Research Data Management at SUNY Geneseo

Sharing and publishing the data will bring another set of criteria to consider. Sharing data makes it possible for researchers to validate research results, to reuse data for teaching and further research, and can increase the impact of that research (Piwowar 2007). Sharing is also required by an increasing number of funders and publishers who seek to maximize the impact of research, ensure results are reproducible, and that sufficient information is included for the scholarly record. 

This page gives an overview of:

• Options for publishing & sharing your data
• General guidance on copyright, privacy, and getting your data cited

Sharing detailed research data is associated with increased citation rate. Heather A. Piwowar, Roger S. Day, Douglas D. Fridsma. PLoS ONE 2(3):e308. 2007. https://dx.doi.org/doi:10.1371/journal.pone.0000308.

Content adapted from RDMS Cornell University licensed CC BY.

There are a range of options for sharing your data with a broad audience, including a number of data repositories that provide varying levels of access and support. Archives and data repositories with data experts can provide curation services and long-term management of your data will allow for the data to be preserved into the future. Some examples include:

• deposit to a discipline-specific data center or repository such as: 
  • CCSS Data Archive (primarily social sciences)
  • CUGIR (Cornell University Geospatial Information Repository)
  • NCBI databases (National Center for Biotechnology Information)
• deposit to a curated discipline agnostic repository e.g. Dryad
• deposit to Geneseo's digital repository (KnightScholar)

Other options for sharing that may be preferred or required by a publisher may not be curated and do not guarantee long-term preservation, e.g.:

• submission to a journal publisher in conjunction with a related publication
• publication in a data journal
• submission to non-curated repositories such as Cornell's Open Science Framework instance, figshare, and Zenodo

While personal or lab websites, Electronic Lab Notebooks (ELNs), wikis, and similar tools may be sufficient for short-term sharing, they are usually not great choices for the long term. The Library can help researchers select an appropriate repository, data journal, or other strategy for sharing data that will ensure the data is discoverable, accessible, and preserved as part of the scholarly record.

Repository policies vary; confer with potential repositories or publishers to determine:

• what data they accept, e.g. limits on file and submission sizes, format requirements
• requirements for submission
• long-term preservation policy
• whether there are any fees associated with deposit or curation services

Content adapted from RDMS Cornell University licensed CC BY.

There are some complex issues associated with making data broadly accessible that researchers need to be aware of, including (but not limited to): 

• Intellectual property rights
• Conditions for reuse (e.g., licensing)  
• Restricted access data, for example private and confidential data, or data with commercial implications  

Intellectual property

Intellectual property issues related to research data are complex. Ownership of data may rest with the researcher, the institution, or the funder, depending on the nature of the researcher's appointment, grant contract conditions, and whether there are patent implications. Consult the Intellectual Property section of the Data Management Planning guide, under “Section 5. Policies for public access, data sharing, and reuse" for more help explaining circumstances that prevent data sharing in a data management plan. You can also consult Cornell services related to intellectual property and copyright for a list of services related to copyright, technology transfer, university policies and more.

Conditions for reuse

When sharing data, it is important to document conditions for reuse. Documentation should include a description of standard licenses applied to the data, and any additional terms of use. We recommend the use of CC0, which is intended to reduce legal and technical impediments to the reuse of data. 

Why CC0? Attribution can become increasingly complex as multiple datasets are combined and reused because derivative work must be licensed under the most restrictive license of all the contributing data sets. This can lead to a difficult-to-navigate situation called “license stacking” or “attribution stacking,” where each reuse of a dataset leads to more restrictive conditions. To prevent this situation, we encourage you to consider CC0, CC-BY, or similar. The use of CC0 does not prevent anyone from following community norms; data citation is always recommended. For a deeper investigation of issues associated with managing intellectual property rights in data projects, see the Introduction to Intellectual Property Rights in Data Management and Cornell University Library's Copyright Information Center.

Private and confidential data, or data with commercial implications

Researchers may have ethical or legal obligations to maintain confidentiality and to protect the privacy of research subjects, or may have other circumstances requiring secure data storage or restricted access to data, such as licensing restrictions that prohibit data sharing. Data may also be part of a research project with commercialization potential. Funders and publishers recognize that there are legitimate circumstances under which an investigator cannot share their data, and a data management plan should explain those circumstances.