FAIR Data Sharing
Activity: Make it FAIR
If you haven’t already, play Level 3 in the League of Data game: https://lod.sshopencloud.eu/LodGame/ It helps you break down what advantages depositing in a FAIR data repository there are compared to posting it with your journal article.Source: SSHOC. (2020). Data Publication Challenge [video game]. Social Sciences and Humanities Open Cloud (SSHOC) League of Data (LOD). https://lod.sshopencloud.eu/
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Open Science has increasingly been promoting F.A.I.R.--Findable, Accessible, Interoperable, and Reusable. FAIR sharing includes principles that individually can increase the discovery and usability of your final research data for others. There are no hard and fast rules to completing each and every one of these features; in addition, there may be cases that you absolutely do NOT want to be 100% FAIR, as total openness and reproducibility could cause harm to your research subjects. Therefore, view the features of FAIR as individual opportunities you can select that will increase the value of your data.
You can make your data findable is to deposit it into a data repository, that has a DOI and that has machine-readable metadata fields. This will make it easier for people to get to your data, based on their search results. It will be even more findable to relevant audiences if the metadata you use is subject-specific.
You could make your data accessible by providing a link to be able to open or download your data. Can someone actually access your data? You could make this totally accessible by uploading the file into a repository and enabling access to it. (Making it slightly less accessible would be if you uploaded the file but it’s a proprietary file type that someone needs a specific software in order to open and see the data).
Interoperability can automatically occur if you deposit your data in approved repositories set up for interoperability, such as Dataverse. Doing so will make your data findable in other search indexes, such as Web of Science and Google, even though you didn’t deposit your data in those systems. Ask your librarian for help selecting an approved data repository.
Reusability can come into play with consideration to the cleanness/organization of your files and your documentation. Can someone understand what your data file is accomplishing? Do they know what your variables and columns are referring to? Are they able to know the methods you used to clean your data, such as a decision you made to remove certain rows because they were outliers or had missing values? The documentation you write can be helpful for someone to know what your data is so they can figure out what they could do themselves, let alone how to reproduce your study themselves.
The Australian Research Data Commons created a FAIR Data Self Assessment Tool to assess the FAIRness of a data and determine how to enhance its FAIRness. Check it out!