Funding knowledgebases: Towards a sustainable funding model for the UniProt use case

Abstract

Millions of life scientists across the world rely on bioinformatics data resources for their research projects. Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases. Despite the increasing need for such highly accurate and reliable sources of scientific information, most of them do not have secured funding over the near future and often depend on short-term grants that are much shorter than their planning horizon. Additionally, they are often evaluated as research projects rather than as research infrastructure components. In this work, twelve funding models for data resources are described and applied on the case study of the Universal Protein Resource (UniProt), a key resource for protein sequences and functional information knowledge. We show that most of the models present inconsistencies with open access or equity policies, and that while some models do not allow to cover the total costs, they could potentially be used as a complementary income source. We propose the Infrastructure Model as a sustainable and equitable model for all core data resources in the life sciences. With this model, funding agencies would set aside a fixed percentage of their research grant volumes, which would subsequently be redistributed to core data resources according to well-defined selection criteria. This model, compatible with the principles of open science, is in agreement with several international initiatives such as the Human Frontiers Science Program Organisation (HFSPO) and the OECD Global Science Forum (GSF) project. Here, we have estimated that less than 1% of the total amount dedicated to research grants in the life sciences would be sufficient to cover the costs of the core data resources worldwide, including both knowledgebases and deposition databases.

Quantifying the impact of public omics data

“…At the time of writing (March 2018), more than 200 datasets have been claimed into ORCID profiles through OmicsDI. In our view, following the same system for monitoring the impact of individual datasets, these metrics could also be used to measure at least some aspects of the impact of public omics data resources [36, 37]. By March 2018, GEO, PRIDE and MetaboLights datasets are the most cited, re-used, and connected in their transcriptomics, proteomics and metabolomics, respectively ( Figure 3 ).…”

The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences

“…These resources are critical for ensuring the reproducibility and integrity of the entire life sciences research enterprise (Bourne et al, 2015). Despite their importance, many are supported in whole or in part by shortterm grants, and there is little coordination of funding across these resources (Berman, 2008;Gabella et al, 2017; https://www.biorxiv.org/content/10.1101/110825v3).…”

The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences

“…These resources are critical for ensuring the reproducibility and integrity of the entire life sciences research enterprise ( Bourne et al , 2015 ). Despite their importance, many are supported in whole or in part by short-term grants, and there is little coordination of funding across these resources ( Berman, 2008 ; Gabella et al , 2017 ; https://www.biorxiv.org/content/10.1101/110825v3 ).…”