AFAIK, these differences are minor, numeric differences. I would consider them equivalent.

rogue-scholar.org

Nice post!
How did you generate the doi-link for a blog post?

We try to avoid that by using this with preprocessed data only. All the heavy lifting is done with nextflow pipelines before. Datasets up to tens of GBs have worked well so far.

Finally, many thanks to my colleagues @alexpeltzer.bsky.social, Daniel Schreyer and Tom Schwarzl for testing, adopting, and contributing to DSO.

If you want to learn more, I'll be presenting this at a @nf-co.re bytesize talk: nf-co.re/events/2025/...

We built this at @boehringerglobal.bsky.social to meet the quality standards required for biomarker analysis in clinical trials.

But I think this is useful for any kind of data analysis project.

One of my favorite features: automated watermarking of all plots in a quarto report. Nobody gonna publish my plots anymore before I think they are ready.

It brings together the best tools:
- git, for code versioning
- dvc, for data versioning and tracking inputs and outputs
- jinja2, for templates
- uv, for Python dep mgmt
- quarto, for authoring reports
- hiyapyco, for hierarchical YAML config
- pre-commit, for linting

(2) Finding the mistake, tracing it back to its origin, and fixing it was only possible because the data and scripts for building the atlas are publicly available and fully reproducible. github.com/icbi-lab/luca

(1) Maintaining a data resource is very much like maintaining software. It is never "done" but constantly improving.