities, foreign governments, individuals		    
2022 - Total in-house R&D expenditures by sources of funds by origin    
2022 - Total in-house R&D expenditures (Canadian and foreign sources)    
Total in-house R&D expenditures previously reported from question 4    

Fields of R&D for in-house R&D expenditures within Canada in 2022

17. In 2022, in which field(s) of research and development did this business have R&D performed in-house within Canada?
Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9
  • capital depreciation.

Select all that apply.

Natural and formal sciences: physical sciences, chemical sciences, earth and related environmental sciences, biological sciences, other natural sciences.

Engineering and technology: civil engineering, electrical engineering, electronic engineering and communications technology, mechanical engineering, chemical engineering, materials engineering, medical engineering, environmental engineering, environmental biotechnology, industrial biotechnology, nanotechnology, other engineering and technologies.

Software-related sciences and technology: software engineering and technology, computer sciences, information technology and bioinformatics.

Medical and health sciences: basic medicine, clinical medicine, health sciences, medical biotechnology, other medical sciences.

Agricultural sciences: agriculture, forestry and fisheries sciences, animal and dairy sciences, veterinary sciences, agricultural biotechnology, other agricultural sciences.

Social sciences and humanities: psychology, educational sciences, economics and business, other social sciences, humanities.

  • Natural and formal sciences
    Exclude computer sciences, information sciences and bioinformatics.
  • Engineering and technology
    Exclude software engineering and technology.
  • Software-related sciences and technology
  • Medical and health sciences
  • Agricultural sciences
  • Social sciences and humanities

18. In 2022, how were this business's total expenditures of $ [Amount] for R&D performed in-house within Canada distributed by field(s) of research and development?

Exclude:

  • payments for outsourced (contracted out or granted) R&D, which should be reported in question 9
  • capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Natural and formal sciences

Mathematics: pure mathematics, applied mathematics, statistics and probability.

Physical sciences: atomic, molecular and chemical physics, interaction with radiation, magnetic resonances, condensed matter physics, solid state physics and superconductivity, particles and fields physics, nuclear physics, fluids and plasma physics (including surface physics), optics (including laser optics and quantum optics), acoustics, astronomy (including astrophysics, space science).

Chemical sciences: organic chemistry, inorganic and nuclear chemistry, physical chemistry, polymer science and plastics, electrochemistry (dry cells, batteries, fuel cells, metal corrosion, electrolysis), colloid chemistry, analytical chemistry.

Earth and related environmental sciences: geosciences, geophysics, mineralogy and palaeontology, geochemistry and geophysics, physical geography, geology and volcanology, environmental sciences, meteorology, atmospheric sciences and climatic research, oceanography, hydrology and water resources.

Biological sciences: cell biology, microbiology and virology, biochemistry, molecular biology and biochemical research, mycology, biophysics, genetics and heredity (medical genetics under medical biotechnology), reproductive biology (medical aspects under medical biotechnology), developmental biology, plant sciences and botany, zoology, ornithology, entomology and behavioural sciences biology, marine biology, freshwater biology and limnology, ecology and biodiversity conservation, biology (theoretical, thermal, cryobiology, biological rhythm), evolutionary biology.

Other natural sciences: other natural sciences.

Engineering and technology

Civil engineering: civil engineering, architecture engineering, municipal and structural engineering, transport engineering.

Electrical engineering, electronic engineering and communications technology: electrical and electronic engineering, robotics and automatic control, micro-electronics, semiconductors, automation and control systems, communication engineering and systems, telecommunications, computer hardware and architecture.

Mechanical engineering: mechanical engineering, applied mechanics, thermodynamics, aerospace engineering, nuclear-related engineering (nuclear physics under Physical sciences), acoustical engineering, reliability analysis and non-destructive testing, automotive and transportation engineering and manufacturing, tooling, machinery and equipment engineering and manufacturing, heating, ventilation and air conditioning engineering and manufacturing.

Chemical engineering: chemical engineering (plants, products), chemical process engineering.

Materials engineering: materials engineering and metallurgy, ceramics, coating and films (including packaging and printing), plastics, rubber and composites (including laminates and reinforced plastics), paper and wood and textiles, construction materials (organic and inorganic).

Medical engineering: medical and biomedical engineering, medical laboratory technology (excluding biomaterials which should be reported under Industrial biotechnology).

Environmental engineering: environmental and geological engineering, petroleum engineering (fuel, oils), energy and fuels, remote sensing, mining and mineral processing, marine engineering, sea vessels and ocean engineering.

Environmental biotechnology: environmental biotechnology, bioremediation, diagnostic biotechnologies in environmental management (DNA chips and bio-sensing devices).

Industrial biotechnology: industrial biotechnology, bioprocessing technologies, biocatalysis and fermentation bioproducts (products that are manufactured using biological material as feedstock), biomaterials (bioplastics, biofuels, bio-derived bulk and fine chemicals, bio-derived materials).

Nanotechnology: nano-materials (production and properties), nano-processes (applications on nano-scale).

Other engineering and technologies: food and beverages, oenology, other engineering and technologies.

Software-related sciences and technologies

Software engineering and technology: computer software engineering, computer software technology, and other related computer software engineering and technologies.

Computer sciences: computer science, artificial intelligence, cryptography, and other related computer sciences.

Information technology and bioinformatics: information technology, informatics, bioinformatics, biomathematics, and other related information technologies.

Medical and health sciences

Basic medicine: anatomy and morphology (plant science under Biological science), human genetics, immunology, neurosciences, pharmacology and pharmacy and medicinal chemistry, toxicology, physiology and cytology, pathology.

Clinical medicine: andrology, obstetrics and gynaecology, paediatrics, cardiac and cardiovascular systems, haematology, anaesthesiology, orthopaedics, radiology and nuclear medicine, dentistry, oral surgery and medicine, dermatology, venereal diseases and allergy, rheumatology, endocrinology and metabolism and gastroenterology, urology and nephrology, and oncology.

Health sciences: health care sciences and nursing, nutrition and dietetics, parasitology, infectious diseases and epidemiology, occupational health.

Medical biotechnology: health-related biotechnology, technologies involving the manipulation of cells, tissues, organs or the whole organism, technologies involving identifying the functioning of DNA, proteins and enzymes, pharmacogenomics, gene-based therapeutics, biomaterials (related to medical implants, devices, sensors).

Other medical sciences: forensic science, other medical sciences.

Other medical sciences: forensic science, other medical sciences.

Agricultural sciences

Agriculture, forestry and fisheries sciences: agriculture, forestry, fisheries and aquaculture, soil science, horticulture, viticulture, agronomy, plant breeding and plant protection.

Animal and dairy sciences: animal and dairy science, animal husbandry.

Veterinary sciences: veterinary science (all).

Agricultural biotechnology: agricultural biotechnology and food biotechnology, genetically modified (GM) organism technology and livestock cloning, diagnostics (DNA chips and biosensing devices), biomass feedstock production technologies and biopharming.

Other agricultural sciences: other agricultural sciences.

Social sciences and humanities

Psychology: cognitive psychology and psycholinguistics, experimental psychology, psychometrics and quantitative psychology, and other fields of psychology.

Educational sciences: education, training and other related educational sciences.

Economics and business: micro-economics, macro-economics, econometrics, labour economics, financial economics, business economics, entrepreneurial and business administration, management and operations, management sciences, finance, pharmacoeconomics, and all other related fields of economics and business.

Other social sciences: anthropology (social and cultural) and ethnology, demography, geography (human, economic and social), planning (town, city and country), management, organisation and methods (excluding market research unless new methods/techniques are developed), law, linguistics, political sciences, sociology, miscellaneous social sciences and interdisciplinary, and methodological and historical science and technology activities relating to subjects in this group.

Humanities: history (history, prehistory and history, together with auxiliary historical disciplines such as archaeology, numismatics, palaeography, genealogy, etc.), languages and literature (ancient and modern), other humanities (philosophy (including the history of science and technology)), arts (history of art, art criticism, painting, sculpture, musicology, dramatic art excluding artistic "research" of any kind), religion, theology, other fields and subjects pertaining to the humanities, and methodological, historical and other science and technology activities relating to the subjects in this group.

In 2022, how were this business's total expenditures of $ [Amount] for R&D performed in-house within Canada distributed by field(s) of research and development?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Natural and formal sciences
Exclude computer sciences, information technology and bioinformatics (to be reported at lines s. and t.)
Mathematics  
Physical sciences  
Chemical sciences  
Earth and related environmental sciences  
Biological sciences  
Other natural sciences  
Total natural and formal sciences  
Engineering and technology
Exclude: software engineering and technology (to be reported at line r.)
Civil engineering  
Electrical engineering, electronic engineering and communications technology  
Mechanical engineering  
Chemical engineering  
Materials engineering  
Medical engineering  
Environmental engineering  
Environmental biotechnology  
Industrial biotechnology  
Nanotechnology  
Other engineering and technologies  
Total engineering and technology  
Software-related sciences and technology
Software engineering and technology  
Computer sciences  
Information technology and bioinformatics  
Total software-related sciences and technology  
Medical and health sciences
Basic medicine  
Clinical medicine  
Health sciences  
Medical biotechnology  
Other medical sciences  
Total medical and health sciences  
Agricultural sciences
Agriculture, forestry and fisheries sciences  
Animal and dairy sciences  
ab. Veterinary sciences  
ac. Agricultural biotechnology  
ad. Other agricultural sciences  
Total agricultural sciences  
Social sciences and humanities
ae. Psychology  
af. Educational sciences  
ag. Economics and business  
ah. Other social sciences  
ai. Humanities  
Total social sciences and humanities  
2022 - Total in-house R&D expenditures within Canada by field of research and development  
Total in-house R&D expenditures previously reported from question 4  
   

19. Summary of 2022 total in-house R&D expenditures within Canada distributed by field(s) of research and development.

19. Summary of 2022 total in-house R&D expenditures within Canada distributed by field(s) of research and development.
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Total natural and formal sciences  
Total engineering and technology  
Total software-related sciences and technology  
Total medical and health sciences  
Total agricultural sciences  
Total social sciences and humanities  
Total in-house R&D expenditures within Canada by fields of research and development  

Nature of R&D for in-house R&D expenditures within Canada in 2022

20. In 2022, how were this business's total expenditures for R&D performed in-house within Canada of $ [Amount] distributed by nature of R&D?

Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.

Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective.

Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.
(OECD. Frascati Manual: Proposed Standard for Surveys on Research and Experimental Development, 2015)

In 2022, how were this business's total expenditures for R&D performed in-house within Canada of $ [Amount] distributed by nature of R&D?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Percentage of total in-house R&D expenditures
Basic research  
Applied research  
Experimental development  
Total percentage  

Results of R&D expenditures from 2020 to 2022

21. During the three (3) years 2020, 2021 and 2022, did this business's total expenditures for R&D performed in-house and outsourced (contracted out or granted) within Canada or outside Canada lead to new or significant improvements to the following?

Goods
Goods developed through new knowledge from research discoveries include determination of effectiveness of existing treatment protocols, establishment of new treatment protocols (including diagnostic procedures, tests and protocols), and creation of new service delivery models and reference tools (including electronic applications).

During the three (3) years 2020, 2021 and 2022, did this business's total expenditures for R&D performed in-house and outsourced (contracted out or granted) within Canada or outside Canada lead to new or significant improvements to the following?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Yes No
Goods
Include goods developed through new knowledge from research discoveries		    
Services
Include on-going knowledge transfer to physicians, first responders, patients and the general public.		    
Methods of manufacturing or producing goods and services    
Logistics, delivery or distribution methods for this business's inputs, goods or services    
Supporting activities for this business's processes, such as maintenance systems or operations for purchasing, accounting or computing    

Energy-related R&D by area of technology

22. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include energy-related R&D in the following categories?

  1. Fossil fuels: crude oils and natural gas exploration, crude oils and natural gas production, oil sands and heavy crude oils surface and sub-surface production and separation of the bitumen, tailings management, refining, processing and upgrading, coal production, separation and processing, transportation of fossil fuels.
  2. Renewable energy resources: solar photovoltaics (PV), solar thermal-power and high-temperature applications, solar heating and cooling, wind energy, bio-energy - biomass production, bio-energy - biomass conversion to fuels, bio-energy - biomass conversion to heat and electricity, and other bio-energy, small hydro (less than 10 MW), large hydro (greater than or equal to 10 MW), other renewable energy.
  3. Nuclear: materials exploration, mining and preparation, tailings management, nuclear reactors, other fission, fusion.
  4. Electric power: generation in utility sector, combined heat and power in industry and in buildings, electricity transmission, distribution and storage of electricity.
  5. Hydrogen and fuel cells: hydrogen production for process applications, hydrogen production for transportation applications, hydrogen transport and storage, other hydrogen, fuel cells, both stationary and mobile.
  6. Energy efficiency: industry, residential and commercial, transportation, other energy efficiency.
  7. Other energy-related technologies: carbon capture, transportation and storage for fossil fuel production and processing, electric power generation, industry in end-use sector, energy systems analysis, all other energy-related technologies.
In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include energy-related R&D in the following categories?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Yes No
Fossil fuels    
Renewable energy resources    
Nuclear fission and fusion    
Electric power    
Hydrogen and fuel cells    
Energy efficiency    
Other energy-related technologies    

Energy-related R&D by area of technology - Fossil fuels

23. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include fossil fuels-related R&D in the following categories?

Select all that apply.

Crude oils and natural gas exploration:
Includes development of advanced exploration methods (geophysical, geochemical, seismic, magnetic) for on-shore and off-shore prospecting.

Crude oil and natural gas production (including enhanced recovery) and storage:
Includes on-shore and off-shore deep drilling equipment and techniques for conventional oil and gas, secondary and tertiary recovery of oil and gas, hydro fracturing techniques, processing and cleaning of raw product, storage on remote platforms (e.g., Arctic, off-shore), safety aspects of off-shore platforms.

Oil sands and heavy crude oils surface and sub-surface production and separation of the bitumen, tailings management:
Includes surface and in-situ production (e.g., SAGD), tailings management.

Refining, processing and upgrading:
Includes processing of natural gas to pipeline specifications, and refining of conventional crude oils to refined petroleum products (RPPs), and the upgrading of bitumen and heavy oils either to synthetic crude oil or to RPPs. Upgrading may be done at an oil sands plant, regional merchant upgraders or integrated into a refinery producing RPPs.

Coal production, separation and processing:
Includes coal, lignite and peat exploration, deposit evaluation techniques, mining techniques, separation techniques, coking and blending, other processing such as coal to liquids, underground (in-situ) gasification.

Transportation of fossil fuels:
Includes transport of gaseous, liquid and solid hydrocarbons via pipelines (land and submarine) and their network evaluation, safety aspects of LNG transport and storage.

  • Crude oils and natural gas exploration
  • Crude oils and natural gas production and storage

Include enhanced recovery natural gas production.

  • Oil sands and heavy crude oil surface and sub-surface production and separation of bitumen, tailings management
  • Refining, processing and upgrading of fossil fuels
  • Coal production, separation and processing
  • Transportation of fossil fuels
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

24. In 2022, what were this business's energy R&D expenditures on crude oils and natural gas exploration?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Crude oils and natural gas exploration:

Include development of advanced exploration methods (geophysical, geochemical, seismic, magnetic) for on-shore and off-shore prospecting.

In 2022, what were this business's energy R&D expenditures on crude oils and natural gas exploration?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

25. In 2022, what were this business's energy R&D expenditures on crude oils and natural gas production and storage?

Include enhanced recovery.

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Crude oil and natural gas production (including enhanced recovery) and storage:

Include on-shore and off-shore deep drilling equipment and techniques for conventional oil and gas, secondary and tertiary recovery of oil and gas, hydro fracturing techniques, processing and cleaning of raw product, storage on remote platforms (e.g., Arctic, off-shore), safety aspects of off-shore platforms.

In 2022, what were this business's energy R&D expenditures on crude oils and natural gas production and storage?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

26. In 2022, what were this business's energy R&D expenditures on oil sands and heavy crude oil surface and sub-surface production and separation of bitumen, tailings management?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Oil sands and heavy crude oils surface and sub-surface production and separation of the bitumen, tailings management:

Include surface and in-situ production (e.g., SAGD), tailings management.

In 2022, what were this business's energy R&D expenditures on oil sands and heavy crude oil surface and sub-surface production and separation of bitumen, tailings management?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

27. In 2022, what were this business's energy R&D expenditures on refining, processing and upgrading of fossil fuels?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Refining, processing and upgrading:

Include processing of natural gas to pipeline specifications, and refining of conventional crude oils to refined petroleum products (RPPs), and the upgrading of bitumen and heavy oils either to synthetic crude oil or to RPPs. Upgrading may be done at an oil sands plant, regional merchant upgraders or integrated into a refinery producing RPPs.

In 2022, what were this business's energy R&D expenditures on refining, processing and upgrading of fossil fuels?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

28. In 2022, what were this business's energy R&D expenditures on coal production, separation and processing?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Coal production, separation and processing:

Include coal, lignite and peat exploration, deposit evaluation techniques, mining techniques, separation techniques, coking and blending, other processing such as coal to liquids, underground (in-situ) gasification.

In 2022, what were this business's energy R&D expenditures on coal production, separation and processing?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for fossil fuels within this reporting unit.

29. In 2022, what were this business's energy R&D expenditures on transportation of fossil fuels?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Transportation of fossil fuels:

Include transport of gaseous, liquid and solid hydrocarbons via pipelines (land and submarine) and their network evaluation, safety aspects of LNG transport and storage.

In 2022, what were this business's energy R&D expenditures on transportation of fossil fuels?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Renewable energy resources

30. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include renewable energy resources-related R&D in the following categories?

Select all that apply.

Solar photovoltaics (PV):
Include solar cell development, PV-module development, PV-inverter development, building-integrated PV-modules, PV-system development, other.

Solar thermal-power and high-temperature applications:
Include solar chemistry, concentrating collector development, solar thermal power plants, high-temperature applications for heat and power.

Solar heating and cooling:
Include daylighting, passive and active solar heating and cooling, collector development, hot water preparation, combined-space heating, solar architecture, solar drying, solar-assisted ventilation, swimming pool heating, low-temperature process heating, other.

Wind energy:
Include technology development, such as blades, turbines, converters structures, system integration, other.

Bio-energy - Biomass production/supply and transport:
Include improvement of energy crops, research on bio-energy production potential and associated land-use effects, supply and transport of bio-solids, bio-liquids, biogas and bio-derived energy products (e.g., ethanol, biodiesel), compacting and baling, other.

Bio-energy - Biomass conversion to fuels:
Include conventional bio-fuels, cellulosic-derived alcohols, biomass gas-to-liquids, other energy-related products and by-products.

Bio-energy - Biomass conversion to heat and electricity:
Include bio-based heat, electricity and combined heat and power (CHP).
Exclude multi-firing with fossil fuels.

Other bio-energy:
Include recycling and the use of municipal, industrial and agricultural waste as energy not covered elsewhere.

Small hydro - (less than 10 MW):
Include plants with capacity below 10 MW.

Large hydro - (greater than or equal to 10 MW):
Include plants with capacity of 10 MW and above.

Other renewable energy:
Include hot dry rock, hydro-thermal, geothermal heat applications (including agriculture), tidal power, wave energy, ocean current power, ocean thermal power, other.

  • Solar photovoltaics (PV)
  • Solar thermal-power and high-temperature applications
  • Solar heating and cooling
  • Wind energy
  • Bio-energy - biomass production and transportation
  • Bio-energy - biomass conversion to transportation fuel
  • Bio-energy - biomass conversion to heat and electricity
  • Other bio-energy
  • Small hydro (less than 10 MW)
  • Large hydro (greater than or equal to 10 MW)
  • Other renewable energy
    Include ocean and geothermal.
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

31. In 2022, what were this business's energy R&D expenditures on solar photovoltaics (PV)?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Solar photovoltaics (PV):

Include solar cell development, PV-module development, PV-inverter development, building-integrated PV-modules, PV-system development, other.

In 2022, what were this business's energy R&D expenditures on solar photovoltaics (PV)?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

32. In 2022, what were this business's energy R&D expenditures on solar thermal-power and high-temperature applications?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Solar thermal-power and high-temperature applications:

Include solar chemistry, concentrating collector development, solar thermal power plants, high-temperature applications for heat and power.

In 2022, what were this business's energy R&D expenditures on solar thermal-power and high-temperature applications?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

33. In 2022, what were this business's energy R&D expenditures on solar heating and cooling?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Solar heating and cooling:

Include daylighting, passive and active solar heating and cooling, collector development, hot water preparation, combined-space heating, solar architecture, solar drying, solar-assisted ventilation, swimming pool heating, low-temperature process heating, other.

In 2022, what were this business's energy R&D expenditures on solar heating and cooling?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

34. In 2022, what were this business's energy R&D expenditures on wind energy?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Wind energy:

Include technology development, such as blades, turbines, converters structures, system integration, other.

In 2022, what were this business's energy R&D expenditures on wind energy?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

35. In 2022, what were this business's energy R&D expenditures on bio-energy - biomass production and transport?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Bio-energy - Biomass production/supply and transport:

Include improvement of energy crops, research on bio-energy production potential and associated land-use effects, supply and transport of bio-solids, bio-liquids, biogas and bio-derived energy products (e.g., ethanol, biodiesel), compacting and baling, other.

In 2022, what were this business's energy R&D expenditures on bio-energy - biomass production and transport?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

36. In 2022, what were this business's energy R&D expenditures on bio-energy - biomass conversion to transportation fuel?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Bio-energy - Biomass conversion to transportation fuel:

Include conventional bio-fuels, cellulosic-derived alcohols, biomass gas-to-liquids, other energy-related products and by-products.

In 2022, what were this business's energy R&D expenditures on bio-energy - biomass conversion to transportation fuel?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

37. In 2022, what were this business's energy R&D expenditures on bio-energy - biomass conversion to heat and electricity?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Bio-energy - Biomass conversion to heat and electricity:

Include bio-based heat, electricity and combined heat and power (CHP).

Exclude multi-firing with fossil fuels.

In 2022, what were this business's energy R&D expenditures on bio-energy - biomass conversion to heat and electricity?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

38. In 2022, what were this business's energy R&D expenditures on other bio-energy?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other bio-energy:

Include recycling and the use of municipal, industrial and agricultural waste as energy not covered elsewhere.

In 2022, what were this business's energy R&D expenditures on other bio-energy?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

39. In 2022, what were this business's energy R&D expenditures on small hydro (less than 10 MW)?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Small hydro - (less than 10 MW):

Include plants with capacity below 10 MW.

In 2022, what were this business's energy R&D expenditures on small hydro (less than 10 MW)?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

40. In 2022, what were this business's energy R&D expenditures on large hydro (greater than or equal to 10 MW)?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Large hydro - (greater than or equal to 10 MW):

Include plants with capacity of 10 MW or greater.

In 2022, what were this business's energy R&D expenditures on large hydro (greater than or equal to 10 MW)?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for renewable energy resources within this reporting unit.

41. In 2022, what were this business's energy R&D expenditures on other renewable energy?

Include ocean and geothermal.

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other renewable energy:

Include hot dry rock, hydro-thermal, geothermal heat applications (including agriculture), tidal power, wave energy, ocean current power, ocean thermal power, other.

In 2022, what were this business's energy R&D expenditures on other renewable energy?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Nuclear fission and fusion

42. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include nuclear fission and fusion-related R&D in the following categories?

Select all that apply.

Exploration, mining and preparation, tailings management:
Include development of advanced exploration methods (geophysical, geochemical) for prospecting, ore surface and in-situ production, uranium and thorium extraction and conversion, enrichment, handling of tailings and remediation.

Nuclear reactors:
Include nuclear reactors of all types and related system components.

Other fission:
Include nuclear safety, environmental protection (emission reduction or avoidance), radiation protection and decommissioning of power plants and related nuclear fuel cycle installations, nuclear waste treatment, disposal and storage, fissile material recycling, fissile materials control, transport of radioactive materials.

Fusion:
Include all types (e.g., magnetic confinement, laser applications).

  • Nuclear materials exploration, mining and preparation, tailings management
  • Nuclear reactors
  • Other fission
  • Fusion
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for nuclear fission and fusion within this reporting unit.

43. In 2022, what were this business's energy R&D expenditures on nuclear materials exploration, mining and preparation, tailings management?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Exploration, mining and preparation, tailings management:

Include development of advanced exploration methods (geophysical, geochemical) for prospecting, ore surface and in-situ production, uranium and thorium extraction and conversion, enrichment, handling of tailings and remediation.

In 2022, what were this business's energy R&D expenditures on nuclear materials exploration, mining and preparation, tailings management?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for nuclear fission and fusion within this reporting unit.

44. In 2022, what were this business's energy R&D expenditures on nuclear reactors?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Nuclear reactors:

Include nuclear reactors of all types and related system components.

In 2022, what were this business's energy R&D expenditures on nuclear reactors?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for nuclear fission and fusion within this reporting unit.

45. In 2022, what were this business's energy R&D expenditures on other fission?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other fission:

Include nuclear safety, environmental protection (emission reduction or avoidance), radiation protection and decommissioning of power plants and related nuclear fuel cycle installations, nuclear waste treatment, disposal and storage, fissile material recycling, fissile materials control, transport of radioactive materials.

In 2022, what were this business's energy R&D expenditures on other fission?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for nuclear fission and fusion within this reporting unit.

46. In 2022, what were this business's energy R&D expenditures on fusion?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Fusion:

Include all types (e.g., magnetic confinement, laser applications).

In 2022, what were this business's energy R&D expenditures on fusion?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Electric power

47. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include electric power-related R&D in the following categories?

Select all that apply.

Electric power generation in utility sector:
Include conventional and non-conventional technology (e.g., pulverised coal, fluidised bed, gasification-combined cycle, supercritical), re-powering, retrofitting, life extensions and upgrading of power plants, generators and components, super-conductivity, magneto hydrodynamic, dry cooling towers, co-firing (e.g., with biomass), air and thermal pollution reduction or avoidance, flue gas cleanup (excluding CO2 removal), CHP (combined heat and power) not covered elsewhere.

Electric power - combined heat and power in industry, buildings:
Include industrial applications, small scale applications for buildings.

Electricity transmission, distribution and storage:
Include solid state power electronics, load management and control systems, network problems, super-conducting cables, AC and DC high voltage cables, HVDC transmission, other transmission and distribution related to integrating distributed and intermittent generating sources into networks, all storage (e.g., batteries, hydro reservoirs, fly wheels), other.

  • Electric power generation in utility sector
  • Electric power - combined heat and power in industry, buildings
  • Electricity transmission, distribution and storage
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for electric power within this reporting unit.

48. In 2022, what were this business's energy R&D expenditures on electric power generation in utility sector?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Electric power generation in utility sector:

Include conventional and non-conventional technology (e.g., pulverised coal, fluidised bed, gasification-combined cycle, supercritical), re-powering, retrofitting, life extensions and upgrading of power plants, generators and components, super-conductivity, magneto hydrodynamic, dry cooling towers, co-firing (e.g., with biomass), air and thermal pollution reduction or avoidance, flue gas cleanup (excluding CO2 removal), CHP (combined heat and power) not covered elsewhere.

In 2022, what were this business's energy R&D expenditures on electric power generation in utility sector?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for electric power within this reporting unit.

49. In 2022, what were this business's energy R&D expenditures on electric power - combined heat and power in industry, buildings?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Electric power - combined heat and power in industry, buildings:

Include industrial applications, small scale applications for buildings.

In 2022, what were this business's energy R&D expenditures on electric power - combined heat and power in industry, buildings?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for electric power within this reporting unit.

50. In 2022, what were this business's energy R&D expenditures on electricity transmission, distribution and storage?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Electricity transmission, distribution and storage:

Include solid state power electronics, load management and control systems, network problems, super-conducting cables, AC and DC high voltage cables, HVDC transmission, other transmission and distribution related to integrating distributed and intermittent generating sources into networks, all storage (e.g., batteries, hydro reservoirs, fly wheels), other.

In 2022, what were this business's energy R&D expenditures on electricity transmission, distribution and storage?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Hydrogen and fuel cells

51. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include hydrogen and fuel cells-related R&D in the following categories?

Select all that apply.

Other hydrogen:
Include end uses (e.g., combustion), other infrastructure and systems R&D (refuelling stations).

Stationary fuel cells:
Include electricity generation, other stationary end-use.

Mobile fuel cells:
Include portable applications.

  • Hydrogen production for process applications
  • Hydrogen production for transportation applications
  • Hydrogen transport and storage
  • Other hydrogen
  • Stationary fuel cells
  • Mobile fuel cells
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

52. In 2022, what were this business's energy R&D expenditures on hydrogen production for process applications?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2022, what were this business's energy R&D expenditures on hydrogen production for process applications?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

53. In 2022, what were this business's energy R&D expenditures on hydrogen production for transportation applications?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2022, what were this business's energy R&D expenditures on hydrogen production for transportation applications?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

54. In 2022, what were this business's energy R&D expenditures on hydrogen transport and storage?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2022, what were this business's energy R&D expenditures on hydrogen transport and storage?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

55. In 2022, what were this business's energy R&D expenditures on other hydrogen?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other hydrogen:

Include end uses (e.g., combustion), other infrastructure and systems R&D (refuelling stations).

In 2022, what were this business's energy R&D expenditures on other hydrogen?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

56. In 2022, what were this business's energy R&D expenditures on stationary fuel cells?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Stationary fuel cells:

Include electricity generation, other stationary end-use.

In 2022, what were this business's energy R&D expenditures on stationary fuel cells?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for hydrogen and fuel cells within this reporting unit.

57. In 2022, what were this business's energy R&D expenditures on mobile fuel cells?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Mobile fuel cells:

Include portable applications.

In 2022, what were this business's energy R&D expenditures on mobile fuel cells?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Energy efficiency

58. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include energy efficiency-related R&D in the following categories?

Select all that apply.

Energy efficiency for industry:
Include reduction of energy consumption through improved use of energy and/or reduction or avoidance of air and other emissions related to the use of energy in industrial systems and processes (excluding bio-energy-related) through the development of new techniques, new processes and new equipment, other.

Energy efficiency for residential, institutional and commercial:
Include space heating and cooling, ventilation and lighting control systems other than solar technologies, low energy housing design and performance other than solar technologies, new insulation and building materials, thermal performance of buildings, domestic appliances, other.

Energy efficiency for transportation:
Include analysis and optimisation of energy consumption in the transport sector, efficiency improvements in light-duty vehicles, heavy-duty vehicles, non-road vehicles, public transport systems, engine-fuel optimisation, use of alternative fuels (liquid and gaseous, other than hydrogen), fuel additives, diesel engines, Stirling motors, electric cars, hybrid cars, air emission reduction, other.

Other energy efficiency:
Include waste heat utilisation (heat maps, process integration, total energy systems, low temperature thermodynamic cycles), district heating, heat pump development, reduction of energy consumption in the agricultural sector.

  • Energy efficiency applications for industry
  • Energy efficiency for residential, institutional and commercial sectors
  • Energy efficiency for transportation
  • Other energy efficiency
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for energy efficiency within this reporting unit.

59. In 2022, what were this business's energy R&D expenditures on energy efficiency applications for industry?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Energy efficiency for industry:

Include reduction of energy consumption through improved use of energy and/or reduction or avoidance of air and other emissions related to the use of energy in industrial systems and processes (excluding bio-energy-related) through the development of new techniques, new processes and new equipment, other.

In 2022, what were this business's energy R&D expenditures on energy efficiency applications for industry?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for energy efficiency within this reporting unit.

60. In 2022, what were this business's energy R&D expenditures on energy efficiency for residential, institutional and commercial sectors?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Energy efficiency for residential, institutional and commercial:

Include space heating and cooling, ventilation and lighting control systems other than solar technologies, low energy housing design and performance other than solar technologies, new insulation and building materials, thermal performance of buildings, domestic appliances, other.

In 2022, what were this business's energy R&D expenditures on energy efficiency for residential, institutional and commercial sectors?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for energy efficiency within this reporting unit.

61. In 2022, what were this business's energy R&D expenditures on energy efficiency for transportation?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Energy efficiency for transportation:

Include analysis and optimisation of energy consumption in the transport sector, efficiency improvements in light-duty vehicles, heavy-duty vehicles, non-road vehicles, public transport systems, engine-fuel optimisation, use of alternative fuels (liquid and gaseous, other than hydrogen), fuel additives, diesel engines, Stirling motors, electric cars, hybrid cars, air emission reduction, other.

In 2022, what were this business's energy R&D expenditures on energy efficiency for transportation?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for energy efficiency within this reporting unit.

62. In 2022, what were this business's energy R&D expenditures on other energy efficiency?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other energy efficiency:

Include waste heat utilisation (heat maps, process integration, total energy systems, low temperature thermodynamic cycles), district heating, heat pump development, reduction of energy consumption in the agricultural sector.

In 2022, what were this business's energy R&D expenditures on other energy efficiency?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Energy-related R&D by area of technology - Other energy-related technologies

63. In 2022, did this business's total in-house and outsourced (contracted out or granted) R&D expenditures include other energy-related R&D in the following categories?

Select all that apply.

Carbon capture end-use:
Include industry in the end-use sector, such as steel production, manufacturing, etc. (exclude fossil fuel production and processing and electric power production).

Energy system analysis:
Include system analysis related to energy R&D not covered elsewhere, sociological, economical and environmental impact of energy which are not specifically related to one technology area listed in the sections above.

All other energy technologies:
Include energy technology information dissemination, studies not related to a specific technology area listed above.

  • Carbon capture, transport and storage related to fossil fuel production and processing
  • Carbon capture, transport and storage related to electric power production
  • Carbon capture, transport and storage related to industry in end-use sector
  • Energy system analysis
  • All other energy-related technologies
  • None of the above

Energy-related R&D by area of technology

Report all 2022 R&D expenditures for other energy-related technologies within this reporting unit.

64. In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to fossil fuel production and processing?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to fossil fuel production and processing?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for other energy-related technologies within this reporting unit.

65. In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to electric power production?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to electric power production?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for other energy-related technologies within this reporting unit.

66. In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to industry in end-use sector?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Carbon capture end-use:

Include industry in the end-use sector, such as steel production, manufacturing, etc. (exclude fossil fuel production and processing and electric power production).

In 2022, what were this business's energy R&D expenditures on carbon capture, transport and storage related to industry in end-use sector?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for other energy-related technologies within this reporting unit.

67. In 2022, what were this business's energy R&D expenditures on energy system analysis?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Energy system analysis:

Include system analysis related to energy R&D not covered elsewhere, sociological, economical and environmental impact of energy which are not specifically related to one technology area listed in the sections above.

In 2022, what were this business's energy R&D expenditures on energy system analysis?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

Report all 2022 R&D expenditures for other energy-related technologies within this reporting unit.

68. In 2022, what were this business's energy R&D expenditures on other energy-related technologies?

Exclude capital depreciation.

Please report all amounts in thousands of Canadian dollars.

Report '0' for no R&D expenditures.

Other energy-related technologies:

Include energy technology information dissemination, studies not related to a specific technology area listed above.

In 2022, what were this business's energy R&D expenditures on other energy-related technologies?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  CAN$ '000
Funds from this business  
Funds from federal, provincial or territorial government(s)  
All other Canadian sources of funds  
All foreign sources of funds  
Total in-house R&D  
Outsourced (contracted out or granted) within Canada  
Outsourced (contracted out or granted) outside Canada  
Total outsourced R&D  

69. Summary of total 2022 energy-related and total R&D expenditures

69. Summary of total 2022 energy-related R&D and total R&D expenditures
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Total energy-related R&D Total R&D
Total funds from this business    
Total funds from federal, provincial or territorial government(s)    
Total all other Canadian sources of funds    
Total all foreign sources of funds    
Total in-house R&D expenditures    
Total outsourced (contracted out or granted) within Canada    
Total outsourced (contracted out or granted) outside Canada    
Total outsourced (contracted out or granted) R&D expenditures    
Total R&D expenditures    

In-house R&D personnel in 2022

70. In 2022, how many in-house R&D personnel within Canada did this business have in the following R&D occupations?

Full-time equivalent (FTE)

R&D may be carried out by persons who work solely on R&D projects or by persons who devote only part of their time to R&D and the balance to other activities such as testing, quality control and production engineering. To arrive at the total effort devoted to R&D in terms of personnel, it is necessary to estimate the full-time equivalent of these persons working only part-time in R&D.

Full-time equivalent (FTE) = Number of persons who work solely on R&D projects + the time of persons working only part of their time on R&D.

Example calculation: If out of four scientists engaged in R&D work, one works solely on R&D projects and the remaining three devote only one quarter of their working time to R&D, then: FTE = 1 + 1/4 + 1/4 + 1/4 = 1.75 scientists.

R&D personnel

Include:

  • permanent, temporary and casual R&D employees
  • independent on-site R&D consultants and contractors working in your business's offices, laboratories, or other facilities
  • employees engaged in R&D -related support activities.

Researchers and research managers are composed of:

  • Scientists, social scientists, engineers and researchers are professionals engaged in the conception or creation of new knowledge. They conduct research and improve or develop concepts, theories, models, techniques instrumentation, software or operational methods. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.
  • Senior research managers plan or manage R&D projects and programs. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.

R&D technical, administrative and support staff are composed of:

  • Technicians and technologists and research assistants are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, the physical and life sciences, or the social sciences, humanities and the arts. They participate in R&D by performing scientific and technical tasks involving the application of concepts, operational methods and the use of research equipment, normally under the supervision of researchers. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.
  • Other R&D technical, administrative support staff include skilled and unskilled craftsmen, and administrative, secretarial and clerical staff participating in R&D projects or directly associated with such projects.

On-site R&D consultants and contractors are individuals hired 1) to perform project-based work or to provide goods at a fixed or ascertained price or within a certain time or 2) to provide advice or services in a specialized field for a fee and, in both cases, work at the location specified and controlled by the contracting company or organization.

In 2022, how many in-house R&D personnel within Canada did this business have in the following R&D occupations?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Male (FTEs) Female (FTRs) Another Gender (FTEs) Total (FTEs)
Researchers and research managers 
Scientists, social scientists, engineers and researchers
Include software developers and programmers.		        
Senior research managers        
Total researchers and research managers        
R&D technical, administrative and support staff 
Technicians, technologists and research assistants
Include software technicians.		        
Other R&D technical, administrative and support staff        
Total R&D technical, administrative and support staff        
Other R&D occupations 
On-site R&D consultants and contractors        
Total in-house R&D personnel within Canada        

71. Of this business's total in-house R&D personnel reported above, what percentage performed software-related activities?

Software-related sciences and technologies

  • Software engineering and technology: computer software engineering, computer software technology and other related computer software engineering and technologies.
  • Computer sciences: computer science, artificial intelligence, cryptography and other related computer sciences.
  • Information technology and bioinformatics: information technology, informatics, bioinformatics, biomathematics and other related information technologies.

Percentage of software-related activities

72. In 2022, how were the [Amount] total in-house R&D personnel distributed by province or territory?

Please report in full time equivalents (FTE).

R&D personnel

Include:

  • permanent, temporary and casual R&D employees
  • independent on-site R&D consultants and contractors working in your business's offices, laboratories, or other facilities
  • employees engaged in R&D -related support activities.

Researchers and research managers are composed of:

  • Scientists, social scientists, engineers and researchers are professionals engaged in the conception or creation of new knowledge. They conduct research and improve or develop concepts, theories, models, techniques instrumentation, software or operational methods. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.
  • Senior research managers plan or manage R&D projects and programs. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.

R&D technical, administrative and support staff are composed of:

  • Technicians and technologists and research assistants are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, the physical and life sciences, or the social sciences, humanities and the arts. They participate in R&D by performing scientific and technical tasks involving the application of concepts, operational methods and the use of research equipment, normally under the supervision of researchers. They may be certified by provincial or territorial educational authorities, provincial, territorial or national scientific or engineering associations.
  • Other R&D technical, administrative support staff include skilled and unskilled craftsmen, and administrative, secretarial and clerical staff participating in R&D projects or directly associated with such projects.

On-site R&D consultants and contractors are individuals hired 1) to perform project-based work or to provide goods at a fixed or ascertained price or within a certain time or 2) to provide advice or services in a specialized field for a fee and, in both cases, work at the location specified and controlled by the contracting company or organization.

Full-time equivalent (FTE)

R&D may be carried out by persons who work solely on R&D projects or by persons who devote only part of their time to R&D, and the balance to other activities such as testing, quality control and production engineering. To arrive at the total effort devoted to R&D in terms of personnel, it is necessary to estimate the full-time equivalent of these persons working only part-time in R&D.

Full-time equivalent (FTE): Number of persons who work solely on R&D projects + the time of persons working only part of their time on R&D.

Example calculation: If out of four scientists engaged in R&D work, one works solely on R&D projects and the remaining three devote only one quarter of their working time to R&D, then: FTE = 1 + 1/4 + 1/4 + 1/4 = 1.75 scientists.

In 2022, how were the [Amount] total in-house R&D personnel distributed by province or territory?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Number of researchers and research managers Number of R&D technical, administrative and support staff Number of on-site R&D consultants and contractors
Newfoundland and Labrador      
Prince Edward Island      
Nova Scotia      
New Brunswick      
Quebec      
Ontario      
Manitoba      
Saskatchewan      
Alberta      
British Columbia      
Yukon      
Northwest Territories      
Nunavut      
Total in-house R&D personnel within Canada      
Total R&D personnel previously reported from question 70      

Technology and technical assistance payments in 2022

73. In 2022, did this business make or receive payments inside or outside Canada for the following technology and technical assistance?

Technology and technical assistance payments

Definitions (equivalent to the Canadian Intellectual Property Office - opens in a new browser window)

  1. Patent
    Government grant giving the right to exclude others from making, using or selling an invention.
  2. Copyright
    Legal protection for literary, artistic, dramatic or musical works, computer programs, performer's performances, sound recordings, and communication signals.
  3. Trademark
    A word, symbol or design, or combination of these, used to distinguish goods or services of one person or organization from those of others in the marketplace.
  4. Industrial design
    Legal protection against imitation of the shape, pattern, or ornamentation of an object.
  5. Integrated circuit topography
    Three-dimensional configurations of the elements and interconnections embodied in an integrated circuit product.
  6. Original software
    Computer programs and descriptive materials for both systems and applications. Original software can be created in-house or outsourced and includes packaged software with customization.
  7. Packaged or off-the-shelf software
    Packaged software purchased for organizational use and excludes software with customization.
  8. Databases
    Data files organized to permit effective access and use of the data including access clinical trial registries and administrative health data for research purposes. Includes partnerships supporting the development of databases, such as patient or clinical trial registries or biobanks, to be used for research purposes (e.g., developing a national bladder cancer patient registry with Bladder Cancer Canada for future research).
  9. Other technology and technical assistance
    Technical assistance, industrial processes and know-how including technology transfer and know how such as batch pilot production, method develop, and validation related to technology or manufacturing transfer.
In 2022, did this business make or receive payments inside or outside Canada for the following technology and technical assistance?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Made Payments Received Payments Both made and received payments Not applicable
Patents        
Copyrights        
Trademarks        
Industrial designs        
Integrated circuit topography        
Original software        
Packaged or off-the-shelf software        
Databases
Useful life exceeding one year.		        
Other technology and technical assistance
Include technical assistance, industrial processes and know-how.		        

74. In 2022, how much did this business pay to other organizations for technology and technical assistance?

Please report all amounts in thousands of Canadian dollars.

Report '1' for payments made between $1 and $999.

Technology and technical assistance payments

Definitions (equivalent to the Canadian Intellectual Property Office - opens in a new browser window)

  1. Patent
    Government grant giving the right to exclude others from making, using or selling an invention.
  2. Copyright
    Legal protection for literary, artistic, dramatic or musical works, computer programs, performer's performances, sound recordings, and communication signals.
  3. Trademark
    A word, symbol or design, or combination of these, used to distinguish goods or services of one person or organization from those of others in the marketplace.
  4. Industrial design
    Legal protection against imitation of the shape, pattern, or ornamentation of an object.
  5. Integrated circuit topography
    Three-dimensional configurations of the elements and interconnections embodied in an integrated circuit product.
  6. Original software
    Computer programs and descriptive materials for both systems and applications. Original software can be created in-house or outsourced and includes packaged software with customization.
  7. Packaged or off-the-shelf software
    Packaged software purchased for organizational use and excludes software with customization.
  8. Databases
    Data files organized to permit effective access and use of the data including access clinical trial registries and administrative health data for research purposes. Includes partnerships supporting the development of databases, such as patient or clinical trial registries or biobanks, to be used for research purposes (e.g., developing a national bladder cancer patient registry with Bladder Cancer Canada for future research).
  9. Other
    Technical assistance, industrial processes and know-how including technology transfer and know how such as batch pilot production, method develop, and validation related to technology or manufacturing transfer.
In 2022, how much did this business pay to other organizations for technology and technical assistance?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Payments made within Canada
CAN$ '000		 Payments made outside Canada
CAN$ '000
Payments made to parent, affiliated or subsidiary companies 
Patents    
Copyrights    
Trademarks    
Industrial designs    
Integrated circuit topography    
Original software    
Packaged or off-the-shelf software    
Databases
Useful life exceeding one year.		    
Other technology and technical assistance
Include technical assistance, industrial processes and know-how.		    
Total payments made to parent, affiliated or subsidiary companies    
Payments made to other companies, organizations or individuals 
Patents    
Copyrights    
Trademarks    
Industrial designs    
Integrated circuit topography    
Original software    
Packaged or off-the-shelf software    
Databases
Useful life exceeding one year.		    
Other technology and technical assistance
Include technical assistance, industrial processes and know-how.		    
Total payments made to other companies, organizations or individuals    
Total payments made to other organizations for technology and technical assistance    

75. In 2022, how much did this business receive from other organizations for technology and technical assistance?

Please report all amounts in thousands of Canadian dollars.

Report '1' for payments received between $1 and $999.

Technology and technical assistance payments

Definitions (equivalent to the Canadian Intellectual Property Office - opens in a new browser window)

  1. Patent
    Government grant giving the right to exclude others from making, using or selling an invention.
  2. Copyright
    Legal protection for literary, artistic, dramatic or musical works, computer programs, performer's performances, sound recordings, and communication signals.
  3. Trademark
    A word, symbol or design, or combination of these, used to distinguish goods or services of one person or organization from those of others in the marketplace.
  4. Industrial design
    Legal protection against imitation of the shape, pattern, or ornamentation of an object.
  5. Integrated circuit topography
    Three-dimensional configurations of the elements and interconnections embodied in an integrated circuit product.
  6. Original software
    Computer programs and descriptive materials for both systems and applications. Original software can be created in-house or outsourced and includes packaged software with customization.
  7. Packaged or off-the-shelf software
    Packaged software purchased for organizational use and excludes software with customization.
  8. Databases
    Data files organized to permit effective access and use of the data including access clinical trial registries and administrative health data for research purposes. Includes partnerships supporting the development of databases, such as patient or clinical trial registries or biobanks, to be used for research purposes (e.g., developing a national bladder cancer patient registry with Bladder Cancer Canada for future research).
  9. Other
    Technical assistance, industrial processes and know-how including technology transfer and know how such as batch pilot production, method develop, and validation related to technology or manufacturing transfer.
In 2022, how much did this business receive from other organizations for technology and technical assistance?
Table summary
This table contains no data. It is an example of an empty data table used by respondents to provide data to Statistics Canada.
  Payments received from within Canada
CAN$ '000		 Payments received from outside Canada
CAN$ '000
Payments received from parent, affiliated or subsidiary companies
Patents    
Copyrights    
Trademarks    
Industrial designs    
Integrated circuit topography    
Original software    
Packaged or off-the-shelf software    
Databases
Useful life exceeding one year.		    
Other technology and technical assistance
Include technical assistance, industrial processes and know-how.		    
Total payments received from parent, affiliated or subsidiary companies    
Payments received from other companies, organizations or individuals
Patents    
Copyrights    
Trademarks    
Industrial designs    
Integrated circuit topography    
Original software    
Packaged or off-the-shelf software    
Databases
Useful life exceeding one year.		    
Other technology and technical assistance
Include technical assistance, industrial processes and know-how.		    
Total payments received from other companies, organizations or individuals    
Total payments received from other organizations for technology and technical assistance    

Environmental and clean technology R&D expenditures in 2022

76. In 2022, what percentage of this business's total expenditures of $ [Amount] for R&D performed in-house within Canada was related to research and development of environmental and clean technologies?

Environmental and clean technology is defined as any process, product, or service that reduces environmental impacts: through environmental protection activities that prevent, reduce or eliminate pollution or any other degradation of the environment, resource management activities that result in the more efficient use of natural resources, thus safeguarding against their depletion; or the use of goods that have been adapted to be significantly less energy- or resource-intensive than the industry standard.

Report '0' for no environmental and clean technology R&D expenditures.

If precise figures are not available, please provide your best estimate.

Percentage of environmental and clean technology R&D

77. In 2022, in which of the following categories of environmental and clean technology did this business perform R&D activities?

Select all that apply.

Air pollution management: Activities aimed at reducing the emissions of pollutants (including greenhouse gases) to the atmosphere. Include pollution abatement and control (e.g., end-of-pipe processes) and pollution prevention (e.g., integrated processes), as well as related measurement, control, laboratories and the like.

Solid waste management: Activities related to the collection, treatment, storage, disposal, and recycling of all domestic, industrial, non-hazardous and hazardous waste (including low-level radioactive waste). Include monitoring activities. Exclude radioactive waste and mine tailings handling and treatment (to be reported under Protection against radiation and Wastewater management, respectively).

Wastewater management: Activities aimed at pollution reduction or prevention through the abatement of pollutants or the reduction of the release of wastewater. Include measures aimed at reducing pollutants before discharge, reducing the release of wastewater, septic tanks, treatment of cooling water, handling and treatment of mine tailings, etc.

Protection and remediation of soil, groundwater and surface water: Activities aimed at the prevention of pollution infiltration: remediation or cleaning up of soils and water bodies; protection of soil from erosion, salinization and physical degradation; monitoring, control, laboratories and the like. Exclude management of wastewater released to surface waters, municipal sewer systems or soil, or injected underground (to be reported under Wastewater management) and protection of biodiversity and habitat (to be reported under Protection of biodiversity and habitat).

Protection of biodiversity and habitat: Activities related to protecting wildlife and habitat from the effects of economic activity, and to restoring wildlife or habitat that has been adversely affected by such activity. Include related environmental measurements, monitoring, control, laboratories and the like.

Noise and vibration abatement: Activities aimed at controlling or reducing industrial and transport noise and vibration for the sole purpose of protecting the environment. Include preventive in-process modifications at the source, construction of anti-noise/vibration facilities, measurement, control, laboratories and the like.

Protection against radiation: Activities aimed at preventing, reducing, or eliminating the negative consequences of radiation on the environment. This includes all handling, transportation, and treatment of radioactive waste (i.e. waste that requires shielding during normal handling and transportation due to high radionuclide content), the protection of ambient media, measurement, control, laboratories and the like, as well as any other activities related to the containment of radioactive waste. Exclude activities and measures related to low-level radioactive waste (to be reported under Solid waste management), the prevention of technological hazards (e.g., external safety of nuclear power plants), and measures taken to protect workers.

Heat or energy savings and management: Activities aimed at reducing the intake of energy through in-process modifications (such as adjustment of production processes or heat and electricity co-generation), as well as reducing heat and energy losses. This includes insulation activities, energy recovery, measurement, control, laboratories and the like.

Renewable energy: Energy obtained from resources that naturally replenish or renew within a human lifespan (i.e. the resource is a sustainable source of energy). This includes wind, solar, aero-thermal, geothermal, hydrothermal and ocean energy, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases.

  • Air pollution management
    e.g., greenhouse gas control technologies or management services, physical or chemical treatment technologies, air pollution modeling and mapping services
  • Solid waste management
    e.g., collection of waste, recycling and organics, compaction-related technologies, landfill leachate collection and containment technologies
  • Wastewater management
    e.g., physical or chemical treatments of industrial wastewater, mine tailing handling and treatment, biological treatments of sewage
  • Protection and remediation of soil, groundwater and surface water
    e.g., in situ and ex situ biological, physical, chemical, thermal treatments, containment
  • Protection of biodiversity and habitat
  • Noise and vibration abatement
    Exclude R&D related to workers' health and safety.
  • Protection against radiation
    Exclude R&D related to workers' health and safety.
  • Heat and energy savings and management
    e.g., efficient equipment (advance insulation, high efficiency pumps or burners (Energy Star certified), etc.), energy storage technologies (flywheels, fuel cells, etc.), lighting upgrades, smart grid services and associated technologies
  • Fuel efficient vehicles and transportation goods or technologies
    e.g., electric and hybrid vehicles, vehicles using alternative fuels, alternative fuel retrofits on existing vehicles, low-rolling resistance tires
  • Production of energy from renewable sources
    e.g., equipment, services, and technologies used to produce electricity or heat from renewable sources
  • Production of nuclear energy
    e.g., equipment, services, and technologies used to produce electricity or heat from nuclear energy
    Exclude the R&D on feedstock used to produce energy (such as uranium).
  • Other environmental protection or resource management activities
    Specify the other environmental protection or resource management activities

Notification of intent to extract web data

79. Does this business have a website?

Notification of intent to extract web data

Statistics Canada engages in web-data extraction, also known as web scraping, which is a process by which information is gathered and copied from the Web using automated scripts or robots, for retrieval and analysis. As a result, we may visit the website for this business to search for and compile additional information. The use of web scraping is part of a broader effort to reduce the response burden on businesses, as well as produce additional statistical indicators to ensure that our data remain accurate and relevant.
We will strive to ensure that the data collection does not interfere with the functionality of the website. Any data collected will be used by Statistics Canada for statistical and research purposes only, in accordance with the agency's privacy and confidentiality mandate. All information collected by Statistics Canada is strictly protected.

More information regarding Statistics Canada's web scraping initiative.

Learn more about Statistics Canada's transparency and accountability.

If you have any questions or concerns, please contact Statistics Canada Client Services, toll-free at 1-877-949-9492 (TTY: 1-800-363-7629) or by email at infostats@statcan.gc.ca- this link will open in a new window. Additional information about this survey can be found by selecting the following link: Information for survey participants (ISP).

Changes or events

80. Indicate any changes or events that affected the reported values for this business, compared with the last reporting period.

Select all that apply.

  • Outsourcing of R&D project(s)
  • Initiation of new R&D project(s)
  • Completion of existing R&D project(s)
  • Major change in funding of R&D project(s) (loss of funding)
  • Major change in funding of R&D project(s) (increase in funding)
  • Organizational change that affected R&D activities (expansion, reduction, restructuring)
  • Economic change that affected R&D activities
  • Lack of availability of qualified R&D personnel
  • Other
    Specify the other changes or events:
  • No changes or events

Contact person

81. Statistics Canada may need to contact the person who completed this questionnaire for further information.

Is the provided given names and the provided family name the best person to contact?

  • Yes
  • No

Who is the best person to contact about this questionnaire?

  • First name:
  • Last name:
  • Title:
  • Email address:
  • Telephone number (including area code):
  • Extension number (if applicable):
    The maximum number of characters is 5.
  • Fax number (including area code):

Feedback

82. How long did it take to complete this questionnaire?

Include the time spent gathering the necessary information.

  • Hours:
  • Minutes:

83. Do you have any comments about this questionnaire?

Low Code UI with Plotly Dash

By: Jeffery Zhang, Statistics Canada

Introduction

Often with data science work, we build models that are implemented in R or Python. If these models are intended for production, they'll need to be accessible to non-technical users.

A major problem with making data models accessible to non-technical users in production is the friction of creating accessible user interfaces. While it is acceptable for a research prototype to be run via a command line, this type of interface, with all its complexities, is very daunting to a non-technical audience.

Most data scientists are not experienced user interface (UI) developers, and most projects don't have the budget for a dedicated UI developer. In this article, we introduce a tool that allows non-UI specialists to quickly create good enough data UI using Python.

What is Plotly Dash?

Plotly is an open-source data visualization library. Dash is an open-source low-code data application development framework that is built on top of Plotly. Plotly Dash offers a solution to the data UI problem. A non-UI specialist data scientist can develop good enough UI for a data app in just a few days with Plotly Dash in Python. In most projects, investing 2-5 extra work-days to develop an interactive graphical UI is well worth the investment.

How does Plotly Dash work?

Plotly and Dash can be thought of as domain specific languages (DSL). Plotly is a DSL for describing graphs. The central object of Plotly is a Figure, which describes every aspect of a graph such as the axes, as well as graphical components such as bars, lines, or pie slices. We use Plotly to construct Figure objects and then use one of the available renderers to render it to the target output device such as a web browser.

Figure 1 - An example of a Plotly figure.
Description - Figure 1: Example of a Plotly figure

This is an example of a figure generated by Plotly. It is an interactive bar chart that allows the user to hover over the individual bars with the mouse and see the data values associated with each bar.

Dash provides two DSLs and a web renderer for Plotly Figure objects.

The first Dash DSL is for describing the structure of a web UI. It includes components for HTML elements such as div, p as well as UI controls such as Slider, DropDown. One of the key components of the Dash web DSL is the Graph component, which allows us to integrate a Plotly Figure into the Dash web UI.

Here's an example of a minimal Dash application.

From dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')

app = Dash(__name__)

app.layout = html.Div([
    html.H1(children='Title of Dash App', style={'textAlign':'center'}),
    dcc.Dropdown(df.country.unique(), 'Canada', id='dropdown-selection'),
    dcc.Graph(id='graph-content')
])

if __name__ == '__main__':
    app.run_server(debug=True)

This is what it looks like in a web browser.

Figure 2 - Minimal Dash application displayed in a web browser.
Description - Figure 2: Minimal app displayed in a browser.

This is an example of a minimal application created with Plotly Dash. It is a sample application that visualizes the growth of the Canadian population from 1950 to present using a line chart. The visualization is interactive and the user can hover the mouse over points on the blue line to see the data values associated with that point.

The second Dash DSL is for describing reactive data flows. This allows us to add interactivity to the data app by describing how data flows from user input components to the data model, and then back out to the UI.

Adding the following code to the above example creates a reactive data flow between the input component dropdown-selection, the function update graph, and the output graph. Whenever the value of the input component dropdown-selection changes, the function update graph is called with the new value of dropdown-selection and the return value of update-graph is output to the figure property of the graph-content object. This updates the graph based on the user's selection in the drop-down component.

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def update_graph(value):
    dff = df[df.country==value]
    return px.line(dff, x='year', y='pop')

Useful features of Dash

Below are some common data app scenarios and how Dash features support those scenarios.

Waiting for long computations

Sometimes a data model will take a long time to run. It makes sense to give the user some feedback during this process so they know the data model is running and the application hasn't crashed. It would be even more useful to give a progress update so the user knows roughly how much work has been completed and how much is remaining.

We may also realize we made a mistake when setting the parameters of a long running job, and we'd like to cancel the running job and start over after making corrections. The Dash feature for implementing these scenarios is called Background callbacks.

Here's an example of a simple Dash application that features a long running job with the progress bar and cancellation.

Figure 3 - Example of simple Dash application with progress and cancellation.
Description - Figure 3: Long running job with progress bar and cancellation

This is an example of a Plotly Dash application involving a long running task with a progress bar to display the progress of the task. It has 2 buttons. The "Run Job!" button is initially enabled and clicking it starts the task and the progress bar. Once the task is running, the "Run Job!" button becomes disabled, and the "Cancel Running Job!" button becomes enabled while the task is running. Clicking it before the task is complete will cancel the running task.

Duplicate callbacks

Normally, the value of an output is uniquely determined by one callback. If there are multiple callbacks that update the same output, we'd face the scenario that the output has multiple values at the same time, and we'd not know which is the correct one.

However, sometimes we might want to take the risk of binding multiple callbacks to the same output to make things simpler. Dash allows us to do this by explicitly specifying that we're willing to allow duplicate outputs. This feature is enabled by setting the allow duplicate parameter on Output to True. Here's an example:

app.layout = html.Div([
    html.Button('Draw Graph', id='draw-2'),
    html.Button('Reset Graph', id='reset-2'),
    dcc.Graph(id='duplicate-output-graph')
])

@app.callback(
    Output('duplicate-output-graph', 'figure', allow_duplicate=True),
    Input('draw-2', 'n_clicks'),
    prevent_initial_call=True
)
def draw_graph(n_clicks):
    df = px.data.iris()
    return px.scatter(df, x=df.columns[0], y=df.columns[1])

@app.callback(
    Output('duplicate-output-graph', 'figure'),
    Input('reset-2', 'n_clicks'),
)
def reset_graph(input):
    return go.Figure()

app.run_server(debug=True)
Figure 4 - Example of a Dash application that uses duplicate callbacks.
Description - Figure 4: Graph that is updated by two different buttons.

This is an example of a Plotly Dash application that uses duplicate callbacks. It has 2 buttons that both target the same output, which is the graph below. Clicking the "Draw Graph" button renders the graph, while clicking the "Reset Graph" button clears the graph. Since both buttons target the same output, this scenario requires the duplicate callback feature of Dash.

In this case, we have two buttons for updating a graph: Draw and Reset. The graph will be updated by the last button that was pressed. While this is convenient, there's a risk to designing UI this way. On the desktop with one mouse pointer, button clicks can be assumed to be unique in time. However, on a multi-touch screen such as a smartphone or tablet, two buttons can be clicked at the same time. In general, once we allow duplicate callbacks, the output becomes potentially indeterminate. This can lead to some bugs that are very difficult to replicate.

This feature is both convenient and potentially dangerous. So use at your own risk!

Custom components

Sometimes the set of components that come with Dash are not enough. The web UI of Dash is built with React, and Dash provides a convenient tool for integrating custom React components into Dash. It's beyond the scope of this article to go into the details of React and Dash-React integration. However, you can read more about this – see: Build your own components.

Error display

Sometimes an error occurs during computation that is due to problems with the data, the code, or user error. Instead of crashing the application, we might want to display the error to the user and provide some feedback on what they can do to rectify it.

There are two Dash features that are used for this scenario: multiple outputs and dash.no_update.

multiple outputs is a Dash feature that allows callbacks to return multiple outputs in the form of a tuple.

dash.no_update is a value that can be returned in an output slot to represent no change in that output.

Here's an example that uses both of these features to implement an error display:

@app.callback(
    Output('out', 'text'),
    Output('err', 'text'),
    Input('num', 'value')
)
def validate_num(num):
    if validate(num):
        return "OK", ""
    else:
        return dash.no_update, "Error"

Partial Updates

Since Dash callback computations occur on the server, to display the results on the client, all the return values from the callback have to be sent to the client on each update.

Sometimes these updates will involve very large Figure objects, which consume a lot of bandwidth and slow the update process. This will negatively impact the user experience. The simple way to implement callback updates is to perform monolithic updates on large data structures such as Figure even if only a small part of it has change, such as the title.

To optimize bandwidth usage and improve the user experience, Dash has a feature called Partial Update. This feature introduces a new type of return value to callbacks called a Patch. A Patch describes which subcomponents of a larger data structure should be updated. This allows us to avoid sending an entire data structure across the network when only a portion of it needs to be updated.

Here is an example of Partial Updates that updates only the font colour title for the figure instead of the whole figure:

From dash import Dash, html, dcc, Input, Output, Patch
import plotly.express as px
import random

app = Dash(__name__)

df = px.data.iris()
fig = px.scatter(
    df, x="sepal_length", y="sepal_width", color="species", title="Updating Title Color"
)

app.layout = html.Div(
    [
        html.Button("Update Graph Color", id="update-color-button-2"),
        dcc.Graph(figure=fig, id="my-fig"),
    ]
)

@app.callback(Output("my-fig", "figure"), Input("update-color-button-2", "n_clicks"))

def my_callback(n_clicks):
    # Defining a new random color
    red = random.randint(0, 255)
    green = random.randint(0, 255)
    blue = random.randint(0, 255)
    new_color = f"rgb({red}, {green}, {blue})"

    # Creating a Patch object
    patched_figure = Patch()
    patched_figure["layout"]["title"]["font"]["color"] = new_color
    return patched_figure

if __name__ == "__main__":
    app.run_server(debug=True)

Dynamic UI and pattern matching callbacks

Sometimes, we can't define the data flow statically. For example, if we want to implement a filter stack that allows the user to flexibly add filters, the specific filters that the user will add won't be known ahead of time. If we want to define data flows involving the input components that the user adds at runtime, we can't do it statically.

Here's an example of a dynamic filter stack where the user can add new filters by clicking the ADD FILTER button. The user can then select the value of the filter via the drop down that is dynamically added.

Figure 5 - Example of dynamic UI in Dash.
Description - Figure 5: Dynamic filter stack

This is an example of a Plotly Dash application that uses dynamic UI and pattern matching callbacks. Clicking the "Add Filter" button adds an additional dropdown box. Since the dropdown boxes are added dynamically, we cannot bind them to callbacks ahead of time. Using the pattern matching callback feature of Dash allows us to bind dynamically created UI elements to callbacks by using a pattern predicate.

Dash supports this scenario by allowing us to bind callbacks to data sources dynamically via a pattern matching mechanism.

The follow code implements the above UI:

From dash import Dash, dcc, html, Input, Output, ALL, Patch

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Add Filter", id="add-filter-btn", n_clicks=0),
        html.Div(id="dropdown-container-div", children=[]),
        html.Div(id="dropdown-container-output-div"),
    ]
)


@app.callback(
    Output("dropdown-container-div", "children"), Input("add-filter-btn", "n_clicks")
)
def display_dropdowns(n_clicks):
    patched_children = Patch()
    new_dropdown = dcc.Dropdown(
        ["NYC", "MTL", "LA", "TOKYO"],
        id={"type": "city-filter-dropdown", "index": n_clicks},
    )
    patched_children.append(new_dropdown)
    return patched_children


@app.callback(
    Output("dropdown-container-output-div", "children"),
    Input({"type": "city-filter-dropdown", "index": ALL}, "value"),
)
def display_output(values):
    return html.Div(
        [html.Div(f"Dropdown {i + 1} = {value}") for (i, value) in enumerate(values)]
    )


if __name__ == "__main__":
    app.run_server(debug=True)

Instead of defining the DropDown components statically, we create a dropdown-container-div which serves as a container for all the DropDown components that the user will create. When we create the DropDown components in display_dropdowns, each new DropDown component is created with an id. Normally this id value would be a string, but in order to enable pattern matching callbacks, Dash also allows the id to be a dictionary. This could be an arbitrary dictionary, so the specific keys in the above example are not special values. Having a dictionary id allows us to define very fine-grained patterns to be matched over each key of the dictionary.

In the above example, when the user adds new DropDown components, the id of the dynamic DropDown components are tagged with ids in sequence that looks like this:

  1. '{"type": "city-filter-dropdown", "index": 1}
  2. '{"type": "city-filter-dropdown", "index": 2}
  3. '{"type": "city-filter-dropdown", "index": 3}

Then, in the metadata for the display_output callback, we define its input as Input({"type": "city-filter-dropdown", "index": ALL}, "value") which then match all components where the id has type equal to city-filter-dropdown. Specifying "index": ALL means that we match any index value.

In addition to ALL, Dash also supports additional pattern matching criteria such as MATCH and ALLSMALLER. To learn more about this feature, visit Pattern Matching Callbacks.

Examples

Here are some examples of apps built with Dash:

Figure 6 - Dash application for object detection.
Description - Figure 6: Object detection

This is an example of a Plotly Dash application that is used for Object Detection. It visualizes the bounding boxes of the detected objects in a scene.

Figure 7 - Dash built dashboard for wind data.
Description - Figure 7: Dashboard

This is an example of a Plotly Dash dashboard application. It visualizes wind speed and direction data.

Figure 8 - Dash application for visualizing Uber rides in New York City.
Description - Figure 8: Uber Rides

This is an example of a Plotly Dash dashboard application. It visualizes the temporal and spatial distribution of Uber rides in Manhattan.

Figure 9 - Dash dashboard for US opioid data.
Description - Figure 9: Opioid map

This is an example of a Plotly Dash dashboard application. It visualizes the spatial distribution of opiod deaths in the US at the county level.

Figure 10 - Dash UI for visualizing point clouds.
Description - Figure 10: Point Cloud

This is an example of a 3D visualization application developed using Plotly Dash. It visualizes the 3D point cloud data collected from a LIDAR from the perspective of a car.

Figure 11 - Dash UI with component for visualizing 3D meshes.
Description - Figure 11: 3D Mesh

This is an example of a 3D mesh visualization application developed using Plotly Dash. It visualizes the reconstruction of the brain using MRI data.

For more examples, visit the Dash Enterprise App Gallery.

Conclusion

Good UI has the potential to add value to projects by making the project deliverables more presentable and usable. For production systems that will be used for a long time, the upfront investment in UI can pay dividends over time with a lower learning curve, reduce user confusion, and improve user productivity. Plotly Dash helps to significantly lower the cost of UI development for data apps, this can help increase the return on investment in UI development for data apps.

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Reference

  1. Plotly: Low-Code Data App Development
  2. Background callbacks: Plotly - Background Callbacks
  3. Custom Components: Plotly - Build Your Own Components
  4. Pattern matching callbacks: Plotly - Pattern-Matching Callbacks
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Self-Supervised Learning in Computer Vision: Image Classification

By: Johan Fernandes, Statistics Canada

Introduction

Computer Vision (CV) comprises tasks such as Image Classification, Object Detection and Image SegmentationFootnote 1. Image Classification deals involves assigning an entire image to one of several finite classes. For example, if an image contains a "Dog" occupies 90% of the space, then it is labeled as a "Dog". Multiple Deep Learning (DL) models using Neural Networks (NN) have been developed to accurately classify images with high accuracy. The state-of-the-art models for this task utilize NNs of various depths and widths.

These DL models are trained on multiple images of various classes to develop their classification capabilities. Like training a human child to distinguish between images of a "Car" and a "Bike", these models need to be shown multiple images of classes such as "Car" and "Bike" to generate this knowledge. However, humans have the additional advantage of developing context through observing our surroundings. Our minds can pick up sensory signals (audio and visual) that help us develop this knowledge for all types of objectsFootnote 2. For instance, when we observe a car on the road our minds can generate contextual knowledge about the object (car) through visual features such as location, color, shape, lighting surrounding the object, and the shadow it creates.

On the other hand, a DL model specifically for CV must be trained to develop such knowledge which is stored in the form of weights and biases it utilizes in its architecture. These weights and biases are updated with this knowledge by training the model. The most popular training process, called Supervised Learning, involves training the model with the image and the corresponding label to improve its classification capability. However, generating labels for all images is time consuming and costly, as it involves human annotators manually generating labels for each image. On the other hand, Self-Supervised Learning (SSL) is a new training paradigm that can be used to train DL models to classify images without the bottleneck of having well-defined labels for each image during training. In this work I, will describe the current state of SSL and its impact on image classification.

Significance of Self-Supervised Learning (SSL)

SSL aims to set up an environment to train the DL model to extract maximum features or signals from the image. Recent studies have shown that the feature extraction capability DL models is restricted when trained with labels, as they must pick signals that will help them develop a pattern to associate similar images with that labelFootnote 2Footnote 3. With SSL the model is trained to understand the sensory signals (e.g., shape and outline of objects) from the input images without being shown the associated labels.

Additionally, since SSL does not limit the model to develop a discrete representation (label) of an image, it can learn to extract much richer features from an image than its supervised counterpart. It has more freedom to improve how it represents an image, as it no longer needs to be trained to associate a label with an imageFootnote 3. Instead, the model can focus on developing a representation of the images through the enhanced features it extracts and identifying a pattern so that images from the same class can be grouped together.

SSL uses more feedback signals to improve its knowledge of an image than supervised learningFootnote 2. As a result, the term self-supervised is being used more frequently in place of unsupervised learning as an argument can be made that DL models receive input signals from the data rather than labels.  However, they do have some form of supervision and are not completely unsupervised in the training process. In the next section I will describe the components needed for self-supervised learning.

These signals are enhanced through a technique known as data augmentation, in which the image is cropped, certain sections of the image are hidden, or the color scheme of the image is modified. With each augmentation, the DL model receives a different image of the same class or category as the original image. By exposing the model to such augmented images, it can be trained to extract rich features based on the visible sections of the imageFootnote 4. Furthermore, this training method removes the overhead of generating labels for all images, opening up the possibility of adapting image classification in fields where labels are not readily available.

Components of self-supervised learning methods: 

Encoder / Feature Extractor:

As humans, when we look at an image, we can automatically identify features such as the outline and colour of objects to determine the type of object in the image. For a machine to perform such a task, we utilize a DL model, which we refer to as an encoder or a feature extractor since it can automatically encode and extract features of an image. The encoder consists of sequentially ordered NN layers, as shown in Fig 1.

Figure 1: Components of a Deep Learning encoder / feature extractor

Figure 1: Components of a Deep Learning encoder / feature extractor.

The image describes the structure of an encoder or feature extractor along with an example of the input it receives and the output it provides. The input to the encoder is an image which is shown as an image of a dog in this instance and the output is a vector that can represent that image in a higher dimensional space. The encoder consists of multiple single layered neural layers that are stacked on top of each other or next to each other as shown in this image. Each layer consists of multiple convolutional neurons. These layers will pick essential features that will help the encoder to represent the image as a vector which is the final output of the encoder. The vector that it produces at the end will have n dimensions where each dimension will be reserved for a feature. This vector can be projected in n dimension space and can be used for clustering vectors of the same class such as a dog or a cat.

An image contains multiple features. The encoder's job is to extract only the essential features, ignore the noise, and convert these features into a vector representation. This encoded representation of the image can be projected into n-dimensional or latent space, depending on the size of the vector. As a result, for each image, the encoder generates a vector to represent the image in that latent space. The underlying principle is to ensure that vectors of images from the same class can be grouped together in that latent space. Consequently, vectors of "Cats" will be clustered together while vectors of "Dogs" will form a separate group, with both groups of vectors distinctly separated from each other.

The encoders are trained to improve their representation of images so that they can encode richer features of the images into vectors that will help distinguish these vectors in latent space. The vectors generated by encoders can be used to address multiple CV tasks, such as image classification and object detection. The NN layers in the encoder would traditionally be convolutional neural network (CNN) layers as shown in Fig 1; however, the latest DL models utilize Attention Network (AN) layers in their architecture. These encoders are called Transformers, and recent works have begun to use them to address image classification due to the impact they have provided in the field of natural language processing. The vectors can be fed to classification models, which can be a series of NN layers or a clustering-based models such as K-Nearest Neighbor (KNN) classifier. Current literature on self-supervised learning utilizes KNN classifiers to cluster images, as they only require the number of clusters as an argument and do not need labels.

Data Augmentation:

Labels of images are not provided to encoders trained in a self-supervised format. Consequently, the representation capability of encoders has to be improved solely from the images they receive. As humans, we can look at objects from different angles and perspectives to understand the shape and outline of objects. Similarly, augmented images assist encoders by providing different perspectives of the original training images. These image perspectives can be developed by applying strategies such as Resized Crop and Color Jitter to the image, as shown in Fig 2. Augmented images enhance the encoder's ability to extract rich features from an image by learning from one section or patch of the image and applying that knowledge to predict other sections of the imageFootnote 4.

Figure 2: Augmentation strategies that can be used to train encoders in a self-supervised format. These augmentation strategies are randomly applied to the image when the encoders are trained.

Figure 2: Augmentation strategies that can be used to train encoders in a self-supervised format. These augmentation strategies are randomly applied to the image when the encoders are trained.

The image contains four ways to represent an image for SSL training. An image of a Corgi dog is used as a sample in this case. The first way is the original image by itself with no additional filters to the image. The second way is to horizontally flip the image. Hence the image of the Corgi Dog which was originally looking to its left is now looking to its right. The third way is to resize the image and to crop a section of the image which has the object of interest. In this case the Corgi dog is in the center of the image so a crop of the dog’s head and part of it’s body is used as an augmented image. The last way is to change the color scale of the image through color jitter augmentation. The color of the dog which was golden in color in the original image will change to blue color as per this augmentation strategy.

Siamese Network architecture:

Many self-supervised learning methods use the Siamese Network architecture to train encoders. As shown in Fig 3, a Siamese Network consists of two encoders that could share the same architecture (example: ResNet-50 for both encoders)Footnote 3. Both encoders receive batches of images during training (training batches). From each batch, both encoders will receive an image, but with different augmentation strategies applied to the images they receive. As shown in Fig 3, we consider the two encoders E1 and E2. In this network, image (x) is augmented by two different strategies to generate x1 and x2, which are respectively are fed to E1 and E2. Each encoder then provides a vector representation of the image, which can be used to measure similarity and calculate loss.

During the training phase, the weights between the two encoders are updated through a process known as knowledge distillation. This involves a student-teacher training format. The student encoder is trained in an online format where undergoes forward and backward propagation, while the weights of the teacher encoder are updated at regular intervals using stable weights from the student with techniques such as Exponential Moving Average (EMA)Footnote 3.

Figure 3 : A Siamese Network consisting of two encoders trained in parallel to generate representations of images, ensuring that representations of images from the same class are similar to each other.

Figure 3: A Siamese Network consisting of two encoders trained in parallel to generate representations of images, ensuring that representations of images from the same class are similar to each other.

The image describes the layout of a Siamese network which is a popular technique for training self supervised encoders. The Siamese network consists of two encoders which will have the same neural network architecture. Both encoders are trained in parallel. The image shows that an image of a corgi dog is sent to both encoders. One encoder behaves as a student which is called E1 while the other encoder behaves as a teacher which is called E2. E1 receives an image of a corgi dog with resize crop and horizontal flip augmentation. E2 receives an image of the same corgi dog as E1 with resize crop and color jitter augmentation. The image also shows that both encoders share their knowledge through weights at regular intervals in the training phase. Both encoders provide vector representations as their final output. A similarity score is calculated to measure if the E1 and was able to learn from the stable weights of the E2 and improve its representational knowledge.

Contrastive vs Non-contrastive SSL methods:

All available SSL methods utilize these components, with some additional changes to improve each other's performance. These learning methods can be grouped into two categories:

Figure 4: Positive and negative pair of image patches.

Figure 4: Positive and negative pair of image patches.

The image shows how you can create positive and negative pairs of images. The image is split into two parts. In the first part there are two different images of corgi dogs. Resize crop augmentation is used to extract the important sections such as the face and body of the dogs and to create two new images. The new augmented cropped images from both corgi dog images can now be considered as a positive pair as shown in the image. In the second part of this image an example of a negative pair of images is shown. Unlike the first part there is one original image of a Corgi dog and another of a cat. After resize crop augmentation is performed on these images we get to see two new images of the originals. One has the cat's face while the other has the Corgi dog's face. These new images will be considered as negative pair of images.

Contrastive learning methods

These methods require positive and negative pairs of each image to train and improve the representation capability of encoders. They utilize contrastive loss to train the encoders in a Siamese network with knowledge distillation. As shown in Fig 4, a positive pair would be an augmented image or patch from the same class as the original image. A negative pair would be an image or patch from another image that belongs to a different class. The underlying function of all contrastive learning methods is to help encoders generate vectors so that vectors of positive pairs are closer to each other, while those of negative pairs are further away from each other in latent space.

Many popular methods such as SimCLRFootnote 4 and MoCoFootnote 5, are based on this principle and work efficiently on large natural object datasets like ImageNet. Positive and negative pairs of images are provided in each training batch to prevent the encoders from collapsing into a state where they produce vectors of only a single class. However, to train the encoders with negative pairs of images, these methods rely on large batch sizes (upwards of 4096 images in a training batch). Furthermore, many datasets, unlike ImageNet, do not have multiple images per class, making generating negative pairs in each batch a difficult, if not impossible, task. Consequently, recent research is leaning towards non-contrastive based methods.

Non-Contrastive learning methods

Methods such as DINOFootnote 3, BYOLFootnote 6 and BarlowTwinsFootnote 7 train encoders in a self-supervised format without the need to distinguish images as positive and negative pairs in their training batches. Methods like DINO continue to use the Siamese Network in a student-teacher format and rely on heavy data augmentation. However, they improve on contrastive methods with a few enhancements:

  • Patches of images provide a local view of the image to the student and a global view of the image to the teacher encoderFootnote 3.
  • A prediction layer is added to the student encoder to generate a probability-based outputFootnote 3. This layer is only used during training.
  • Instead of calculating contrastive loss between pairs of images, the output from the encoders is used to calculate a classification type of loss, such as cross entropy or L2 loss to determine if the output vectors from the student and teacher encoders are similar or not Footnote 3, Footnote 6, Footnote 7, Footnote 8.
  • Employing EMA or any moving average method to update the teacher network's weights from the online weights of the student network, while avoiding backpropagation on the teacher network Footnote 3, Footnote 6, Footnote 7, Footnote 8.

Unlike contrastive methods, these methods do not require a large batch size for training and do not need additional overhead to ensure negative pairs in each training batch. Additionally, deep learning (DL) models such as Vision Transformer, which have the capability to learn from the local view of an image and predict other similar local views while also considering at the global view have replaced conventional CNN encoders. These models have enhanced non-contrastive methods to surpass image classification accuracies of supervised learning techniques.

Conclusion

Self-supervised learning is a training process that can help DL models train more efficiently than popular supervised learning methods without the use of labels. This efficiency is evident in the higher accuracy that DL models have achieved on popular datasets such as ImageNet when trained in a self-supervised setup compared to a supervised setup. Furthermore, self-supervised learning eliminates the need for labeling images before training, providing an additional advantage. The future looks bright for solutions that adopt this type of learning for image classification tasks as more research is being conducted on its applications in fields that do not involve natural objects, such as medical and document images.

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Evaluation of Statistics Canada's Centre for Gender, Diversity and Inclusion Statistics

Evaluation Report

August 2023

How the report is structured

The report in short

In line with the Government of Canada's priority to make more detailed data accessible on gender and diversity characteristics to support and inform the development of equitable policies and programs, Budget 2018 committed funds for Statistics Canada to create the Centre for Gender, Diversity and Inclusion Statistics (CGDIS). While the original focus of the CGDIS was mainly on Gender-based Analysis Plus (GBA Plus), the scope and mandate broadened over time and now include other groups such as 2SLGBTQI+ and racialized groups.

Through its work, the CGDIS aims to:

  • Address important gaps in available data and analysis to make detailed data accessible for increased analysis on gender, diversity and inclusion (GDI) in Canada;
  • Support data development and capacity-building strategies to measure GDI outcomes; and
  • Increase knowledge and capacity on GDI.

These measures extend to conducting intersectional analyses and contributing to training initiatives that will help build an understanding of the barriers that different groups face and how best to support them with evidence-based policies.

The CGDIS strives to achieve its mandate through the following three objectives:

  1. Reporting to Canadians about the state and progress of GDI in Canada;
  2. Generating new information to increase knowledge and develop new concepts and measures to address intersecting identity characteristics; and
  3. Building statistical capacity to increase knowledge of GDI and GBA Plus.

Moreover, as Statistics Canada's centre of excellence for GDI-related data, the CGDIS has also been assigned a key role in supporting the Disaggregated Data Action Plan, which aims to fill data and knowledge gaps around GDI.

This evaluation was conducted by Statistics Canada in accordance with the Treasury Board Policy on Results and Statistics Canada's Integrated Risk-Based Audit and Evaluation Plan (2021/2022 to 2025/2026). The objective of the evaluation is to assess the relevance of the CGDIS, its effectiveness in achieving intended results and its readiness to move forward. Effectiveness was examined in terms of its progress towards its objectives.

The evaluation methodology consisted of a document review and interviews. Interviews were conducted with Statistics Canada's staff (i.e., CGDIS staff, staff from other divisions that partnered or worked with the CGDIS, and regional staff), as well as with partners and users external to Statistics Canada. The findings outlined in this report are based on the triangulation of these data collection methods.

Key findings and recommendations

The CGDIS made progress on all three key objectives and has met the key deliverables outlined in its strategic proposal. The Gender, Diversity and Inclusion Statistics (GDIS) Hub was launched in September 2018, and the Centre delivered several products over the period. In addition, GBA Plus training material was developed in collaboration with Women and Gender Equality Canada and the Canada School of Public Service, and hub tours were delivered.

The CGDIS' engagement strategy promoted the awareness and use of the GDIS Hub and related products but mainly among federal government organizations (FGOs). Overall, the CGDIS met the needs of most FGOs, particularly those who were regular clients of the Centre. While the needs of non-FGOs were met to a lesser extent, most of them still supported the work of the CGDIS and underscored the importance of the GDIS Hub. Data needs varied across user groups, but they all expressed a desire to have more disaggregated data to better understand the intersectionality as opposed to binary interactions. Many opportunities for improvements were also noted for the presentation of data, overall functionality of the Hub and outreach efforts to non-FGOs.

The CGDIS has strategically transformed its organization to adapt and evolve. However, gaps in its overall program management, oversight and capacity are risks. Having robust planning and prioritization processes, relevant performance measurement, clearly defined roles and responsibilities, and proper governance are key considerations as the CGDIS moves forward. A more strategic targeted approach is also required to improve outreach and engagement with non-FGOs.

In light of these findings, the following recommendations are proposed:

Recommendation 1

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes a recurring activity prioritization and decision-making process at the CGDIS level which would facilitate the ongoing review and rebalancing of activities; and
  • Develops and implements key performance measures that will support the monitoring of activities in view of the objectives set forth by the Centre.

Recommendation 2

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes or leverages a senior-level governance body to provide guidance; and
  • Formalizes roles and responsibilities with internal partners such as other program areas.

Recommendation 3

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS takes steps to broaden and enhance its outreach, consultation and engagement efforts to include external non-FGO stakeholders.

Acronyms and abbreviations

CGDIS
Centre for Gender, Diversity and Inclusion Statistics
DDAP
Disaggregated Data Action Plan
FGO
Federal government organization
GBA Plus
Gender-based Analysis Plus
GDI
Gender, diversity and inclusion
GDIS
Gender, Diversity and Inclusion Statistics
2SLGBTQI+
Two-spirit, lesbian, gay, bisexual, transgender, queer, intersex and additional sexually and gender diverse
WAGE
Women and Gender Equality Canada

What is covered

The evaluation was conducted in accordance with the Treasury Board Policy on Results and Statistics Canada's Integrated Risk-Based Audit and Evaluation Plan (2021/2022 to 2025/2026). The objective of the evaluation is to assess the relevance of the Centre for Gender, Diversity and Inclusion Statistics (CGDIS), its effectiveness in achieving intended results and its readiness to move forward. Effectiveness was examined in terms of its progress towards its objectives.

The Centre for Gender, Diversity and Inclusion Statistics

Mandate and objectives

In line with the Government of Canada's priority to make more detailed data accessible on gender and diversity characteristics to support and inform the development of equitable policies and programs, Budget 2018 committed funds for Statistics Canada to create the CGDIS. While the original focus of the Centre was mainly on Gender-based Analysis Plus (GBA Plus)Footnote 1, the scope and mandate broadened over time and now include other groups such as 2SLGBTQI+ and racialized groups.

Through its work, the CGDIS aims to:

  • Address important gaps in available data and analysis to make detailed data accessible for increased analysis on gender, diversity and inclusion (GDI);
  • Support data development and capacity-building strategies to measure GDI outcomes; and
  • Increase knowledge and capacity on GDI in Canada.

These measures extend to conducting intersectional analyses and contributing to training initiatives that will help build an understanding of the barriers that different groups face and how best to support them with evidence-based policies.

The CGDIS strives to achieve its mandate through the following three objectives:

  1. Reporting to Canadians about the state and progress of GDI in Canada;
  2. Generating new information to increase knowledge and develop new concepts and measures to address intersecting identity characteristics; and
  3. Building statistical capacity to increase knowledge of GDI and GBA Plus.

Budget 2021 also committed funds to Statistics Canada to implement the Disaggregated Data Action Plan (DDAP), which aims to fill data and knowledge gaps around GDI. The funding is intended to enhance statistics on diverse populations and support more representative data collection. In doing so, the DDAP is expected to support the Government's and society's efforts to address gender gaps and systemic racism. As Statistics Canada's centre of excellence for GDI-related data, the CGDIS has also been assigned a key role in supporting the DDAP.

Key activities

Table 1 below highlights the CGDIS' key activities under each of its three objectives, including examples of actions.

Table 1. Centre for Gender, Diversity and Inclusion Statistics' key activities under each objective
Reporting to Canadians Generating new information Building statistical capacity
Key activities
  • Tracking, aggregating and disseminating relevant data through the Gender, Diversity and Inclusion Statistics (GDIS) Hub
  • Supporting evidence-based analysis and policy development
  • Producing new analytical products and innovative visual tools
  • Providing insights on important issues faced by different gender and diversity groups
  • Increasing new knowledge on gender, diversity and other intersecting identities
  • Identifying data gaps for more inclusive data
  • Developing new concepts and measures to address identified data gaps
  • Using leading-edge methods and techniques to link or integrate data from various sources
  • Establishing partnerships to build and enhance statistical knowledge and literacy on Gender-based Analysis Plus (GBA Plus), diversity and inclusion
  • Developing, sharing, and advising on standards and best practices for measuring concepts related to gender, diversity and inclusion
Examples of work being carried out by the Centre for Gender, Diversity and Inclusion Statistics (CGDIS)
  • Developing, launching, updating and maintaining the GDIS Hub
  • Developing the Gender Results Framework indicators and tables
  • Posting research and analytical products and infographics on the GDIS Hub
  • Providing corporate support on GBA Plus
  • Developing research and analytical products, including infographics on gender, diversity and inclusion in Canada.
  • Providing subject-matter expertise and support (e.g., developing standards) to external and internal stakeholders
  • Establishing and leading Statistics Canada's GBA Plus Responsibility Network
  • Providing training (including hub tours and workshops) to internal and external stakeholders
  • Conducting outreach and raising awareness of the CGDIS and the Hub to internal and external stakeholders
  • Leading the update of the visible minority concept and corresponding statistical standards

Structure

As depicted by Figure 1, the CGDIS is led by an assistant director who reports to the Director of the Diversity and Sociocultural Statistics Division. The division falls under the responsibility of the Director General of the Justice, Diversity and Population Statistics Branch, which is within the Social, Health and Labour Statistics Field.

At the time of the evaluation, the CGDIS was comprised of the following two sectionsFootnote 2:

  • Leadership, Engagement and Partnership; and
  • Research, Analysis and Subject Matter.

Both sections directly mirrored the three key objectives of the CGDIS. The Leadership, Engagement and Partnership section largely delivered on the CGDIS' Reporting to Canadians and Building statistical capacity objectives, while the Research, Analysis and Subject Matter section largely delivered on the Generating new information objective.

The Leadership, Engagement and Partnership section was responsible for updating and maintaining the GDIS Hub, delivering GDIS Hub tours, conducting engagement and outreach activities, and holding consultations to ensure that the Hub is meeting the needs of users.

The Research, Analysis and Subject Matter section consisted of two teams, each led by a chief:

  • Gender Research and GBA Plus Corporate Reporting; and
  • Immigration and Ethnocultural Statistics and the DDAP Secretariat.

The GBA Plus Corporate Reporting team was established to respond to corporate requests, including reviewing documents such as Treasury Board submissions, and to ensure that the GBA Plus lens and analysis were complete and integrated. Immigration and Ethnocultural Statistics was previously a centre on its own at Statistics Canada; however, because of the broadening mandate of the CGDIS and the resulting overlap, it was subsequently integrated into the CGDIS. Finally, the DDAP Secretariat, responsible for monitoring the implementation of the DDAP across Statistics Canada, was added.

To help with the high volume of requests that the CGDIS receives, a SWAT Team was established to channel ad hoc and urgent requests to the appropriate areas in the Centre for action. However, since the SWAT Team does not have the authority to assign requests to other divisions within or outside the field, it is dependent on their availability to help.

Figure 1. Structure of the Centre for Gender, Diversity and Inclusion Statistics at the time of the evaluation
The figure 1 depicts the structure of the Centre for Gender, Diversity and Inclusion Statistics (CGDIS) at the time of the evaluation.
Description - Figure 1. Structure of the Centre for Gender, Diversity and Inclusion Statistics at the time of the evaluation

The figure 1 depicts the structure of the Centre for Gender, Diversity and Inclusion Statistics (CGDIS) at the time of the evaluation.

At the time of the evaluation, the CGDIS was comprised of the Leadership, Engagement and Partnership Section, which is led by a chief, as well as the Research, Analysis and Subject Matter Section, which consisted of the following two teams, each led by a chief:

  1. Gender Research and GBA Plus Corporate Reporting; and
  2. Immigration and Ethnocultural Statistics and the DDAP Secretariat.

The CGDIS is led by an assistant director who reports to the Director of the Diversity and Sociocultural Statistics Division. The division falls under the responsibility of the Director General of the Justice, Diversity and Population Statistics Branch.

The evaluation

The scope of the evaluation included the key activities of the CGDIS, which support its three objectives (namely, reporting to Canadians, generating new information and building statistical capacity). Nonetheless, activities undertaken by the CGDIS beyond the three objectives were also captured to provide a complete picture of the activities performed by the Centre.

The scope was established in consultation with CGDIS management.

The evaluation was conducted from January to November 2022. The three evaluation issues and questions that were identified for review are outlined in Table 2.

Table 2. Evaluation issues and questions
Evaluation issues Evaluation questions
1. Effectiveness: Progress towards objectives

To what extent is the Centre for Gender, Diversity and Inclusion Statistics (CGDIS) progressing towards the achievement of its three key objectives?

  1. Reporting to Canadians about the state and progress of gender, diversity and inclusion (GDI) in Canada.
  2. Generating new information to increase new knowledge on GDI and develop new concepts and measures to address intersecting identity characteristics.
  3. Building statistical capacity to increase knowledge of GDI and Gender-based Analysis Plus.
2. Effectiveness and relevance: Awareness and usefulness To what extent are stakeholders aware of the CGDIS' key activities and to what extent are their needs met?
3. Moving forward To what extent is the CGDIS well positioned to fulfill its objectives and evolving role in the future (such as the Disaggregated Data Action Plan)?

Guided by a utilization-focused evaluation approach, the data collection methods outlined in Figure 2 were used.

Figure 2. Data collection methods
The figure 2 depicts the three collection methods used for the evaluation: external interviews, internal interviews, and document review.
Description - Figure 2. Data collection methods

The figure 2 depicts the three collection methods used for the evaluation: external interviews, internal interviews, and document review.

The external interviews included semi-structured interviews with other federal government organizations, academia and non-profit organizations. There were 30 external interviews conducted with 30 people.

The internal interviews included semi-structured interviews with Centre for Gender, Diversity and Inclusion Statistics representatives, as well as partners and users within Statistics Canada. There were 22 internal interviews conducted with 29 people.

The document review included a review of Statistics Canada's documents and web trends information.

Three main limitations were identified, and mitigation strategies were employed, as outlined in Table 3.

Table 3. Limitations and mitigation strategies
Limitations Mitigation strategies
Given the wide range of products and services offered by the Centre for Gender, Diversity and Inclusion Statistics (CGDIS), the perspectives gathered through external interviews may not fully apply to the entire range of CGDIS products and services.

Key deliverables were selected under each of the CGDIS' three key objectives using specific criteria to maximize the value of external input for the Centre.
Given the very broad nature of the subject areas covered by the CGDIS, the perspectives gathered through external interviews may not be fully representative of all those who have used CGDIS products or services. External interviewees were selected using specific criteria to give as broad a range as possible, and attempts were made to pre-qualify potential interviewees. Those interviewees who never used CGDIS products or services were still able to provide information on the usefulness and relevance of the CGDIS' deliverables and awareness efforts.
Given that the proportion of the available lists of external stakeholders was largely skewed towards federal government organizations (FGOs), the perspectives gathered from external interviews may be heavily biased towards the needs and priorities of the federal government. External interviewees were selected early and using specific criteria to maximize reach to non-FGO stakeholders. The CGDIS and evaluators were aware of the skew and its implications and, where possible, findings were presented separately for FGO and non-FGO stakeholders.

What we learned

1. Effectiveness: Progress towards objectives

Evaluation question

To what extent is the CGDIS progressing towards the achievement of its three key objectives?

  1. Reporting to Canadians about the state and progress of GDI in Canada.
  2. Generating new information to increase new knowledge on gender, diversity, and develop new concepts and measures to address intersecting identity characteristics.
  3. Building statistical capacity to increase knowledge of GDI and GBA Plus.
Summary

The CGDIS made progress on all three key objectives and has met the key deliverables outlined in its strategic proposal. The GDIS Hub was launched in September 2018, and the Centre delivered several products over the period. In addition, GBA Plus training materials were developed in collaboration with Women and Gender Equality Canada (WAGE) and the Canada School of Public Service, and hub tours were delivered.

One of the CGDIS key deliverables is the GDIS Hub. Launched in 2018, the Hub brings together GDI data from several Statistics Canada sources and presents the information in a single interactive analytical environment.

When the CGDIS was first established, its key activities were set forth by senior management and guided by the original mandate of its strategic proposal, namely, to deliver the GDIS Hub and to provide GBA Plus information.

The GDIS Hub is a platform on the Statistics Canada website which brings together GDI-related data from several Statistics Canada sources and presents the information in a single interactive analytical environment. It provides users with consolidated access to research and detailed statistical information. It also includes links to recent releases, key indicators, a data visualization tool, infographics, information on the DDAP and engagement opportunities.

Subject-specific pages within the GDIS Hub include:

  • accessibility statistics, which are produced in collaboration with Employment and Social Development Canada;
  • the Sex, Gender and Sexual Orientation Statistics Hub, which includes indicators for the Gender Results Framework;
  • the Dimensions of Poverty Hub;
  • immigration and ethnocultural diversity statistics;
  • statistics on Indigenous peoples;
  • language statistics;
  • statistics on older adults and population aging; and
  • rural Canada statistics.

While the original scope was for the CGDIS to create a statistical hub that would present data and products on the topic of gender, the GDIS Hub has expanded to include the concepts of diversity and inclusion.

The planning and development of the GDIS Hub began during the summer of 2018, and the launch took place in September 2018. The CGDIS was required to launch the Hub six weeks after the Centre was first established; given the short turnaround, no formal consultations took place with stakeholders. However, a crowdsourcing consultation did take place soon after the launch to gather input from Canadians to inform continued development.

In addition to the GDIS Hub, the Centre delivered many products and services under each of its three key objectives. These included GBA Plus training materials, which were developed in collaboration with WAGE and the Canada School of Public Service, and hub tours.

In addition to the creation, launch and upgrade of the GDIS Hub, the Centre has delivered many other products and services under each of its three objectives.

Under the Reporting to Canadians objective, the CGDIS launched the newly developed Sex, Gender and Sexual Orientation Statistics Hub; included a DDAP tile within the GDIS Hub; and tracked the 29 indicators for the Gender Results FrameworkFootnote 3.

Under Generating new information, the CGDIS delivered the portrait of Canada's Black population; developed products pertaining to definitions and addressing measurement issues with respect to racism and discrimination; undertook and released research and analytical products under letters of agreement with other federal government organizations (FGOs); and produced infographics to augment the presentation of related data.

Finally, under its Building statistical capacity objective, the CGDIS established Statistics Canada's GBA Plus Responsibility Network to further spread GBA Plus knowledge and awareness across the agency and to support timeliness with corporate requests with GBA Plus components (e.g., GBA Plus annexes in funding proposals). The CGDIS also provided GDIS Hub tours, which are training sessions that walk participants through the GDIS Hub's different features and information. It also developed GBA Plus training material in collaboration with WAGE and the Canada School of Public Service.

It was also noted that the CGDIS has met the key implementation milestones in its strategic proposal and that targets in its performance measurement framework were either being met or were under development. With the key deliverables from the proposal met, activities evolved to correspond with the Centre's expanding scope and mandate.

2. Effectiveness and relevance: Awareness and usefulness

Evaluation question

To what extent are stakeholders aware of the CGDIS' key activities and to what extent are their needs met?

Summary

The CGDIS' engagement strategy promoted the awareness and use of the GDIS Hub and related products but mainly among FGOs. Overall, the CGDIS met the needs of most FGOs, particularly those who were regular clients of the Centre. While the needs of non-FGOs were met to a lesser extent, most of them still supported the work of the CGDIS and underscored the importance of the GDIS Hub. Data needs varied across user groups, but they all expressed a desire to have more disaggregated data to better understand the intersectionality as opposed to binary interactions. Many opportunities for improvements were also noted for the presentation of data, overall functionality of the Hub and outreach efforts to non-FGOs.

The CGDIS promoted the awareness of the GDIS Hub by engaging external stakeholders in various ways; many communication channels, both formal and informal, and social media platforms were leveraged.

The CGDIS engaged external stakeholders in various ways and leveraged many communication channels. This included the ongoing engagement of existing clients (e.g., WAGE, Canadian Heritage), where regular discussions took place to collaboratively identify research priorities to fill data gaps and create new GDI products. One of the various GDI initiatives the Centre is involved in is the Justice Data Modernization initiative, which is a data and research initiative led by the Department of Justice Canada and Statistics Canada to improve the collection and use of disaggregated data in view of addressing the over-representation of Indigenous, Black and racialized people in the criminal justice system.

The CGDIS also participates in various committees, networks and working groups, such as the Sexual Orientation Standards Working Group, which conducts consultations with various stakeholders, including 2SLGBTQI+ experts, academic groups and the general public. The CGDIS also engaged external stakeholders through Statistics Canada's regional representatives, who serve as a point of contact between the public and the agency. For example, the CGDIS delivered a series of webinars for the Library of Parliament through this channel. The Centre also maintains a social media presence reaching out to the public on several digital platforms.

The CGDIS' engagement strategy promoted the awareness of the GDIS Hub and research and analytical products, particularly among interviewees from FGOs who had worked with the Centre.

Most external interviewees, especially users from FGOs who had worked directly with the CGDIS either as a client of the Centre, were aware of the GDIS Hub and the CGDIS' research and analytical products either through a letter of agreement/memorandum of understanding, or as a partner helping to develop analytical products or provide input. Most were made aware of the CGDIS through colleagues, or through the various official messages (e.g.: Budget 2018 or the email announcing the CGDIS' launch) or from the notifications from Statistics Canada's official release bulletin, The Daily. Some also discovered the GDIS Hub and the research and analytical products by navigating through Statistics Canada's website or via an Internet search engine, such as Google.

Nearly all of those who were aware of the GDIS Hub or the CGDIS' research and analytical products have used them. However, the frequency of use reported by users of the GDIS Hub varied greatly. Some used it daily, while others reported that they use it only occasionally. The few who did not use the Hub had access to other sources of data to conduct their work. A few interviewees mentioned that they were uncertain whether the research and analytical products they accessed were from the CGDIS.

Figure 3. Awareness and use of the Centre for Gender, Diversity and Inclusion Statistics' products and services by federal government organization and non-federal government organization interviewees
The figure 3 depicts the awareness and use of the Centre for Gender, Diversity and Inclusion Statistics' (CGDIS') products and services by federal government organization and non-federal government organization interviewees.

Notes: Among the external interviewees, 70% (n=30) were from federal government organizations (FGOs), who were the main target audience for the Gender, Diversity and Inclusion Statistics (GDIS) Hub.

Description - Figure 3. Awareness and use of the Centre for Gender, Diversity and Inclusion Statistics' products and services by federal government organization and non-federal government organization interviewees

The figure 3 depicts the awareness and use of the Centre for Gender, Diversity and Inclusion Statistics' (CGDIS') products and services by federal government organization and non-federal government organization interviewees.

Among the external interviewees, 70% (n=30) were from federal government organizations (FGOs), who were the main target audience for the Gender, Diversity and Inclusion Statistics (GDIS) Hub.

Out of these FGO interviewees:

  • 33% have used the training, workshops and hub tours provided by the CGDIS, while 67% were unaware of them.
  • 81% have used CGDIS' research and analytical products, while 5% were aware of them but did not use them, and 14% were unaware of them.
  • 90% have used the GDIS Hub, while 10% were aware of it but did not use it.

Out of the external interviewees who were not from federal government organizations (i.e., non-FGO interviewees):

  • 11% were aware of the training, workshops and hub tours provided by the CGDIS but did not use them, while 89% were unaware of them.
  • 44% have used CGDIS' research and analytical products, while 56% were unaware of them.
  • 56% have used the GDIS Hub, while 11% were aware of it but did not use it, and 33% were unaware of it.
Most external interviewees were unaware of training and learning opportunities.

The CGDIS partnered with FGOs to build and enhance the statistical knowledge and literacy of policy analysts and researchers on GDI by contributing to various learning activities. These included collaborating with WAGE and the Canada School of Public Service to develop training material for GBA Plus courses, conducting GDIS Hub tours, delivering workshops (e.g., data interpretation workshops), and presenting to specific user groups (e.g., webinars to researchers of the Library of Parliament).

However, there was a gap in the awareness of the training and learning opportunities. Nearly all the interviewees from non-FGOs and 67% of the interviewees from FGOs were unaware of the GDIS Hub tours and learning activities provided by the CGDIS. All the interviewees who had participated in the hub tours were from FGOs who were either clients of the CGDIS or had partnered with it.

Furthermore, while the hub tours were offered to promote the GDIS Hub when it was launched, they were no longer promoted afterwards but were available upon request only. Many external interviewees, from both FGOs and non-FGOs, indicated that they and their teams would have benefitted from the hub tours, workshops and presentations.

Overall, the CGDIS met the needs of most interviewees from FGOs, particularly those who were ongoing, regular clients of the Centre; many noted that without the Hub and the research and analytical products, their ability to carry out their work effectively and efficiently would be greatly hindered.

Interviewees from FGOs used the GDIS Hub and the CGDIS' research and analytical products to support their work in advancing their organizational agenda on GDI. This work included meeting organizational requirements on GBA Plus, analyzing the implications of policies and programs through a GBA Plus or GDI lens, and informing various governmental initiatives on GDI-related matters.

Many of the interviewees from FGOs reported that without the GDIS Hub and the research and analytical products, their ability to carry out their work efficiently would have been greatly hindered. Those who participated in the hub tours or training provided by the CGDIS also reported that these sessions helped to reduce the learning curve on aspects of their work, such as finding specific GDI data.

While the needs of interviewees from non-FGOs were met to a lesser extent than those from FGOs, most of them still supported the work of the CGDIS and underscored the importance of the GDIS Hub.

Interviewees from non-FGOs, such as those from academia and not-for-profit organizations, also used the GDIS Hub and the research and analytical products to support their various GDI data needs. These needs included conducting research, monitoring GDI trends and related impacts on different groups, providing information for decision-making to their stakeholders, teaching and raising awareness, communicating with the media, and complementing other data sources.

Although the CGDIS did meet some of the needs of non-FGOs, it was to a lesser extent than for those from FGOs. Nonetheless, nearly all still supported the work of the CGDIS and underscored the importance of the GDIS Hub. Some noted that the GDIS Hub was particularly crucial for smaller not-for-profit organizations which lack the capacity to perform their own extensive analyses due to limited resources. For these organizations, the GDIS Hub provided quick access to some GDI data that supported their work.

Overall, despite some of its limitations, many interviewees noted that the GDIS Hub made GDI data more accessible to Canadians. For some interviewees, not having the GDIS Hub would be detrimental to their work in advancing GDI. One interviewee mentioned that without the Hub, Statistics Canada's reputation as a supporter of the Government of Canada's GDI priority could be undermined.

Although specific data needs varied across user groups, the general consensus was a desire for more disaggregated data to better understand the intersectionality as opposed to binary interactions.

External interviewees identified data needs for at least the following six different population groups: gender groups, racialized groups, Indigenous peoples, immigrants and newcomers, persons with disabilities, and 2SLGBTQI+ groups.

For these population groups, external interviewees were mostly interested in having data on the following six dimensions: population and demography, including sexual orientation as a demographic variable; health and health care; labour and employment; income and wealth; housing and community; and violence and abuse.

Almost all external interviewees expressed a desire to better understand the intersectionality between these various dimensions and their impact on sub-population groups (for example, being able to make a statement such as the following: "The general population experience is […], which is amplified for populations who are also members of the 2SLGBTQI+ community, and further amplified for members who are Black and living with disabilities"). At the same time, some interviewees recognized that it would not always be possible to obtain their desired level of disaggregated data given the need for confidentiality to be protected by Statistics Canada. While Statistics Canada strives to produce more detailed information, it must also ensure confidentiality and privacy are maintained.

Table 4 in Appendix A provides examples of data needs on intersectionality identified by external interviewees.

Several opportunities were identified to enhance the GDIS Hub and its content.

While the GDIS Hub was initially launched in 2018 without client testing or consultation, it was later updated in 2021 to reflect the feedback gathered from a crowdsourcing consultation conducted in 2018, as well as the CGDIS' expanded scope. While some interviewees noted improvements to the GDIS Hub, they largely pertained to the quantity of information.

Many interviewees, particularly those from non-FGOs, identified opportunities for improvement regarding how data were presented in the GDIS Hub. One area for improvement was terminology. For example, many noted the need to have more clarity and consistency on terms such as "community," "ethnocultural group," "visible minority" and "racialized." Furthermore, it was mentioned that there are instances where the GDIS Hub's definitions are inconsistent with other parts of the Statistics Canada website. For example, "non-binary gender identities" is used in one area and "male or female" is used in another. Finally, some interviewees also pointed out the importance of not conflating certain GDI concepts, such as lumping "persons with disabilities" under "accessibility" or "sexual orientation" under "gender identities."

For many, timeliness is one of the GDIS Hub's main challenges, as nearly real-time data are what is desired. For example, labour market information that is one or two years old was viewed as being not useful for planning purposes. Interviewees also noted that some features or data in the Hub are not kept up to date, rendering the information unreliable or irrelevant.

Some interviewees noted that the data presented could be more useful if they were supplemented with contextual information, such as an introductory paragraph, background information or footnotes. Supporting information is deemed necessary to help users better understand the social and cultural dimensions of inequity issues for certain population groups. Data alone without this information could be misinterpreted or misused.

Many of the interviewees, particularly those from non-FGOs, did not find the GDIS Hub to be user-friendly. Most noted that it was difficult to find the information that they needed. As such, several opportunities were identified to enhance the user experience by improving the GDIS Hub's overall functionality. One of the areas for enhancement pertained to its design and layout, such as how the GDIS Hub is organized visually and its ease of navigation. Another area was improving the Hub's search function so that users could find the information they are looking for more easily. The final key enhancement was the use of more data visualization tools, such as infographics and charts.

Table 5 in Appendix B provides further examples of improvements to the GDIS Hub suggested by external interviewees.

It is important to note that many aspects of the Hub fall outside the control of the CGDIS; technically and functionally, the Hub (and more broadly the Statistics Canada website) is based on corporate solutions supported by corporate resources, and content from other parts of Statistics Canada falls under the responsibility of those groups. For example, the presentation of the data and the overall functionality of the Hub are dependent on the structure of the website and availability of information technology resources, which are factors beyond the control of the CGDIS. In addition, the current Statistics Canada website follows a subject structure that does not include the representative groups and the intersectional identity factors.

Outreach efforts to non-FGOs could be improved.

Most interviewees encouraged the CGDIS to enhance its outreach efforts to further promote awareness of the Hub and related products and services. Opportunities for improvement in this area included having more frequent communication on GDIS Hub updates. Having a sign-up option in the Hub for users to receive updates would help support this enhancement. Having a dedicated point of contact on the Hub for users to ask questions through a live chat function or general mailbox was also suggested.

Another opportunity is for the CGDIS to enhance its outreach efforts to non-FGOs. These non-FGOs include:

  • organizations with a social purpose and grassroots organizations;
  • academics;
  • networks of immigrant employment service and settlement agencies across Canada, many of which have their own provincial or municipal networks;
  • immigrant employment councils; and
  • chambers of commerce.

These organizations often work directly with local and diverse communities and have their own channels and networks that could be leveraged to help raise awareness of the CGDIS. For example, universities and social science associations such as the Canadian Sociological Association and the Canadian Population Society would be groups that could disseminate information about the CGDIS. Beyond outreach, the CGDIS could develop closer relationships with these organizations to leverage their expertise, experience, networks and ideas.

3. Moving forward

Evaluation question

To what extent is the CGDIS well positioned to fulfill its objectives and evolving role in the future (such as the DDAP)?

Summary

The CGDIS has strategically transformed its organization to adapt and evolve. However, gaps in its overall program management, oversight and capacity are risks. Having robust planning and prioritization processes, relevant performance measurement, clearly defined roles and responsibilities, and proper governance are key considerations as the CGDIS moves forward. A more strategic targeted approach is also required to improve outreach and engagement with non-FGOs.

Given the broad mandate of the CGDIS and its prominence in the current social and political environment, the demands on the Centre are significant and growing. The Centre receives both external requests from stakeholders and internal requests from senior management and other program areas. It undertakes a vast array of activities, including:

  • Providing expertise on frameworks, papers, and definitions and terminology;
  • Disseminating data for FGOs through the GDIS Hub;
  • Fulfilling contractual work under letters of agreement and memoranda of understanding; and
  • Performing a coordination function for interactions between FGOs such as WAGE and other Statistics Canada divisions.

Program areas work with the CGDIS on developing standards and concepts, conducting analyses, disseminating products and infographics, and actioning requests for information and support (both on an ad hoc basis and on an ongoing basis, such as through letters of agreement and memoranda of understanding).

Regarding ad hoc requests that the CGDIS receives, it should be noted that some fall outside of the Centre's three objectives. Examples include some corporate reporting responsibilities and speech and presentation materials for the field. Furthermore, the Centre also receives requests that are not related to its mandate, such as employment equity requests that pertain to human resources. These requests add to the overall burden the Centre faces.

The CGDIS has taken progressive steps to meet its objectives and mature as a Centre; this includes aligning its organizational structure, establishing a SWAT Team and a GBA Plus Corporate Reporting Team, incorporating the DDAP Secretariat, and updating its planning processes.

In exploring the Centre's readiness to meet upcoming challenges, several dimensions of its program management were examined: governance, planning and prioritization, roles and responsibilities, capacity, and connection to stakeholders.

There is no formal governance body to provide guidance to the CGDIS on areas such as priorities, accountabilities, and roles and responsibilities.

The CGDIS follows Statistics Canada's regular governance structure (i.e., Tier 1 committees); the Centre falls under the responsibility of the Diversity and Sociocultural Statistics Division within the Justice, Diversity and Population Statistics Branch. There is no additional oversight, advisory or other formal governance body in place to provide guidance on areas such as priorities, accountabilities, and roles and responsibilities. While the Centre participated in working groups such as the Sexual Orientation Standards Working Group and the DDAP Engagement and Communications Working Group, managed the Advisory Committee on Ethnocultural and Immigration Statistics, and operated the GBA Plus Responsibility Network, none of these provide the Centre with organizational strategic-level guidance.

Several considerations are taken into account by the CGDIS when identifying and prioritizing activities, including alignment with government-wide and agency priorities, input from stakeholders, and obligations under letters of agreement and memoranda of understanding.

The process to identify and prioritize key activities takes place at the division, Centre and section levels. It includes a bottom-up approach whereby input is provided by the sections to management. All levels take similar information into consideration: Government of Canada, Statistics Canada and divisional priorities and objectives; emerging trends; input from clients and internal and external stakeholders; and obligations under letters of agreement and memoranda of understanding. For example, the Research, Analysis, and Subject Matter section has an annual unit research plan that is developed in consultation with its partners (e.g., Canadian Heritage, WAGE and others) and in alignment with Government of Canada and Statistics Canada priorities. At the section level, each section independently identifies and prioritizes its key activities according to its respective objectives and informs management. The sections are ultimately responsible for operationalizing activities.

Working relationships between partner divisions and the CGDIS are mostly informal in nature.

As noted previously, the work of the CGDIS is often intertwined with other areas of Statistics Canada. Leadership, input and support from the CGDIS are viewed as invaluable by internal partners. CGDIS analysts are viewed as subject-matter experts and are often tasked with new requests and activities for cross-cutting work, which frequently overlaps with other program areas. Interviewees noted that this raises some challenges in terms of clarity around accountability, such as who should be the lead. In addition, it was also noted that operational discussions between the CGDIS and other program areas are generally informal in nature and occur at the working level. This may result in varying roles and relationships that are people-dependent; this is a risk given challenges around capacity and an increasing volume of requests.

Finally, while the CGDIS' mandate and objectives are aligned with the DDAP, many interviewees noted that the relationship between the two, including roles and responsibilities, lacked clarity.

A lack of resources was a significant concern; growing demands have placed significant pressure on the Centre.

A lack of capacity and resources to respond to significant and growing demands was a concern identified by many internal interviewees. There was a strong belief that as the scope and volume of work have increased for the CGDIS, capacity and resources have not increased correspondingly. The CGDIS has recognized this and is trying to build up its human resource capacityFootnote 4. However, many reported that it is challenging to find the right complement of individuals with the required skills, lived experiences and agile mindsets.

Some partners and stakeholders noted there were delays for some deliverables from the CGDIS. They also questioned whether the Centre could meet its many demands given its capacity challenges. Additionally, turnover and fatigue among staff were noted by many interviewees. In addition to establishing the SWAT Team, there are also efforts to leverage corporate resources, such as those from the Strategic Engagement Field and the Data Analytics as a Service Division, to help inform the CGDIS' external consultations and to assess the behavioural user experience of the GDIS Hub, respectively.

While the CGDIS maintains good working relationships with its key FGO stakeholders, engagement with those from non-FGOs was lacking.

Overall, the CGDIS is meeting the needs of the FGOs it works with on a regular basis. Regular communication takes place with these stakeholders, and their needs are well understood and are considered in planning. For non-FGOs such as academia and not-for-profit organizations, their needs are being met to a lesser degree. However, the mandate and objectives of the CGDIS still resonated with them.

More recently, the CGDIS has moved to expand and formalize its consultations and engagements. For example, the CGDIS has had consultations on the standard on sexual orientation, and a communication plan on the standard for visible minorities is in development. Some other activities include giving presentations on the GDIS Hub organized through Statistics Canada regional representatives and formally gathering feedback from its training session.

The CGDIS recognizes its engagements and consultations need to be expanded and is currently developing a plan in consultation with the Stakeholder Relations and Engagement Division to identify gaps in addressing external stakeholders' needs, broaden the current pool of users, and inform the next iterations of the GDIS Hub and related CGDIS products and services.

Having robust planning and prioritization processes, relevant performance measurement, clearly defined roles and responsibilities, and proper governance are key considerations as the CGDIS moves forward. A more strategic targeted approach is also required to improve outreach and engagement with non-FGOs.

As Statistics Canada's centre for expertise on GDI statistics, the CGDIS plays a vital role in leading, coordinating and supporting activities in a highly visible evolving domain. Current demands are significant and will increase further under the DDAP. Within this context, proper governance, robust formal planning and prioritization processes, and relevant performance indicators are imperative to efficiently and effectively deliver products and services that align with the needs and priorities of users and stakeholders.

As the CGDIS enhances its planning processes, it should consider establishing a recurring activity prioritization and decision-making process at the CGDIS level. This would allow priorities to be regularly reviewed and rebalanced. The process would include bottom-up input and top-down guidance and facilitate the examination of priorities across and between sections. Supporting the planning and prioritization processes would be a robust performance measurement framework (with indicators and operational metrics) to monitor progress and performance and to highlight areas of challenge, such as capacity.

Given the strong links to other program areas in Statistics Canada, a senior-level governance body to provide guidance on areas of priorities, roles and responsibilities, and accountabilities should be considered. The body should have broad representation so that a more holistic perspective is provided. For example, the new body could help establish accountabilities when requests or projects overlap between the CGDIS and other program areas (i.e., who leads and under what circumstances). In addition, the CGDIS should move to formalize its relationship with other areas to ensure roles and responsibilities are clear and appropriate.

While the CGDIS maintains strong relationships with its key FGOs, the outreach to and engagement of non-FGOs are recognized gaps. While recent activities have tended to be more encompassing, a more strategic targeted approach is required given that non-FGOs are a very diverse group.

How to improve the Centre

Recommendation 1

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes a recurring activity prioritization and decision-making process at the CGDIS level which would facilitate the ongoing review and rebalancing of activities; and
  • Develops and implements key performance measures that will support the monitoring of activities in view of the objectives set forth by the Centre.

Recommendation 2

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes or leverages a senior-level governance body to provide guidance; and
  • Formalizes roles and responsibilities with internal partners such as other program areas.

Recommendation 3

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS takes steps to broaden and enhance its outreach, consultation and engagement efforts to include external non-FGO stakeholders.

Management response and action plan

Recommendation 1

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes a recurring activity prioritization and decision-making process at the CGDIS level which would facilitate the ongoing review and rebalancing of activities; and
  • Develops and implements key performance measures that will support the monitoring of activities in view of the objectives set forth by the Centre.

Management response

Management agrees with the recommendation.

Recurring activity prioritization and decision-making process

  • The CGDIS will establish a recurring activity prioritization and decision-making process that uses a Gantt chart tracker.
  • The tracker will support the management of incoming and ad hoc requests, as well as the prioritization of planned activities on an ongoing basis. The tracker will also outline the resources to support these priorities and will be reviewed regularly and readjusted as needed to rebalance activities and in light of changing priorities.
  • The tracker will include a ranking of activities (high, medium and low) based on a number of criteria, such as urgency, cost recovery or client-funded, and linkages and interdependencies with other program areas.

Key performance measures

  • The CGDIS will develop and implement key performance measures based on SMART (specific, measurable, achievable, relevant and time-bound) principles to monitor its activities, including the reporting on the GBA Plus and DDAP indicators.

Deliverables and timelines

  • A centre-wide, recurring prioritization and decision-making process that is supported by a CGDIS project tracker will be delivered by September 2023.
  • Pilot measures, which will be adjusted over time, as required, will be delivered by April 2024.

Recommendation 2

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS:

  • Establishes or leverages a senior-level governance body to provide guidance; and
  • Formalizes roles and responsibilities with internal partners such as other program areas.

Management response

Management agrees with the recommendation.

Governance

  • The CGDIS will leverage an existing governance structure, in particular the DDAP governance, as a governing body to provide guidance and to enable the Centre to align its work better strategically and to enable collaboration with other programs. This will help the Centre to meet emerging needs from its users, as well as align demands based on resources.

Roles and responsibilities

  • The CGDIS will formalize its roles and responsibilities in collaboration with internal partners, assess current collaborations, and identify opportunities for further collaboration as needed, with the aim of reducing costs and improving timeliness. As part of this assessment, readiness and capacity of internal partners will also be considered.

Deliverables and timelines

  • Updated terms of reference for the DDAP governance reflecting the additional responsibilities will be delivered by October 2023.
  • Documented roles and responsibilities developed in collaboration with internal partners will be delivered by April 2024.

Recommendation 3

The Assistant Chief Statistician, Social, Health and Labour Statistics Field (Field 8), should ensure that the CGDIS takes steps to broaden and enhance its outreach, consultation and engagement efforts to include external non-FGO stakeholders.

Management response

Management agrees with the recommendation.

  • The CGDIS will continue to maintain the existing engagement with key federal departments. Furthermore, the CGDIS will expand its engagement with community groups.
  • The Centre will continue to participate in and use its partners' consultation activities with stakeholders to inform its research priorities.
  • The Centre will also develop an engagement plan for non-governmental organizations (which will include a list of organizations and timelines) and undertake regular consultations with external non-governmental organization stakeholders on the Centre's research priorities and emerging issues.
  • The engagement plan will use the Engagement Playbook developed by the Stakeholder Relations and Engagement Division.

Deliverables and timelines

  • An engagement plan for non-governmental organizations will be approved by the director by August 2023 and refreshed on an annual basis.

Appendix A – Examples of data needs on intersectionality

Table 4. Examples of data needs on intersectionality identified by external interviewees
Example of data needs on intersectionality identified by external interviewees 2SLGBTQI+ groups Gender groups Indigenous groups Persons with disabilities Racialized groups Immigrants and newcomers
Employment data (e.g., business ownership by sector, manufacturing data, labour market activity, inflation, wage gaps, skills and employment and tax filer data) by population group 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4
Representation of population groups in an authoritative role in various public and private sector organizations (e.g., members of Parliament, boards of directors, chief executive officers, First Nations chiefs, federal judges) 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4 1, 3, 4
Data on Indigenous youth (e.g., language, gender identity) living in northern and rural regions, on-reserve versus off-reserve   1, 5 1, 5      
Program and policy impacts on categories of newcomers related to access to health care, employment and social services; indicators of integration; overqualifications of newcomers relative to job type; labour market and other outcomes for newcomers by educational background, ethnic origin, ethnicity, language and other data points         1, 2, 3, 4, 5 1, 2, 3, 4, 5
Data on persons with disabilities (e.g., labour market participation of persons with disabilities, abuse of persons with disabilities)       3, 6    
Data on housing, homelessness and poverty by population group, including sexual orientation and other sociodemographic factors 1, 4, 5 1, 4, 5 1, 4, 5 1, 4, 5 1, 4, 5 1, 4, 5
Community-based data, including qualitative data on the lived experiences of Indigenous and other racialized communities     5   5  
Violence against gender groups (e.g., women) and 2SLGBTQI+ groups 6 6        
Violence against racialized groups         6 6

Legend

1
Population and demography
2
Health and health care
3
Labour and employment
4
Income and wealth
5
Housing and community
6
Violence and abuse

Appendix B: Suggested improvements to the Gender, Diversity and Inclusion Statistics Hub

Table 5. Examples of improvements to the Gender, Diversity and Inclusion Statistics Hub suggested by external interviewees
Design and layout
  • Streamline the Gender, Diversity and Inclusion Statistics Hub. There are currently too many links that make it challenging from an accessibility perspective.
  • Improve navigability by:
    • Displaying and organizing links for infographics, analytical reports and data tables by topic all in one place (make them easier to find).
    • Having individual landing pages or tabs for each designated population group.
  • Make Gender Results Framework indicators easier to find.
  • Hire a user experience (UX) designer to examine the Hub from a gender diversity lens.
Search function
  • Improve the search function. Currently, users find it easier to use a search engine, such as Google, to find information on the Hub, rather than the search function on the Hub.
  • Improve the filter function. Currently, users are unable to determine whether data are unavailable or that they cannot find them.
  • Have a centralized searchable database.
  • Provide links to other related information within or outside Statistics Canada (i.e., other data products from Statistics Canada or other organizations).
Data visualization tools
  • Enhance data visualization tools on the Hub, including more infographics, charts, tip sheets, graphic-informed labels, mobile-friendly components, videos and podcasts.
  • Have pre-set and customizable charts (i.e., interactive data visualization tools) to help smaller organizations with less capacity.
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