Just pushed some updates. I have posted the #verilog serial link code. This involved getting a set of scripts for simulation set up.
If anybody is interested in helping on this project, I'd love to have somebody try to follow the readme.md to install the prerequisites and run the ./run_sim script.
The goal would be to take notes and flesh out what things aren't in the prerequisite list and how to install them.
I'd be happy to do the same for somebody else's project.
To ensure AI is genuinely open source, we must have complete access to:
1. The datasets used for its training and evaluation
2. The underlying code
3. The design of the model
4. The model's parameters
Without these elements, transparency and the ability to replicate results remain incomplete.
7/ Wei Mun Chan, Research Integrity Manager
With 10+ years in publishing and data curation, Wei Mun ensures every paper meets our high standards for ethics and #reproducibility. From image checks to data policies, he’s the quiet force keeping the scientific record trustworthy.
Are you an educator and wondering if and how to include #preregistration into student assignments? Then join our #webinar and learn from our speakers!
🗓️ 26 June
⌚ 13:30 -15 hrs
register here:
https://events.teams.microsoft.com/event/d5795f72-3c31-4962-97f7-62911cffc909@335122f9-d4f4-4d67-a2fc-cd6dc20dde70
Ewout Meijer from Maastricht University and Elen Le Foll from the University of Cologne will share their experiences with having students preregister their term papers and theses work.
We will make a recording of the webinar available.
#openScience #reproducibility
#Checklists have massive benefit in #aviation, #engineering, and #medicine.
Now #cognitiveScience has a list of 51 simple yes-no questions (from experts around the world) to design and assess #research — its quality, #reproducibility, etc.
Rejoignez nous !
Une super occasion de contribuer à une science plus ouverte, de collaborer et de mieux comprendre les défis de la reproductibilité.
📅 3 octobre 2025
📍 INRIA Paris
#ReplicationGames #Reproducibility #ScienceOuverte #I4R #RFRR
🔬 Validated antibodies = trusted results
We test for specificity, selectivity, activity, and more – across species and applications.
🧪 Knockout. Arrays. In vivo. Data you can trust.
🔗 https://www.antibodies.com/company/product-validation
Calkit now has its own pipeline syntax that forces you to define an environment for every stage, but manages those environments for you automatically. No more pip installs, Docker builds, etc. Your project will just be reproducible with a single command.
Here is how PCI is elevating the standards for open and reproducible science: introducing 4 already-existing and 2 new features 🧵👇 #OpenScience #Reproducibility
Here is how PCI is elevating the standards for open and reproducible science: introducing 4 already-existing and 2 new features 🧵👇
#OpenScience #Reproducibility
Introduction to @swheritage for software developers, in Spanish https://www.youtube.com/watch?v=Pilimdyr5Ac at @commitconf
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/
Enjoy the freshness of astro updates in Guix
Thanks @avp for the merge assistance
https://codeberg.org/guix/guix/pulls/58
#astronomy #software #python #reproducibility
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
🎙"#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
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
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!
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/
