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Quantifying the impact of public omics data
The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683138/ https://www.ncbi.nlm.nih.gov/pubmed/31383865 http://dx.doi.org/10.1038/s41467-019-11461-w |
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author | Perez-Riverol, Yasset Zorin, Andrey Dass, Gaurhari Vu, Manh-Tu Xu, Pan Glont, Mihai Vizcaíno, Juan Antonio Jarnuczak, Andrew F. Petryszak, Robert Ping, Peipei Hermjakob, Henning |
author_facet | Perez-Riverol, Yasset Zorin, Andrey Dass, Gaurhari Vu, Manh-Tu Xu, Pan Glont, Mihai Vizcaíno, Juan Antonio Jarnuczak, Andrew F. Petryszak, Robert Ping, Peipei Hermjakob, Henning |
author_sort | Perez-Riverol, Yasset |
collection | PubMed |
description | The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets. |
format | Online Article Text |
id | pubmed-6683138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66831382019-08-07 Quantifying the impact of public omics data Perez-Riverol, Yasset Zorin, Andrey Dass, Gaurhari Vu, Manh-Tu Xu, Pan Glont, Mihai Vizcaíno, Juan Antonio Jarnuczak, Andrew F. Petryszak, Robert Ping, Peipei Hermjakob, Henning Nat Commun Article The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets. Nature Publishing Group UK 2019-08-05 /pmc/articles/PMC6683138/ /pubmed/31383865 http://dx.doi.org/10.1038/s41467-019-11461-w Text en © The Author(s) 2019 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/. |
spellingShingle | Article Perez-Riverol, Yasset Zorin, Andrey Dass, Gaurhari Vu, Manh-Tu Xu, Pan Glont, Mihai Vizcaíno, Juan Antonio Jarnuczak, Andrew F. Petryszak, Robert Ping, Peipei Hermjakob, Henning Quantifying the impact of public omics data |
title | Quantifying the impact of public omics data |
title_full | Quantifying the impact of public omics data |
title_fullStr | Quantifying the impact of public omics data |
title_full_unstemmed | Quantifying the impact of public omics data |
title_short | Quantifying the impact of public omics data |
title_sort | quantifying the impact of public omics data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683138/ https://www.ncbi.nlm.nih.gov/pubmed/31383865 http://dx.doi.org/10.1038/s41467-019-11461-w |
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