Research Artifacts and Reproducibility

A research result is easier to trust when the artifacts around it are clear. Code, data descriptions, experiments, logs, configuration, and limitations all help readers understand what was actually done.

Reproducibility is not only about rerunning a script. It is about making the work inspectable. What inputs were used? What assumptions were made? Which results are stable, and which are sensitive to data or parameter choices?

I want this website to become a place where projects can show that context instead of hiding it in private notes or scattered repositories.