Data sharing is the practice of making research data accessible to other stakeholders, including investigators, research subjects, and the broader public. This often involves submitting data to repositories like Harvard Dataverse, Figshare, or institutional repositories, allowing others to discover and use it. Increasingly, funding agencies, publishers, and research institutions mandate or strongly encourage data sharing to promote transparency, enhance research reproducibility, and increase the impact of research. Proper data citation and publication also ensure that the original researchers receive appropriate credit for their contributions.
Data repositories are tools for sharing and preserving research data. There are hundreds of repositories worldwide. Some cater to a specific research community, while others are general-purpose. Repositories may be called data centers, data archives, or scientific databases.
They are often divided into three categories:
Institutional Repositories (IRs) are affiliated with a researcher’s institution.
Domain-specific or Disciplinary Repositories (DRs) are discipline-specific and often operated by a professional organization, a consortium of researchers, or a similar group.
General-purpose or Open Repositories (ORs) allow researchers to deposit and make their data available regardless of disciplinary or institutional affiliation.
Disciplinary data repositories are set up to accommodate the data needs of a specific research community. They are the most likely to offer both the specialist domain knowledge and the data management expertise needed to ensure data are properly kept and used.
They may provide the ideal solution to meet data archiving and public access expectations of funding agencies, publishers, and the researcher community. However, they are also the most likely to be selective, requiring advance planning to meet standards for metadata and documentation.
Using Disciplinary Data Repositories
Since there are many data repositories, it is important to review terms and conditions before use.
1. Is the repository reputable and who supports it?
It may be listed in re3data, FAIRSharing, or broadly recognized by the research community. Better yet, it is endorsed by a journal, funder, or professional society.
2. Will it take data you want to deposit and how are data deposited?
Data may need to be of a particular type and file format. Some repositories allow self-deposit while others mediate deposit.
3. Will the repository be safe in legal terms?
Some repositories may be capable of safely storing sensitive or restricted data, while others may not. Ideally the repository allows depositors to assign terms of use and licenses.
4. Will the repository sustain the data value?
A repository can add value by making data findable, accessible, interoperable and reusable (FAIR) for the long term. This includes assigning persistent identifies (like DOIs) to datasets, requiring standard metadata for discoverability, and conducting file preservation activities.
5. Will it support analysis and track data usage?
Repositories may also provide citation information to users and usage tracking for the depositor.
From: Whyte, A. (2015). ‘Where to keep research data:DCC checklist for evaluating data repositories’ v.1 Edinburgh: Digital Curation Centre. Available online: www.dcc.ac.uk/resources/how-guides
Generatlist repositories accept data regardless of data type, format, content, or disciplinary focus. Subject specific repositories are preferred if an appropriate one is available for your research topic or data type.
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