Hi 👋 — I'm building BioEval, an open evaluation of publicly available insect colony / swarm simulations, scoring each on data transparency, code availability, and computational reproducibility. Your project was included, and I wanted to share the results directly and — more importantly — ask for your feedback. If I've gotten something wrong or missed context, I'd genuinely like to correct it.
Overall: 64.3/100
| Dimension |
Score |
| Data Disclosure |
82 |
| Dataset Resolvability |
75 |
| Code Availability |
72 |
| Traceability |
48 |
| Simulation Clarity |
52 |
| Reproducibility Package |
58 |
(Scores include a documented +20 baseline calibration, capped at 100; the overall is the weighted mean of the six dimensions.)
Ways to improve the score:
- Map algorithmic choices back to their sources to raise traceability
- Add a versioned release / pinned commit / Zenodo archive
- General: pin dependency versions + add a deterministic seed; add a parameters table mapping each constant to its source; tag a release and archive to Zenodo for a DOI; commit one example output with a checksum.
Methodology and full rubric: https://github.com/fractastical/bioinformatics-eval
Thanks for building and open-sourcing this — any corrections welcome!
Hi 👋 — I'm building BioEval, an open evaluation of publicly available insect colony / swarm simulations, scoring each on data transparency, code availability, and computational reproducibility. Your project was included, and I wanted to share the results directly and — more importantly — ask for your feedback. If I've gotten something wrong or missed context, I'd genuinely like to correct it.
Overall: 64.3/100
(Scores include a documented +20 baseline calibration, capped at 100; the overall is the weighted mean of the six dimensions.)
Ways to improve the score:
Methodology and full rubric: https://github.com/fractastical/bioinformatics-eval
Thanks for building and open-sourcing this — any corrections welcome!