# Welcome to the Open Image Data Handbook

```{warning}
This is very much a work in progress. There will likley be significant changes to both the structure and content in the coming weeks. Links should not be treated as stable.
```

## Objective
This book aims to collect best practices for working with and producing open image data and image analysis pipelines. This book focuses mostly on how to utilize open infrastructure and standards to work collaboratively with the broader image analysis community. This book does not aim to teach the fundamentals of image processing. If you would like to learn about the fundamentals of image processing, please see the ["Related books and resources section"](content:references:related_resources) below.

## Structure and usage
Currently this document is mostly a collection of thoughts. As more content is added, I will refactor into a more structured book. For now, the best place to start is the "data story".

(content:references:related_resources)=
## Related books and resources

### Image analysis
- **[Introduction to Bioimage Analysis](https://bioimagebook.github.io/README.html) by Pete Bankhead**. An amazing reference with clear techincal content and figures to illustrate the fundamental concepts in image processing.
- **[Bio-image Analysis with Python](https://github.com/BiAPoL/Bio-image_Analysis_with_Python) by Anna Poetsch, Biotec Dresden, Marcelo Leomil Zoccoler, Johannes Richard Müller and Robert Haase**. A well-made course on how to perform key bioimage analysis and data science tasks in python.
- **[BiA-PoL blog](https://biapol.github.io/blog/) by the Bio-image Analysis Technology Development group**. A collection of blog posts with helpful tips for bioimage analysis and data science.
- **[Image Analysis Training Resources](https://neubias.github.io/training-resources/) by NEUBIAS**. Training materials for teaching/learning image processing on a variety of platforms (e.g., ImageJ/FIJI, python, and MATLAB)


### Data science
- **[The Turing Way](https://the-turing-way.netlify.app/welcome.html) by the Turing Way Community**. A comprehensive guide for reproducible data science in research starting from project design through communication of results.
- **[UvA Deep Learning Tutorials](https://uvadlc-notebooks.readthedocs.io/en/latest/) by Phillip Lippe**. A series of notesbooks giving practical explanations of deep learning concepts with exercises implemented as Google Collab notebooks.