I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training
Creators
- 1. German Cancer Research Center
- 2. Department Enabling Technology
- 3. Single-cell Open Lab
- 4. Heinrich Heine University Düsseldorf
- 5. Center for Advanced Imaging
- 6. Max Planck Institute for Evolutionary Biology, Plön, Germany
- 7. Osnabrück University
- 8. Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases
- 9. University of Cologne
- 10. University of Münster
Description
The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers' daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.
Notes
Notes
Files
2023_Schmidt_etal_I3Dbio_OMERO_Training_Material_10.5281_zenodo.8323588.pdf
Files
(143.4 MB)
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Additional details
Related works
- Is referenced by
- Lesson: https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU (URL)
References
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- Ouyang, W. and C. Zimmer, The imaging tsunami: Computational opportunities and challenges. Current Opinion in Systems Biology, 2017. 4: p. 105-113. DOI: 10.1016/j.coisb.2017.07.011
- Andreev, A. and D.E.S. Koo, Practical Guide to Storage of Large Amounts of Microscopy Data. Microscopy Today, 2020. 28(4): p. 42-45. DOI: 10.1017/S1551929520001091
- Goldberg, I.G., et al., The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol, 2005. 6(5): p. R47. DOI: 10.1186/gb-2005-6-5-r47
- Allan, C., et al., OMERO: flexible, model-driven data management for experimental biology. Nat Methods, 2012. 9(3): p. 245-53. DOI: 10.1038/nmeth.1896
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- Schmidt, C., Hanne, J., et al., Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey. F1000Res, 2022. 11: p. 638. DOI: 10.12688/f1000research.121714.2
- Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. DOI: 10.1038/sdata.2016.18
- Rigano, A., et al., Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications. Nat Methods, 2021. 18(12): p. 1489-1495. DOI: 10.1038/s41592-021-01315-z
- Ryan, J., et al., MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text. Nat Methods, 2021. 18(12): p. 1414-1416. DOI: 10.1038/s41592-021-01290-5
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- Hammer, M., et al., Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model. Nat Methods, 2021. 18(12): p. 1427-1440. DOI: 10.1038/s41592-021-01327-9
- Sarkans, U., et al., REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology. Nature Methods, 2021. DOI: 10.1038/s41592-021-01166-8
- Sivagurunathan, S., et al., Bridging imaging users to imaging analysis - A community survey. J Microsc, 2023. DOI: 10.1111/jmi.13229
- Power, R., Maximizing the Impact of Instructional Video Length., in Integration of Instructional Design and Technology: Volume 2. 2022: Pressbooks.com
- Guo PJ, K.J. and R. R. How video production affects student engagement: an empirical study of MOOC videos. in First ACM Conference on Learning at Scale. 2014. New York: L@S'14 Proceedings of the First ACM Conference on Learning at Scale. DOI: 10.1145/2556325.2566239
- Hartley, M., et al., The BioImage Archive - Building a Home for Life-Sciences Microscopy Data. J Mol Biol, 2022. 434(11): p. 167505. DOI: 10.1016/j.jmb.2022.167505
- Rocca-Serra, P., et al., ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics, 2010. 26(18): p. 2354-6. DOI: 10.1093/bioinformatics/btq415
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Subjects
- presentation
- http://purl.org/spar/fabio/Presentation
- training material
- http://edamontology.org/data_3669
- Portable Document format
- http://edamontology.org/format_3508
- pptx
- http://edamontology.org/format_3838
- FAIR data
- http://edamontology.org/topic_4012
- Data Management
- http://edamontology.org/topic_3071
- Database management
- http://edamontology.org/topic_3489