Data Sharing and Governance

Satisfying FAIR data management principles for data sharing involves several administrative and technical parts including hosting data, providing access permission, and enabling discovery of that data. These services need to be maintained beyond the life of the project.

Indigenous data has special governance concerns: For more information, see OCAP & CARE.

Considerations before sharing data

1. Are you the owner or data-steward of the data and have the right to make decisions about sharing the data?
2. Is it necessary to connect with the institution’s Intellectual Property office?
3. Are there third-party agreements or funding restrictions (such as CIHR, NSERC, industry partners) on sharing the data?
   1. If yes, have they been reviewed and complied with the terms?
4. Have the institutional data sharing and open science policies been reviewed?
5. Is the research subject to approval by a Research Ethics Board (REB)?
   1. If yes, does the REB approval cover the proposed data sharing?
6. Does your data involve Indigenous communities, knowledge, or participants?
   1. If yes, have the OCAP principles been followed (Ownership, Control, Access, and Possession)? Has there been consulted or obtained community consent where required?
7. Does your dataset include sensitive or personal information?
   1. If yes, has it been de-identified to meet privacy standards?
8. Has a license or terms of use been determined for your dataset (ex. restricted access)?
9. Is the metadata and documentation sufficient for reuse and understanding by others?
10. If the data is being deposited, is there a primary repository the data will be deposited in, how long can it store the data, and how accessible will it be?
11. Has a location and method of data sharing been identified? (repository, platform)?
    1. Is the repository compliant with privacy or security requirements?
12. Are there clearly defined attribution and citation expectations?

Governance:

• Identifying a person or team responsible for managing project shared data specifications, storage and access.
  • This includes the responsibility of determining what shared data is as a subset of raw or analyzed project data.
  • Data privacy and anonymization requirements need to be defined.
• Making clear how project data may be used and what attribution is required, if any.
  • Data licensing and governance documentation need to be provided.
• Identify contact role(s) and processes within an organization for handling access requests.
• It may be possible to pre-define data usage agreements between trusted parties.

Hosting:

• Determining a public or access-controlled repository to host project data. We recommend
  • Borealis, the Canadian Dataverse Repository (previously called Dataverse). See Data Sharing using Borealis for an introduction to data sharing and the Borealis platform.
• Ensuring Secure (tamperproof) upload and download of data.
• Implementing and maintaining access control as necessary.

Data Discovery:

• Utilizing FAIR data catalogues to enable researchers to become aware of project datasets in terms of available data types, fields, size of content, and even experimental designs that lead to the content. Providing this information usually benefits from dataset scheme and standardization work.
  • https://fairsharing.org/

Author: Damion Dooley