Thinking out loud
about R&D data

Réflexions sur
la data en R&D

Articles on statistics, data engineering, and AI applied to pharmaceutical, biotech, and cosmetic research. In French and English.

· 7 min read

The GxP data science stack for a 5-person biotech team

S3, dbt, renv, signed Git tags, Quarto. A lean, audit-ready architecture for small teams who cannot afford to get compliance wrong.

· 7 min read

Your PowerPoint deck is not audit-ready: what regulators actually expect

ICH E6(R2) requires traceable, reproducible outputs. A number manually pasted into a slide satisfies none of those requirements.

· 7 min read

The reproducibility trap: when your R&D analysis depends on who runs it

Most biotech R&D analyses live in one person's environment. When that person leaves, the analysis leaves with them.

· 7 min read

How to build a reproducible R&D data pipeline from scratch

Most R&D pipelines start as ad hoc scripts, then accumulate debt. Build reproducibility from day one with a clear architecture.

Statistics Coming soon

How to automate your NCA workflow in R: from raw data to CDISC-ready output

Replace your Excel NCA spreadsheet with a reproducible R pipeline that produces CDISC PC domain output automatically.

StatisticsPharmacokinetics Coming soon

PK/PD modeling for non-statisticians: a visual introduction

Half-life, AUC, Emax models. The pharmacokinetic concepts every drug development professional should understand, explained without equations.

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