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- Abrahamsson, Sanna, et al.
(författare)
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Comparison of online learning designs during the COVID-19 pandemic within bioinformatics courses in higher education
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059. ; 37:Suppl 1, s. I9-I15
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Due to the worldwide COVID-19 pandemic, new strategies had to be adopted to move from classroom-based education to online education, in a very short time. The lack of time to set up these strategies, hindered a proper design of online instructions and delivery of knowledge. Bioinformatics-related training and other onsite practical education, tend to rely on extensive practice, where students and instructors have a face-to-face interaction to improve the learning outcome. For these courses to maintain their high quality when adapted as online courses, different designs need to be tested and the students' perceptions need to be heard. Results: This study focuses on short bioinformatics-related courses for graduate students at the University of Gothenburg, Sweden, which were originally developed for onsite training. Once adapted as online courses, several modifications in their design were tested to obtain the best fitting learning strategy for the students. To improve the online learning experience, we propose a combination of: (i) short synchronized sessions, (ii) extended time for own and group practical work, (iii) recorded live lectures and (iv) increased opportunities for feedback in several formats.
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- Tischer, Christian, et al.
(författare)
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BigDataProcessor2 : A free and open-source Fiji plugin for inspection and processing of TB sized image data
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059. ; 37:18, s. 3079-3081
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Tidskriftsartikel (refereegranskat)abstract
- SUMMARY: Modern bioimaging and related areas such as sensor technology have undergone tremendous development over the last few years. As a result, contemporary imaging techniques, particularly electron microscopy (EM) and light sheet microscopy, can frequently generate datasets attaining sizes of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2-a Fiji plugin facilitating processing workflows for TB sized image datasets.AVAILABILITY AND IMPLEMENTATION: BigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2).
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