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ronfinn (Ron Finn) · GitHub

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Building open-source scientific data governance tools
💭
Building open-source scientific data governance tools
25 followers · 170 following

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ronfinn/README.md

Data Swamp Biosystems  ·  Cell Painting AnnData Validator  ·  Bio Run Crate  ·  BioDataset Scout

Building trustworthy scientific data systems

I work across scientific research, data strategy and software engineering. My focus is making complex research data discoverable, reproducible, traceable and ready for responsible AI/ML use.

Scientific data strategy

Target operating models, platform architecture, data products, metadata strategy, stewardship and governance-as-code.

Computational biology

Genomics, single-cell and spatial omics, bioimaging, phenotypic profiling and translational research data.

Software and data engineering

Typed Python, validation tooling, Nextflow, containers, cloud platforms, CI/CD and reproducible scientific workflows.

Programme delivery

Technical roadmaps, architecture decisions, cross-functional delivery, platform evaluation and measurable governance outcomes.

Flagship open-source systems

Data Swamp Biosystems

A deterministic synthetic biotech data estate for benchmarking catalogues, lineage systems, governance controls and AI agents.

synthetic-data data-governance AI agents

Cell Painting AnnData Validator

Semantic, provenance and AI-readiness validation for Cell Painting datasets represented as AnnData.

anndata phenotypic-profiling validation

Bio Run Crate

Validation of biological analysis-run metadata and packaging of traceable outputs as RO-Crate research objects.

RO-Crate FAIR reproducibility

BioDataset Scout

Evidence-backed discovery, qualification and acquisition intelligence for biomedical datasets, with explainable scoring and preserved provenance.

biomedical-data data-discovery provenance

Open-source portfolio pulse

The two panels above are generated as local SVG files by GitHub Actions. The profile does not depend on a public badge or statistics-image service.

Engineering principles

  • Contracts before convenience: make schemas, interfaces and expectations explicit.
  • Reproducibility by default: deterministic execution, versioned configuration and traceable outputs.
  • Governance in the workflow: validation, lineage, ownership and quality controls belong in the delivery path.
  • Build for scientific users: technically rigorous systems must remain understandable and usable by research teams.

Technical landscape

Scientific computing
Python · R · AnnData · Scanpy · Squidpy · Cell Painting · single-cell omics · spatial transcriptomics · bioimaging

Engineering
Nextflow · Docker · Kubernetes · GitHub Actions · pytest · Ruff · mypy · Pydantic · uv

Data platforms
AWS · Google Cloud · S3 · DataHub · OpenMetadata · OpenLineage · Benchling · Seqera

Strategy and governance
FAIR · ALCOA++ · metadata management · data quality · data lineage · data products · stewardship · governance as code

Scientific data should be understandable, reproducible and trustworthy by design.
London, United Kingdom

Pinned Loading

  1. dataswamp-biosystems dataswamp-biosystems Public

    Deterministic synthetic biotech data platform for generating governed organisations, metadata, scientific datasets, lineage and defects for testing data catalogues, governance platforms, AI agents …

    Python

  2. cell-painting-anndata-validator cell-painting-anndata-validator Public

    Validate the semantic correctness, metadata completeness, provenance and AI readiness of Cell Painting datasets represented in AnnData.

    Python

  3. bio-run-crate bio-run-crate Public

    Validate and package reproducible computational biology analysis runs using RO-Crate.

    Python

  4. openauc-io openauc-io Public

    Open-source Python toolkit for ingesting, validating, plotting, generating and archiving analytical ultracentrifugation data.

    Python

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