Quantifying the impact of public omics data

Abstract

The amount of omics data in the public domain is increasing every year [1, 2]. Public availability of datasets is growing in all disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility), and/or it is mandated by funding agencies, scientific journals, etc. Science is now a data intensive discipline and therefore, new and innovative ways for data management, data sharing, and for discovering novel datasets are increasingly required [3, 4]. However, as data volumes grow, quantifying its impact becomes more and more important. In this context, the FAIR (Findable, Accessible, Interoperable, Reusable) principles have been developed to promote good scientific practises for scientific data and data resources [5]. In fact, recently, several resources [1, 2, 6] have been created to facilitate the Findability (F) and Accessibility (A) of biomedical datasets. These principles put a specific emphasis on enhancing the ability of both individuals and software to discover and re-use digital objects in an automated fashion throughout their entire life cycle [5]. While data resources typically assign an equal relevance to all datasets (e.g. as results of a query), the usage patterns of the data can vary enormously, similarly to other "research products" such as publications. How do we know which datasets are getting more attention? More generally, how can we quantify the scientific impact of datasets?Recently, several authors [7][8][9] and resources [10] pointed out the importance of evaluating the impact of each research product, including datasets.

Integrated omics: tools, advances and future approaches

“…The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data (Barrett & Edgar 2006). The most current omics-driven data are archived at OmicsDI (www.omicsdi.org) that houses 149,702 datasets, covering 3926 diseases, 2773 tissues from 6428 species (Perez-Riverol et al 2018).…”

Histone modifications during the life cycle of the brown algaEctocarpus

“…Spectra were generated using Xcalibur (version 2.0.7) and analysed with Mascot TM (version 1.4, Thermo Scientific) using an in-house database consisting of the complete Ectocarpus proteome available at Orcae [66]. The mass spectrometry proteomics data have been deposited in the PRIDE database [67] via ProteomeXchange with identifier PXD013535. Details of the liquid chromatography-tandem mass spectrometery parameters are provided in the Supplementary Methods.…”

Decoding communication patterns of the innate immune system by quantitative proteomics

“…This challenge is also associated with experimental limitations of biological follow‐up required for each additional factor. Therefore, the deposition of proteomic datasets into publicly‐available repositories (e.g., PRIDE, PASSEL) is strongly supported to allow these unexplored biological insights to be prioritized and further investigated by other researchers …”