Contents for analysing data - Revision history

Valerias at 15:11, 17 November 2022

2022-11-17T15:11:55Z

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	* [https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/ FAIR Evaluation Services]		* [https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/ FAIR Evaluation Services]

			* [https://www.fairsfair.eu/f-uji-automated-fair-data-assessment-tool F-UJI Automated FAIR Data Assessment Tool]

			* [https://fair-checker.france-bioinformatique.fr/ FAIR-Checker]

Valerias at 14:29, 17 November 2022

2022-11-17T14:29:25Z

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	* 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.

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

			* [https://fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/ FAIR Evaluation Services]

Valerias at 14:26, 17 November 2022

2022-11-17T14:26:46Z

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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 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.

Valerias at 14:25, 17 November 2022

2022-11-17T14:25:23Z

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	This step analyses the data to support the FAIRification step:		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?

	* 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~~		* 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.

	~~The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving~~
	~~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~~
	~~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~~
	~~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~~.

Valerias at 14:21, 17 November 2022

2022-11-17T14:21:36Z

← Older revision		Revision as of 14:21, 17 November 2022
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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. To name a few examples, such an analysis involves:

	This step analyses the data to support the		This step analyses the data to support the FAIRification step:

			*

	* 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		* 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

Valerias at 11:15, 17 November 2022

2022-11-17T11:15:43Z

← Older revision		Revision as of 11:15, 17 November 2022
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	A proper understanding of the data is an ~~essential~~		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:

	This step analyses the data to support the		This step analyses the data to support the


	~~As a preparation of the FAIRification itself, this step also includes checking if~~ some FAIR features already		* 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
	are existing in the data such as persistent identifiers. If the data are extensive, running

	The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving		The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving

Valerias at 11:08, 17 November 2022

2022-11-17T11:08:09Z

← Older revision		Revision as of 11:08, 17 November 2022
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			A proper understanding of the data is an essential

	This step analyses the data to support the		This step analyses the data to support the

Valerias: Created page with "This step analyses the data to support the As a preparation of the FAIRification itself, this step also includes checking if some FAIR features already are existing in the..."

2022-11-17T10:56:31Z

Created page with "This step analyses the data to support the As a preparation of the FAIRification itself, this step also includes checking if some FAIR features already are existing in the..."

New page

This step analyses the data to support the

As a preparation of the FAIRification itself, this step also includes checking if some FAIR features already
are existing in the data such as persistent identifiers. If the data are extensive, running

The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving
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
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
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.