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1. Move acknowledgments to intro.md.
2. Move domain specific tutorial links to "Keep Exploring"
3. Add Pythia material to "Keep Exploring"
4. Add CONTRIBUTING.md
5. Redirect SciPy 2022 link to Fundamental Learning Path
6. Reorganize with Data Tidying and Learning Path subheadings
Closes#49Closes#81Closes#176Closes#179
Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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##Contributing
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# Contributing Guide
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This repository is a great opportunity to start contributing to Xarray.
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This tutorial repository is a great opportunity to start contributing to Xarray.
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- Report bugs, request features or submit feedback as a [GitHub Issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues). First check existing issues: https://github.com/xarray-contrib/xarray-tutorial/issues !
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- Report bugs, request features or submit feedback as a [GitHub Issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues). First check existing [issues](https://github.com/xarray-contrib/xarray-tutorial/issues) !
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- Make fixes, add content or improvements using [GitHub Pull Requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests), the sections below go over this process in more detail:
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```{seealso}
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The Project Pythia Foundations material on [Github](https://foundations.projectpythia.org/foundations/getting-started-github.html) and Github workflows is a great place to start if you are new to this.
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```
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## Content Guidelines
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Please note that examples submitted to this repository should follow these
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guidelines:
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1. Run top-to-bottom without intervention from the user
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1. Not require external data sources that may disappear over time (external data sources that are highly unlikely to disappear are fine)
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1. Not require external data sources that may disappear over time (external data sources that are highly unlikely to disappear are fine). Small datasets for tutorial purposes can be added [here](https://github.com/pydata/xarray-data/) if necessary.
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1. Not be resource intensive, and should run within 2GB of memory
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1. Be clear and contain enough prose to explain the topic at hand
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1. Be concise and limited to one or two topics, such that a reader can get through the example within a few minutes of reading
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You'll need `conda` or `mamba`, which can be installed from https://github.com/conda-forge/miniforge
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We also use [conda-lock](https://conda-incubator.github.io/conda-lock/) to ensure we have reproducible environments across different operating systems
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We also use [conda-lock](https://conda.github.io/conda-lock/) to ensure we have reproducible environments across different operating systems
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We also use [pre-commit hooks](https://pre-commit.com) to run styling and other checks before committing code.
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## Contributing
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Contributions are welcome and greatly appreciated! See our [CONTRIBUTING.md](./CONTRIBUTING.md) document.
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## Acknowledgements
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This website is the result of many contributions from the Xarray community! We're very grateful for everyone's volunteered effort as well as [sponsored development](https://xarray.dev/#sponsors). Funding for SciPy 2022, SciPy 2023 tutorial materialdevelopment specifically was supported by NASA's Open Source Tools, Frameworks, and Libraries Program (award 80NSSC22K0345).
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