Metroline Step: Data access and retrieval

status: future work

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…

The How to section should:

• be split into easy to follow steps;

  • Step 1 - Title of the step

  • Step 2 - Title of the step

  • etc.

• help the reader to complete the step;

• aspire to be readable for everyone, but, depending on the topic, may require specialised knowledge;

• be a general, widely applicable approach;

• if possible / applicable, add (links to) the solution necessary for onboarding in the Health-RI National Catalogue;

• aim to be practical and simple, while keeping in mind: if I would come to this page looking for a solution to this problem, would this How-to actually help me solve this problem;

• contain references to solutions such as those provided by FAIR Cookbook, RMDkit, Turing way and FAIR Sharing;

• contain custom recipes/best-practices written by/together with experts from the field if necessary. 

Expertise requirements for this step 

Describes the expertise that may be necessary for this step. Should be based on the expertise described in the Metroline: Build the team step.

Practical examples from the community 

This section should show the step applied in a real project. Links to demonstrator projects. 

Training

Relevant training will be added soon.

Suggestions

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