PLoS Computational Biology (Jul 2020)

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

  • Kim T Gurwitz,
  • Prakash Singh Gaur,
  • Louisa J Bellis,
  • Lee Larcombe,
  • Eva Alloza,
  • Balint Laszlo Balint,
  • Alexander Botzki,
  • Jure Dimec,
  • Victoria Dominguez Del Angel,
  • Pedro L Fernandes,
  • Eija Korpelainen,
  • Roland Krause,
  • Mateusz Kuzak,
  • Loredana Le Pera,
  • Brane Leskošek,
  • Jessica M Lindvall,
  • Diana Marek,
  • Paula A Martinez,
  • Tuur Muyldermans,
  • Ståle Nygård,
  • Patricia M Palagi,
  • Hedi Peterson,
  • Fotis Psomopoulos,
  • Vojtech Spiwok,
  • Celia W G van Gelder,
  • Allegra Via,
  • Marko Vidak,
  • Daniel Wibberg,
  • Sarah L Morgan,
  • Gabriella Rustici

DOI
https://doi.org/10.1371/journal.pcbi.1007976
Journal volume & issue
Vol. 16, no. 7
p. e1007976

Abstract

Read online

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.