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If you do not yet have the data which you aim to FAIRify, you will need to gain access to it. There get the data.

https://static-content.springer.com/esm/art%3A10.1038%2Fs41597-023-02167-2/MediaObjects/41597_2023_2167_MOESM1_ESM.pdf table 2:

1. Get the data

   1. 1.1 Data access: Considerations relating to how data is accessed, eg through APIs, via controlled access

      1. FCB014, FCB015, FCB073

   2. 1.2 Data retrieval Considerations relating to data retrieval, eg query language, results representation and exporting capabilities

      1. FCB040, FCB046, FCB060, FCB070

As explained by RDMKit, there are many aspects to consider to transfer data[RDMKit_DataTransfer]when transfering data. Life Sciences often generate massive amounts of data, such as digital images and output from “omics” techniques. Such datasets cannot simply be sent via email and require a different approach. For example, to transfer such data, you could consider usage of Cloud Storage Services offered by the data owner’s institute, usage of secure File Transfer Protocols to transfer files and usage of checksums to verify the data’s integrity. Furthermore, rules and legislation, such as the GDPR, may require specific measures to be taken before data can be transferred. For example, you may have to establish a data processing agreement, before you can transfer the data. 

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