How reproducible is research in digital art history?
Béatrice Joyeux-Prunel argues for a post-computational framework that complements FAIR data with ethics, expertise & interpretive validation.
https://link.springer.com/article/10.1007/s42803-023-00079-6
#DigitalHumanities #Reproducibility #FAIREST #DigitalArtHistory #OpenScience
Digital humanities in the era of digital reproducibility: towards a fairest and post-computational framework - International Journal of Digital Humanities

Reproducibility has become a requirement in the hard sciences, and its adoption is gradually extending to the digital humanities. The FAIR criteria and the publication of data papers are both indicative of this trend. However, the question that arises is whether the strict prerequisites of digital reproducibility serve only to exclude digital humanities from broader humanities scholarship. Instead of adopting a binary approach, an alternative method acknowledges the unique features of the objects, inquiries, and techniques of the humanities, including digital humanities, as well as the social and historical contexts in which the concept of reproducibility has developed in the human sciences. In the first part of this paper, I propose to examine the historical and disciplinary context in which the concept of reproducibility has developed within the human sciences, and the disciplinary struggles involved in this process, especially for art history and literature studies. In the second part, I will explore the question of reproducibility through two art history research projects that utilize various computational methods. I argue that issues of corpus, method, and interpretation cannot be separated, rendering a procedural definition of reproducibility impractical. Consequently, I propose the adoption of ‘post-computational reproducibility’, which is based on FAIREST criteria as far as digital corpora are concerned (FAIR + Ethics and Expertise, Source mention + Time-Stamp), but extended to include further sources that confirm computational results with other non-computational methodologies.

SpringerLink

🎙"#Reproducibility isn’t just about repeating results, it’s about making the #research process transparent, so others can follow the path you took and understand how you got there."

🎧Listen to our new OpenScience podcast with Sarahanne Field @smirandafield

🔗 https://www.rug.nl/research/openscience/podcast/#Field

⏳ In this 10 min episode, Sarahanne reimagines reproducibility for #qualitative research.
She addresses challenges in ethical #data sharing of transcripts, and the importance of clear methodological reporting.

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: https://terminological.github.io/dtrackr/ #rstats #reproducibility
Track your Data Pipelines

Track and document dplyr data pipelines. As you filter, mutate, and join your way through a data set, dtrackr seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.

How to best create, maintain and archive custom environments from within Jupyter? .. just updated the documentation for Carto-Lab Docker with examples for Python [1] and R [2].

The tricky part is linking Kernels from custom envs with a Jupyter kernelspec (specifically if the Jupyter server and the Kernel are in two different environments). However, most of this can be stored in Jupyter notebook cells, for reproducibility.

There's also a section on archival of package versions with Conda's `env export` (yml approach) and `conda list --explicit` (full archival).

[1]: https://cartolab.theplink.org/use-cases/#create-your-own-environment-in-a-bind-mount-and-install-the-ipkernel
[2]: https://cartolab.theplink.org/use-cases/#example-create-an-environment-with-a-specific-r-version

#jupyter #R #ipython # #reproducibility #Notebooks #conda

User Workflows - Carto-Lab Docker Documentation

If you share all of your code and data but users can't rerun with a single command (including environment setup), that's commendable, but your project is not reproducible.

In other words, don't write a non-automated "pipeline" as a list of manual steps in your README!

#reproducibility #openscience

Tiny changes to a matrix can flip signs while breaking it into simpler parts, due to floating point quirks—causing big differences in results even with the same random seed. Safer methods exist, but tradeoffs apply. Full deep dive:

https://blog.djnavarro.net/posts/2025-05-18_multivariate-normal-sampling-floating-point/

#infosec #reproducibility #simulation #computing

When good pseudorandom numbers go bad – Notes from a data witch

Multivariate normal sampling can be wildly irreproducible if you’re not careful. Sometimes more than others. There are eldritch horrors, ill-conditioned matrices, and floating point nightmares in here. Teaching sand to do linear algebra was a mistake

Notes from a data witch
@SURF
Congrats on an inspirational and activating day in Hilversum! Lots of #reproducibility related content this year: automation of computational reproducibility, research support - researcher gap (and solutions to bridge that "divide" - see research cockpit at TU/e), building communities to create cultures of support
Very few cardiovascular health studies include enough information to allow other scientists to verify their results, suggesting a need for greater transparency in the field. #Reproducibility #Medicine #Cardiology
https://elifesciences.org/digests/81051/improving-trust-and-transparency-in-heart-research?utm_source=mastodon&utm_medium=social&utm_campaign=organic
Improving trust and transparency in heart research

Very few cardiovascular health studies include enough information to allow other scientists to verify their results, suggesting a need for greater transparency in the field.

eLife Sciences Publications, Ltd
[PATCH 00/31] Astro update 2025/05.

🧰 JUBE Workflow Training (May 26)

🚀 Level up your HPC workflow game!
Join our free online training on Reproducible HPC workflows using JUBE – learn how to benchmark & run applications like GROMACS.

📅 May 26 | 12:00–14:30 CEST
🔗 https://buff.ly/98CbDL5

#HPC #Reproducibility #JUBE #ResearchTraining

HPC Series: Reproducible HPC workflows using JUBE

This course provides an introduction into the Jülich Benchmarking Environment (JUBE), a workflow management system created for running performance benchmarks on high-performance computing (HPC) systems. JUBE can also be used for general workflows involving running applications on HPC systems, and this course will further provide an example on how to run the molecular-dynamic code GROMACS and will discuss aspects of workflow reproducibility in JUBE. Target audience: Users of HPC systems that...

DRA event platform (Indico) (Indico)
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🫖 Reproducibili HIGH Tea with Don van Ravenzwaaij
📅 Tue, May 20 | 🕑 2–3 PM CET
📍 H.0431 (Heijmans) & https://osc-international.us4.list-manage.com/track/click?u=fa279e9b279d77a612c19105f&id=c54b2c5758&e=c7c7b4acc0
🔍 Learn how to de-identify data for open sharing!
👉 Don’t miss it!
#OpenScience #Reproducibility #DataPrivacy