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Research Data Management

This guide contains information about research data management and best practices for faculty, researchers and graduate students

Cite Your Data

Like any content that is used in the publication process, it is important to cite your sources, and this applies to using research data as well. Research data citation standards are still being developed, so the following elements are important when citing or providing citations for your own research data:

  • Author or Principal Investigator
  • Title of dataset used
  • Source Name
  • Version identifier
  • Publication Date
  • Access Date
  • URL or DOI

Other sources

Rowan Digital Works Data Repository

Data produced at Rowan University (any discipline)

Rowan Digital Works -  This institutional repository was created to capture, distribute, and preserve the scholarly and creative works of Rowan University faculty, researchers and students. Authors can archive their digital works in a variety of formats, including datasets. For more information on how to deposit data into Rowan Digital Works, contact rdw (at)

Other non-Rowan Affiliated Data Repositories

Researchers can use FAIRsharing as a lookup resource to identify and cite the standards, databases or repositories that exist for their data and discipline, for example, when creating a data management plan for a grant proposal or funded project; or when submitting a manuscript to a journal, to identify the recommended databases and repositories, as well as the standards they implement to ensure all relevant information about the data is collected at the source. Today’s data-driven science, as well as the growing demand from governments, funders and publishers for FAIRer data, requires greater researcher responsibility. Acknowledging that the ecosystem of guidance and tools is still work in progress, it is essential that researchers develop or enhance their research data management skills, or seek the support of professionals in this area.

Typically researchers share data via email, posting it to personal or via Google Drive or Amazon. However, these methods make it challenging to discover research data. Depositing data in repositories helps to discover, manage, cite and preserve data for the long-term. The options below by no means comprise a comprehensive list of repositories.

If you would like to suggest repositories to include in this list or need assistance with depositing your data to one of these repositories, please email Rowan's Research Data Management Working Group at . Also, you can share sharing data with multiple repositories which will help increase visibility and preservation of your research, so carefully consider which repositories will help you achieve this.

Search data repositories

  • Registry of Research Data Repositories - is a global registry of research data repositories that covers research data repositories from different academic disciplines. It presents repositories for the permanent storage and access of data sets to researchers, funding bodies, publishers and scholarly institutions. promotes a culture of sharing, increased access and better visibility of research data.
  • Open Access Directory of Data Repositories - Listed by discipline.

Data repositories (general)

  • Zenodo - is a free cloud-based service based on CERN's data repository platform
  • FigShare - is a free cloud-based service run by Nature Publishing Group
  • OSF- You can make your OSF project private or public and alternate between the two settings. You can have different privacy settings on your project and components, controlling which parts are public or private.

Data repositories by discipline

Agricultural Sciences

Biological & Life Sciences

  • ArrayExpress - is an archive of Functional Genomics Data stores data from high-throughput functional genomics experiments, and provides these data for reuse to the research community.
  • Barcode of Life Data Systems - is designed to support the generation and application of DNA barcode data.
  • BioModels.Net - is a repository of computational models of biological processes. Models described from literature are manually curated and enriched with cross-references. All models are provided in the Public Domain.
  • DigiMorph- Digital Morphology library is a dynamic archive of information on 3D scans, animations, and high-resolution X-ray computed tomography of biological specimens.
  • Dryad - Dryad is an international repository of data underlying peer-reviewed articles in the basic and applied biosciences.
  • GenBank - is the NIH genetic sequence database, an annotated collection of all publicly available DNA sequences (Nucleic Acids Research, 2013 Jan;41(D1):D36-42).  GenBank is part of the International Nucleotide Sequence Database Collaboration , which comprises the DNA DataBank of Japan (DDBJ), the European Nucleotide Archive (ENA), and GenBank at NCBI.
  • PLEXdb - Gene expression data for plants and plant pathogens.
  • Protein DataBank- Experimentally determined structures for macromolecules (protein and nucleic acids). The site includes search and visualization tools
  • The Cell: An Image Library- Images of all cell types from all organisms, including intracellular structures and movies or animations demonstrating functions. This project relies upon the cell biology community to populate the library.
  • UniProt - Access protein sequences and functional information.
  • XNAT Central - is a database for sharing neuroimaging and related data with select collaborators or the general community.
  • NIH repositories list - lists NIH-supported data repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems.


  • Cambridge Structural Database- Small molecule crystal structures
  • eCrystals - x-ray crystallographic data
  • PubChem- NCBI's repository of bioactivy/bioassay data and information for "small" molecules (i.e. not macromolecular). Both text-based and structure-based search tools are provided


Computer Science

Earth, Environmental and Geosciences


GIS and Geography




Social Sciences


Legal and Policy Considerations

Sharing data: legal and policy considerations

Best Practice

All research requires the sharing of information and data. The general philosophy is that data are freely and openly shared. However, funding organizations and institutions may require that their investigators cite the impact of their work, including shared data. By creating a usage rights statement and including it in data documentation, users of your data will be clear what the conditions of use are, and how to acknowledge the data source.

Include a statement describing the "usage rights" management, or reference a service that provides the information. Rights information encompasses Intellectual Property Rights (IPR), copyright, cost, or various Property Rights. For data, rights might include requirements for use, requirements for attribution, or other requirements the owner would like to impose. If there are no requirements for re-use, this should be stated.

Usage rights statements should include what are appropriate data uses, how to contact the data creators, and acknowledge the data source. Researchers should be aware of legal and policy considerations that affect the use and reuse of their data. It is important to provide the most comprehensive access possible with the fewest barriers or restrictions.

There are three primary areas that need to be addressed when producing sharable data:

  1. Privacy and confidentiality: Adhere to your institution's policy
  2. Copyright and intellectual property (IP): Data is not copyrightable. Ensure that you have the appropriate permissions when using data that has multiple owners or copyright layers. Keep in mind that information documenting the context of data collection may be under copyright.
  3. Licensing: Data can be licensed. The manner in which you license your data can determine its ability to be consumed by other scholars. For example the Creative Commons Zero License provides for very broad access.

If your data falls under any of the categories below there are additional considerations regarding sharing:

  • Rare, threatened or endangered species
  • Cultural items returned to their country of origin
  • Native American and Native Hawaiian human remains and objects
  • Any research involving human subjects

If you use data from other sources, you should review your rights to use the data and be sure you have the appropriate licenses and permissions.

Description Rationale

When sharing data, or using data shared by others, researchers should be aware of any policies that might affect the use of the data. Including a usage rights statement makes clear to data repository users what the conditions of use are, and how to acknowledge the data source.