Data Management and Record Keeping: Sharing & Reuse

• Creating a plan for data sharing and reuse is integral to data management plans, and the earlier this aspect of data management is considered, the better, especially because...
• The most common type of data-sharing will occur with your colleagues and with your future self
• Be aware of any issues regarding intellectual property, including copyright, licensing and contracts, and patents
• Once data is shared publicly, it can be cited like any other scholarly output, and can be included as research output on your CV. Make sure you have a unique identifier to serve as a stable link for your data so others can continue to find it. 
  • Data citation format example: Creator (Publication Year). Title. Version. Publisher. Resource Type. Identifier
• Consider your data's file formats and how easily they can be re-used; check to see if there are any file format requirements from your funding source of the data repository you plan to use. More information on file formats and their capabilities is available from the Open Data Handbook.
• Privacy & Security: 
  • Be aware of any sensitive data you might have, including data with personally identifiable information, and what you need to do to anonymize the data (this might involve removing direct and indirect identifiers from the data). 
  • Follow good computer security practices (make sure your software is up to date and that you have a firewall and antivirus software in place; use strong passwords).

Data Science

Research Methodology

Copyright

Data Licenses

• From Creative Commons: CC0 Use for Data
• List of all Creative Commons Licenses

Data Identifiers

• DOI (Digital Object Identifier)
• DataCite

Data Repositories

Some particularly popular/well-known data repositories for sharing your data. Be sure to check for intellectual property policies, metadata standards or requirements, and data storage and backup policies when using these services.

All data types:

• figshare
• Dataverse

Science-related data:

• ZENODO
• Dryad (mostly biology)
• GenBank (genome)
• DataONE (Earth observational data)
• arXiv.org (primarily physics, math, and computer science)

Other specific data types:

• GitHub (code)
• ICPSR (social science)