image-processing
image-processing copied to clipboard
datacarpentry
Collecting existing resources
Use this thread to collect links to existing resources that could be useful for inspiration, resusing content, suggesting to learners as further reading after the lesson, images that might make good examples, etc.
EMBL Bio-IT Python Workshop - Image Processing by Jonas Hartmann (@whoisjack), Karin Sasaki (@karinsasaki), and me. MIT licensed, so we can reuse anything here that seems like it could help. However, the material is tailored towards a molecular biology audience, and was not designed for The Carpentries approach, so this may be more in the "inspiration" than "reuse" category.
We should point learners to https://forum.image.sc/ as a source of support as they continue to learn image processing beyond the workshop.
Introduction to image processing with scikit-image http://justinbois.github.io/bootcamp/2021/lessons/l38_intro_to_image_processing.html
Basic image quantification http://justinbois.github.io/bootcamp/2021/lessons/l39_segmentation.html
This was an intermediate python course we ran internally at EMBL - the numpy section I wrote might be helpful for examples on pros/cons of numpy: https://grp-bio-it-workshops.embl-community.io/intermediate-python/02-data/index.html Repo here: https://git.embl.de/grp-bio-it-workshops/intermediate-python
These links should be converted to a list in a section to be presented to the learner at the end of the lesson (appending to the last episode), pointing them to resources where they can learn more. Once that section exists, we can close this issue.
I think that will be a good place to mention napari and other image viewers too.
To anyone who wants to make a pull request to tackle this: feel free to tag me for a review!
For further exploring after the lesson, learners might be interested in scikit-image tutorials that @alexdesiqueira and I recently presented to the Data Umbrella community. The videos for the two webinars are available under a “scikit-image” playlist on their YouTube channel: https://www.youtube.com/playlist?list=PLBKcU7Ik-ir9Fi_hM_A6_U2UTpm7ACUtl
Resources that came to mind immediately:
- Mentioned in the curriculum advisory committee: Checklist for publishing images and analyses https://www.nature.com/articles/s41592-023-01987-9
- REMBI: Recommended Metadata for Biological Images https://www.nature.com/articles/s41592-021-01166-8
- "Twenty questions": a schema for a set of questions to guide analyses https://www.nature.com/articles/s41592-023-01919-7
- Pete Bankhead's outstanding gitbook on image analysis https://bioimagebook.github.io/index.html
- The "Python for Bioimage Analysis" materials from the RMS-DAIM group https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis
