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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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
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
Descripción
Sumario: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.