Here is how PCI is elevating the standards for open and reproducible science: introducing 4 already-existing and 2 new features 
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My proposed #tutorial "From your source code to a reproducible online demonstration" has been accepted in #ACMRep 2025! If you're around, don't hesitate to join, or to follow online otherwise! https://acm-rep.github.io/2025/
ACM REP 2025ACM REP 2025ACM Conference for Reproducibility and Replicability
"#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
terminological.github.ioTrack your Data PipelinesTrack 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.
“Non-Markovian” and “directional” errors inhibit scientific self-correction and can lead fields of study astray: an illustration using gardening and obesity-related outcomes | BMC Medical Research Methodology https://www.allforgardening.com/1307754/non-markovian-and-directional-errors-inhibit-scientific-self-correction-and-can-lead-fields-of-study-astray-an-illustration-using-gardening-and-obesity-related-out/ #garden #gardening #HealthSciences #Medicine #NonMarkovianErrors #Reproducibility #rigor #ScientificErrors #ScientificSelfCorrection #StatisticalTheoryAndMethods #StatisticsForLifeSciences #TheoryOfMedicine/Bioethics #transparency
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@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
eLife Sciences Publications, Ltd · Improving trust and transparency in heart researchVery few cardiovascular health studies include enough information to allow other scientists to verify their results, suggesting a need for greater transparency in the field.
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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
DRA event platform (Indico) (Indico)HPC Series: Reproducible HPC workflows using JUBEThis 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...
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
Had a great time at the CSS workshop in Mannheim organized by Ruben Bach & Valerie Hase. You can find the slides from my presentation on "Reproducibility & Replicability in Computational Social Science Research" here: https://doi.org/10.6084/m9.figshare.29086382.v1
#reproducibility #replicability #computationalsocialscience
figshareReproducibility & Replicability in Computational Social Science ResearchPresentation on "Reproducibility & Replicability in Computational Social Science Research" given at the workshop "Computational Social Science: AI and Society – Exploring Inequality in the Digital Age", Mannheim, Germany, May 15-16, 2025.
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TODAY (Monday) 16-17:30 CEST #ReproducibiliTea in the HumaniTeas goes qualitative! 
Nathan Dykes (Department of #DigitalHumanities and Social Studies @FAU) will give a 20-min input talk entitled "Beyond the gold standard: Transparency in qualitative corpus analysis", followed by a 60-min open discussion on applying the principles of #OpenScience to qualitative research. 
Everyone is welcome, whether on-site @unibibkoeln (where you can also enjoy a range of teas and snacks) or online via Zoom. Please join our mailing list to receive the Zoom link: https://lists.uni-koeln.de/mailman/listinfo/reproducibilitea-humaniteas (or DM me if you read this after 14:00 CEST). 
New study: #ChatGPT is not very good at predicting the #reproducibility of a research article from its methods section.
https://link.springer.com/article/10.1007/s10115-025-02428-z
PS: Five years ago, I asked this question on Twitter/X: "If a successful replication boosts the credibility a research article, then does a prediction of a successful replication, from an honest prediction market, do the same, even to a small degree?"
https://x.com/petersuber/status/1259521012196167681
What if #LLMs eventually make these predictions better than prediction markets? Will research #assessment committees (notoriously inclined to resort to simplistic #metrics) start to rely on LLM replication or reproducibility predictions?
SpringerLinkChatGPT struggles to recognize reproducible science - Knowledge and Information SystemsThe quality of answers provided by ChatGPT matters with over 100 million users and approximately 1 billion monthly website visits. Large language models have the potential to drive scientific breakthroughs by processing vast amounts of information in seconds and learning from data at a scale and speed unattainable by humans, but recognizing reproducibility, a core aspect of high-quality science, remains a challenge. Our study investigates the effectiveness of ChatGPT (GPT $$-$$ - 3.5) in evaluating scientific reproducibility, a critical and underexplored topic, by analyzing the methods sections of 158 research articles. In our methodology, we asked ChatGPT, through a structured prompt, to predict the reproducibility of a scientific article based on the extracted text from its methods section. The findings of our study reveal significant limitations: Out of the assessed articles, only 18 (11.4%) were accurately classified, while 29 (18.4%) were misclassified, and 111 (70.3%) faced challenges in interpreting key methodological details that influence reproducibility. Future advancements should ensure consistent answers for similar or same prompts, improve reasoning for analyzing technical, jargon-heavy text, and enhance transparency in decision-making. Additionally, we suggest the development of a dedicated benchmark to systematically evaluate how well AI models can assess the reproducibility of scientific articles. This study highlights the continued need for human expertise and the risks of uncritical reliance on AI.
https://www.europesays.com/uk/84682/ Reproducibility of genetic risk factors identified for long COVID using combinatorial analysis across US and UK patient cohorts with diverse ancestries | Journal of Translational Medicine #Biomedicine #CombinatorialAnalytics #general #Genetics #LongCOVID #Medicine/PublicHealth #PASC #PostCOVID19Condition #PostAcuteCOVID19 #Reproducibility #SARSCoV2 #Science #UK #UnitedKingdom
On peut suivre depuis quelques jours les travaux du groupe Recherche Reproductible sur Mastodon. Bienvenue à @reproductible_fr sur le Fediverse ! #reproducibility #reproductibilité http://www.recherche-reproductible.fr/
French Reproducible Research NetworkFrench Reproducible Research NetworkA national network of scientists for promoting reproducible research.
New RFP Open! COS is funding open source developers to build or enhance tools that integrate with the Open Science Framework (OSF).
Got an idea to make open science more powerful? We want to hear it.
cos.ioFunding for Open-Source Development to Advance OSF IntegrationsThe Center for Open Science (COS) is seeking proposals for open-source development projects that will improve usability, interoperability, and research workflows on the Open Science Framework (OSF)—a free, open-source platform maintained by COS to support researchers throughout the entire research lifecycle.
Want to use {pak} as backend for {renv} actions? Set the variable RENV_CONFIG_PAK_ENABLED = TRUE https://github.com/rstudio/renv/issues/1210 #renv #pak #reproducibility #RLang
GitHub[pak] Use pak for package installation · Issue #1210 · rstudio/renv
#statstab #332 {rix} Reproducible Environments with Nix
Thoughts: Docker, renv, groundhog? Now a new option!
docs.ropensci.orgReproducible Data Science Environments with NixSimplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using Nix. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools or programming languages such as Python or Julia. Additional helpers make it easy to run R code in Nix software environments for testing and production.
On #Reproducibility.
I've had a few conversations over the years, one recently, where I've been working with a published model and interacted with others that have used it as well and I was asking questions about their methods. I've received many responses like, "that's x years ago, I don't know", "it's too long ago, I can't remember". Folks, code doesn't change over time. You should be able to go back to what you did and tell me.
I feel like I'm somehow resented for asking these questions.
Another study confirms lack of #reproducibility in parts of biological research. Measures to improve reproducibility are known but still widely ignored. https://www.nature.com/articles/d41586-025-01266-x
www.nature.comHuge reproducibility project fails to validate dozens of biomedical studiesUnique reproducibility effort in Brazil focuses on common methods rather than a single field ― and prompts call for reform.
A propos of nothing in particular 
a rough estimate of the size of the papermill problem in the scientific literature:
https://bjoern.brembs.net/2024/02/how-reliable-is-the-scholarly-literature/
(from last year)
bjoern.brembs.blog · How reliable is the scholarly literature?A few years ago, I came across a cartoon that seemed to capture a particular aspect of scholarly journal publishing quite well: The academic journal publishing system sure feels all too often a bit like a sinking boat. There are […] <a class="more-link" href="https://bjoern.brembs.net/2024/02/how-reliable-is-the-scholarly-literature/">↓ Read the rest of this entry...</a>