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Why Open Source Matters for Reproducible Science

I develop almost everything in open source. People ask why I spend so much time on documentation, examples, and polish for free software.

The answer is simple: science should be reproducible, and code is increasingly central to scientific claims.

The Reproducibility Crisis

Academia has a problem: published papers often can’t be reproduced. Reasons include:

  • Methods described too vaguely
  • Data not available
  • Code never released
  • Dependencies undocumented
  • Computational environment not preserved

This isn’t just inefficient—it undermines the scientific method.

Code as Scientific Artifact

When your research involves computation (and whose doesn’t these days?), your code is part of your methodology. Hiding it is like a biologist refusing to describe their experimental protocol.

Open source isn’t charity. It’s scientific rigor.

Why I Document Obsessively

Every library I publish includes:

  • Clear installation instructions
  • Reproducible examples
  • API documentation
  • Tests that demonstrate usage
  • Version-controlled history showing evolution

This takes time. But it means someone in 2028 can:

  • Understand what I did
  • Reproduce my results
  • Build on my work
  • Identify my errors

The Broader Impact

Open source accelerates science by:

  • Enabling replication
  • Facilitating collaboration
  • Preventing redundant work
  • Building cumulative knowledge

This is why I’ll keep publishing everything. Not for recognition, but because science is a collective enterprise that only works if we show our work.


This philosophy shapes every project I touch. Check the repos—documentation is never an afterthought.

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