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A dataset for assessing phytolith data for implementation of the FAIR data principles

Phytolith research contributes to our understanding of plant-related studies such as plant use in archaeological contexts and past landscapes in palaeoecology. This multi-disciplinarity combined with the specificities of phytoliths themselves (multiplicity, redundancy, naming issues) produces a wide...

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Autores principales: Kerfant, Céline, Ruiz-Pérez, Javier, García-Granero, Juan José, Lancelotti, Carla, Madella, Marco, Karoune, Emma
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361956/
https://www.ncbi.nlm.nih.gov/pubmed/37479711
http://dx.doi.org/10.1038/s41597-023-02296-8
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author Kerfant, Céline
Ruiz-Pérez, Javier
García-Granero, Juan José
Lancelotti, Carla
Madella, Marco
Karoune, Emma
author_facet Kerfant, Céline
Ruiz-Pérez, Javier
García-Granero, Juan José
Lancelotti, Carla
Madella, Marco
Karoune, Emma
author_sort Kerfant, Céline
collection PubMed
description Phytolith research contributes to our understanding of plant-related studies such as plant use in archaeological contexts and past landscapes in palaeoecology. This multi-disciplinarity combined with the specificities of phytoliths themselves (multiplicity, redundancy, naming issues) produces a wide variety of methodologies. Combined with a lack of data sharing and transparency in published studies, it means data are hard to find and understand, and therefore difficult to reuse. This situation is challenging for phytolith researchers to collaborate from the same and different disciplines for improving methodologies and conducting meta-analyses. Implementing The FAIR Data principles (Findable, Accessible, Interoperable and Reusable) would improve transparency and accessibility for greater research data sustainability and reuse. This paper sets out the method used to conduct a FAIR assessment of existing phytolith data. We sampled and assessed 100 articles of phytolith research (2016–2020) in terms of the FAIR principles. The end goal of this project is to use the findings from this dataset to propose FAIR guidance for more sustainable publishing of data and research in phytolith studies.
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spelling pubmed-103619562023-07-23 A dataset for assessing phytolith data for implementation of the FAIR data principles Kerfant, Céline Ruiz-Pérez, Javier García-Granero, Juan José Lancelotti, Carla Madella, Marco Karoune, Emma Sci Data Data Descriptor Phytolith research contributes to our understanding of plant-related studies such as plant use in archaeological contexts and past landscapes in palaeoecology. This multi-disciplinarity combined with the specificities of phytoliths themselves (multiplicity, redundancy, naming issues) produces a wide variety of methodologies. Combined with a lack of data sharing and transparency in published studies, it means data are hard to find and understand, and therefore difficult to reuse. This situation is challenging for phytolith researchers to collaborate from the same and different disciplines for improving methodologies and conducting meta-analyses. Implementing The FAIR Data principles (Findable, Accessible, Interoperable and Reusable) would improve transparency and accessibility for greater research data sustainability and reuse. This paper sets out the method used to conduct a FAIR assessment of existing phytolith data. We sampled and assessed 100 articles of phytolith research (2016–2020) in terms of the FAIR principles. The end goal of this project is to use the findings from this dataset to propose FAIR guidance for more sustainable publishing of data and research in phytolith studies. Nature Publishing Group UK 2023-07-21 /pmc/articles/PMC10361956/ /pubmed/37479711 http://dx.doi.org/10.1038/s41597-023-02296-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Kerfant, Céline
Ruiz-Pérez, Javier
García-Granero, Juan José
Lancelotti, Carla
Madella, Marco
Karoune, Emma
A dataset for assessing phytolith data for implementation of the FAIR data principles
title A dataset for assessing phytolith data for implementation of the FAIR data principles
title_full A dataset for assessing phytolith data for implementation of the FAIR data principles
title_fullStr A dataset for assessing phytolith data for implementation of the FAIR data principles
title_full_unstemmed A dataset for assessing phytolith data for implementation of the FAIR data principles
title_short A dataset for assessing phytolith data for implementation of the FAIR data principles
title_sort dataset for assessing phytolith data for implementation of the fair data principles
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361956/
https://www.ncbi.nlm.nih.gov/pubmed/37479711
http://dx.doi.org/10.1038/s41597-023-02296-8
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