Research Data Management: Institutional Strategies

The first requirement of the Tri-Agency Research Data Management Policy is an institutional strategy. All institutions eligible for Tri-Agencies funding must provide the Tri-Agencies with and have a published institutional strategy by March 1, 2023.

The purpose of an Institutional RDM Strategy is to foster a culture and develop capacity that supports researchers in adopting responsible RDM practices, following the FAIR Principles to make research data Findable, Accessible, Interoperable and Reusable.

The Portage Network* defines an institutional RDM strategy as: "a concise and directive document that outlines how an institution, such as a university or research institute, will increase its capacity for effectively managing its research data". (Portage Network, 2020)

In other words, an institutional RDM strategy provides a roadmap for all people in an organization to understand their role and responsibility for good data management and the resources and tools available.

There are four main components of an institutional strategy:

1. Raise awareness: Researchers’ perspectives towards data sharing are varied, as are levels of expertise in terms of good data management practices. There is a need to raise awareness within the research community about the benefits of and best practices for good data management, as well as funder and journal policy requirements.
2. Assess institutional readiness: To effectively manage data holdings and fully realize their potential, an organization must first be aware of the location, condition, estimated growth, and value of those data sets. Institutions can undertake a review of the data landscape on campus, using one of a number of existing tools.
3. Formalize RDM practices: Formalizing the expected practices around RDM through the adoption of guidelines, procedures or policies is an important step in establishing an effective and sustainable approach to RDM at the institution. This will set the tone for research undertaken at the institution and underscore institutional commitment and expectations. Depending on the institution, this could be implemented through a set of coherent guidelines or procedures, or through the implementation of a cohesive policy. Community engagement and consultation are essential prerequisites for researcher and institutional buy-in.
4. Define a roadmap: A pragmatic roadmap will help institutions build capacity for RDM over the medium term. Best practices in RDM contribute to research excellence, greater efficiency, and greater transparency of research. The defined roadmap will ensure that institutions are able to adhere to RDM requirements and continue to improve institutional capacity for RDM activities.

* The Portage Network is the RDM service of the Digital Research Alliance of Canada (the Alliance) and is dedicated to the shared stewardship of research data in Canada through:

• Developing a national research data culture
• Fostering a community of practice for research data
• Building national research data services and infrastructure

Knowing the FAIR (findable, accessible, interoperable, reusable) principles of RDM and what they mean in a practical way informs an institutional strategy and data management plans.

(Pundir, 2016)

Findable

• Data and accompanying materials have sufficiently rich metadata and a unique and persistent identifier
• Metadata are indexed in a searchable resource

Accessible

• Metadata and data are understandable to humans and machines
• Metadata are retrievable by their persistent identifier
• Data are deposited in a trusted repository - this does not mean open
• Metadata are accessible even when the data are not

Interoperable

• Metadata and data use a formal, accessible, shared, and broadly applicable language for knowledge representation
• Metadata and data include references to other data when applicable

Reusable

• Metadata and data are richly described with accurate and relevant attributes
• The provenance and source of the metadata and data are clearly stated
• Data usage permissions and licenses are clear and accessible 

There is a misconception that FAIR principles dictate that data must be open.  Neither the Tri-Agency policy nor the FAIR principles prescribe that data must be openly accessible.  Instead, the FAIR principles provide the factors to consider when assessing data within an institutional strategy and data management plan. 

In 2019 the Portage Network Research Intelligence Expert Group conducted a survey on behalf of the Canadian Association of Research Libraries of post-secondary institutions and government agencies to assess the readiness of those organizations to adopt and implement an institutional strategy.

In addition to serving the purpose of the survey, the questions asked provide organizations with the framework of the requirements to prepare and implement an institutional strategy.

Key questions to consider include:

1. What data management positions (if any) have your institution created or reassigned (please provide associated job titles)? (for example, data management coordinator, data protection officer, data management librarian, data curation specialist, data managers, or data scientists)
2. How are different stakeholders within your institution working collaboratively to tackle research data management challenges? (for example, cross-campus working group, Research Data Management steering committee, responsibility is spread across multiple departments, etc.)
3. What research data management infrastructure does your institution have access to?
   1. Active data storage services (storage used during the actual research process)
   2. Data repository (where data are deposited/published for discovery and/or appropriate access)
   3. Long term data preservation (the process through which some data may be stored for the longer term post project)
   4. Infrastructure for protecting and preserving sensitive data
   5. Other research data management infrastructures
4. What resources are required at this time to streamline research data management at your institutional level?
   1. Human resources
   2. Policy guidance
   3. Legal guidance ((licensing, Intellectual Property, etc.)
   4. Financial resources
   5. Technical resources
5. Different skills are needed to further support research data management. At what level are the following skills available at your institution?
   1. Technical skills in managing sensitive data
   2. Data security and risk management skills
   3. Data analysis and visualization skills
   4. Metadata creation skills
   5. Data curation skills
   6. Data preservation skills
   7. Technical skills in the area of e-infrastructures
   8. Research software development skills
   9. Researchers’ operational data management skills
   10. Knowledge of national policies
   11. Advisory skills on technical, organizational and operational matters
6. Research data management support can be a composite of services provided by different units at an institution, as well as regional and national efforts. Infrastructure may also consist of open platform/not-for-profit and commercial/publisher packages. Which units or products within and outside your institution support each of the following services?  Support include, but are not limited to: library, IT, shared, regional/consortial, national, commercial.
   1. Informational website(s) on research data management
   2. Advisory services
   3. Technical support with data encryption, anonymization, etc.
   4. Data computing services
   5. General RDM best practices or DMP training
   6. Targeted hands-on RDM workshops
   7. Curation support
   8. Specific financial support to researchers
7. Does your institution currently have a research data management strategy? RDM Policy? Elements /  provisions to cover are: 
   1. Make research data FAIR (findable, accessible, interoperable and reusable)
   2. Specific guidelines for sensitive data
   3. Provisions for data storage
   4. Provisions for specific disciplinary areas
   5. Provision for handling personal data
   6. Legal support
   7. Licenses
8. Is there a guideline/policy at your institution on the ownership of research data?
   1. Guideline or policy on minimum retention period
   2. Guideline or policy on how researchers license research data
9. How does your institution budget for research data management services?
10. Institutional awareness
11. What are the barriers/challenges for your institution to support research data management and adherence to FAIR principles (i.e. making data Findable, Accessible, Interoperable, and Reusable)?
    1. Limited awareness of the benefits of RDM
    2. Resistance to making data available or to share data
    3. Concerns over legal frameworks
    4. Concerns over increased costs
    5. Limitations of confidentiality clauses
    6. Lack of time to clean and prepare data and metadata
    7. Absence of incentives to promote RDM
    8. Lack of skilled staff with expertise on the topic
    9. Lack of support for researchers
    10. Lack of coordination among the relevant actors
    11. Lack of funding
    12. Lack of awareness raising, including training opportunities
12. What are the most important actions that could help promote research data management and adherence to FAIR principles (Findability, Accessibility, Interoperability, and Reusability) of research data at your institution?