Tri-Agency Research Data Management Policy - Frequently Asked Questions

Table of contents

1. Data and data management

a. What are data?

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

b. What are research data?

Research data are data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or creative practice, 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. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. What is considered relevant research data is often highly contextual, and determining what counts as such should be guided by disciplinary norms.

c. 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.

Examples of research data corresponding to these materials include gene sequence data, chronological analyses of ideas and contributions, and data on the behaviour of the zebrafish under certain conditions, respectively. “Research material” is a general concept that spans disciplines and may be digital or analogue.

d. What is research data management?

Research data management (RDM) refers to the processes applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data.Footnote 2

RDM is essential throughout the data lifecycle—from data creation, processing, analysis, preservation, storage and access, to sharing and reuse (where appropriate), at which point the cycle begins again. 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, disseminating data, and preserving data for the long term after the research investigation has concluded.Footnote 3

The agencies acknowledge the diversity of models of scientific and scholarly inquiry that advance knowledge within and across the disciplines represented by agency mandates. The agencies, therefore, recognize that significant differences exist in standards for RDM—including what counts as relevant research data—among and across the disciplines, areas of research, and modes of inquiry that the agencies support.

e. Why is RDM important?

RDM enables researchers to organize, store, access, reuse and build upon digital research data. RDM is essential to Canadian researchers’ capacity to securely preserve and use their research data throughout their research projects, reuse their data over the course of their careers and, when appropriate, share their data. Furthermore, as an acknowledged component of research excellence, strong RDM practices support researchers in achieving scientific rigor and enable collaboration in their fields.

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2. Indigenous research

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

The agencies acknowledge the importance of Indigenous data sovereignty and RDM principles that recognize and respect self-determination for First Nations, Inuit and Métis Peoples through a distinctions-based approach. As a result, the Tri-Agency Research Data Management Policy includes language that recognizes Indigenous data sovereignty, notably in the preamble and under each requirement (subsections 3.1, 3.2 and 3.3).

The policy aligns with the CARE Principles for Indigenous Data Governance (Collective benefit, Authority to control, Responsibility, and Ethics), which reflect the crucial role of data in advancing Indigenous innovation and self-determination (see Global Indigenous Data Alliance below).

In an effort to support Indigenous rights-holders to conduct research and partner with the broader research community, the agencies recognize that data related to research by and with Indigenous rights-holders must be managed in accordance with data governance frameworks developed and approved by these rights-holders. The CARE Principles represent an overarching framework for Indigenous data governance principles, and their implementation should be consistent with and reinforce locally derived principles and protocols where they existFootnote 4. In Canada, the principles of ownership, control, access and possession (OCAP®) are one model for First Nations data governance, but this model does not necessarily respond to the distinct needs and values of distinct First Nations, Inuit and Métis communities. Additional guidance on Indigenous data governance is provided by the National Inuit Strategy on Research principles and the Manitoba Métis principles of ownership, control, access and stewardship.

With respect to Indigenous research, the agencies acknowledge the importance of ethical considerations and refer grant recipients to the framework for the ethical conduct of research involving First Nations, Inuit, and Métis Peoples outlined in Chapter 9 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2). Decisions to deposit and/or share Indigenous research data and knowledge must be guided by principles of research with Indigenous Peoples.

Moving forward, the agencies plan to support the development of Indigenous RDM protocols that aim to ensure community consent, access and ownership of Indigenous data, and protection of Indigenous intellectual property rights. This next phase in advancing Indigenous RDM in Canada is outlined in Setting New Directions to Support Indigenous Research and Research Training in Canada.

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3. Policy development

a. Why did the agencies develop the Tri-Agency Research Data Management Policy?

RDM is a key element of research excellence. The agencies have a responsibility to ensure the research they fund is conducted according to the highest standards.

By developing an RDM policy, the agencies aim to enable a research culture that sees:

  • strong data management as an accepted signifier of research excellence across disciplines;
  • more Canadian data sets cited;
  • Canadian researchers recognized and rewarded for the research data they produce and share;
  • Canadian researchers equipped and ready to engage in international research collaboration where data management requirements are standard practice;
  • Canadian research institutions ready to support the management of the data their researchers produce; and
  • increased ability for research data to be archived, discoverable and, where appropriate, reused to support links with other data and research to fuel new discovery and innovation.

b. How did the agencies develop the Tri-Agency Research Data Management Policy?

The agencies took their first step towards developing a harmonized RDM 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 research data policy framework.

Following further discussions with the community, the agencies released the Tri-Agency Statement of Principles on Digital Data Management in spring 2016, which outlines the agencies’ overarching expectations concerning RDM, 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 RDM requirements, as found in the policy.

The agencies then re-engaged the community to discuss how to realize the principles and, specifically, possible policy directions they could take. As part of this engagement, they held discussions with stakeholders and partners from various fields, communities and groups, across Canada and internationally, for feedback on (what would become) the RDM Policy’s requirements. The agencies also held an online consultation for input on a draft version of the policy text. A summary of that consultation is available online.

c. How does the Tri-Agency Research Data Management Policy fit into the Government of Canada’s wider RDM and open science agenda?

Governments and research funders across the globe recognize the value of research data and the need for policies to enable excellence in data management. Canada has joined many other countries at the forefront of this movement, as shown through its support for the Organisation for Economic Co-operation and Development (OECD)’s Declaration on Access to Research Data from Public Funding (2004), Recommendation of the Council concerning Access to Research Data from Public Funding (2021), and Declaration on Transformative Science, Technology and Innovation Policies for a Sustainable and Inclusive Future (2024); its commitment to the Open Government Partnership and declaration (2011); and its approval of G7 science and technology ministers’ communiqués (2017, 2022, 2023, 2024) calling to expand open science with equitable and responsible dissemination of scientific knowledge and appropriate research outputs, including open and public access to publicly funded scholarly publications and scientific data.

As part of the Open Government Partnership, the Government of Canada has, through its biennial national action plans on open government, committed to making government-funded science open and transparent to Canadians. Specifically, the plans have expressed the Government of Canada’s commitment to open science through working with international partners in developing open science policies, exploring supportive incentive structures, and identifying good practices for promoting increased access to the results of publicly funded research, including scientific data and publications. The Chief Science Advisor of Canada’s Roadmap for Open Science (2020) provides overarching principles and recommendations to guide open science activities in Canada.

Canada’s national Digital Research Infrastructure (DRI) Strategy led Innovation, Science and Economic Development Canada (ISED) to create the Digital Research Alliance of Canada (the Alliance). The Alliance is tasked with co-ordinating and funding activities in advanced research computing, RDM, and research software of the DRI strategy, working collaboratively with interest-holders across the country.

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4. Policy elements

Institutional strategy

a. What is an institutional RDM strategy?

An institutional RDM strategy describes how the institution will provide its researchers with an environment that enables and supports RDM practices. Developing these strategies will help research institutions identify and address gaps and challenges in infrastructure, resources and practices related to RDM.

Each strategy should reflect the institution’s particular circumstances, including the institution’s size and capacity, geography, and other contextual factors. The strategy would likely require input from various institutional units such as the administrative research office, the research ethics board, library services, IT services, and departments and faculties.

b. Which organizations are required to develop institutional strategies?

Each postsecondary institution and research hospital eligible to administer Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council (NSERC) or Social Sciences and Humanities Research Council (SSHRC) funds is required to create an institutional RDM strategy.

Some institutions, such as research hospitals or university colleges, have a formal affiliation with a parent institution that is also subject to the institutional strategy requirement. In this case, the research hospital or university college may develop its strategy in collaboration with the parent institution, or the parent institution may develop a strategy that encompasses its affiliates.

c. Why are the agencies requiring institutional strategies?

Research institutions have a significant role to play in supporting RDM. Developing a RDM strategy provides institutions with an opportunity to think through where gaps exist and how to address them from an institutional perspective. RDM strategies allow institutions to develop solutions that work for them, while encouraging alignment and collaboration with other institutions. The information in these institutional strategies is intended to help research funders and the Canadian research community gain a better understanding of RDM capacity across the country.

The agencies will not be evaluating the strategies.

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

The Portage Network (legacy organization) and the Canadian Association for Research Administrators, with representatives from the three federal research funding agencies, Research Data Canada (legacy organization) and the Canadian University Council of Chief Information Officers, have developed an institutional strategy template and guidance documentation (available on the Alliance website) to assist postsecondary institutions and research hospitals. Institutions may also find it helpful to review other institutions’ strategies and consult Ripp et al. (2024), Mapping Canadian institutional research data management strategies: A cross-sectional study.

The Alliance has published videos to help guide research institutions in creating effective institutional RDM strategies. The videos cover the first two strategy components outlined in the Institutional RDM Strategy Template: Raising Awareness and Assessing Institutional Readiness. Each module is also accompanied by discussion prompts:

Data management plans

e. What is a data management plan?

A data management plan (DMP) is a living document, typically associated with an individual research project or program that consists of the practices, processes and strategies that pertain to a set of specified topics related to data management and curation. DMPs should be modified throughout the course of a research project to reflect changes in project design, methods, or other considerations.

DMPs guide researchers in articulating their plans for managing data; they do not necessarily compel researchers to manage data differently.

A DMP template provides guidance on creating DMPs (see question h below for tools to help researchers create DMPs).

f. Why is it important to have a DMP?

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 anticipate and identify opportunities and challenges in managing their data (whether ethical, methodological, financial or other), before those opportunities and challenges emerge. DMPs, therefore, 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 partners and collaborators in ongoing conversation about how to best manage research data. Thus, DMPs improve the design and efficiency of the research project, and are an important tool to ensure research excellence.

g. What are the key components of DMPs?

While specific details and information contained within DMPs differ according to the nature and type of research being conducted, DMPs typically include sections on roles and responsibilities, ethical and legal considerations, and (if applicable) Indigenous data management considerations; data collection; documentation and metadata; storage, security and access; and data retention, deposit and sharing.

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

h. Are there tools to help researchers create DMPs?

While not required for the purposes of this policy, when developing a DMP, researchers are encouraged to consider using tri-agency guidance as well as the Alliance’s DMP Assistant, a free, bilingual online service for creating DMPs. To use the DMP Assistant, researchers need to create a free account. Once they have created an account, users can develop DMPs. Users are encouraged to revisit their plan throughout their project’s lifecycle, reviewing and revising as needed. Options exist to publish a full or partial plan to share with others.

There are various other online tools that guide researchers through the elements of a DMP. Researchers can consult discipline-specific examples from organizations like the Digital Curation Centre or the California Digital Library, or refer to resources offered through their institution.

Data deposit

i. What is “data deposit”?

“Data deposit” refers to when the research data collected as part of a research project are transferred to a data repository. The repository should have easily accessible policies or information describing user licenses, data curation, access control (when implemented), storage and backup, preservation, and sustainability and succession plans. The deposit of research data supports ongoing data-retention and, where appropriate, access to the data.

Ideally, data deposits will include accompanying documentation (e.g., a “readme” file), metadata, source code, custom software, and any supplementary materials that provide additional information about the data, including the context in which it was collected and used to inform the research project. This additional information facilitates the discoverability, accessibility, interoperability and reuse of the data.

j. Why is it important to deposit data?

By depositing their data, researchers ensure that the data are retained and accessible 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 links with other data and research findings.

k. Do CIHR-funded researchers still have to comply with the deposit requirement in the Tri-Agency Open Access Policy on Publications?

Yes. CIHR-funded researchers still have to comply with the deposit requirement in the Tri-Agency Open Access Policy on Publications, which requires that CIHR-funded researchers: 1) deposit bioinformatics, atomic, and molecular co-ordinate data into an appropriate public database immediately upon publication of results, and 2) retain all data sets associated with a given grant for a minimum of five years.

l. What are “metadata”?

“Metadata” are data about data—data that define and describe the characteristics of other data. Accurate and relevant metadata are essential for making research data findable. A principle to help determine what information should be included in metadata is the open archival information system model criterion that the information be “independently understandable.” “Independently understandable” means enough information has been provided in the metadata for someone else to be able to understand the data set without needing its creator explain it.

There are many metadata standards (often referred to as “schemas”) prescribing how to treat metadata, and they vary greatly across disciplines. However, metadata generally state who created the data and when, and include information on how the data were created, their quality, accuracy and precision, and other features necessary to enable discovery, understanding and reuse.

m. What are the FAIR principles?

The FAIR principles for scientific data management and stewardship are an international best practice for improving the findability, accessibility, interoperability and reuse of digital assets.

  • Findable: The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of data sets and services.
  • Accessible: Once the user finds the required data, the user needs to know how they can be accessed, possibly including authentication and authorization.
  • Interoperable: The data usually need to be integrated with other data. In addition, the data need to be interoperable and able to function with applications (including computer software and hardware) or workflows for analysis, storage and processing.
  • Reusable: The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

More information about the FAIR principles is available in CIHR’s data management plan guidance and at the GO FAIR website.

The application of the FAIR principles should not supersede Indigenous data sovereignty or other cultural, ethical, legal or commercial considerations. The FAIR principles are complemented by the CARE Principles for Indigenous Data Governance (see Global Indigenous Data Alliance below).

n. How does data sharing benefit the creator of the data?

The Tri-Agency Research Data Management Policy does not require grant recipients to share their data. However, the agencies do expect researchers to provide appropriate access to the data, where ethical, cultural, legal and commercial requirements allow, and in accordance with the FAIR principles and the standards of their disciplines.

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 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. 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. As signatories of the San Francisco Declaration on Research Assessment, the agencies are committed to considering the value and impact of data sets and software in research assessment.

o. Will the data deposit requirement apply to collaborations with non-agency-funded researchers?

The deposit requirement will apply to the digital research data, metadata and code that directly support the research conclusions in journal publications and preprints that arise from agency-supported research, regardless of where the research is conducted or with whom the funded researchers have collaborated.

Agency-funded researchers are encouraged to consider how collaborations with international or other partners could affect their ability to comply with the data deposit requirement of the policy prior to beginning the research project. These types of considerations would be included in a DMP.

p. Where can data be stored during the course of a 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 or 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 are 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.

Canada’s Advanced Research Computing platform

Canada’s Advanced Research Computing (ARC) platform is provided by The Digital Research Alliance of Canada (the Alliance) with its regional partners BC DRI Group, Prairies DRI Group, Compute Ontario, Calcul Québec and ACENET. In addition to these organizations, the ARC federation is composed of 38 partner universities and a national office. Together, members of the federation play a crucial role in supporting research in Canada, offering essential ARC infrastructure, software and services to research projects across a broad spectrum of scope and need, ranging from small, individual initiatives or collaborations to Canada’s largest “big science” projects.

Cloud Storage

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 you 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 data centre, as provincial privacy legislation may prevent this approach to storing data with personal information.

q. 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 and resources on desirable characteristics of data repositories for additional guidance on identifying appropriate options (see Resources below).

Institutional repositories

Researchers working within a university setting should have access to their institutional data repository. It is always advisable for researchers to deposit data in their institutional data 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 data repository. One benefit of using an institutional repository is the possibility of getting help from an RDM specialist at the institution, and long-term stewardship associated with the service.

Dataverse network

Many Canadian universities provide institutional research data storage and repository services, and many make use of an open source research data repository software called Dataverse, which can be used to deposit and share data associated with a research project. Dataverse includes a range of flexible, customizability options, built-in mechanisms for data citation and attribution of credit, robust file permissions and options, data analysis and exploration tools, and strong sharing and linking capabilities.

Dataverse is being used by an increasing number of Canadian universities, colleges and research networks. Notable examples include Borealis, the Canadian Dataverse Repository, a national research data repository initiative provided by the University of Toronto Libraries in partnership with Canadian academic library consortia (the Partenariat des bibliothèques universitaires du Québec , Ontario Council of University Libraries, Council of Prairie and Pacific University Libraries, and Council of Atlantic Academic Libraries), with support from the Digital Research Alliance of Canada. Borealis has over 75 institutional members across Canada, with each institution maintaining their own collection spaces and offering services to support researchers and collaborators.

Discipline-specific repositories

In addition to their institutional repository, researchers can deposit data into thematically focused repositories, such as GenBank (for nucleic acid sequences), the Polar Data Catalogue (for data on the Arctic and Antarctic), or Inter-university Consortium for Political and Social Research (ICPSR, 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. Some journal publishers will recommend repositories that provide the best fit for specific types of research data (e.g., Nature), whether publishing in that journal or another.

General purpose repositories

There are many general purpose repositories that can house data. The long-term capacity of these resources to make data available relies upon a variety of factors. The timeline for data retention and preservation in a given repository will typically be specified in its data retention, preservation and/or deaccession policy.

General purpose online repositories are short- to mid-term data storage options, but might not provide sufficient guarantees for long-term preservation. When selecting a general purpose repository, researchers are advised to consider whether long-term preservation of the data is warranted, and to plan accordingly.

Examples of Canadian general purpose repositories include the national Federated Research Data Repository, hosted by the Alliance, and the many instances of Borealis hosted in universities and regions across the country. More repository options can be found through the Re3data Repository Registry (Re3data.org) and the 2023 Summary Report: Canadian Research Data Repositories and the Re3data Repository Registry.

Researchers should consult Section 5, More Information, below for links to additional information on repositories.

r. How should researchers consider and incorporate security into their RDM planning?

When conducting research that involves sensitive data or has potential for dual use, researchers may need to take additional measures to balance the need for data sharing and access with that for protection from threats. To ensure the integrity of their research is not compromised, and research results (e.g., data sets, publications, patents) are secure and protected until they choose to disseminate them, researchers should put in place good physical and cyber security practices and infrastructure. These practices should be agreed to by all research team members as well as collaborators and partners.

Canadian-led research can be an attractive target for those seeking to steal, use or adapt research for their own priorities and gain. In some scenarios, research could lead to advancements in the strategic, military or intelligence capabilities of other countries, or be used to purposely cause harm. It is, therefore, important that researchers consider the possible research security risks and vulnerabilities associated with their research. They should also assess and clarify the intentions of their research collaborators and partners, and take reasonable, risk-based measures to safeguard their research.

Sound data management practices can be an integral part of mitigating research security risks. Accordingly, researchers applying to funding opportunities where both the National Security Guidelines for Research Partnerships and the RDM policy’s requirement for DMPs apply should identify in their Risk Assessment Form (RAF) risks that will be mitigated—in part or in whole—by a DMP. In these cases, the risk mitigation plan section of the RAF must also describe how the DMP will address those data-related risks.

Researchers are also responsible for complying with any additional legislative and regulatory requirements related to data and information management to which they are subject for their area of research (e.g., researchers working at facilities subject to Canadian Biosafety Standards and Guidelines requirements).

For more information on safeguarding research, conducting risk assessments, or finding best practices for travelling internationally, researchers should consult the Safeguarding Your Research portal and any guidance provided by their institution. For more information about the federal research granting agencies’ implementation of research security measures, and for additional research security resources, consult the Tri-agency guidance on research security.

Resources for research security:

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5. More information

a. Where can I find additional resources and information on RDM?

Research institution libraries

Many universities offer data services at their libraries. Sometimes called “scholarly communications,” there may also be data or research librarians available for consultation with researchers. Some institutions also provide support in data management planning and guidance during the course of the research project. Other services include providing advice on data storage or file security, research documentation and metadata considerations, research data-sharing, and curation (selection, preservation, archiving, citation) of completed projects and published data.

Guidance in Applying TCPS 2 - Guidance related to data management

The Panel on Research Ethics publishes a collection of guidance documents as a resource for the community. The guidance documents focus on specific topics or areas of research based on input from experts in the field and guided by the core principles of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS). They expand beyond the TCPS provisions and their interpretations by offering context-specific considerations, examples, and options to assist researchers and research ethics boards in conducting and reviewing research involving the specific issues or topics.

Guidance documents:

The Secretariat on Responsible Conduct of Research and the Panel on Research Ethics are developing a guidance document on broad consent and creation of repositories. If you have questions about the interpretation of Article 3.13 (broad consent) or other sections of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans – TCPS (2022), contact secretariat@srcr-scrr.gc.ca.

The Digital Research Alliance of Canada

Research data management at the Alliance

As part of its core activities, the Alliance integrates, champions and funds infrastructure, tools and services to promote RDM in Canada. Historically, the Government of Canada supported two initiatives dedicated to RDM in Canada: Research Data Canada and Portage. Both have been integrated into the Digital Research Alliance of Canada as part of the federal DRI Strategy.

Services provided by the Alliance are intended to guide the collection, documentation, storage, sharing and preservation of research data, and allow researchers to find and access data. They include the following:

  • DMP Assistant

    A national, online, bilingual data management planning tool to help researchers in preparing DMPs. The tool is freely available to all researchers, and develops a DMP through a series of key data management questions, supported by best-practice guidance and examples.

  • Federated Research Data Repository

    A national, bilingual platform for sharing and preserving Canadian research data. The service provides Canadian researchers in any discipline with a robust repository option into which large research datasets can be ingested, curated, processed for preservation, discovered, cited, and shared.

  • Lunaris

    A bilingual, scalable, national research data discovery service, the platform provides a single point of search for Canada’s multidisciplinary research data held in a variety of repositories, including those of postsecondary institutions, departments at all levels of government, research organizations, and national repository initiatives.

  • Learning & Resources

    The Alliance provides tools and resources developed with stakeholders and partners to help with data management planning, access, preservation and discovery, including guidance on developing an institutional RDM strategy.

International repository options:

Directory of Open Access Repositories

The Directory of Open Access Repositories (OpenDOAR) provides a quality-assured list 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.

Re3data registry

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.

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 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 advanced research computing services and infrastructure for Canadian researchers and their collaborators.

Digital Curation Centre

The Digital Curation Centre (DCC) is an internationally recognized centre of expertise in digital curation with a focus on building capability and skills for RDM. The DCC provides expert advice and practical help to researchers and research organizations on storing, managing, safeguarding and sharing digital research data.

Inter-university Consortium for Political and Social Research

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

Scholarly Publishing and Academic Resources Coalition

The Scholarly Publishing and Academic Resources Coalition (SPARC) is a global coalition of academic and research libraries that use the resources and support provided by SPARC to actively promote open access to scholarly articles, open sharing of research data, and creation and adoption of open educational resources on their campuses. SPARC works to enable open sharing of research outputs and educational materials to democratize access to knowledge, accelerate discovery, and increase return on investment in research and education.

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.

Canadian organizations:

Canadian Association of Research Libraries

Canadian Association of Research Libraries (CARL) 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

The Canadian Institute for Health Information 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.

CANARIE

CANARIE provides an internationally competitive, ultra-high-speed network for Canada’s research, innovation and advanced education communities; develops, demonstrates and implements next generation technologies; and assists firms operating in Canada and Canadian institutions to advance innovation and commercialization of products and services to bolster Canada’s technology capabilities.

Données de la Recherche Apprentissage Numérique

Données de la Recherche Apprentissage Numérique (DoRANum) offers a co-ordinated access, distance training system, integrating various self-training resources on RDM and sharing. Topics covered include:

  • access and viewing
  • legal and ethical aspects
  • data papers and data journals
  • depots and warehouses
  • stakes and benefits
  • perennial identifiers
  • metadata
  • data management planning
  • storage and archiving

Érudit

Érudit is the largest disseminator of French-language resources in North America. Through its research platform, Érudit offers centralized access to the majority of francophone publications in the social sciences and humanities from North America, including scholarly and cultural journals, books, conference proceedings, theses and dissertations, and various research documents and data.

International organizations:

The Committee on Data of the International Science Council

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

Consortia Advancing Standards in Research Administration Information

The Consortia Advancing Standards in Research Administration Information (CASRAI) is an international, not-for-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. Among CASRAI’s resources is an RDM glossary that was referred to in the development of these FAQs. The glossary is now managed by the Committee on Data of the International Science Council, known as CODATA.

Global Indigenous Data Alliance (GIDA)

The Global Indigenous Data Alliance (GIDA) is a network of Indigenous researchers, data practitioners, and policy activists advocating for Indigenous Data Sovereignty within their nation-states and at an international level. GIDA endorses and hosts the CARE Principles for Indigenous Data Governance. The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles, encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.

GO FAIR

GO FAIR is a stakeholder-driven, self-governed initiative that aims to implement the FAIR data principles, making data findable, accessible, interoperable and reusable. It offers an open and inclusive ecosystem for individuals, institutions and organizations working together, through implementation networks. Networks operate along three activity pillars: GO CHANGE, GO TRAIN and GO BUILD.

Research Data Alliance

The Research Data Alliance is an international, community-driven organization committed to building the social and technical infrastructure to enable open sharing of data.

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

If you have questions not covered above, contact:

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