RESEARCH DATA MANAGEMENT SERVICE GROUP
Comprehensive Data Management Planning & Services

Sharing data

Sharing data makes it possible for researchers to validate research results and to reuse data for teaching and further research. Furthermore, sharing can increase the impact of research (Piwowar 2007). Sharing is also required by an increasing number of funders and publishers. Funders seek to maximize the impact of the research they fund by encouraging or requiring data sharing. Publishers seek to ensure the research they publish is reproducible, and that sufficient information is included for the scholarly record. While sharing data may pose challenges of "ethical, cultural, legal, financial, or technical nature," it can also pave the way for "more open, ethical, and sustainable science" (Figueiredo 2017).

Strategies for sharing

Data sharing encompasses all strategies by which an investigator might make their data available to a broader audience, including:

  • deposit to a discipline-specific data center or repository (recommended, if possible) or a curated discipline agnostic repository like Dryad
  • deposit to Cornell's digital repository (eCommons)
  • submission to a journal publisher in conjunction with a related publication
  • publication in a data journal
  • Cornell's Open Science Framework instance, non-curated repositories such as figshare and Harvard Dataverse, independently-developed infrastructure for data distribution

While there are many strategies for sharing, we recommended that researchers submit data to an established data system or repository whenever possible.  Depositing to an established repository will help to ensure that data are consistently available and accessible, and preserved for future use. 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 best solution will ensure that data is discoverable, accessible, and preserved over the long term. The RDMSG can help researchers select an appropriate repository, data journal, or other strategy for sharing data.

Choosing a repository

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

  • that they will accept the data
  • requirements for submission
  • long-term preservation policy
  • whether there are any fees associated with deposit

In order to identify potential places to publish or share data, researchers may:

Issues and exceptions

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 Intellectual Property under Section 5. Policies for public access, data sharing, and reuse of the Data Management Planning guide for more help explaining circumstances that prevent data sharing in a data management plan, and 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 any conditions for reuse. Documentation should include a description of any standard licenses applied to the data, as well as any additional terms of use. For an overview of issues associated with managing intellectual property rights in data projects, see the Introduction to Intellectual Property Rights in Data Management. For additional support contact 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.

References

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.

Data Sharing: Convert Challenges into Opportunities. Ana Sofia Figueiredo. Frontiers in Public Health 5(327). 2017. https://doi.org/10.3389/fpubh.2017.00327

Related information

Data citation. Cornell Research Data Management Service Group. http://data.research.library.cornell.edu/content/data-citation. Information and guidance about citing data sets.

Frequently asked questions. Cornell Research Data Management Service Group. http://data.research.library.cornell.edu/content/frequently-asked-questions#question1 Addresses more questions about data sharing.

Metadata and describing data. Cornell Research Data Management Service Group. http://data.research.library.cornell.edu/content/writing-metadata. Information about documenting data for sharing.

Preparing tabular data for description and archiving. Cornell Research Data Management Service Group. http://data.research.library.cornell.edu/content/tabular-data. An outline of best practices for preparing spreadsheets and other tabular data for publication.