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Research Guides

Research Data Management: Wrapping Up: Preparing Your Data for Sharing

Guide of resources related to the many aspects of research data management. Data management encompasses the processes surrounding collecting, organizing, describing, sharing, and preserving data.

Why do I need to prepare my data for sharing?

There are several practical and ethical reasons why you might need to take additional steps to prepare your data for sharing. You might add context to your data — like a README file — to help others interpret your data in the future. The funder for your project may require that your published data be consistent with FAIR data principles. Your data may contain sensitive information, and you may need to anonymize the data for publication and restrict access.

Whatever your situation is, if you are planning on sharing your data, it will require some level of preparation before publishing.

How do I make my data FAIR?

Making your data FAIR means making sure it is Findable, Accessible, Interoperable, and Reusable. Much of what has already been covered on this page will already set you on the right course towards making your data FAIR. Creating comprehensive metadata and choosing future proof file formats go a long way towards ensuring that your data will be:

  • Findable: Your machine-readable metadata makes your data easy to find for both humans and computers.
  • Accessible: Your data and metadata can be accessed once it has been found
  • Interoperable: You data can be integrated with other data and can be interoperated with applications or workflows for analysis, storage, and processing
  • Reusable: You data is well-described so that it can be replicated and combined

Learn more about the FAIR Principles by visiting the GO FAIR site or watching the video below.

How do I prepare data for future use?

Two key considerations will allow your data to have utility in the future: creating metadata and selecting an appropriate file format for your work. Making wise choices about both of these considerations will allow your data to be understandable and accessible after your project has come to a close.

Creating Metadata

Metadata — or "data about data" — describes the characteristics of your data like its content, conditions, and analysis. When you describe your data well it is more easily organized and managed, preserved long term, findable, and reused.

Some examples of metadata include:

Date: When the data was created
Creators: Who created the data
Source: Where did the data come from?
Purpose: Why was the data collected?
Structure: Organizational structure of your data files
Versions: Changes made between different versions of data
Codes: Codes used for variables and missing values
Methods & Instruments: How and with what did you collect the data
Anonymization: What steps were taken to anonymize the data


Different disciplines use different metadata standards. Explore metadata standards by discipline in the Digital Curation Centre's disciplinary metadata catalog.

You may want to create a README file associated with your data. The goal of a README file is to provide the context you or another researcher may need in the future to navigate, comprehend, and potentially re-use your data. README files should be written in a plain text format (.txt) to ensure future accessibility rather than proprietary formats such as MS Word or Rich Text Format (RTF). See Cornell University's guide to writing README style metadata.

Choosing File Formats

It is important to choose file formats with long-term software compatibility and sustainability in mind.

  • Open formats like CSV for tabular data and JPEG for images are the preferred file format. Being open ensures that regardless of how trends in software shift in the future, the file can still be opened.
  • Widely-used proprietary formats that will likely continue to endure like MP3 and WAV for audio are also suitable.

See the Library of Congress's Recommended Formats Statement to discover the best file format for various types of digital media.


How do I deal with sharing sensitive or confidential data?

Sometimes, sharing your data involves considering potential privacy concerns, risks to confidentiality, and other protections. Although mitigating these risks in your data's final, sharable form may take some additional steps, it is possible to publish your data ethically and safely.

Ultimately many funders will require you to share you data publicly. You can balance these requirements with ethical and legal data sharing practices by:

  • Anonymizing your data
  • Sharing processed, aggregated data rather than raw, individual data
  • Choosing a repository with gated entry, so that only individuals with the right credentials may view the data

Read about the information security and privacy laws and regulations that may apply to your project on this list from the UW Privacy Office.

Tools & Resources


If you have questions about preparing your data for sharing or would like to request a consultation with a member of the Scholarly Communications and Publishing Team, please email