STATUS: IN DEVELOPMENT
Short Description
If you do not yet have the data which you aim to FAIRify, you will need to gain access to it. There are many aspects to consider to transfer data[RDMKit_DataTransfer]. 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.
Why is this step important
You need the data to be able to FAIRify it. By completing this step, you should have the data.
Expertise requirements for this step
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
How to
There’s some nice information here. It’s a bit too much to copy-paste it. Could be a great basis for how-to.
[FAIRInAction]
Get the data
Data access Considerations relating to how data is accessed, eg through APIs, via controlled access
Data retrieval Considerations relating to data retrieval, eg query language, results representation and exporting capabilities
Note: Don’t think all are relevant for what we’re trying to do here…
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
Training
Add links to training resources relevant for this step.
Further reading
Additional resources on this step.