Grants

To support continued maintenance and development of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Project Lead: Tom Augspurger (NumFOCUS)

To complete the design, implementation, and rollout of pip’s next-generation dependency resolver, and permanently improve pip’s maintainer capacity and user experience.

Project Lead: Ernest W. Durbin III (Python Software Foundation)

To develop an open standard and API for phylogenetic models and improve the speed and scalability of the IQ-TREE software for phylogenetic inference from ultra-large genomic data.

Project Lead: Minh Bui (Australian National University)

To put Rocker, the de facto standard for reproducible, containerized R analyses, on a path to sustainable maintenance through refactoring, improving the quality of documentation, expanding the community, and targeting new hardware platforms.

Project Lead: Carl Boettiger (University of California, Berkeley)

To improve the bcbio-nextgen toolkit, focusing on maintaining existing variant calling functionality and extending support for structural and RNA-seq variant analyses.

Project Lead: Rory Kirchner (Harvard Chan School of Public Health)

To enable Matplotlib to continue as the core plotting library of the scientific Python ecosystem by addressing the maintenance backlog and planning Matplotlib’s evolution to meet the community’s visualization challenges for the next decade.

Project Lead: Thomas A. Caswell (Brookhaven National Laboratory, NumFOCUS)

To support the open source µManager optical microscopy acquisition platform to improve its architecture, infrastructure, and support to ensure many years of growth, both in user base and capabilities.

Project Lead: Kevin Eliceiri (University of Wisconsin, Madison)

To construct a solid foundation for the next generation of Protege using a modern web stack that will make Protege easier to maintain, extend, and — crucially — make it easier for third parties to contribute to the code base.

Project Lead: Mark Musen (Stanford University)

To provide open-source, interoperable, and extensible statistical software for quantitative mass spectrometry, which enables experimentalists and developers of statistical methods to rapidly respond to changes in the evolving biotechnological landscape.

Project Lead: Olga Vitek (Northeastern University)