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Data Management Plan (DMP) Guide

Learn how to write a data management plan!

Step 6. Data Sharing

It is important to understand that data sharing is an expected part of every data management plan requirement.

There are a few cases when data sharing may not be appropriate. In general data which may compromise research subjects or be tied to intellectual property such as patents, are acceptable exceptions.

Writing prompts

Are there acceptable reasons for not sharing the data?

In select circumstances it may be acceptable to not share your research data. These reasons may include research subject privacy or protection concerns or intellectual property rights such as pending patents. If your research falls into one of these categories make sure to articulate why publicly sharing the research data would be detrimental or illegal. It may be possible to simply limit access to the data, or to apply usage requirements as a compromise.

Examples of data sharing concerns:
The data cannot be publicly shared because it contains potentially identifying information of human subjects.
The data contains the locations of endangered/threatened species or valuable artifacts and will only be shared with trusted parties who agree to the reuse criteria.
Data cannot be released until the patents related to this research are issued.

When will the data be available?

Different agencies have different requirements on when research data should be made available. Some agencies require that the data be made available at the time of publication while others simply require the data to be available within 12 months or within a "reasonable time" after publication. Make sure to check the exact requirements of the sponsor as they may have a specific time period that you need to comply with.

Examples of data available statements:
Data will be available at the time of publication.
Supporting data will be available upon the acceptance of the research paper.
Data will be available no later than 6 months following publication.
All data gathered by this research will be available within a year after the grant funding has ceased.

How will others access the data?

A statement that "data is available upon request" does not show a strong commitment to data sharing. Because of this it's a good idea to share research data in a more formal matter, in effect "publishing" the data. Sharing data in a formal way also provides some additional benefits such as:

  • Improved discoverability - published datasets are easier to find as they are assigned metadata.
  • Citable - published datasets are often accompanied by a recommended citation.
  • Stable - published datasets are often assigned a permanent identifier such as a DOI (digital object identifier) which does not change even if the URL to access the data changes.

Not all data publishing venues are created equal. A common way to "publish" data is to publish data as journal article supplementary information (SI) file. While this is an easy way to share data, not all SI files are publicly available, assigned a DOI, or assigned metadata different from that of the article (which will make it more difficult to find and track). Make sure to share your data via a method that satisfies all requirements and fits your needs.

Examples of shared data access:
Data will be publicly available on Figshare where it will be shared under a CC0 license and assigned a DOI.
Data will be available as journal article supplementary information (SI) files. These SI files will be open access.

Will there be any restrictions on the data?

If there is an acceptable reason to restrict access to your research data then make sure that the method of data sharing you have chosen is compatible with your restriction needs. Keep in mind that many data repositories require you to apply a CC0 license to your datasets which is not compatible with usage restrictions.

Example data restrictions:
Data will be deposited with ICPSR but access will be restricted due to the sensitive nature of its contents. Anyone wishing to use the data must first contact and receive permission from the PI.

Will the data have enough documentation to be useful?

Making data accessible is only one-half of data sharing. The other half is making sure your data has enough documentation for it to be meaningful and useful to others. Some repositories will not accept your data unless it is accompanied with a readme file, data dictionary, or other form of metadata or documentation. See section 3: Data Documentation for more details.

Examples of data documentation:
Shared data will be accompanied by a readme file which will provide additional information such as instrument calibrations and data coding details.


  • Valuable data should be shared when possible. Examples of valuable data include data of one-time events, data that are expensive to collect, and data that validate research findings.
  • Try to deposit your data in a repository that will handles both data sharing and preservation.
  • Try to share both the raw and analyzed data whenever possible as analyzed data often contains computed values which cannot be reversed back into their individual variables. 

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