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learners/further-reading.md

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## Where to go from here
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- [Scikit-image YouTube tutorials](https://www.youtube.com/playlist?list=PLBKcU7Ik-ir9Fi_hM_A6_U2UTpm7ACUtl)
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- [Principles of Scientific Imaging](https://imagej.net/imaging/principles)
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- [Data Handling and Management Training](https://carpentries-incubator.github.io/Data-Management-Training/)
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- [Introduction to Bioimage Analysis by Pete Bankhead](https://bioimagebook.github.io/index.html)
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- [Bio-image Analysis Notebooks by Robert Haase](https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html)
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- [Introduction to artificial neural networks in Python](https://carpentries-incubator.github.io/machine-learning-neural-python/index.html)
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- [Building Better Research Software](https://carpentries-incubator.github.io/better-research-software/)
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- [Data Handling and Management Training](https://carpentries-incubator.github.io/Data-Management-Training/)
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## Where to find more help
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## Scientific Literature
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- [Digital Image Processing](https://www.imageprocessingplace.com), Textbook by Rafael C. Gonzalez and Richard E. Woods
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- [Checklist for publishing images and analyses ](https://www.nature.com/articles/s41592-023-01987-9)
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- [Studies on application of image processing in various fields: An
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overview](https://dx.doi.org/10.1088/1757-899X/961/1/012006)
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- [REMBI: Recommended Metadata for Biological Images](https://www.nature.com/articles/s41592-021-01166-8)
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- ["Twenty questions": a schema for a set of questions to guide analyses](https://www.nature.com/articles/s41592-023-01919-7)
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- [From cells to pixels: A decision tree for designing bioimage analysis pipelines](https://onlinelibrary.wiley.com/doi/10.1111/jmi.70021)

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