The funded projects are:
- Project Jupyter (JupyterHub & Binder)
In total, 32 proposals were selected for funding by CZI; proposals from NumFOCUS sponsored projects account for nearly 20% of the funded grants. Two proposals by NumFOCUS affiliated projects (one for scikit-learn and one for scikit-image and Dash) were also selected for funding.
NumFOCUS especially applauds CZI’s leadership in making grant funding available for maintenance of foundational open source scientific software projects. In the current environment, opportunities for funding necessary maintenance and community building activities are unfortunately rare.
“The impact of CZI’s funding extends well beyond project contributor communities.”
“The impact of CZI’s funding extends well beyond project contributor communities,” said Leah Silen, NumFOCUS Executive Director. “Maintenance support creates positive ripple effects for the large, diverse and growing user base around these tools.”
CZI supports several areas of basic science and technology with the goal of making it possible to cure, prevent, or manage all diseases by the end of this century. Many NumFOCUS sponsored projects serve as the foundational building blocks for more specialized downstream software, such as biomedical tools. NumFOCUS projects play an important role in advancing biomedical research through improvements in scientific software, for example by making it possible to discover new cures for disease through the quantitative analysis of cell changes.
For example, CellProfiler, an open source Python tool that quantitatively analyzes and tracks the size and shape of cells, is built on top of NumFOCUS-supported open source projects including NumPy, SciPy, Matplotlib, scikit-image and scikit-learn. CZI previously funded Allen Goodman in the Carpenter lab to support CellProfiler; Goodman is one of three CZI-funded Software Fellows working on the most critical open-source bioimaging software packages.
NumFOCUS applauds CZI for funding the maintenance of foundational open source scientific software tools. In addition to directly benefiting the biomedical research community, the impacts of the funded proposals will ripple out to many other areas of scientific research that rely on NumFOCUS computing tools.
Proposals by NumFOCUS Sponsored Projects, funded by CZI Science:
Matplotlib: Foundation of Scientific Visualization in Python
Thomas A. Caswell, Brookhaven National Laboratory, NumFOCUS, United States
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.
Strengthening NumPy’s Foundations: Growing Beyond Code
Ralf Gommers, Quansight, NumFOCUS, United States
To grow the maturity of the NumPy project through governance, documentation, and website work by improving the robustness of its links with OpenBLAS, and through diversifying the core team beyond the developer role.
Ensuring the Continued Growth of pandas
Tom Augspurger, NumFOCUS, United States
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.
A Solid Foundation for Statistics in Python with SciPy
Warren Weckesser, University of California, Berkeley; NumFOCUS, United States and
Matt Haberland, California Polytechnic State University; NumFOCUS, United States
The project will improve the SciPy library’s statistics functionality to better serve biomedical research and downstream projects. In addition, an outreach component will engage female students, inspiring them to participate in open source code development.
JupyterHub Contributor in Residence Program
Chris Holdgraf, University of California, Berkeley; NumFOCUS, United States
To improve community support and technical maintenance across the JupyterHub repositories.
Scalable Storage of Tensor Data for Scientific Computing
Ryan Williams, Mount Sinai School of Medicine; NumFOCUS, United States
To establish Zarr as a foundation for scientific data storage, with clear data format and protocol specifications, implementations in multiple programming languages, and a community process for evolving to support new scientific applications.