Aims and Scope¶
pyOpenSci aims to support Python packages that enable reproducible research and managing the data lifecycle for scientists. Packages submitted to pyOpenSci should fit into one or more of the categories outlined below. If you are unsure whether your package fits into one of these categories, please open an issue as a pre-submission inquiry. We will let you know if we are able to review it at this time.
Data retrieval: Packages for accessing and downloading data from online sources. Includes wrappers for accessing APIs.
Data extraction: Packages that aid in retrieving data from unstructured sources such as text, images and PDFs.
Data munging: Tools for processing data from scientific data formats.
Data deposition: Tools for depositing data in scientific research repositories.
Reproducibility: Tools to scientists ensure that their research is reproducible. E.g. version control, automated testing, or citation tools.
Geospatial: Packages focused on the retrieval, manipulation, and analysis of spatial data.
Education: Packages to aid with instruction.
Data visualization:* Packages for visualizing and analyzing data.
* Please fill out a pre-submission inquiry before submitting a data visualization package. See the notes below.
Notes on Categories¶
pyOpenSci is young and this list is tentative. If your scientific Python package does not fit into one of the categories or if you have any other questions, we’d 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 on first.
Note that we cannot currently accept statistics and/or machine learning packages. We don’t feel that we can give these the deep, thorough review that they require.
pyOpenSci encourages competition among packages, forking and re-implementation as they improve options of users overall. However, as we want packages in the pyOpenSci suite to be our top recommendations for the tasks they perform, we aim to avoid duplication of functionality of existing Python packages in any repo without significant improvements. A Python package that replicates the functionality of an existing package may be considered for inclusion in the pyOpenSci suite if it significantly improves on alternatives by 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
Better in usability and performance
Actively maintained while alternatives are poorly or no longer actively maintained
These factors should be considered as a whole to determine if the package is a significant improvement. A new package would not meet this standard only by following our package guidelines while others do not, unless this leads to a significant difference in the areas above.
We recommend that packages highlight differences from and improvements over overlapping packages in their README and/or vignettes.
We encourage developers whose packages are not accepted due to overlap to still consider submittal to other repositories or journals.