ORCID Profile
0000-0002-9464-6640
Current Organisation
Albert-Ludwigs-Universität Freiburg
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Publisher: Springer Science and Business Media LLC
Date: 12-07-2021
Publisher: Cold Spring Harbor Laboratory
Date: 03-06-2022
DOI: 10.1101/2022.06.02.494505
Abstract: There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform ( training.galaxyproject.org ) an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.
Publisher: Public Library of Science (PLoS)
Date: 13-08-2020
Publisher: Wiley
Date: 03-04-2023
DOI: 10.1111/ANAE.16015
Abstract: Virtual reality is a form of high‐fidelity simulation that may be used to enhance the quality of medical education. We created a bespoke virtual reality trainer software using high resolution motion capture and ultrasound imagery to teach cognitive‐motor needling skills necessary for the performance of ultrasound‐guided regional anaesthesia. The primary objective of this study was to determine the construct validity between novice and experienced regional anaesthetists. Secondary objectives were: to create learning curves for needling performance compare the virtual environment immersion with other high‐fidelity virtual reality software and compare cognitive task loads imposed by the virtual trainer compared with real‐life medical procedures. We recruited 21 novice and 15 experienced participants, each of whom performed 40 needling attempts on four different virtual nerve targets. Performance scores for each attempt were calculated based on measured metrics (needle angulation, withdrawals, time taken) and compared between the groups. The degree of virtual reality immersion was measured using the Presence Questionnaire, and cognitive burden was measured using the NASA‐Task Load Index. Scores by experienced participants were significantly higher than novices (p = 0.002) and for each nerve target (84% vs. 77%, p = 0.002 86% vs. 79%, p = 0.003 87% vs. 81%, p = 0.002 87% vs. 80%, p = 0.003). Log–log transformed learning curves demonstrated in idual variability in performance over time. The virtual reality trainer was rated as being comparably immersive to other high‐fidelity virtual reality software in the realism, possibility to act and quality of interface subscales (all p 0.06) but not in the possibility to examine and self‐performance subscales (all p 0.009). The virtual reality trainer created workloads similar to those reported in real‐life procedural medicine (p = 0.53). This study achieved initial validation of our new virtual reality trainer and allows progression to a planned definitive trial that will compare the effectiveness of virtual reality training on real‐life regional anaesthesia performance.
Publisher: Oxford University Press (OUP)
Date: 21-04-2022
DOI: 10.1093/NAR/GKAC247
Abstract: Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with & integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Switzerland
No related grants have been discovered for Wolfgang Maier.