- 1. Data and Data Management
- 2. Indigenous research
- 3. Policy Development
- 4. Policy Elements
- Institutional Strategy
- Data Management Plans
- Data Deposit
- i. What is “data deposit”?
- j. Why is it important to deposit data?
- k. Do CIHR-funded researchers still have to comply with the deposit requirement in the Tri-Agency Open Access Policy on Publications?
- l. What are “metadata”?
- m. What are the FAIR principles?
- n. How does data sharing benefit the creator of the data?
- o. Will the data deposit requirement apply to collaborations with non-agency-funded researchers?
- p. Where can data be stored during the course of a research project?
- q. Where can data be stored after the research project?
- 5. More Information
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.
f. 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 that 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 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 assess and clarify the intentions of their research partners, and take reasonable, risk-based measures to safeguard their research.
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.
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 RDM, 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 communities to conduct research and partner with the broader research community, the agencies recognize that data related to research by and with Indigenous communities must be managed in accordance with data management principles developed and approved by these communities. These include, but are not limited to considerations of data collection, ownership, protection, use and sharing. 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.
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 should 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 2019-2022.
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 with the Statement has been 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 received stakeholder feedback on the document in summer 2015 before launching the Statement.
The agencies subsequently re-engaged the community to discuss how to realize the principles, and possible policy directions. 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 the proposed 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’s Declaration on Access to Research Data from Public Funding (2004) and Recommendation of the Council concerning Access to Research Data from Public Funding (2021); its commitment to the Open Government Partnership and declaration (2011); and its approval of the G7 Science Communiqué (2017).
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 DRI (Digital Research Infrastructure) strategy calls for Innovation, Science and Economic Development Canada (ISED) to establish a New Digital Research Infrastructure Organization (NDRIO). NDRIO will co-ordinate and fund activities in advanced research computing, RDM, and research software components of the DRI strategy, working collaboratively with stakeholders across the country.
4. Policy elements
a. What is an institutional strategy for RDM?
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 CIHR, NSERC or 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 will allow institutions to develop solutions that work for them, while encouraging alignment and collaboration with other institutions. The information in these institutional strategies will 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 and the Canadian Association for Research Administrators, with representatives from the three federal research agencies, Research Data Canada and the Canadian University Council of Chief Information Officers, have developed an institutional strategy template and guidance documentation (available on the Portage website) to assist postsecondary institutions and research hospitals. Institutions may also find it helpful to review other institutions’ strategies.
Portage has published videos to help guide research institutions in creating effective institutional RDM strategies. The videos cover the first two strategy components outlined in Portage’s 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 management plan 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 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. DMPs should also outline any ethical, legal or commercial constraints relevant to the data.
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 the Portage Network’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.
i. 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. The repository should have 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 deposits will include accompanying documentation, source code, software, metadata, 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 curation, discoverability, accessibility and reuse of the data.
j. Why is it important to deposit data?
By depositing their data, researchers ensure that the data are securely preserved 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.
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 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?
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; these details should be captured in a DMP.
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.
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.
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, and the Abacus Dataverse Network, which includes several universities in British Columbia.
Compute Canada, 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.
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 for additional guidance on identifying appropriate options.
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.
In addition to their institutional repository, researchers can 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. 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
Researchers are encouraged to deposit their data into a repository. Depositing data into a repository helps ensure data are curated, securely preserved, discovered, cited and appropriately shared.
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. 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.
The Portage Network provides suggested repositories, including the national Federated Research Data Repository, co-developed by Compute Canada and Portage, and the many instances of Dataverse hosted in universities and regions across the country. More repository options can be found through Portage’s Research Data Repositories page and Repository Options in Canada guide.
Researchers should consult Section 5, More Information, below for links to additional information on repositories.
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.
Safeguarding Your Research
The Government of Canada recognizes that open and collaborative research is indispensable to pushing the boundaries of science and addressing complex economic and societal challenges. Unfortunately, open and collaborative research environments may be vulnerable to abuse through theft of data and cyber threats. As outlined by a joint policy statement on research security and COVID-19 from the Minister of Innovation, Science and Industry, Minister of Health, and Minister of Public Safety and Emergency Preparedness, espionage and foreign interference activities by both human and cyber actors pose real threats not only to the integrity of Canada’s research ecosystem, but also to Canadian national security, safety, and economic prosperity.
Research security is a collective responsibility, and all forms of research could potentially be subject to research security risks. The Government of Canada encourages all members of the research ecosystem to remain vigilant and to ensure that they balance collaborative research with risk and science-appropriate safeguards. This includes employing strong cybersecurity and physical security protocols. Research should be as open as possible and as safeguarded as necessary, and should be practiced in full respect of privacy, security, ethical considerations and appropriate intellectual property protections.
To address these research security risks, the Government of Canada, including Canada’s security agencies, federal research granting agencies—the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Social Sciences and Humanities Research Council (SSHRC)—and the Canada Foundation for Innovation (CFI) have worked in collaboration with university organizations to provide resources for the academic community to self-evaluate and, when necessary, take actions to mitigate any security, safety, economic, or geopolitical risks associated with their research. With the right tools and awareness of the potential risks to Canada’s world-leading science and research, we can all play an active role in making sure the benefits of Canadian research and development are realized by those that perform it and for the benefit of Canadians.
- Safeguarding Your Research Portal: The Canadian Government’s newly launched research security portal – developed by a joint Government of Canada-Universities Working Group – aims to provide the research community with guidance, information, and tools to help them protect their research and intellectual property. This includes guidance for Mitigating economic and/or geopolitical risks in sensitive research projects as well as a Travel security guide for university researchers and staff and a series of case studies that illustrate tangible risks, possible consequences, and applicable resources and best practices.
- Safeguarding Science: Public Safety Canada, in coordination with nine other federal departments and agencies, hosts an interactive workshop, which informs participants about Canada's counter-proliferation efforts and commitments, and offers tools to help recognize and mitigate the specific risks Canadian institutions are facing, including those posed to their research and development. The workshops include a facilitator-led group exercise incorporating realistic running scenarios, aimed at challenging participants to react to evolving security threats and a concluding “lessons learned” dialogue.
- Canadian Centre for Cyber Security: Offers resources on the cyber threat environment, including cyber threat actors and their motivations, sophistication, techniques, tools, and strategies to address the potential threat. These resources include a Learning Hub, Interactive content, Publications and Alerts and advisories
- Canadian Cyber Security Tool: A voluntary, user-friendly, and brief self-assessment survey that provides an overview of your organization’s operational resilience and cyber security posture. It also provides comparative results between your organization and peer entities within your sector.
- Public Health Agency of Canada: PHAC offers a self-paced online course as well as guidance to help researchers identify Dual-Use in Life Science Research, as well as a self-paced online course on Insider and Outsider Threats that describes the motives, tactics, and indicators of insider/outsider threats alongside mitigation strategies.
- Regional Resilience Assessment Program (RRAP): Public Safety Canada works with IT departments across Canada to evaluate cyber security protocols through the Canadian Cyber Resilience Review (CCRR) process. The CCRR is a free, voluntary, non-regulatory, non-technical cyber security assessment, delivered by a Public Safety facilitator.
The Portage Network works within the library and research communities to co-ordinate expertise, technology, and services in RDM, as well as promote collaboration between research libraries and other data management stakeholders.
Portage has published videos to help guide research institutions in creating effective institutional RDM strategies. The videos cover the first two strategy components outlined in Portage’s Institutional RDM Strategy Template: Raising Awareness and Assessing Institutional Readiness. Each module is also accompanied by discussion prompts:
Institutional RDM strategy template
Portage has released a template and supporting guidance document designed to assist Canadian research institutions in developing an overarching strategy for RDM. The resources will exist as living documents, to be updated by Portage’s working group as needed.
Canadian repository options
The Portage Network (sponsored by the Canadian Association of Research Libraries) provides suggested repositories, including the national Federated Research Data Repository, co-developed by Compute Canada and Portage, or one of the many instances of Dataverse hosted in universities and regions across the country.
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 version open to all Canadian researchers.
Federated Research Data Repository
The Federated Research Data Repository provides a national repository option through which data can be entered, 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, the Repository’s federated search tool provides a focal point to discover and access Canadian research data housed in over 30 Canadian data repositories.
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.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.
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 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 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
- data management planning
- storage and archiving
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.
New Digital Research Infrastructure Organization
The New Digital Research Infrastructure Organization (NDRIO) is a national, not-for-profit organization that plays a critical role in helping establish a researcher-focused, accountable, agile, strategic and sustainable digital research infrastructure (DRI) ecosystem for Canadian researchers. As part of Canada’s national DRI strategy, Innovation, Science and Economic Development Canada has set aside up to $375 million of its five-year funding to establish this new organization. NDRIO will co-ordinate and fund activities in advanced research computing, RDM, and research software components of the DRI strategy, working collaboratively with stakeholders across the country.
Research Data Canada
Research Data Canada (RDC) is dedicated to collaborating with stakeholders from across the country to enhance access to research data and improve RDM within Canada. RDC is an organizational member of the international Research Data Alliance (see below).
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.
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 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 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 a government-funded, 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:
- SSHRC: ResearchData-Donneesderecherche@sshrc-crsh.gc.ca
- NSERC: ResearchData-Donneesderecherche@nserc-crsng.gc.ca
- CIHR: ResearchData-Donneesderecherche@cihr-irsc.gc.ca