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Research Data Management

A comprehensive guide to the best practices for planning, collecting, working with, sharing and reusing research data

How to Share Research Data?

The best way to share your research data is via a trusted open data repository. Data repositories not only provide long-term storage space for researchers to deposit their research data, but also enable research data to be discoverable, accessible and reusable. Apart from depositing data in a repository, you can also publish your data in a data journal, and link your data to your publications to maximize the impact of your research data!

There are thousands of data repositories available online. Here are the best practices for choosing a repository for your research data:

  • Check whether your funder or publisher has a list of recommendations for data repositories
    • Some funders and publishers may require or suggest researchers to deposit data in specific data repositories
    • The recommendations can often be found in the "Data Policies" or "Instructions for Authors"
    • Alternatively, you can use FAIRsharing to search for data repositories recommended by funder and journal data policies
  • Choose disciplinary repositories that are commonly used in your field to facilitate data discoverability
  • Use certified repositories, preferably with a Data Seal of Approval (DSA) or CoreTrustSeal, whenever possible
    1. Use re3data to browse data repositories by subject, or to search for suitable disciplinary data repositories
    2. Then, filter your results to "DSA" or "CoreTrustSeal" under "Certificates" to look for the certified repositories
  • Use general repositories when you cannot find a suitable discipline-specific repository for your research data
    • See "Data Repositories" for examples of some of the most widely used general repositories
  • Select a data repository that issues a persistent identifier, preferably a Digital Object Identifier (DOI)
    • A persistent identifier can help others to easily locate your research data
    • Data repositories indexed at re3data can be filtered by PID systems, e.g. DOI, Handle, ARK, PURL

Data paper is an emerging scholarly genre that focuses on the description of research data objects. Unlike traditional research papers, data papers do not include any data analysis or results. Instead, they provide concise descriptions of published research datasets, including information about the methods used to collect and verify the data and the conditions of access to the data, which aims to make research data more reusable, discoverable, interpretable, and citable.

Publishing a data paper in a data journal can greatly improve the visibility and transparency of your research data. Here are the examples of some leading data journals:

lightbulbPlease note that most data journals do not archive data in-house. Instead, they require authors to deposit the dataset to an approved repository as a condition of publication.

For more data journals, please refer to this list prepared by the University of Edinburgh.

Include a Data Availability Statement (DAS) in your research paper

Funders and publishers increasingly encourage or require authors to include a DAS in their manuscripts to indicate where, and under what conditions, the data associated with the paper can be accessed. Adding a DAS to your research paper not only helps you to comply with funder's and publisher's requirement, but also allows readers to easily locate your research data hosted in the repository.

Here are some major publishers' guideline on writing a DAS:

Cite your research data

Don't forget also to cite your data in your publication to get credit for your work! See "Data Citation" to learn about how to cite data properly.

lightbulbPlease note that not all research data can be made open. Data containing sensitive or confidential information should never be shared openly without appropriate safeguards.

Sensitive Data

Sensitive data refers to any data that would have cause potential harm if exposed to the public, which includes but not limited to:

  • Personal data: identifiers such as names or identification numbers, physical, physiological, genetic, mental, economic, cultural or social characteristics, and location data from GPS or mobile phones, etc.
  • Confidential data: trade secrets, investigations, data protected by intellectual property rights.
  • Security data: passwords, financial information, national safety, military information, etc.
  • Combination of different datasets that can be combined into sensitive or personal data
  • Biological data: endangered (plant or animal) species, where their survival is dependent on the protection of their location data (biodiversity community)
  • Personal and sensitive metadata

Adapted from: in accordance with a Creative Commons Attribution 4.0 License

Image source: Ground Labs. (n.d.). Sensitive Data Discovery Software and Tools [Pinterest post]. Pinterest. Retrieved March 7, 2022, from

Sharing Sensitive Data

Sensitive data have to be protected against unauthorized access or unwarranted disclosure. When sharing sensitive research data, you will need to pay careful attention to ensure that the data is shared ethically and legally. Here are some useful strategies to share sensitive data responsibly: