Frequently Asked Questions
Tri-Agency Research Data Management Policy

  1. Data Management
    1. What are data?
    2. What are research data?
    3. How are research materials related to research data?
    4. What is research data management?
    5. Why is research data management important?
  2. Policy Development
    1. Why are the tri-agencies developing a data management policy?
    2. Do the tri-agencies currently have data management requirements?
    3. How long have the tri-agencies been engaged in data management policy development, and what has been involved in this process?
    4. When will the draft policy be finalized, launched and implemented?
  3. Policy Elements
    Institutional Strategy
    1. What is an institutional strategy for research data management?
    2. Why are the tri-agencies considering requiring institutional strategies?
    3. Where can institutions find guidance on how to develop their institutional strategies?
    Data Management Plans
    1. What is a data management plan?
    2. Why is it important to have a data management plan?
    3. What are the key components of data management plans?
    4. What are metadata?
    5. How do I determine which metadata standard to use for my research project?
    6. Is there a tool to help researchers create data management plans?
    7. How can researchers access and use the Portage DMP assistant?
    Data Deposit
    1. What is “data deposit”?
    2. Why is it important to deposit data?
    3. Do the tri-agencies already have policies on data deposit?
    4. What research data does the draft Tri-Agency Research Data Management Policy propose to be deposited?
    5. The draft policy encourages researchers to provide access to the data where ethical, legal, and commercial requirements allow, and in accordance with the standards of their disciplines. How does providing access to the data benefit the creator of the data?
    6. Where can data be stored during the course of a research project?
    7. Where can data be stored after the research project?
    8. How does this policy relate to the management of Indigenous research, knowledge and data?
  4. More Information
    1. Educational resources, supports, etc.
    2. Canadian organizations
    3. International organizations
    4. I have specific concerns. Who can I contact?

1. Data and Data Management

  1. What are data?

    Data are facts, measurements, recordings, records, or observations about the world collected by researchers and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.Footnote 1

  2. What are research data?

    Research data are data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or artistic activity, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. All other digital and non-digital content have the potential of becoming research data. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. Footnote 2

  3. How are research materials related to research data?

    Research materials serve as the object of an investigation, whether scientific, scholarly, literary or artistic, and are used to create research data. Research materials are transformed into data through method or practice. Examples of research materials may include bio-samples for a geneticist, primary sources in an archival fonds for an historian, or a school of zebrafish for a biologist; and the corresponding research data could be gene sequence data, chronological analyses of ideas and contributions, and the behaviour of the zebrafish under certain conditions, respectively. “Research material” is a general concept that spans disciplines and may be digital or analogue.

  4. What is research data management?

    Research data management (RDM) includes the collection, storage, preservation and, where appropriate, access to data produced from a given investigation. Data management should be practiced over the entire lifecycle of the data, including planning the investigation, conducting the research, backing up data as it is created and used, and long term preservation of data after the research investigation has concluded.Footnote 3

  5. Why is research data management important?

    Research data management enables Canadian researchers to store, access, reuse and build upon digital research data. RDM is essential to Canadian researchers’ capacity to remain current and collaborative in their fields, enabling them to generate and contribute critical research that underpins Canada’s economic and social well-being. Accessible digital research data also hold great potential benefits for the Canadian non-profit and private sectors in helping them to advance critical social and commercialization goals.

2. Policy Development

  1. Why are the agencies developing a data management policy?

    The agencies must ensure that the research they fund is conducted according to the highest standards. By adopting a research data management policy the agencies aim to contribute to a future research culture that sees:

    • strong data management as an accepted signifier of research excellence across disciplines;
    • more Canadian datasets cited;
    • Canadian researchers recognized and rewarded for excellent data management (valued as a product of research);
    • Canadian researchers equipped and ready to engage in international research collaboration where data management requirements are becoming the norm;
    • Canadian research institutions ready to support the management of the data their researchers produce; and
    • an increased ability for research data to be archived, found and reused to support reproducibility, the responsible conduct of research, and to fuel new discovery and innovation.

  2. Do the agencies currently have data management requirements?

    As per the Tri-Agency Statement of Principles on Digital Data Management, the agencies expect grant holders to incorporate data management best practices into their research, and research institutions to provide their researchers with an environment that enables world class data stewardship practices.

    With the exception of data deposit requirements for CIHR-funded researchers in the Tri-Agency Open Access Policy on Publications, and ethical requirements as outlined in the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans and the Tri-Agency Framework: Responsible Conduct of Research, the agencies do not currently have mandatory data management requirements.

  3. How long have the agencies been engaged in data management policy development, and what has been involved in this process?

    The tri-agencies took their first step towards developing a harmonized data management policy in 2013, when they released Toward a Policy Framework for Advancing Digital Scholarship in Canada. This document was shared with the research community as part of a broad consultation to inform the development of a data policy framework.

    Following further discussions with the community, the agencies developed the Tri-Agency Statement of Principles on Digital Data Management, which outlines the agencies’ overarching expectations concerning research data management, and the roles and responsibilities of researchers, research communities, research institutions and research funders.

    The objective of the Statement is to promote excellence in digital data management practices and data stewardship in agency-funded research. The Statement was also meant to establish a common set of principles that would serve as the basis for the development of tri-agency data management requirements. The agencies received stakeholder feedback on the document in summer 2015 and launched the Statement in spring 2016. They then re-engaged the community to discuss realizing the principles and possible policy directions. These discussions informed the draft Tri-Agency Research Data Management Policy.

  4. When will the draft policy be finalized, launched and implemented?

    The agencies plan to launch a final policy in winter 2019 and implement the policy incrementally, as determined through ongoing engagement with the research community and in step with the continuing development of research data practices and capacity in Canada and internationally. Consultation feedback could result in modifications to this timeline.

3. Policy Elements

Institutional Strategy

  1. What is an institutional strategy for research data management?

    An institutional research data management strategy describes how the institution will provide its researchers with an environment that enables and supports best practices for research data management. These strategies also help research institutions identify and address gaps and challenges in their research data management capacity. A strategy document could be in the format of a policy, a statement, or a workflow process. Strategies would typically be part of the institution’s overall research policy framework and require input from various institutional units, such as the research office (including ethics administrators), library services, IT services and departments and/or faculties.

  2. Why are the agencies considering requiring institutional strategies?

    Research institutions have a significant role to play in supporting research data management. Developing a data management strategy provides institutions with an opportunity to think through where gaps exist, and how to address them from an institutional perspective. These data management strategies should allow institutions to develop solutions that work for them, while encouraging alignment and collaboration with other institutions. They would also provide the agencies with information about data management capacity across Canada.

  3. Where can institutions find guidance on how to develop their institutional strategies?

    The Portage Network and the Canadian Association for Research Administrators, with representatives from the tri-agencies, Research Data Canada and the Canadian University Council of Chief Information Officers, have developed an institutional strategy template and guidance documentation designed to assist Canadian research institutions in fostering a culture of support for research data management. The template and guidance document can be found on the Portage website.

Data Management Plans

  1. What is a data management plan?

    A data management plan (DMP) is a living document, typically associated with an individual research project or programme, that consists of the practices, processes, and strategies that pertain to a set of specified topics related to data management and curation in research. DMPs guide researchers in articulating their plans for managing data; they do not necessarily compel researchers to manage data differently. DMPs differ from data management plan platforms, which are instruments (such as web-based systems) that help researchers prepare and continuously update a DMP. A data management plan template, which consists of a set of topics that make up the content of a DMP, provides guidance on creating DMPs.

  2. Why is it important to have a data management plan?

    DMPs assist researchers in proactively establishing how they will manage their data through all stages of a research project and beyond. DMPs are an excellent way for researchers to identify opportunities and challenges in managing their data (whether ethical, methodological, financial or other), well before those opportunities and challenges emerge, and thereby enable researchers to better adapt their projects to unanticipated obstacles and to integrate necessary adaptations and improvements. DMPs can also be an excellent way to engage other teams, in the institution and beyond, in an ongoing conversation about how to best manage data produced from a research project. DMPs improve the design and efficiency of the research project and are emerging as an important tool to ensure research excellence.

  3. What are the key components of data management plans?

    While specific details and information contained within data management plans differ according to the nature and type of research being conducted, data management plans typically include sections on data collection, data storage and backup, data security, data preservation, data sharing and reuse (if applicable), and the roles and responsibilities within the research team for managing the data. They should also outline any ethical, legal or commercial constraints relevant to the data.

    DMPs do not set standards for what constitute acceptable data management practice (e.g., metadata standards, disciplinary expectations about data sharing, etc.). However, by documenting how researchers plan to manage data, they do allow for a level of internal and external review and could compel the emergence of or adherence to a certain institutional or disciplinary standard.

  4. What are metadata?

    Metadata are "data about data" – data that define and describe the characteristics of other data.Footnote 4 Accurate and complete metadata are essential for making research data findable, and for the systems that use or mine the data. A principle to help determine what information should be included in the metadata is the OAIS criterion that it be “independently understandable”.Footnote 5 This means that enough information is provided in the metadata for someone else to be able to understand the data without its originator having to be present to explain it. Metadata standards (often referred to as ‘schemas’) are diverse and vary across disciplines, but metadata generally state who created the data and when, and include information on how the data were created, their quality, accuracy and precision, as well as other features necessary to enable discovery, understanding and reuse.

  5. How do I determine which metadata standard to use for my research project?

    Determining the “right” metadata standard to use can be difficult. It may not be so much that a single, “right” standard exists for your research but that you must choose one from several existing standards that may be used in your discipline or that are applicable to your research. It is important to consider the needs of the project and the potential users of your data. Some research institutions have dedicated data management staff or librarians who can provide advice on data management standards. The Portage Network website lists contacts at various Canadian research institutions. You may also wish to contact your scholarly association to inquire about disciplinary standards, or disciplinary journals to inquire about recommended data standards. The Digital Curation Centre and systems like FAIRsharing maintain databases of metadata standards across multiple disciplines.

  6. Is there a tool to help researchers create data management plans?

    There are various online tools available. Research institutions may have recommended templates, and researchers can consult discipline-specific examples from organizations like the Digital Curation Centre and the California Digital Library. Researchers are encouraged to consider using the Portage Network’s DMP Assistant, a free, bilingual online service for creating data management plans. The tool’s templates guide researchers through the key elements in developing a data management plan.

  7. How can researchers access and use the Portage DMP assistant?

    Researchers can access the Portage DMP Assistant on the Portage Network website. In order to use the DMP Assistant researchers will need to create a free account. Once the account is created, users can develop data management plans. Users are encouraged to revisit the plan throughout their research projects, reviewing and revising their plans as needed. Options exist to publish a full or partial plan to share with others.

Data Deposit

  1. What is "data deposit"?

    'Data deposit' refers to when the research data collected as part of a research project are transferred to a research data repository with easily accessible policies describing deposit and user licenses, access control, preservation procedures, storage and backup practices, and sustainability and succession plans. The deposit of research data into appropriate repositories supports ongoing data-retention and, where appropriate, access to the data.

    Ideally, data deposit will include accompanying documentation, source code, software, metadata, and any supplementary materials which provide additional information about the data and the context in which it was collected and used to inform the research project. This additional information facilitates curation, discoverability, accessibility and reuse of the data.

  2. Why is it important to deposit data?

    By depositing their data, researchers ensure that the data is securely preserved and available to them following the completion of the research project. Data deposit also enables researchers to choose to what extent the data may be accessible to others, and under what terms. Making the data accessible to others supports reuse, validation, replication, and linkage with other data and research findings.

    The agencies believe that data are significant and legitimate products of research and must be recognized as such. Any time data are made accessible, all users of the data should acknowledge – through citation and other practices or standards relevant to their disciplines – the sources of the data they are using, and respect the terms and conditions under which these data were accessed. Repositories facilitate these important considerations by, among other things, assigning persistent identifiers (such as ORCIDs for authors, and DOIs for articles and datasets) that ensure greater citability and impact of research results. Researchers who responsibly and effectively share their data should be recognized by funders, their academic institutions and users benefiting from the reuse of the data.

  3. Do the tri-agencies already have policies on data deposit?

    The Tri-Agency Open Access Policy on Publications currently requires that CIHR-funded researchers deposit bioinformatics, atomic, and molecular coordinate data into an appropriate public database immediately upon publication of results. CIHR also requires retention of all data sets associated with a given grant for a minimum period of five years. Many researchers and institutions commit to retaining research data and associated materials much longer than this minimum period.

    In addition to the tri-agencies, an increasing number of academic journals require researchers to deposit data within a defined timeline after publication (e.g., one year).

  4. What research data does the draft Tri-Agency Research Data Management Policy propose to be deposited?

    The draft policy proposes that grant recipients be required to deposit, in a recognized digital repository, all digital research data, metadata and code that directly support the research conclusions in journal publications, pre-prints and other research outputs that arise from agency-supported research. This will ensure safe storage, preservation, and curation of the data. This pertains to the research data that would be required to confirm or reproduce the conclusions in the research output, as per the standards of the discipline.

    CIHR researchers are required to deposit all bioinformatics, atomic and/or molecular coordinate data immediately upon publication of research results, as per section 3.2 of the Tri-Agency Open Access Policy on Publications.

    When depositing data, researchers must ensure that ethical, legal and commercial obligations have been met. Researches should consult the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans and the Tri-Agency Framework: Responsible Conduct of Research for guidance on responsible data deposit.

  5. The draft policy encourages researchers to provide access to the data where ethical, legal, and commercial requirements allow, and in accordance with the standards of their disciplines. How does providing access to the data benefit the creator of the data?

    There are numerous reasons why researchers decide to share data, such as raising awareness of their research, broadening dissemination of their research results, and increasing the citation rates of papers linked to the data. Sharing data also helps other researchers, within one’s discipline and beyond, build on the research results. However, researchers should only make data accessible if doing so is ethical, legal, and is in consonance with any commercial or other agreements the researcher has entered into. In some cases, access may be restricted to certain outside parties.

    The agencies believe that data are significant and legitimate products of research and must be recognized as such. Any time data is made accessible, all users of the data should acknowledge, through citation and other practices or standards relevant to their disciplines, the sources of the data they are using, and respect the terms and conditions under which these data were accessed. Researchers who responsibly and effectively share their data should be recognized by funders, their academic institutions and users benefiting from the reuse of the data.

  6. Where can data be stored during the course of a research project?

    Data should be collected and stored throughout the research project using software and formats that ensure secure storage, access to analysis and visualization tools, and facilitate preservation of and access to the data well beyond the duration of the research project.

    Although storing data on a personal computer may be practical in some situations, data stored on a personal computer is not secure. If the computer is corrupted (e.g., through viruses, malware, ransomware, accidental damage, etc.), the research data may become irretrievable, corrupt and useless.

    Normally, researchers will use multiple data storage solutions during the course of a research project. Several options are listed below.

    Networked drives

    Researchers that are working as part of an institution, such as a university, will likely have access to a networked drive, maintained by the institution. Saving data to a networked drive will ensure that the data is backed up and safeguarded. Should the researcher’s computer be compromised, the data will still be safe. Networked drives are supported by dedicated staff who can help determine how best to meet the data storage and access requirements of the project. Institutionally maintained network drives dedicated to research are preferred to network drives open for administrative and instructional purposes, which have greater security vulnerabilities.

    Institutional repositories

    Institutional repositories, such as those found in most Canadian universities, are used to store the intellectual and research outputs of members of the university. These outputs can include lectures, analyses, pre-prints of scholarly publications, presentation slides, video recordings, and research data.

    While most institutional repositories focus on storing resources that are completed (i.e., finished products not subject to update), researchers may wish to speak with their computing centre or university library to see what options exist for storing data during the active phase of the research project.

    Dataverse networks

    Many universities make active use of an open-source research data repository software called Dataverse, which may be used to store data during the course of a research project. Dataverse includes a range of flexible customizability options, built-in mechanisms for data citation and attribution of credit, robust permissions and options, data analysis and exploration tools, and strong sharing and linking capabilities.

    Dataverse is being used in an increasing number of Canadian universities and university networks. Notable examples include Scholars Portal Dataverse, maintained by the Ontario Council of University Libraries (OCUL), and the Abacus Dataverse Network, which includes several universities in British Colombia.

    Compute Canada, the National Research and Education Network, and Regional Partners

    Compute Canada is one of the primary sources of active storage for Canadian researchers. The Compute Canada framework also provides a host of software tools and resources for working with research data from multiple disciplines. Researchers may wish to consult Compute Canada directly, or via regional partners such as WestGrid, ACENET, Compute Ontario, or Calcul Québec. Canada’s National Education and Research Network also provides some options for the storage of data during the research project (e.g. Cybera’s Rapid Access Cloud), and in some cases a service such as SOCIP can provide advanced research computing and links with commercial enterprises.

    Caution with the Cloud

    While many cloud-based data storage options are secure, researchers should be cautious when using these solutions. Institutional librarians and ethics officers, as well as members of one’s professional society or disciplinary community may help identify appropriate cloud-based options. One consideration when using commercial cloud services (e.g., DropBox or Google) is whether the data is stored in a Canadian datacentre, as provincial privacy legislation may prevent this approach to storing data with personal information.

  7. Where can data be stored after the research project?

    Below are listed several mid- to long-term data storage options that could be pursued as data archiving solutions. Researchers should consult their institution’s library for additional guidance on identifying appropriate options.

    Institutional repositories

    Researchers working within a university setting should have access to their institutional repository. It is always advisable for researchers to deposit data in their institutional repository, especially when it comes to ensuring the long-term preservation of that material. Researchers should contact their university library to learn how to store data in their institutional repository.

    Discipline specific repositories

    In addition to their institutional repository, researchers should deposit data into thematically focused repositories, such as GenBank (for nucleic acid sequences), Gene Expression Omnibus (for gene expression data), Dryad Digital Repository (for data underlying scientific and medical publications), or Inter-university Consortium for Political and Social Research (for social science data). Normally, discipline-specific repositories are the best option to ensure that researchers in a specific discipline will find data, thereby increasing the impact of that research.

    Discipline-specific repositories enable researchers to house their data in a resource that is tailor-made to the specific type of content focused on in their work. Many journal publishers will recommend repositories that provide the best fit for specific types of research data (e.g. Nature, PLOS), whether publishing in that journal or another.

    General purpose repositories

    There are many general purpose repositories which can house data. The long-term capacity of these resources to make data available relies upon a variety of factors. An online repository that is operational today might not be 10 to 20 years from now, or longer.

    General purpose online repositories are short to mid-term data storage options, but might not provide sufficient guarantees for long-term storage. When selecting a general purpose repository, researchers are advised to maintain preservation copies of the data elsewhere (such as their institutional repository) in order to ensure long-term availability.

    Researchers are encouraged to deposit their data into an appropriate repository. Depositing data into a repository helps ensure data are curated, preserved, discovered, cited and appropriately shared. The Portage Network (sponsored by the Canadian Association of Research Libraries) provides suggested repositories, including the national Federated Research Data Repository (FRDR), co-developed by Compute Canada and Portage, or one of the many instances of Dataverse hosted in Universities and regions across the country. More repository options can be found on the Portage Research Data Repositories page.

    Researchers are encouraged to consult Section 4 (More Information) for links to additional information on repositories.

  8. How does this policy relate to the management of Indigenous research, knowledge and data?

    With respect to research with Indigenous peoples, the Tri-Agencies acknowledge the importance of ethical considerations and refer grant recipients to the framework for the ethical conduct of research involving Indigenous peoples outlined in Chapter 9 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS). Decisions to deposit and/or share Indigenous research data and knowledge should be guided by principles of research with Indigenous peoples.

    The agencies welcome feedback on the implications this policy could have in terms of Indigenous research, knowledge and data.

4. More Information

  1. Educational Resources, Supports, etc.

    Data Management Plans

    Portage Network
    The Portage Network works within the library and research communities to coordinate expertise, technology, and services in research data management, as well as promote collaboration between research libraries and other data management stakeholders.

    DMP Assistant
    Portage has developed an online, bilingual, data management planning tool (the DMP Assistant) and associated templates, to support researchers in developing data management plans.

    Canadian Repository Options

    The Portage Network (sponsored by the Canadian Association of Research Libraries) provides suggested repositories, including the national Federated Research Data Repository (FRDR), co-developed by Compute Canada and Portage, or one of the many instances of Dataverse hosted in Universities and regions across the country.

    Dataverse
    The many regional/institutional instances of Dataverse in Canada provide researchers with a robust data repository option. The Portage Network is working on a number of developments related to Dataverse, including the possibility of a national instance open to all Canadian researchers.

    Federated Research Data Repository
    The Federated Research Data Repository (FRDR), co-created by Portage and Compute Canada, provides a national repository option into which data can be ingested, curated, preserved, discovered, cited and shared. Features include: ‘big data’ upload/download capacity, ability to maintain file hierarchies, preservation processing support, and provision of a national discovery platform – specifically, FRDR’s federated search tool provides a focal point to discover and access Canadian research data housed in over 30 Canadian data repositories.

    Repositories – International

    Re3data Registry (re3data.org)
    Re3data.org is a global registry of research data repositories that covers research data repositories from different academic disciplines. It presents repositories for the permanent storage and access of data sets to researchers, funding bodies, publishers and scholarly institutions. Re3data.org promotes a culture of sharing, increased access and better visibility of research data. The registry went live in autumn 2012 and is funded by the German Research Foundation (DFG).

    The Directory of Open Access Repositories (OpenDOAR)
    OpenDOAR provides a quality-assured listing of open access repositories around the world. OpenDOAR staff harvest and assign metadata to allow categorization and analysis to assist the wider use and exploitation of repositories. Each of the repositories has been visited by OpenDOAR staff to ensure a high degree of quality and consistency in the information provided. OpenDOAR is maintained by SHERPA Services, based at the Centre for Research Communications at the University of Nottingham.

    Other Resources

    Compute Canada
    Compute Canada, in partnership with regional organizations ACENET, Calcul Québec, Compute Ontario and WestGrid, leads the acceleration of research innovation by deploying state-of-the-art advanced research computing (ARC) systems, storage and software solutions. Together these organizations leverage a team of over 200 experts employed by 35 partner universities and research institutions in order to provide essential ARC services and infrastructure for Canadian researchers and their collaborators.

    Digital Curation Centre (DCC)
    The Digital Curation Centre (DCC) is an internationally-recognized centre of expertise in digital curation with a focus on building capability and skills for research data management. The DCC provides expert advice and practical help to research organizations wanting to store, manage, protect and share digital research data.

    UK Data Archive
    The UK Data Archive is an internationally acknowledged centre of expertise in acquiring, curating and providing access to social science and humanities data.

    Inter-university Consortium for Political and Social Research (ICPSR)
    The world’s largest archive of social science data.

  2. Canadian Organizations

    Research Data Canada (RDC)
    RDC is dedicated to collaborating with stakeholders from across the country to enhance access to research data and improve research data management within Canada. RDC is an organizational member of the international Research Data Alliance (see below).

    Canadian Association of Research Libraries (CARL)
    CARL’s members include 29 major academic research libraries across Canada, as well as Library and Archives Canada, and Canada’s National Science Library. CARL provides leadership on behalf of Canada’s research libraries and enhances capacity to advance research and higher education. It promotes effective and sustainable knowledge creation, dissemination, and preservation, and public policy that enable broad access to scholarly information.

    Canadian Institute for Health Information (CIHI)
    CIHI provides stakeholders with essential information on Canada's health care system and the health of Canadians. With 28 pan-Canadian databases, this health information acts as an enabler for stakeholders to perform evidence-based decision-making.

  3. International Organizations

    Canadian National Committee for CODATA (CNC/CODATA)
    CODATA is an interdisciplinary Scientific Committee of the International Council for Science (ICSU) that works to improve the quality, reliability, management, and accessibility of numerical data to all fields within the science and technology community. CNC/CODATA presents the Canadian perspective within international CODATA discussions.

    Consortia Advancing Standards in Research Administration Information (CASRAI)
    CASRAI is an international non-profit membership initiative with a mission to adapt the best practices of open standards and data governance across all areas of requirement for data research stakeholders. Standard Information Agreements developed by CASRAI cover all key information areas required for the management of research at every stage of the investigation process.

    Research Data Alliance (RDA)
    The Research Data Alliance is a government-funded, community-driven organization committed to building the social and technical infrastructure to enable open sharing of data.

  4. I have specific concerns. Who can I contact?

    If you have questions that are not covered above, you can contact:

    SSHRC: ResearchData-Donneesderecherche@sshrc-crsh.gc.ca

    NSERC: ResearchData-Donneesderecherche@nserc-crsng.gc.ca

    CIHR: ResearchData-Donneesderecherche@cihr-irsc.gc.ca

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