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Quantifying the impact of public omics data

Author

Listed:
  • Yasset Perez-Riverol

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Andrey Zorin

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Gaurhari Dass

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Manh-Tu Vu

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Pan Xu

    (National Center for Protein Sciences (The PHOENIX Center, Beijing))

  • Mihai Glont

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Juan Antonio Vizcaíno

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Andrew F. Jarnuczak

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Robert Petryszak

    (EMBL-European Bioinformatics Institute (EMBL-EBI))

  • Peipei Ping

    (University of California
    University of California)

  • Henning Hermjakob

    (EMBL-European Bioinformatics Institute (EMBL-EBI)
    National Center for Protein Sciences (The PHOENIX Center, Beijing))

Abstract

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.

Suggested Citation

  • Yasset Perez-Riverol & Andrey Zorin & Gaurhari Dass & Manh-Tu Vu & Pan Xu & Mihai Glont & Juan Antonio Vizcaíno & Andrew F. Jarnuczak & Robert Petryszak & Peipei Ping & Henning Hermjakob, 2019. "Quantifying the impact of public omics data," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11461-w
    DOI: 10.1038/s41467-019-11461-w
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    Cited by:

    1. Tine Claeys & Tim Van Den Bossche & Yasset Perez-Riverol & Kris Gevaert & Juan Antonio Vizcaíno & Lennart Martens, 2023. "lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation," Nature Communications, Nature, vol. 14(1), pages 1-4, December.
    2. Joshua Borycz & Robert Olendorf & Alison Specht & Bruce Grant & Kevin Crowston & Carol Tenopir & Suzie Allard & Natalie M. Rice & Rachael Hu & Robert J. Sandusky, 2023. "Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    3. Nathaniel T. Hawkins & Marc Maldaver & Anna Yannakopoulos & Lindsay A. Guare & Arjun Krishnan, 2022. "Systematic tissue annotations of genomics samples by modeling unstructured metadata," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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