Sharing data is now required by major public and private funding agencies and many journals require it as a prerequisite for publication. Sharing data encourages reproducibility, reduces duplication, and allows for re-use and re-purposing.
There are many trusted digital data repositories for storing and sharing data. These repositories can be institutional (TUScholarShare), disciplinary (PANGAEA), or cross-disciplinary (Dryad; ICPSR); publicly-owned (Zenodo) or privately-owned (figshare); curated (QDR) or non-curated (Harvard Dataverse Network).
Repositories create metadata and documentation to ensure that the data will be discoverable and intelligible to future researchers. Repositories also provide regular back ups and may even migrate file formats to avoid digital obsolescence. These active measures may vary depending on the repository, so choose your repository carefully.
The best approach to finding a repository is to think about this as you are writing a data management plan at the start of your research project.
Reproducibility and replicability are concepts that have received increased scrutiny over the past decade. Although their meanings can vary depending on discipline and research community, they both point to the fact that advances in the scientific enterprise depend on the credibility of previous work. Improving reproducibility and replicability are goals supported by major funders and scientific organizations.
The authors of Reproducibility and Replicability in Science (National Acadamies Press, 2019) define them broadly in this way:
Other researchers and organizations have come up with different definitions, summarized at Curating Reproducibility (CURE). A more comprehensive treatment of reproducibility can be found in the Stanford Encyclopedia of Philosophy, Reproducibility of Scientific Results.
Best practices in research data management, like those articulated in this guide, were developed to address growing concerns over reproducibility and replicability. Developing a data management plan, creating file naming conventions and data workflows, carefully recording protocols, and cleaning data and making calculations using open source software, among other things, can help to ensure that your work can be reproduced or replicated by others. Refer to the contents of this guide and to the Further Reference on this page.