Two weeks ago, we held a scikit-learn sprint in Austin and Paris. Here is a brief report, on progresses and challenges.
Several sprints
We actually held two sprint in Austin: one open sprint, at the scipy conference sprints, which was open to new contributors, and one core sprint, for more …
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Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state …
At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.
“Without engineering, science is not more than philosophy” — the community
How do we enable better Science? Why do we do software …
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The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.
I give links to the videos. Some machine-learning researchers have great thoughts about growing communities of coders, about code as a process and a deliverable …
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tl;dr: Reproducibilty is a noble cause and scientific software a promising vessel. But excess of reproducibility can be at odds with the housekeeping required for good software engineering. Code that “just works” should not be taken for granted.
This post advocates for a progressive consolidation effort of scientific …
Christophe Pradal, Hans Peter Langtangen, and myself recently edited a version of the Journal of Computational Science on scientific software, in particular those written in Python. We wrote an editorial defending writing and publishing open source scientific software that I wish to summarize here. The full text preprint is openly …
