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135 changes: 79 additions & 56 deletions about/package-scope.md
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# The Scope of Packages that pyOpenSci Reviews
# The Scope of Packages that pyOpenSci Reviews

The mission of pyOpenSci's open peer review process is to:

Expand All @@ -10,19 +10,32 @@ of Open Source software for those who wish to obtain a Journal paper
through our review.

## What types of packages does pyOpenSci review?
pyOpenSci reviews higher level software packages that support scientific workflows.

pyOpenSci reviews Python packages that support scientific workflows and research.
Our scope is intentionally broad to accommodate the diverse ways scientists use
Python in their work.

**Scientific workflows** include activities such as:

- Data collection, retrieval, and processing
- Data analysis, modeling, and simulation
- Data visualization and exploration
- Research reproducibility and automation
- Scientific communication and collaboration

Packages that enable, enhance, or streamline these activities for researchers
across any scientific domain are within our scope.

:::{figure-md} fig-target

<img src="../images/python-stack-jupyter-earth.png" alt="Image showing the tiers of software in the python ecosystem starting with Python itself and as you move out packages become more domain specific. In this image packages like xarray and numpy are considered core to scientific python. Packages and distributions like astropy, simpeg and metpy are considered to be domain specific." width="700px">

Diagram showing the tiers of software in the python ecosystem starting with Python itself and as you move out packages become more domain specific. In this image, packages such as xarray and numpy are considered core to scientific python. Packages and distributions like astropy, simpeg and metpy are considered domain specific. pyOpenSci's review
process focuses on domain specific packages rather than core packages as
these packages tend to have more variability in long term maintenance and
package infrastructure and quality compared with established core packages. **Source: ["Jupyter meets earth" project](https://jupytearth.org/jupyter-resources/introduction/ecosystem.html)**
Diagram showing the tiers of software in the Python ecosystem starting with Python itself and as you move out, packages become more domain specific. In this image, packages such as xarray and numpy are considered core to scientific Python. Packages and distributions like Astropy, SunPy, and MetPy are considered domain specific. pyOpenSci's review
process focuses on domain-specific packages and tools that support scientific workflows rather than core infrastructure packages, as
these packages tend to have more variability in long-term maintenance and
package infrastructure and quality compared with established core packages. Examples of pyOpenSci-reviewed packages include MovingPandas (geospatial data), Pandera (data validation), PyGMT (geophysical mapping), and xclim (climate data analysis). **Source: ["Jupyter meets earth" project](https://jupytearth.org/jupyter-resources/introduction/ecosystem.html)**
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@coatless coatless Dec 4, 2025

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Maybe add great-tables for table creation?

pyOpenSci/software-submission#202

:::


:::{admonition} This is a living document
:class: note

Expand Down Expand Up @@ -66,15 +79,23 @@ fit into at least one scope category below. We also welcome mature packages with
a growing or established community!
```


## Package categories that are in-scope for pyOpenSci

The following are the current categories that fall into scope for
pyOpenSci. In addition to fitting into one or more of these categories, your package should have some level of
demonstrated scientific application. This could be a use case that you can
link to or a tutorial that demonstrates its potential application for science.
pyOpenSci. In addition to fitting into one or more of these categories, your package should support
scientific or research activities. This support can be demonstrated through:

- Documentation showing how the package is used in research workflows
- Examples or tutorials demonstrating scientific applications
- Use cases in scientific publications or projects
- Relevance to data collection, analysis, or visualization in research contexts

Below we provide examples of packages from pyOpenSci ecosystem.
We interpret "scientific application" broadly to include any research domain—from
physical and life sciences to social sciences, digital humanities, and beyond—as well
as tools that support general research infrastructure (e.g., data validation, workflow
automation, reproducibility).

Below we provide examples of packages from the pyOpenSci ecosystem.

```{note}
Many of the example packages below perform tasks that might fit in multiple
Expand All @@ -83,25 +104,26 @@ of packages that would fall into that category.
```

### Data retrieval

Packages for accessing and downloading data from online sources. This category
includes wrappers for accessing APIs.

Our definition of scientific applications is broad, including data storage
services, journals, and other remote servers, as many data sources may be of
interest to scientists. However, retrieval packages should be focused on data
sources / topics, rather than services. For example a general client for Amazon
Web Services data storage would not be in-scope.

* Examples: [OpenOmics](https://github.com/pyOpenSci/software-submission/issues/31), [pyDov](https://github.com/pyOpenSci/software-submission/issues/19), [Physcraper](https://github.com/pyOpenSci/software-review/issues/26)
We interpret scientific application broadly for data retrieval packages, recognizing
that many data sources—including data storage services, journals, repositories, and
other remote servers—may be valuable to researchers. However, retrieval packages should
be focused on data sources or topics relevant to research rather than general-purpose
services. For example, a general client for Amazon Web Services data storage would not
be in scope, but a package that retrieves specific scientific datasets from AWS would be.

- Examples: [OpenOmics](https://github.com/pyOpenSci/software-submission/issues/31), [pyDov](https://github.com/pyOpenSci/software-submission/issues/19), [Physcraper](https://github.com/pyOpenSci/software-review/issues/26)
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Is there a way we can make these example sections more up to date? Maybe a page on the pyopensci website filtering by category?


### Data extraction

These packages aid in retrieving data from unstructured sources such as text,
images, and PDFs. They might also parse scientific data types and outputs from
scientific equipment.

* Examples: [devicely](https://github.com/pyOpenSci/software-submission/issues/37), [jointly](https://github.com/pyOpenSci/software-submission/issues/45)
- Examples: [devicely](https://github.com/pyOpenSci/software-submission/issues/37), [jointly](https://github.com/pyOpenSci/software-submission/issues/45)

### Data processing and munging

Expand All @@ -110,21 +132,20 @@ category focuses on tools for handling data in specific formats that scientists
may be interested in working with. These data may also be generated from
scientific workflows or exported from instruments and wearables.

* Examples: [devicely](https://github.com/pyOpenSci/software-submission/issues/37), [jointly](https://github.com/pyOpenSci/software-submission/issues/45), [MovingPandas](https://github.com/pyOpenSci/software-submission/issues/18), [OpenOmics](https://github.com/pyOpenSci/software-submission/issues/31), [Physcraper](https://github.com/pyOpenSci/software-submission/issues/26)

- Examples: [devicely](https://github.com/pyOpenSci/software-submission/issues/37), [jointly](https://github.com/pyOpenSci/software-submission/issues/45), [MovingPandas](https://github.com/pyOpenSci/software-submission/issues/18), [OpenOmics](https://github.com/pyOpenSci/software-submission/issues/31), [Physcraper](https://github.com/pyOpenSci/software-submission/issues/26)

### Data deposition

Tools for depositing data into scientific research repositories.

* Examples: [This is an example from rOpenSci - eml](https://github.com/ropensci/software-review/issues/80)
- Examples: [This is an example from rOpenSci - eml](https://github.com/ropensci/software-review/issues/80)

### Data validation and testing:

Tools that enable automated validation and checking of data quality and
completeness. These tools should be able to support scientific workflows.

* Example: [pandera](https://github.com/pyOpenSci/software-submission/issues/12)
- Example: [pandera](https://github.com/pyOpenSci/software-submission/issues/12)

### Scientific software wrappers

Expand All @@ -139,15 +160,16 @@ We strongly encourage submissions that wrap tools that are open-source with
an OSI-approved license. Exceptions will be evaluated on a case-by-case basis,
taking into consideration whether open-source options exist.

* Examples: [PyGMT](https://github.com/pyOpenSci/software-submission/issues/43), [python-graphblas](https://github.com/pyOpenSci/software-submission/issues/81)
- Examples: [PyGMT](https://github.com/pyOpenSci/software-submission/issues/43), [python-graphblas](https://github.com/pyOpenSci/software-submission/issues/81)

### Workflow automation and versioning

Tools that automate and link together workflows and as such support
reproducible workflows. These
tools may include build systems and tools to manage continuous integration.
This also includes tools that support version control.

* Examples: Both of these tools are not pyOpenSci reviewed as of yet but are examples of tools that might be in scope for this category - [snakemake](https://snakemake.readthedocs.io/en/stable/), [pyGitHub ](https://github.com/PyGithub/PyGithub)
- Examples: Both of these tools are not pyOpenSci reviewed as of yet but are examples of tools that might be in scope for this category - [snakemake](https://snakemake.readthedocs.io/en/stable/), [pyGitHub ](https://github.com/PyGithub/PyGithub)

### Citation management and bibliometrics:

Expand All @@ -156,17 +178,17 @@ creating CVs or otherwise attributing scientific contributions, or accessing,
manipulating or otherwise working with bibliometric data. (Example: [Example from rOpenSci - RefManageR](https://github.com/ropensci/software-review/issues/119))

### Data visualization and analysis

These are packages that enhance a scientist's experience in visualizing and
analyzing data.

* Examples: [PyGMT - (also spatial and data munging)](https://github.com/pyOpenSci/software-submission/issues/43),
- Examples: [PyGMT - (also spatial and data munging)](https://github.com/pyOpenSci/software-submission/issues/43),

### Database software bindings

Bindings and wrappers for database APIs.

* Example: [Example from rOpenSci - rrlite](https://github.com/ropensci/software-review/issues/6)
Bindings and wrappers for database APIs.

- Example: [Example from rOpenSci - rrlite](https://github.com/ropensci/software-review/issues/6)

## Scope for packages that support analytics, statistics and modeling

Expand All @@ -177,12 +199,12 @@ credible journal.

We consider the following when determining whether an analytics-related package is within our review scope:

1. If your package facilitates a scientist using a **known or vetted statistical, AI or Analytical approach** we consider that in-scope. Before submitting to us, please ensure that your package's documentation directs users to existing paper(s) or pre-print(s) that document that approach's application. Further, be sure to link to these publications in your package review submission.
1. If your package facilitates a scientist using a **known or vetted statistical, AI or Analytical approach** we consider that in-scope. Before submitting to us, please ensure that your package's documentation directs users to existing paper(s) or pre-print(s) that document that approach's application. Further, be sure to link to these publications in your package review submission.

The review for this package:

* requires at least 1 domain specialist
* will never vet the analytical method itself.
- requires at least 1 domain specialist
- will never vet the analytical method itself.

2. If your package introduces a novel or newer analytic approach that is not yet vetted/ accepted by a scientific journal, we can not review it. We cannot review projects that exist as a proof-of-concept demonstration of a model or analytical approach that might accompany a paper. In this case, the approach should be sent to a scientific journal for vetting.

Expand All @@ -202,32 +224,29 @@ we will expand this list.

Packages focused on the retrieval, manipulation, and analysis of spatial data.

* Examples: [PyGmt](https://github.com/pyOpenSci/software-submission/issues/43),
[Moving Pandas ](https://github.com/pyOpenSci/software-submission/issues/18)

- Examples: [PyGmt](https://github.com/pyOpenSci/software-submission/issues/43),
[Moving Pandas ](https://github.com/pyOpenSci/software-submission/issues/18)

### Education

Packages to aid with instruction.

* Examples: [pyrolite](https://github.com/morganjwilliams/pyrolite)


- Examples: [pyrolite](https://github.com/morganjwilliams/pyrolite)

## Partnerships

### Astropy

We have a [community affiliated package partnership with Astropy](../partners/astropy). To see packages currently under review for Astropy affiliation, visit the [open issues page](https://github.com/pyOpenSci/software-submission/issues?q=is%3Aissue+is%3Aopen) and select the `astropy` label.

### Pangeo

We have a [partnership with Pangeo](../partners/pangeo). Often times packages submitted as a part of that partnership are also in the geospatial domain.

* Examples: [xclim](https://github.com/pyOpenSci/software-submission/issues/73)
- Examples: [xclim](https://github.com/pyOpenSci/software-submission/issues/73)

## Package technical scope


### Telemetry & user-informed consent

Your package should not collect collecting usage analytics without first informing your users about what data are being collected and what is being done with that data. With
Expand All @@ -247,12 +266,11 @@ We will evaluate usage data collected by packages on a case-by-case basis
and reserve the right not to review a package if the data collection is overly
invasive.


To be in technical scope for a pyOpenSci review, your package:

* Should have maintenance workflows documented.
* Should declare vendor dependencies using standard approaches rather than including code from other packages within your repository.
* Should not have an exceedingly complex structure. Others should be able to contribute and/or take over maintenance if needed.
- Should have maintenance workflows documented.
- Should declare vendor dependencies using standard approaches rather than including code from other packages within your repository.
- Should not have an exceedingly complex structure. Others should be able to contribute and/or take over maintenance if needed.

```{admonition} pyOpenSci's goal is to support long(er) term maintenance
pyOpenSci has a goal of supporting long term maintenance of open source
Expand All @@ -262,14 +280,15 @@ package's maintenance.
```

### What if my package seems like its category or domain is out of scope?

- pyOpenSci is still developing as a community. If your scientific Python
package does not fit into one of the categories or if you have any other
questions, we encourage you to open a pre-submission inquiry. We're happy to help.
package does not fit into one of the categories or if you have any other
questions, we encourage you to open a pre-submission inquiry. We're happy to help.
- Data visualization packages come in many varieties, ranging from small
hyper-specific methods for one type of data to general, do-it-all packages
(e.g. matplotlib). pyOpenSci accepts packages that are somewhere in between the
two. If you're interested in submitting your data visualization package, please
open a pre-submission inquiry first.
hyper-specific methods for one type of data to general, do-it-all packages
(e.g. matplotlib). pyOpenSci accepts packages that are somewhere in between the
two. If you're interested in submitting your data visualization package, please
open a pre-submission inquiry first.

## Examples of packages that might be out of technical scope

Expand All @@ -279,12 +298,14 @@ Your package **may not be in technical scope** for us to review at this time if
it fulfills any of the out-of-technical-scope criteria listed below.

Your package is in technical scope if it is:
* Pure Python or Python with built extensions
* Available from PyPI and/or community conda channels such as conda-forge or bioconda

- Pure Python or Python with built extensions
- Available from PyPI and/or community conda channels such as conda-forge or bioconda

Your package might be out of in technical scope if it is:
* Not published in a community channel such as PyPI or a channel on anaconda cloud
* Exceedingly complex in its structure or maintenance needs

- Not published in a community channel such as PyPI or a channel on anaconda cloud
- Exceedingly complex in its structure or maintenance needs

A few examples of packages that may be too technically challenging for us to
find a new maintainer for in the future are below.
Expand Down Expand Up @@ -312,7 +333,9 @@ maintenance of the original code base to be independent from your package's
maintenance.

(package-overlap)=

## Package Overlap

pyOpenSci encourages competition among packages, forking and re-implementation
as they improve options of users. However, we strive to make packages in the
pyOpenSci suite to represent our top recommendations for the tasks that they
Expand All @@ -324,7 +347,7 @@ being:

- More open in licensing or development practices
- Broader in functionality (e.g., providing access to more data sets, providing
a greater suite of functions), but not only by duplicating additional packages
a greater suite of functions), but not only by duplicating additional packages
- Better in usability and performance
- Actively maintained while alternatives are poorly or no longer actively maintained

Expand Down
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