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Research Data Management

Sharing & reproducibility

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. 

Choosing a repository: questions to ask

  • Will it accept your data?
    Some data repositories require that your university or organization to hold a membership in the repository or the organization sponsoring the repository.
     
  • Is it a curated or non-curated repository?
    A curated repository provides value-added services that help to organize, preserve, and share your data. This might require that you work with a curator before your data is made available. A non-curated repository allows you to upload your data and make it available without any mediation.
     
  • Are there any fees required?
    There are fees associated with some repositories, so you should figure this out early in your planning.
     
  • Is it a general or disciplinary repository?
    Think about the kind of audience you would like to share your data with. Is it important to target a particular community in a specialized repository, or do you want to reach out to a broader, more general audience?
     
  • What will you need to do to prepare your data for deposit?
    Since repositories may have different requirements for depositing data, think in advance about kinds of file formats and metadata that your repository of choice will accept.
     
  • How long can you store your data?
    Think about how long you would like to share your data and whether a repository is consistent with your short- or long-term plans.

Reproducibility / Replicability

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:

  • ...reproducibility [means] computational reproducibility—obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis
     
  • ...replicability [means] obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data
     
  • ...reproducibility involves the original data and code; replicability involves new data collection and similar methods used by previous studies

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. 

Further reference