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评估 ELIXIR 中生物信息学培训的质量和影响的框架。

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

机构信息

Department of Genetics, University of Cambridge, Cambridge, United Kingdom.

EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.

出版信息

PLoS Comput Biol. 2020 Jul 23;16(7):e1007976. doi: 10.1371/journal.pcbi.1007976. eCollection 2020 Jul.

DOI:10.1371/journal.pcbi.1007976
PMID:
32702016
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7377377/
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.

摘要

ELIXIR 是一个泛欧政府间组织,旨在协调欧洲范围内的生命科学生物信息学资源;生物信息学培训是其战略的核心,旨在培养跨越所有 ELIXIR 成员国的培训社区。在一个基于证据的方法来加强欧洲的生物信息学培训计划中,ELIXIR 培训平台,由 ELIXIR EXCELERATE 质量和影响评估子任务与 ELIXIR 培训协调员小组合作,已经实施了一项评估策略,以衡量其整个培训组合的质量和影响。在这里,我们提出了 ELIXIR 的培训质量和影响评估框架,其中包括:指定评估目标、确定为了达到这些目标需要收集哪些数据,以及我们集中收集数据的策略,以便进行全 ELIXIR 范围的分析。此外,我们还介绍了过去 4 年中收集的 ELIXIR 培训数据的概述。我们强调了协调一致的数据收集方法的重要性,以及为联盟范围内的分析以及同一课程的不同迭代之间的数据比较定义特定指标和答案规模的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/7fce56613113/pcbi.1007976.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/2f59f34e9f3c/pcbi.1007976.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/072e0bfd1d53/pcbi.1007976.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/95807d77bdd6/pcbi.1007976.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/7fce56613113/pcbi.1007976.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/2f59f34e9f3c/pcbi.1007976.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/072e0bfd1d53/pcbi.1007976.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/95807d77bdd6/pcbi.1007976.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f8/7377377/7fce56613113/pcbi.1007976.g004.jpg

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