FAIR-Aware Additional guidance to the Science Europe DMP assessment rubric

M. L Verburg (Maaike); M.J. (Marjan) Grootveld

doi:10.5281/zenodo.6088214

FAIR-Aware Additional guidance to the Science Europe DMP assessment rubric

M. L Verburg (Maaike), M.J. (Marjan) Grootveld

DANS-KNAW

Onderzoeksoutput: Andere bijdrage › Wetenschappelijk

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One way to support data management planning for FAIR data is to incorporate FAIR criteria more explicitly in data management plan (DMP) templates. As DMPs hold an important position in the planning phase of a research project, the inclusion of FAIR data criteria at this stage will facilitate a greater understanding of the necessary steps needed to make data FAIR as well as an increase in the number of FAIR datasets produced as a result. In this output of the FAIRsFAIR project, FAIR explicit guidance has been added to the Science Europe DMP evaluation rubric to help researchers and data stewards better plan for FAIR data. This guidance is based on FAIR-Aware, the FAIR learning tool developed in the FAIRsFAIR project.

FAIR-Aware: https://fairaware.dans.knaw.nl/

NB: This guidance document does not replace the original Science Europe guidance (https://doi.org/10.5281/zenodo.4915862). The original document will always take precedence.

Originele taal-2	Engels
Mijlpalentype toekennen	Lesson
Uitgever	Zenodo
Aantal pagina's	11
DOI's	https://doi.org/10.5281/zenodo.6088214
Status	Gepubliceerd - 22 feb. 2022

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10.5281/zenodo.6088214Licentie: CC BY

FAIRsFAIR
Dillo, I., Grootveld, M. J., von Stein, I., de Vries, J., Coen, G., Leenarts, E., Mokrane, M., van Horik, R., Kalaitzi, V. & Saldner, S.
01/03/2019 → 28/02/2022
Project: Onderzoek

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title = "FAIR-Aware Additional guidance to the Science Europe DMP assessment rubric",

abstract = "One way to support data management planning for FAIR data is to incorporate FAIR criteria more explicitly in data management plan (DMP) templates. As DMPs hold an important position in the planning phase of a research project, the inclusion of FAIR data criteria at this stage will facilitate a greater understanding of the necessary steps needed to make data FAIR as well as an increase in the number of FAIR datasets produced as a result. In this output of the FAIRsFAIR project, FAIR explicit guidance has been added to the Science Europe DMP evaluation rubric to help researchers and data stewards better plan for FAIR data. This guidance is based on FAIR-Aware, the FAIR learning tool developed in the FAIRsFAIR project.FAIR-Aware: https://fairaware.dans.knaw.nl/ NB: This guidance document does not replace the original Science Europe guidance (https://doi.org/10.5281/zenodo.4915862). The original document will always take precedence.",

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AB - One way to support data management planning for FAIR data is to incorporate FAIR criteria more explicitly in data management plan (DMP) templates. As DMPs hold an important position in the planning phase of a research project, the inclusion of FAIR data criteria at this stage will facilitate a greater understanding of the necessary steps needed to make data FAIR as well as an increase in the number of FAIR datasets produced as a result. In this output of the FAIRsFAIR project, FAIR explicit guidance has been added to the Science Europe DMP evaluation rubric to help researchers and data stewards better plan for FAIR data. This guidance is based on FAIR-Aware, the FAIR learning tool developed in the FAIRsFAIR project.FAIR-Aware: https://fairaware.dans.knaw.nl/ NB: This guidance document does not replace the original Science Europe guidance (https://doi.org/10.5281/zenodo.4915862). The original document will always take precedence.

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KW - Research Data Management

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KW - Collaboration

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FAIR-Aware Additional guidance to the Science Europe DMP assessment rubric

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