Access & Reuse slice from biomedical data lifecycle wheelAccess & Reuse Considerations

Who has access to data and how they are permitted to use data are important considerations. When publishing or sharing data, determine how and to whom you will provide access to your data. Also think about what you will allow to be done with your data, and provide provisions for reuse, redistribution, and creation of derivatives.

When thinking about your project:

  • The data you produce should have sustainability, both in your Active and Short Term Projects and in the future

  • Guarantee your work can be continued when you leave Harvard with the RDM Offboarding Checklist

  • Data Reproducibility requires the data to be well-documented, properly organized and stored, licensed, easily identifiable, and preserved for the long term

  • Different levels of access can be placed on research data, from Open Access to restricted access
     

Make It Easier to (Re)Use Your Data

This paper "Nine simple ways to make it easier to (re)use your databy Ethan P. White et al. presents recommendations for scientists on how to to make their data understandable, easy to analyze, and readily available:

  1. Share your data
  2. Provide metadata
  3. Provide an unprocessed form of the data
  4. Use standard data formats for files, tables, and within table cells
  5. Use good null values
  6. Make it easy to combine your data with other datasets
  7. Perform basic quality control
  8. Use an established repository
  9. Use an established and liberal license
  • Provide Access to Data

    Digital Object Identifier: get a DOI to make your data easier to find and cite

    • Digital Object Identifiers (DOIs) are used for identifying intellectual property in the digital environment

    • Many data repositories and publishers provide specific instructions for how to cite their data

    • A dataset should be cited formally in an article's reference list, not just informally in the text

    Data Citation: provide a citation to your data to make it easy for others to reference your work

    • Elements of a Data Citation: (FORCE11 Joint Declaration of Data Citation Principles)

      • Author/Creator(s): the creators of the data; can be one or more people or organizations

      • Title: the title of the data set

      • Version: the exact version or edition of the data set use

      • Publication Date: the date when the data set was published or released

      • Publisher/Archive: the data center or repository that is archiving and distributing the data

      • Identifier/Locator: URL or other linkable locator for the data; a persistent, permanent URL such as a DOI (Digital Object Identifier) or a handle is preferred

    • Example: Rodriguez, Tommy (2013): 17,170 Base Pair Alignment of Thirteen Time-Extended Lineages [data: (complete) mtDNA; format: ClustalW]. figshare. https://dx.doi.org/10.6084/m9.figshare.815894 Retrieved: 16 26, Jan 04, 2016 (GMT)

  • Appropriately License Data

    • Data is not copyrightable, but particular expressions of data, such as a table in a book, can be copyrightable (see Intellectual Property)

    • Promote sharing and unlimited use of your data by making it available under an Open Data Commons or Creative Commons license

      • Public Domain (CC0) is frequently suggested for open data. They rely on community norms for attribution, and they prevent the problem of attribution stacking, where a researcher uses data from multiple datasets and provides attribution for each component

      • Attribution (CC BY) is recommended for maximum dissemination and use of licensed materials

      • Use the License Chooser to explore Creative Commons licenses and determine which license to choose from