Progress toward a comprehensive teaching approach to the FAIR data principles. / Shanahan, Hugh.

In: Data Intelligence, Vol. 2, No. 10, 08.10.2021.

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Progress toward a comprehensive teaching approach to the FAIR data principles. / Shanahan, Hugh.

In: Data Intelligence, Vol. 2, No. 10, 08.10.2021.

Research output: Contribution to journalArticlepeer-review

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@article{92fda7dd605640b1881456f212db9112,
title = "Progress toward a comprehensive teaching approach to the FAIR data principles",
abstract = "The FAIR data principles (Findable, Accessible, Interoperable, and Reusable) provide a useful and accessible guide for preparing data (and other digital outputs of research) in order to achieve maximum reuse value and repurposing with confidence. Providing people with the skills to make data FAIR needs to draw on a wider frame of community knowledge relating to digital curation. Similarly, for FAIR practices to be embedded, it is necessary to incorporate the FAIR principles, and the concepts of reproducible data stewardship and data science at many stages of the research lifecycle. Other areas not to be neglected include the relationship between the ethos of Open and the FAIR to good scientific practice through the importance of facilitating reproducibility, and describing context and provenance.For training to accommodate FAIR principles, it is necessary that the skills needed are clearly defined within a competence framework. Such a framework provides a guide to those building curricula and courses with a guide to what objectives, in terms of learning outcomes, should be envisaged and included. Much work has been done to develop competence frameworks that relate closely to FAIR and on which FAIR depends. It is timely to review and align such work and to ensure that the skills needed to make digital objects FAIR are adequately covered; and that closely related dimensions of data stewardship and data science, are fully incorporated.This article will review, at a relatively high level, the most important current work on competence frameworks and will highlight good practice with a view of ensuring that the skills necessary to make data FAIR and keep them FAIR are fully addressed. It will also provide an extensive set of sources of materials for delivering FAIR training. Examples of initiatives which have been developing training to address FAIR will also be given.",
keywords = "FAIR data, teaching, training",
author = "Hugh Shanahan",
year = "2021",
month = oct,
day = "8",
doi = "10.1016/j.patter.2021.100324",
language = "English",
volume = "2",
journal = "Data Intelligence",
number = "10",

}

RIS

TY - JOUR

T1 - Progress toward a comprehensive teaching approach to the FAIR data principles

AU - Shanahan, Hugh

PY - 2021/10/8

Y1 - 2021/10/8

N2 - The FAIR data principles (Findable, Accessible, Interoperable, and Reusable) provide a useful and accessible guide for preparing data (and other digital outputs of research) in order to achieve maximum reuse value and repurposing with confidence. Providing people with the skills to make data FAIR needs to draw on a wider frame of community knowledge relating to digital curation. Similarly, for FAIR practices to be embedded, it is necessary to incorporate the FAIR principles, and the concepts of reproducible data stewardship and data science at many stages of the research lifecycle. Other areas not to be neglected include the relationship between the ethos of Open and the FAIR to good scientific practice through the importance of facilitating reproducibility, and describing context and provenance.For training to accommodate FAIR principles, it is necessary that the skills needed are clearly defined within a competence framework. Such a framework provides a guide to those building curricula and courses with a guide to what objectives, in terms of learning outcomes, should be envisaged and included. Much work has been done to develop competence frameworks that relate closely to FAIR and on which FAIR depends. It is timely to review and align such work and to ensure that the skills needed to make digital objects FAIR are adequately covered; and that closely related dimensions of data stewardship and data science, are fully incorporated.This article will review, at a relatively high level, the most important current work on competence frameworks and will highlight good practice with a view of ensuring that the skills necessary to make data FAIR and keep them FAIR are fully addressed. It will also provide an extensive set of sources of materials for delivering FAIR training. Examples of initiatives which have been developing training to address FAIR will also be given.

AB - The FAIR data principles (Findable, Accessible, Interoperable, and Reusable) provide a useful and accessible guide for preparing data (and other digital outputs of research) in order to achieve maximum reuse value and repurposing with confidence. Providing people with the skills to make data FAIR needs to draw on a wider frame of community knowledge relating to digital curation. Similarly, for FAIR practices to be embedded, it is necessary to incorporate the FAIR principles, and the concepts of reproducible data stewardship and data science at many stages of the research lifecycle. Other areas not to be neglected include the relationship between the ethos of Open and the FAIR to good scientific practice through the importance of facilitating reproducibility, and describing context and provenance.For training to accommodate FAIR principles, it is necessary that the skills needed are clearly defined within a competence framework. Such a framework provides a guide to those building curricula and courses with a guide to what objectives, in terms of learning outcomes, should be envisaged and included. Much work has been done to develop competence frameworks that relate closely to FAIR and on which FAIR depends. It is timely to review and align such work and to ensure that the skills needed to make digital objects FAIR are adequately covered; and that closely related dimensions of data stewardship and data science, are fully incorporated.This article will review, at a relatively high level, the most important current work on competence frameworks and will highlight good practice with a view of ensuring that the skills necessary to make data FAIR and keep them FAIR are fully addressed. It will also provide an extensive set of sources of materials for delivering FAIR training. Examples of initiatives which have been developing training to address FAIR will also be given.

KW - FAIR data

KW - teaching

KW - training

U2 - 10.1016/j.patter.2021.100324

DO - 10.1016/j.patter.2021.100324

M3 - Article

VL - 2

JO - Data Intelligence

JF - Data Intelligence

IS - 10

ER -