Contents for analysing data: Difference between revisions

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A proper understanding of the data is essential for carrying out any FAIRification activity. If the data are own data or coming from an in-house activity, such an understanding may come easily. But if the data are provided by a third party, a detailed analysis might be necessary. To name a few examples, such an analysis involves:  
A proper understanding of the data is essential for carrying out any FAIRification activity. If the data are own data or coming from an in-house activity, such an understanding may come easily. But if the data are provided by a third party, a detailed analysis might be necessary. This step analyses the data to support the FAIRification step. Issues to be considered are:


This step analyses the data to support the  
* How are the data organized? Do the data meet the intended formatting? Are data missing?


* Are some FAIR features already existing in the data such as persistent identifiers? If the data are extensive, running a (semi-) automatic FAIR assessment tool is helpful.


* Are already some FAIR features already are existing in the data such as persistent identifiers. If the data are extensive, running a (semi-) automatic FAIR assessmen
Here is a list of (semi-) automatic tools:
 
The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving
* [https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/ FAIR Evaluation Services]
interoperability) and is within the pre-FAIRification phase of the workflow. This process may include:
 
1) investigating the data in whatever form(s) it is available (specified in Step 1) and checking whether
* [https://www.fairsfair.eu/f-uji-automated-fair-data-assessment-tool F-UJI Automated FAIR Data Assessment Tool]
both the data representation (format) and the meaning of the data elements (the data semantics) are clear
 
and unambiguous, and 2) checking whether the data already contain FAIR features, such as persistent
* [https://fair-checker.france-bioinformatique.fr/ FAIR-Checker]
unique identifiers for data elements [14] (FAIR principle F1 [1]) by e.g., using FAIRness assessment tooling
[2, 3, 4]. It is evident that this step is tightly connected with Step 1 since e.g., selecting a relevant subset
of the data and defining driving user questions(s) are highly relying on being familiar with the data.

Latest revision as of 15:11, 17 November 2022

A proper understanding of the data is essential for carrying out any FAIRification activity. If the data are own data or coming from an in-house activity, such an understanding may come easily. But if the data are provided by a third party, a detailed analysis might be necessary. This step analyses the data to support the FAIRification step. Issues to be considered are:

  • How are the data organized? Do the data meet the intended formatting? Are data missing?
  • Are some FAIR features already existing in the data such as persistent identifiers? If the data are extensive, running a (semi-) automatic FAIR assessment tool is helpful.

Here is a list of (semi-) automatic tools:

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