A framework to assess the quality and impact of bioinformatics training across ELIXIR.
Détails
Télécharger: 32702016_BIB_3B57279FB31E.pdf (1336.24 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY 4.0
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_3B57279FB31E
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A framework to assess the quality and impact of bioinformatics training across ELIXIR.
Périodique
PLoS computational biology
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Statut éditorial
Publié
Date de publication
07/2020
Peer-reviewed
Oui
Volume
16
Numéro
7
Pages
e1007976
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
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.
Pubmed
Open Access
Oui
Création de la notice
11/08/2020 10:58
Dernière modification de la notice
30/04/2021 6:09