Open data requirements

Many journals now require, or encourage data and other materials to be made publicly available.

Why should you do this?

  • To preserve your scientific contributions
  • To allow others to build on your work, find new uses for your data and use in meta-analysis
  • It allows interested readers to replicate the results and findings of a study
  • It verifies results – readers can identify statistical or methodological errors
  • It can increase citation levels, and helps to ensure robust dissemination and appropriate credits to authors

What types of data should be available?

  • Primary data
  • Datasets
  • Code
  • Details of software required
  • Analysis code, such as R scripts
  • Other digital material

If the standard in the field is to share data that has been processed, this can be submitted instead of raw data. Otherwise, raw data should be made available. Exceptions should be discussed with the editorial office before submission.

When preparing your data, you should ask the following questions:

  • Have you included everything that might be helpful to researchers (including materials, code used to run your analysis, accession IDs for sequence data)?
  • Is it prepared in a useful and clear-to-understand format?
  • Have you taken into account any sensitive data (e.g human subject data)?
  • Is it clear how readers of your paper can access your dataset?

There are many repositories available for authors to deposit data, dependent on type of data and subject area. For example:

  • Sequence data should be uploaded to the NCBI Sequence Read Archive or GenBank
  • Source code and R scripts should be available in a software-focused repository such as GitHub, and then archived into Zenodo
  • Earth and environmental science data can be georeferenced in specific repositories such as PANGAEA
  • Where a data-specific repository is not available, we recommend generalist repositories such as Dryad or Figshare, which can handle a wide variety of data.

Examples of public archives:

Our top tips:

  • Think about creating a data management plan
  • Check with your funder or institution on what they may require
  • See if your institution itself archives data in a repository
  • Ask your colleagues and supervisors for advice
  • Check what the journal guidelines say
  • Contact the editorial office

Find information about the Royal Society journals’ data sharing policy.