How to create FAIR data












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            * 1. Documentation

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            The description of a dataset which is provided as a part of a related publication may ofte
            to provide the full context of data collection and processing. Therefore, a dataset should

            accompanied by rich documentation which provides a detailed description of how the data we
            what instrument was used for taking measurements, who the respondents were, how the data w

            and processed, and other relevant information that are necessary for accurate interpretati
            Documentation should also include links to other related entities (ideally via persistent 
            such as publications stemming from the given dataset, author(s) of the dataset, metadata s

            were use for describing the data and so forth.



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            * 2. Standardised vocabulary
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            Use standardised terminology for your data and their description, which is established in 
            practice ensures that everyone will understand your data and also facilitates the potentia
            your data with other datasets (interoperability). If you are using an established standard

            to describe your data, you should include the information about the used vocabulary in the



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            * 3. Persistent identifiers
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            Use persistent identifiers both for your data and for referencing other related entities (
            authors, institutions, other datasets etc.). By using persistent identifiers, you improve 
            of your data and help users to identify the dataset, its authors and other related entitie

            typically assign persistent identifiers to datasets.



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            * 4. File formats
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            If possible, you should use open file formats or formats which are established in your fie
            and sharing your data. This way, the possibility of data reuse will not be limited by the 
            specific software required for accessing them.



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            * 5. Access to data
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            Define who should have access to the data and under what conditions. Under certain circums

            data with restricted access can comply with the FAIR principles – typically in cases where
            be shared because they contain personal information, sharing would be contrary to intellec
            protection, or because they include data associated with (national) security. If your data

            personal information, consider the possibility of anonymisation to enable sharing with a w
            For sharing research data, use data repositories – either domain specific, where available
            repositories such as Zenodo [ URL "https://zenodo.org/"] , Figshare [ URL "https://figshar

            [ URL "https://datadryad.org/"] .



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            * 6. Licensing
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            Clearly specify the conditions for reuse. For open sharing, Creative Commons [ URL "https:
            creativecommons.org/share-your-work/"] licenses are the most commonly used, but you can al
            license that better suits your needs. You can also use multiple licenses for the same data

            sharing for non-commercial use and available for commercial use at a fee.





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            * How FAIR are your data?
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            Some data management tools support the FAIR principles and will assist you in evaluating h

            are. The Data Stewardship Wizard [ URL "https://ds-wizard.org/"] tool for creating data ma
            for instance, incorporates FAIR metrics within relevant questions, guiding users on how to
            align them as closely with the FAIR principles as possible.


            To assess the ‘FAIRness’ of your data, you can use the FAIR self-assessment tool [ URL "ht
            ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/"] developed b

            initiative ANDS-Nectar-RDS, or you can use this checklist [ URL "https://doi.org/10.5281/z
            which was created for the EUDAT summer school by Sarah Jones and Marjan Grootveld.