[review this in October 2025] Add: data section to guide

data/intro.md

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+# Data for your package
+
+In this section we talk about data for your scientific Python package: 
+when you would need it, and how you can access it and provide it to your users.
+
+```{admonition}
+:class: note
+Some material adapted from:
+https://www.dampfkraft.com/code/distributing-large-files-with-pypi.html  
+https://learn.scientific-python.org/development/patterns/data-files/
+```
+
+## When and why you might need data
+
+First we describe when and why you might need data. 
+Basically there are two cases: 
+for examples, and for tests.
+We'll talk through both in the next couple of sections.
+
+### Data for example usage
+
+It's very common for scientific Python packages to need data that helps their users understand how the library is to be used. 
+Often the package provides functionality to access this data, 
+either by loading it from inside the source code, or by downloading it off of a remote host.
+In fact, the latter approach is so common that libraries have been developed just to "fetch" data, 
+like pooch. 
+We will show you how to use both methods for providing access to data below, 
+but here we present some examples.
+
+#### Examples in pyOpenSci packages
+* movingpandas: <https://movingpandas.github.io/movingpandas-website/2-analysis-examples/bird-migration.html> 
+
+#### Examples in core scientific Python packages
+* scikit-image: <https://github.com/scikit-image/scikit-image/tree/main/skimage/data> 
+* scikit-learn: <https://github.com/scikit-learn/scikit-learn/tree/main/sklearn/datasets/data>
+
+### Data for tests 
+It is common to design your code and tests in such a way that you can quickly test on fake data, 
+ranging from something as simple as a NumPy array of zeros, 
+to something much more complex like a test suite that "mocks" data for a specific domain. 
+This lets you make sure the core logic of your code works, *without* needing real data.
+At the end of the day though, you do want to make sure your code works on real data, 
+especially if it is scientific code that may work with very specific data formats.
+That's why you will often want at least a small amount of real-world test data.
+A good rule of thumb is to have a handful of small files,
+say no more than 10 files that are a maximum of 50 MB each.
+Anything more than that you will probably want to store on-line and download, 
+for reasons we describe in the next section.
+
+## Why you should prefer to download data: size limits
+
+Below we will introduce places you can store data on-line, and show you tools you can use to download that data.
+We suggest you prefer this approach when possible, 
+The main reason for this is that there are limits on file and project sizes for forges, like GitHub and GitLab, 
+and for package indexes--most importantly, PyPI.
+Especially with scientific datasets that can be quite large, 
+we want to be good citizens of the ecosystem and not place unneccesarry demands on the common infrastructure.
+
+### Forges (GitHub, GitLab, BitBucket, etc.)
+
+Forges for hosting source code have maximum sizes for both files and projects. 
+For example, on GitHub, a single file cannot be more than 100 MB. 
+You would be surprised how quickly you can make a csv file this big! 
+You also want to avoid committing larger binary files (like images or audio) 
+to a version control system like git, because it is hard to go back and remove them later, 
+and it can really slow down the speed with which you can clone the project.
+More importantly, it slows down the speed with which potential contributors can clone your project!
+
+### Data size and PyPI
+
+The Python Package Index (PyPI) places a limit on the size of individual files uploaded--where a "file" is either 
+a sdist or a wheel--and also a limit on the total size of the project (the sum of all the "files"). 
+These limits are not documented as far as we can tell, 
+but most estimates are around 100 MB per file and 1 GB for the total project. 
+Files this large place a real strain on the resources supporting PyPI, as discussed here. 
+For this reason, as a good citizen in the Python ecosystem you should do everything you can to minimize your impact. 
+Don't worry, we're here to help you do that! 
+You can request increases for both file size and project size 
+(see [here](https://pypi.org/help/#file-size-limit) 
+and [here](https://pypi.org/help/#project-size-limit)) 
+but we strongly suggest you read about other options here first.
+
+## Where to store your data
+
+Alright, we're strongly suggesting you don't try to cram your data into your code--where should you store it?
+Here we provide several options.
+
+### Within the repository
+
+As stated above, there *are* cases where relatively small datasets 
+can be included in a package.
+If this data consists of examples for usage, 
+then you would likely put it inside your source code 
+so that it will be included in the sdist and wheel.
+If the data is meant only for tests,
+and you have a separate test directory (as we suggest)
+
+* strengths and weaknesses
+  * Strengths
+    * easy to access
+    * can be very do-able for smaller files, e.g. text files used in bioinformatics
+  * Weaknesses
+    * maximum file sizes on forges like GitHub and on PyPI
+    * You want to avoid adding these files to your version control history (git) and draining the resources of PyPI
+* examples:
+    * pyOpenSci:
+      * opengenomics: <https://github.com/JonnyTran/OpenOmics/tree/master/tests/data/TCGA_LUAD>   
+      * jointly:  <https://github.com/hpi-dhc/jointly/tree/master/test-data> 
+    * core scientific-python packages
+      * scikit-learn: <https://github.com/scikit-learn/scikit-learn/tree/main/sklearn/datasets/data> 
+
+### In the cloud
+
+#### scientific data repositories
+* strengths and weaknesses
+  * strength: free, guaranteed lifetime of dataset, often appropriate for pyOpenSci packages
+  * weaknesses: may be hard to automate for data that changes frequently
+* examples
+  * Zenodo
+  * OSF
+  * FigShare
+  * Dryad (paid?)
+
+#### private cloud
+* strengths and weaknesses
+  * strengths: robust; tooling exists to more easily automate updating of packages, but this requires more technical know-how
+  * weaknesses: not free
+* examples
+  * AWS
+  * Google Cloud
+  * Linode
+
+```{admonition} Data version control
+:class: tip
+
+Did you know that tools exist that let you track changes to datasets in the same way version control systems like git 
+lets you track changes to code? Although you don't strictly need data versioning to include data with your package, 
+you probably want to be aware that such tools exist if you are reading this section. 
+Such tools could be particularly important if your package focuses mainly on providing access to datasets.
+Within science, tools have been developed to provide distributed access to datasets. These tools 
+general
+DataLad https://www.datalad.org/ 
+related tools that are used for data engineering and industry (maybe breakout?)
+Git-LFS
+DVC
+Pachyderm (I think it's called?)
+```
+
+```{admonition} Field specific standards + metadata
+:class: tip
+
+It's important to be aware of field-specific standards
+eg astronomy
+neuroscience: DANDI, NWB
+many pyOpenSci tools exist to address these standards or to provide interoperability because these standards don't exist
+see also: FAIR data
+```
+
+## How to access your data
+
+Last but definitely not least, it's important to understand how you *and* your users
+
+### For examples: in documentation, tutorials, docstrings, etc.
+
+### Accessing local files with importlib-resources
+
+If you have included data files in your source code, then you can provide access to these through importlib-resources.
+
+link to PyCon talk w/Barry Warsaw
+code snippet example here
+mention python-3.9 backport
+* examples:
+  * pyOpenSci packages:
+    * crowsetta: <https://github.com/vocalpy/crowsetta/blob/main/src/crowsetta/data/data.py> 
+  * core scientific Python packages:
+    * scikit-learn: <https://github.com/scikit-learn/scikit-learn/blob/f86f41d80bff882689fc16bd7da1fef4a805b464/sklearn/datasets/_base.py#L297> 
+
+### Accessing files that are hosted remotely with pooch
+pooch: 
+https://github.com/fatiando/pooch 
+code snippet example of using pooch
+
+### For tests
+Many of the same tools apply.
+You can download test data as a set-up step in your CI.
+Pytest fixtures for accessing test data.