Skip to Main Content

Research Data Management

Research Data Management

Research data management (RDM) concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. RDM helps researchers navigate the increasingly complex landscape of data planning, storage, and sharing.  It is part of the research process, and aims to make the research process as efficient as possible, and meet expectations and requirements of the university, research funders, and legislation.

RDM  concerns how you:

  • Create or collect data and plan for its use,
  • Organise, structure, and name data (e.g. files),
  • Keep it – make it secure, provide access, store and back it up,
  • Find information resources, and share with collaborators and more broadly, publish and get cited, allowing for your data to be reused.

Tri-Agency RDM Policy

Tri-Agency Research Data Management Policy (March 2021)
  • This policy will be phased in over the next few years and will not apply to research that has already been awarded funding prior to March 2021.
  • The objective of this policy is to support Canadian research excellence by promoting sound RDM and data stewardship practices.
  • This policy is not an open data policy.
  • 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. 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.
  • Data management plans: All grant proposals submitted to the agencies should include methodologies that reflect best practices in RDM. For certain funding opportunities, the agencies will require data management plans (DMPs) to be submitted to the appropriate agency at the time of application, as outlined in the call for proposals; in these cases, the DMPs will be considered in the adjudication process.
    • By spring 2022, the agencies will identify the initial set of funding opportunities subject to the DMP requirement.
    • The agencies will pilot the DMP requirement in targeted funding opportunities before this date.
  • Data deposit: Grant recipients are required to deposit into a digital repository all digital research data, metadata and code that directly support the research conclusions in journal publications and pre-prints that arise from agency-supported research.
    • Determining what counts as relevant research data, and which data should be preserved, is often highly contextual and should be guided by disciplinary norms.
    • Grant recipients are not required to share their data. However, the agencies 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.
    • The deposit must be made by time of publication. 
    • After reviewing the institutional strategies (due 2023) and in line with the readiness of the Canadian research community, the agencies will phase in the deposit requirement.
Tri-Agency RDM Policy Frequently Asked Questions
For McGill researchers and affiliates, please refer to the Digital Research Services Hub for resources related to RDM or with any questions about the Tri-Agency RDM Policy

Why is RDM Important?

Other Granting Agency Requirements



Canadian Institutes of Health Research (CIHR)

  • Policy summary: EnglishFrench

  • Full policy: EnglishFrench

  • "deposit bioinformatics, atomic, and molecular coordinate data into the appropriate public database (e.g. gene sequences deposited in GenBank) immediately upon publication of research results"

  • "retain original data sets for a minimum of five years (or longer if other policies apply)"

Social Sciences and Humanities Research Council (SSHRC)
  • Policy: EnglishFrench
  • "All research data collected with the use of SSHRC funds must be preserved and made available for use by others within a reasonable period of time. SSHRC considers "a reasonable period" to be within two years of the completion of the research project for which the data was collected."
  • "Costs associated with preparing research data for deposit are considered eligible expenses in SSHRC research grant programs."
Government of Canada



Fonds de recherche du Québec
  • Policy covers publishing of results, but does not apply to research data: English. French.
  • "This Policy does not cover the data used or raw data generated by research, but rather the dissemination of the research results (methodology, scientific analysis, figures, graphics, tables, etc.)."



National Science Foundation (NSF)
  • Policy: English
  • " Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants."
National Institutes of Health (NIH)
  • Final policy (released October 2020, effective January 2023): English
  • "The final DMS Policy does not create a uniform requirement to share all scientific data", but there is "an expectation that researchers will maximize appropriate data sharing" as described in Data Management Plans
  • "The final DMS Policy requires submission of a [Data Management and Sharing] Plan for extramural grants at application"
  • The final DMS Policy states that “[s]hared scientific data should be made accessible as soon as possible, and no later than the time of an associated publication, or the end of the award/support period, whichever comes first.”
  • From the DRAFT plan (October 2003): "In NIH's view, all data should be considered for data sharing. Data should be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting confidential and proprietary data."
National Endowment for the Humanities (NEH)
  • Requires data management plan: English
  • Generally encourages data sharing in a timely manner, but does not define timelines


Journal Mandates

Many journals, especially journals with higher impact factors or journals associated with major publishers, have instituted policies regarding the availability of research data underlying publications. In many cases, journals require that data are made openly available as a condition for publishing an article.

The following list covers a few major publishers as examples:

  • Science journals require all data be made available upon reasonable request. See policy here (scroll to "Data and Materials Availability after Publication").
  • PLOS journals require authors to make all data necessary to replicate their study’s findings publicly available without restriction at the time of publication. When specific legal or ethical restrictions prohibit public sharing of a data set, authors must indicate how others may obtain access to the data. Read the full policy here.
  • Nature (SpringerNature) requires authors to make materials, data, code, and associated protocols promptly available to readers without undue qualifications. Read the full policy here.
  • Wiley journals may adopt a range of data sharing policies from less restrictive ("encourages data sharing" to mandatory "mandates data sharing"). Read about the policies and associated journals here.

Open Research Principles

Open data/research is the practice of research in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods. Open research data is data that can be freely accessed, reused, remixed and redistributed, for academic research and teaching purposes and beyond. Ideally, open data have no restrictions on reuse or redistribution, and are appropriately licensed as such. Openly sharing data exposes it to inspection, forming the basis for research verification and reproducibility, and opens up a pathway to wider collaboration.

However, there are also special considerations - not all data can or should be open. For example, to maintain Indigenous Knowledge sovereignty and Indigenous Data sovereignty (see CARE principles below in this page), or to protect the identity of human subjects, limited restrictions of access may be implemented.

Read more about Open research: (CC-0)

FAIR Data Principles

Since the publication in 2016 of "FAIR Guiding Principles for scientific data management and stewardship" in Scientific Data, the best practice for managing data is to adhere to the FAIR principles. The FAIR principles are a framework for ensuring that data collected by researchers across all disciplines and fields meet specific standards to promote open science, reproducibility of research, and maximize the benefits of research to academia and society.

The following description of the FAIR principles is taken directly from 


The first step in (re)using data is to find them. Metadata (the description of the data) and data should be easy to find for both humans and computers. This means assigned a persistent identifier (PID) to the data/dataset (usually in the form of a digital object identifer, or DOI). Identifiers consist of an internet link (e.g., a URL that resolves to a web page where the data are located). Identifiers will help others to properly cite your work when reusing your data.


Once the user finds the required data, they need to know how can they be accessed, possibly including authentication and authorisation. This does not mean that data should be open, necessarily. There are many reasons to restrict access to data (e.g. the data contain personally identifiable information (PII), are proprietary/licensed as intellectual property (IP), or contain other sensitive information). Accessibility essentially means that it should be clear under what conditions access is allowed. The rule with accessibility can be distilled to: "As Open as Possible, as Closed as Necessary"


Interoperability refers to the ease by which data can be integrated with other/new data. In practice, storing data in open formats makes it easier to later integrate new data. On the other hand, storing data in proprietary formats hinders this effort. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. This means that when possible, it's best practice to use standardized vocabularies/variable labels/terms.


The ultimate goal of FAIR is to optimise 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. In practice, this involves creating a README file with details on how to clean, transform, or manage the data, if applicable. This also involves applying a license to let others know if the data are public domain or if copyright is retained to some degree or completely. 

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.

Collective Benefit:

Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.

Authority to Control:

Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data


Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.


Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.

The First Nations Principles of OCAP®

To learn about the principles of Ownership, Control, Access, and Possession, please visit

The following information is quoted from the First Nations Information Governance Centre's website:

"OCAP® asserts that First Nations alone have control over data collection processes in their communities, and that they own and control how this information can be stored, interpreted, used, or shared.

Ownership refers to the relationship of First Nations to their cultural knowledge, data, and information. This principle states that a community or group owns information collectively in the same way that an individual owns his or her personal information.

Control affirms that First Nations, their communities, and representative bodies are within their rights in seeking to control over all aspects of research and information management processes that impact them. First Nations control of research can include all stages of a particular research project-from start to finish. The principle extends to the control of resources and review processes, the planning process, management of the information and so on.

Access refers to the fact that First Nations must have access to information and data about themselves and their communities regardless of where it is held. The principle of access also refers to the right of First Nations’ communities and organizations to manage and make decisions regarding access to their collective information. This may be achieved, in practice, through standardized, formal protocols.

Possession While ownership identifies the relationship between a people and their information in principle, possession or stewardship is more concrete: it refers to the physical control of data. Possession is the mechanism by which ownership can be asserted and protected."

Please note: “OCAP® is a registered trademark of the First Nations Information Governance Centre (FNIGC)”


Profile Photo
Alisa Rod
McGill University Library
Digital Initiatives
550 Sherbrooke West, West Tower
Montreal, QC H3A 1B9

Research Data Management Specialist

McGill LibraryQuestions? Ask us!
Privacy notice