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STATUS: IN DEVELOPMENT

Short Description 

If you do not yet have the data which you aim to FAIRify, you will need to 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 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. 

Why is this step important 

You need the data to be able to FAIRify it. By completing this step, you should have the data.  

How to 

[HANDS] (Not sure, “acquisition” is a pretty broad term)

Why should I consult an expert about data acquisition techniques?

You can use a variety of techniques to generate data. Familiarity with one technique does not necessarily make that technique the best for your particular study. You should consult experts to make sure you make a good choice.

[RDMKit_DataTransfer]  

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 

    • Transferring data with SFTP: FCB014 

    • Downloading data with Aspera: FCB015 

    • Developing FAIR API for the Web: FCB073 

  • Data retrieval Considerations relating to data retrieval, eg query language, results representation and exporting capabilities 

    • Exploring data with SPARQL: FCB040 

    • Identifier resolution services: FCB046 

    • Registering Datasets in Wikidata: FCB060 

    • FAIR and Knowledge graphs: FCB070  

Note: Don’t think all are relevant for what we’re trying to do here…

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.  

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. Since the training aspect is still under development, currently many steps have “Relevant training will be added in the future if available.”

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