PyData Prague Recap
Following our events in New York City and Chicago last year and at UC Berkeley in January, we saw our fourth OSSci-themed meetup the other week in Prague, Czech Republic: Open Source Science @ PyData Prague #20
More than 50 people attended, making this one of our bigger meetups yet.
Thanks to the PyData Prague team for assembling a stellar lineup. Thanks to Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering for providing the venue. And thanks to IBM for covering refreshments.
Evelina Gabašová: Open source and academia: Research software engineering perspective
In the British academic system, a new movement was established in 2012 called “research software engineering”. The goal has been to recognize and promote the vital role of software in research and establish academic career paths for people who develop it. As a Vice-President of the Society of Research Software Engineering (and a research software engineer herself), Evelina presented some of the lessons learned over the past decade and what have been the challenges in recognizing software and open source contributions as fully-fledged academic outputs. Check out the slides.
Derek Homeier: Astropy — a community effort to develop a common core package for Astronomy in Python
Since the early 2010s, Python emerged as a powerful alternative to proprietary platforms like IDL and Matlab for scientific data processing in astrophysics. This led to the development of key modules like Numarray/Numpy and Matplotlib, though individual needs caused a proliferation of independent solutions. Astropy was created to unify these efforts, fostering an ecosystem of interoperable astronomy packages with common standards. A decade later, Python and Astropy have become the dominant data-processing platform in astrophysics, evolving from an informal team effort to a structured and organized project.
Martin Fleischmann: Open by Default: Developing reproducible, computational research
Academic research often relies heavily on open-source software, yet researchers infrequently contribute back due to lack of incentives, time constraints, or imposter syndrome. Integrating research with open-source development can benefit both communities. By choosing to enhance existing libraries rather than creating new, fragmented packages, the Urban Grammar AI project exemplified this approach, contributing significantly to the GeoPandas and PySAL ecosystems, releasing an independent package, and developing a canonical Docker container for geographic data science. Check out the slides.
Tim Bonnemann: Accelerating Science with Open Source — An Introduction to Open-Source Science (OSSci)
Open-Source Science (OSSci) is a NumFOCUS initiative launched in July 2022 to accelerate scientific research by improving open-source software practices in science. OSSci connects various stakeholders through interest groups covering domain-specific and cross-domain topics, with more groups planned for 2024.
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