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Quantifying the impact of public omics data.

Yasset Perez-Riverol1, Andrey Zorin2, Gaurhari Dass2

  • 1European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK. yperez@ebi.ac.uk.

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|August 7, 2019
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Summary

The Omics Discovery Index (OmicsDI) now includes new metrics to measure the impact and attention of biomedical datasets. This system helps manage and discover increasing amounts of omics data.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Genomics
  • Proteomics
  • Metabolomics

Background:

  • The volume of publicly available omics data is rapidly expanding, making data management and discovery challenging.
  • Modern scientific research is increasingly reliant on large datasets, necessitating advanced solutions for data accessibility and utilization.
  • The Omics Discovery Index (OmicsDI) was initially developed to aggregate diverse omics datasets from public resources.

Purpose of the Study:

  • To introduce novel metrics for quantifying the attention and impact of biomedical datasets.
  • To present a comprehensive framework for evaluating these new metrics within the OmicsDI system.
  • To provide recommendations for optimizing dataset impact assessment for authors, journals, and data resources.

Main Methods:

  • Development and integration of a novel metrics framework into the Omics Discovery Index (OmicsDI).
  • Aggregation of diverse omics datasets including genomics, transcriptomics, proteomics, metabolomics, and multiomics data.
  • Implementation of a system to provide and evaluate proposed metrics for biomedical datasets.

Main Results:

  • A new set of metrics has been developed to quantify the attention and impact of biomedical datasets.
  • The developed framework is now integrated into the OmicsDI, enabling the evaluation of these metrics.
  • Recommendations are proposed to enhance the quantification of dataset impact across the scientific community.

Conclusions:

  • The OmicsDI provides an enhanced system for managing and discovering omics data, now including impact metrics.
  • The proposed metrics and framework aim to improve the assessment of biomedical dataset value and utilization.
  • Promoting optimal quantification of dataset impact requires collaborative efforts from authors, journals, and data repositories.