1. Check for existing datasets

Are currently available datasets insufficient to answer your research questions? You can search for data sets in Google's Dataset Search  and re3data .

2. Have consent forms for sharing de-identified data

Research data can only be shared if consent has been obtained from participants and if the privacy of subjects is protected. Make sure your consent forms clearly state your intention to de-identify the data and publish it in a data repository.

3. Before live data collection, test your tools

Test all of your tools before live data collection. Once you have collected mock data: do all of your variables have descriptive and precise metadata? Is your data collection tool yielding high quality data that will be useful to others?

4. Choose a copyright license for your data

Will others be able to reuse your data and build upon your work? Under what terms? Creative Commons  is a good place to start for choosing license.

5. De-identify sensitive data

Before making your data public, remove or alter all identifiers that may lead to the identification of individual participants, such as names, address, telephone numbers, social security numbers and date of births. Consider using an anonymization software like Amnesia .

6. Define what access availability your data will have

Depending on the nature of the dataset and the feasibility to remove all identifiers, researchers may choose to share their data in a controlled way. To learn about different types of repositories visit the Sensitive research data bootcamp  by the University of Bristol.

7. Organize your data to comply with the FAIR principles

The FAIR Data Principles  are a set of guiding principles that make data findable, accessible, interoperable, and reusable.

8. Choose a research data repository

Using a repository will allow your dataset to be preserved over time, be findable by others, and easily citable. Here is a list of data repositories , some of which are free of charge.