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. | 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 FAIRification step: | |||
* How are the data organized? Do the data meet the intended formatting? Are data missing? | * 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 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. | ||
Revision as of 14:26, 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.