Jackson Burns

@fastprop.bsky.social

Researcher

🐍 Want to publish your own Python package? Join pyOpenSci’s 2-hour online workshop Nov 6: ✔️ Build your first package fast ✔️ Learn the core files (pyproject.toml, docs, metadata) ✔️ Publish to PyPI with confidence 📅 Nov 6, 2025 | Online 🎟️ bit.ly/PythonPackaging

Purple graphic with pyOpenSci logo at the top. Large white text reads: From Zero to Python Package. Subheading: A 2-hour packaging workshop with pyOpenSci! A checklist underneath says: Build your first Python package, Publish securely to PyPI, Automate releases with GitHub Actions, Write docs that help users & contributors. Below, text reads: Thursday, 6 November 2025, 10:00 AM MST – 12:15 PM MST. At the bottom: bit.ly/PythonPackaging with a button that says Register Now. A simple robot illustration is on the right side.

🚨 Extension Alert: The intermediate leaderboard submission deadline for the antiviral challenge has been extended to midnight on February 7th! Don't miss this chance to see how you stack up against other participants! Submit your results today: polarishub.io/competitions

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This is obviously how scientific articles should be, and if we had an open ecosystem designed for users rather than profit, we would have had this years ago. Time to dump commercial publishers and their paywalls.

Mike Morrison@mikemorrison.bsky.social · 2y ago

Want to see a quick demo of the technology that might replace citations in scientific papers? Goodbye citations, hello embeds. I was just hoping for links in scientific papers, but this is even better!

📢 Calling all ML practitioners! Have you been waiting for an opportunity to prove how well your model performs on a blind, newly generated and consistent test set? You now have the unique opportunity to! Super excited to launch a first competition on @polarishub.io!

Polaris@polarishub.io · 2y ago

🦠 We’re excited to announce our first competition in partnership with @asapdiscovery.bsky.social and @omsf.io! Test your skills across three sub-challenges revolving around SARS-CoV-2 and MERS-CoV Mpro🧵 Full details: polarishub.io/competitions Blog: polarishub.io/blog/antivir...

My machine learning data splitting library astartes has just hit 64 stars on GitHub! ⭐ We built this to help rigorously quantify how well our models actually work - if you want to quantify how well you models extrapolates into new feature or target space, give it a look: github.com/JacksonBurns...

GitHub - JacksonBurns/astartes: Better Data Splits for Machine Learning

Better Data Splits for Machine Learning. Contribute to JacksonBurns/astartes development by creating an account on GitHub.

github.com