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.

A framework to assess the quality and impact of bioinformatics training across ELIXIR / Gurwitz, Kim T; Singh Gaur, Prakash; Bellis, Louisa J; Larcombe, Lee; Alloza, Eva; Balint, Balint Laszlo; Botzki, Alexander; Dimec, Jure; Dominguez Del Angel, Victoria; Fernandes, Pedro L; Korpelainen, Eija; Krause, Roland; Kuzak, Mateusz; Le Pera, Loredana; Leskošek, Brane; Lindvall, Jessica M; Marek, Diana; Martinez, Paula A; Muyldermans, Tuur; Nygård, Ståle; Palagi, Patricia M; Peterson, Hedi; Psomopoulos, Fotis; Spiwok, Vojtech; van Gelder, Celia W G; Via, Allegra; Vidak, Marko; Wibberg, Daniel; Morgan, Sarah L; Rustici, Gabriella. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - 16:7(2020). [10.1371/journal.pcbi.1007976]

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

Le Pera, Loredana;Via, Allegra;
2020

Abstract

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.
2020
Algorithms; biomedical research; computational biology; curriculum; data collection; databases, factual; education, continuing; europe; program evaluation; quality control; reproducibility of results; research personnel; software; user-computer interface
01 Pubblicazione su rivista::01a Articolo in rivista
A framework to assess the quality and impact of bioinformatics training across ELIXIR / Gurwitz, Kim T; Singh Gaur, Prakash; Bellis, Louisa J; Larcombe, Lee; Alloza, Eva; Balint, Balint Laszlo; Botzki, Alexander; Dimec, Jure; Dominguez Del Angel, Victoria; Fernandes, Pedro L; Korpelainen, Eija; Krause, Roland; Kuzak, Mateusz; Le Pera, Loredana; Leskošek, Brane; Lindvall, Jessica M; Marek, Diana; Martinez, Paula A; Muyldermans, Tuur; Nygård, Ståle; Palagi, Patricia M; Peterson, Hedi; Psomopoulos, Fotis; Spiwok, Vojtech; van Gelder, Celia W G; Via, Allegra; Vidak, Marko; Wibberg, Daniel; Morgan, Sarah L; Rustici, Gabriella. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - 16:7(2020). [10.1371/journal.pcbi.1007976]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1663518
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