👋 Meet Will Landau! 🇺🇸

Statistician and software developer. He maintains {targets} and several related packages to the targets ecosystem. Target is a pipeline toolkit that makes data analysis in R faster and fully reproducible by tracking dependencies and skipping work that is already up to date.

Find Will at:
🐘 fosstodon.org/@landau
💻 wlandau.github.io
🔗 linkedin.com/in/wlandau

#MaintainerMonth #rOpenSci #RStats #ReproducibleResearch

The "Implementing FAIR Workflows project" has contributed a dedicated persistent identifiers chapter to "The Turing Way", a community-driven handbook for reproducible, ethical & collaborative research, raising awareness of how PIDs & metadata & open infrastructure connect: https://doi.org/10.5438/aah4-gj16

#FAIRData #PIDs #Metadata #OpenScience #ReproducibleResearch #TuringWay #OpenInfrastructure #ResearchData #ScholarlyCommunication #PersistentIdentifiers

Répliquer plutôt que répliquer !

#ReproducibleResearch

Version 3.0 of the tdda library and command-line tools has shipped:

python -m pip install -U tdda

or the usual variations.

Source: https://github.com/tdda/tdda.git
Docs: https://tdda.readthedocs.io/en/latest/
Book: https://book.tdda.info
Book 20% discount code: 26SMA1 at https://www.routledge.com/Test-Driven-Data-Analysis/Radcliffe/p/book/9781032897158

- Command-line tools for data validation (including constraint inference from training data)
- Reference testing (semantic testing of complex results)
- Automatic test-generation (any language)
- Format and utilities for working more safely with flat files (e.g. CSV files) with tdda.serial metadata and/or CSVW, Frictionless. Conversion utilities and format inference available.
- Utilities for unicode text (glyph counting and TK normal form, which goes beyond NFKC and NFKD)

3.0 includes
- Support for Pandas 3.0 (original, numpy_nullable, and pyarrow backends)
- Support for Polars in most areas
- Comprehensive parquet suport (replacing feather)
- Man pages for all commands
- Upgraded help docs for the whole library
- Associated book with the methodology
- 22 checklists for methodological support in areas unsuitable for code support.

#tdda #python #data #dataanalysis #ml #AI #rlang #datascience #testing #book #books #reproducibility #reproducibleresearch

💁‍♂️ Want to know how to make computational research more reusable❓
👍 Join our short course on Supporting the understanding and reuse of #reproducible analysis workflows.

📅 Friday, May 8
🕓 8:30-10:15

Check out the #NFDI4Earth poster:
From Data Rocks to #FAIR Peaks: With NFDI4Earth’s services towards Harmonized Metadata and User-Centered Tools for Earth System Research.

📅 Friday, May 8
🕓 16:15–18:00

👉 https://52north.org/events/egu-general-assemly-2026/

#reproducibleresearch #datadiscovery #EGU26 #AquaINFRA

Would @opensustaintech be interested in porting all FOSS licensed projects to @guix ?

I see most of them are Python, it should be simple enough as #Guix has a good coverage for it - about 3000 packages with general and science related purposes

#ReproducibleResearch #ReproducibleBuilds

My forthcoming book, Test-Driven Data Analysis is finally available for pre-order from the publisher, with 20% off for the next three days. (I don’t set the price; if I did it would be much lower.)

It covers data validation, testing of analytical pipelines and a lot more, with exercises, examples, checklists, anecdotes and more. I think it will help almost any data professional/data wrangler/analyst/modeller. I think people have found it more readable than you might expect given the subject matter.

Version 3.0 of the accompanying tdda library will be released slightly before or with the book, around 19th May. It’s at RC10 and has lots of new and extended functionality.

#tdda #data #analysis #reproducibility #ML #AI #reproducibleresearch #ETL #QA

RE: https://fediscience.org/@biohackrxiv/116477505622903440

Today, we published the summary (as a #BioHackrXiv preprint) of the #SnakemakeHackathon2026 . All the accomplishments which contribute once more to improve the #Snakemake "ecosystem" for #reproducibleResearch & #Dataanalysis .

Many thanks to @egonw from BioHackrXiv for helping us!