Deposit DataData sharing is essential for expedited translation of research results into knowledge, products and procedures to improve human health.

In the last decade, it has become increasingly common for researchers to make their data available to others when they complete a study. This is usually referred to as data sharing or data publishing. Data sharing is growing mostly due to recent data policies from journals and funders.

Image: Ainsley Seago. "To deposit or not to deposit, that is the question." doi:10.1371/journal.pbio.1001779. Shared under CC-BY License.

Why Share Data?

Compliance

  • Be compliant with research funding organizations that require data management plans and data accessibility

  • Be compliant with journals that require submission of supporting data files to accompany manuscripts

Research Benefits

  • Find your own data years after you finish a project

  • Enable others to replicate your work

  • Enable others to conduct new analyses using your data

Incentives

  • Data citation is becoming a standard across publishers, and standardized data repositories generate a data citation when you deposit your data. Thus, sharing your data in a repository results in credit for your work.

  • To create incentives for data sharing, some are developing and advocating for tools that track sharing of data and that formally credit investigators that share data:

Other Considerations

  • How can I maximize my data's reuse?

    • Share data and code in open trusted repositories

    • Use persistent links from publication to data and code

    • Citation to data and code should be a standard

    • Document data, code, workflows, and computational environment

    • Use open license for your code and data

    • Make use of a data provenance tool

  • What is reproducibility and why does it matter?

    • Reproducibility and Replication (National Science Foundation) (see Reproducibility)

      • Reproducibility: the ability for a researcher to replicate the results of a prior study using the same materials and procedures used by the original investigator (reproducibility)

      • Replication: the same procedures are followed but new data are collected (replication)

    • Empirical, Computational, Statistical Reproducibility (Stodden, 2014)

      • Empirical: data and collection details are made freely available

      • Computational: code, software, hardware, and implementations details are provided

      • Statistical: details on choice of statistics tests, model parameters are provided

  • How do I handle proprietary data?

    • You may find yourself in a situation where your ideal sharing method or repository is at odds with data sharing requirements

    • For example, your institution or funder may insist the data you’ve collected is proprietary (see Data Security) which could limit you from publishing in journals where open data is required

    • In either case, you should make sure your data is available to editors and reviewers at your selected journal so they can properly evaluate the work

    • In order to share you may be able to make a de-identified or subset of the data available (see Clinical Data Management)

Publishing Data with Research
Learn more about data sharing in this webinar