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Research Data Management

Make a plan

Need to create a data management plan? Create an account with DMP Assistant, a nationally-endorsed tool for effective data stewardship. 

Share your data

To deposit data with Borealis and the York University Dataverse, please follow the instructions listed in the following guides:

Depositing Data in the York University Dataverse: A Quick Guide

York University Dataverse Deposit Submission Guide

To learn more about the York University Dataverse, please consult our data deposit guidelines and collections policy:

York University Dataverse Deposit Guidelines

York University Dataverse Collections Policy

Contact us

Need help with research data management? Contact us at

Expand the below for more information

Frequently asked questions

CODATA’s Research Data Management Glossary defines “Research Data” as sources and evidence that are needed to support and validate findings of scientific enquiry, research scholarship, and artistic activities. There are many different types of research data: experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data.

Research data management (RDM) provides guidelines on the storage, access, and preservation of research data throughout a research project. It covers the entire research project lifecycle, from drafting the research plan, to implementing it and beyond. RDM outlines terms regarding the subsequent deposit of the data with a data repository for sharing long-term management and preservation.

Want to learn more about the basics of research data management? Consult the Digital Research Alliance of Canada website or the CIHR Research Data Management Learning Module.

A data management plan (DMP) is a formal statement describing how research data will be managed, documented, and preserved during the research process and once the project is completed. Data management planning is an international best practice which supports the responsible conduct of research and respects the disciplinary norms inherent in how research is conducted in various fields. Planning early for each stage of the data life cycle will help researchers identify opportunities and anticipate issues earlier, and it will prepare researchers for addressing potential challenges in a holistic and systematic fashion.

To begin drafting a DMP, researchers are invited to consult the Portage Network guide for creating an effective DMP and to create an account with DMP Assistant, a nationally-endorsed tool for effective data stewardship.

Need help getting started on your data management plan? Contact the libraries at to schedule an RDM consultation.

Researchers can improve data organization practices through a variety of strategies such as file naming and folder structure, and version control. Documenting data at both the study-level and data-level is necessary for data to be understood and reused.

To learn more about recommended data documentation strategies, consult the following guide:
Documentation and Supporting Material Required for Deposit (Portage Network)

Need support with data organization or data documentation? Contact the libraries at to schedule an RDM consultation.

Research data are often shared:

  • to meet funding agency requirements;
  • to meet journal data policy requirements;
  • to publish data as a scholarly product according to the FAIR principles; or
  • to adopt open science practices: increasing research accountability, reproducibility, open engagement and reducing duplication.

Balancing data sharing requirements with common directives for research ethics and confidentiality can be achieved through careful data management planning. To consider your options for sharing data, consult the following resources:

Risk and Security Guidelines:

Research Data Retention and Deposit Guidelines for Research Involving Human Participants (York University Libraries)
Data Security Guideline: Research Involving Human Participants (York University Information Technology)
Can I share my data (Portage Network)

Once it is determined that research data can be shared, researchers need to choose a trusted repository for data sharing and preservation. A number of commercial and not-for-profit generalist repositories exist for research data.

A disciplinary data repository can be the best option for data to be properly catalogued and discovered by users in your discipline. You can consult the re3data directory and browse by discipline to generate a potential data repository list for your project.

Canadian researchers also have two national research data repositories they can use to deposit their data:

Borealis, the Canadian Dataverse Repository, is a bilingual, multidisciplinary, secure, Canadian research data repository, supported by academic libraries and research institutions across Canada. Borealis supports open discovery, management, sharing, and preservation of Canadian research data. Users can create robust metadata, track changes across versions of their datasets, mint Digital Object Identifiers (DOIs), and make data open or restricted.

The Federated Research Data Repository (FRDR) is a platform for sharing open and large data sets as well as a national data discovery engine.

Researchers can learn more about repository choices using the following guides:
Repository Options in Canada: A Portage Guide
Generalist Repository Comparison Chart
Recommended Repositories for COVID-19 Research Data

Have questions about your data repository options? Contact us at

The York University Dataverse is the main institutional data repository supported by the Libraries.

To deposit data with Dataverse, please follow the instructions listed in this guide:
Depositing Data in the York University Dataverse: A Quick Guide

For more information on best practices for depositing research data, consult the below guides:
York University Dataverse Deposit Guidelines
Dataverse North Metadata Best Practices Guide (Portage Network)

Research communities and research data stewards are developing principles to guide the practices of RDM. The FAIR (Findable, Accessible, Interoperable, Reusable) Principle has been adopted by many research data stakeholders across research domains. Researchers can assess the FAIRness of their research data using tools such as FAIR-Aware. More recently, the Global Indigenous Data Alliance has also developed the CARE Principles for Indigenous Data Governance in order to reinforce Indigenous Peoples’ rights to engage in RDM decision-making in accordance with Indigenous values and collective interests.

To explore active storage options for your research data, you can consult the below resources:

Storage options:
O365/OneDrive Security and Privacy:
York University Information Technology Research Computing Services:
Compute Canada Rapid Access Service for storage needs:
Compute Canada Research Portal National Services: