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Open Access Week Challenge 2024

This 5-day asynchronous online workshop will guide you through tools and resources that we recommend for a community-first, open approach to knowledge sharing.

Welcome to the 5th Day of the OA Challenge!

Welcome to Day 5 of the Open Access Week Challenge!

Today’s activity will give you a quick introduction to strategies for managing and sharing research data.

In this part of the Open Access Week Challenge, you should expect to learn the following:

  • What is open data vs. controlled access data?
  • What are best practices for research data management?
  • Different types of data protections
  • How to share research data

What is Research Data Management?

What is research data management (RDM)?

Research data management is the set of small, practical strategies you use to collect, organize, describe, analyze, and report the data for a particular project. The goal of research data management is to make it easier for you (the researcher) to find and use the data to answer your research questions. The good news is that good RDM practices also support research integrity, data sharing, and transparency.

For all research projects, the core practices are:

  1. Develop a Data Management and Sharing Plan (DMSP) that documents your obligations and plans
  2. Identify your obligations with respect to data management and sharing
    • Legal requirements - international, federal, state, local (e.g., EU GDPR, the US Common Rule, HIPAA, FERPA, etc.)
    • Ethical obligations of your professional and/or research communities (e.g., privacy & confidentiality, data sharing, etc.)
    • Funder or sponsor requirements (e.g., DMSP, data sharing, data citation, etc.)
    • Institutional requirements and/or policies (e.g., record-keeping, data retention & disposal, data security, etc.)
    • Publisher or journal requirements (e.g., data availability statements, data sharing, data citation)
  3. Determine whether you have the rights or permissions to share the data
    • Did you get consent to share from human research participants?
    • If you received the data from someone else (a vendor, data repository, the government), do you have the rights to share or re-distribute it?
  4. Based on the requirements and rights/permissions above, decide what data to share, with whom, where & how, and when
  5. Use the DMSP to support consistency in practices such as file naming, storing data securely, data curation, data retention and disposal, etc.
  6. Implement relevant data and metadata standards specific to your field of research and project (where they exist)

Good data practices are context-specific. The specific practices and standards for RDM can look very different between research fields (i.e., public health vs. analytical chemistry vs. anthropology). RDM strategies can even change for projects conducted by the same team. Because of those differences, there is not a single list of "best practices" that apply to all research. BUT there are some foundational practices that can be implemented in multiple ways so that they fit into the unique needs and workflows of all projects. To learn more, read the Open Access article:

Briney, K., Coates, H., & Goben, A. (2020). Foundational Practices of Research Data Management. Research Ideas and Outcomes, 6, e56508. https://doi.org/10.3897/rio.6.e56508

How are research data shared?

Data sharing happens on a spectrum from open to closed.

According to the Open Definition, “Open data and content can be freely used, modified, and shared by anyone for any purpose”

Most data shared in open access data repositories are mostly open, but are not fully open according to the Open Definition. For example, many data sets are shared with a Creative Commons license that requires the data source be attributed or cited. For now, data citation is essential for measuring the impact of data sharing and to give data creators credit for their contributions. 

At the other end of the spectrum is restricted access data or data shared under controlled mechanisms. Usually, controlled access requires potential users to apply to get access to the data. The application can be brief or extensive, but is then reviewed by a group of experts such as a Steering Committee or Repository Curators/Managers. This process also typically involves some sort of agreement, such as a Data Use Agreement or Terms of Use, that describe what users can and cannot do with the data. These agreements are signed by University officials with signatory authority to make sure that all legal obligations can be met.

Finally, there are some data that cannot be shared due to lack of consent, association with classified government projects, etc.

Challenge Activity: Spot the Snafus

Watch "Data Sharing & Management Snafu in 3 Short Acts"

https://www.youtube.com/watch?v=66oNv_DJuPc

Can you identify three things the dog researcher (not Dr. Benign) could do to improve how he manages and shares his research data next time?

If you are planning your thesis or dissertation project, start drafting a Data Management & Sharing plan and discuss the draft with your supervisor. The IU template for a living DMSP is available at https://datamanagement.iu.edu/governance/policies/rdm-guidance.html or you can try the DMPTool (log in with your IU account).