ReproducibiliTea Copenhagen is back with the first journal club of the year!
When: Feb 27, 10:30 am
Where: https://lnkd.in/dUTDkWSW
#ReproducibiliTeaCopenhagen #JournalClub #OpenScience #ReproducibleResearch #ResearchReproducibility
ReproducibiliTea Copenhagen is back with the first journal club of the year!
When: Feb 27, 10:30 am
Where: https://lnkd.in/dUTDkWSW
#ReproducibiliTeaCopenhagen #JournalClub #OpenScience #ReproducibleResearch #ResearchReproducibility
📣 Upcoming talk with R-NVSU
I’ll be giving a session on Git & GitHub in RStudio: Practical Version Control for Data Work for the R-NVSU community.
We’ll focus on:
• Git fundamentals as they appear in RStudio
• using the Git pane safely
• when to use the GUI vs the terminal
• practical workflows for R projects
🗓️ Event & registration:
👉 https://www.meetup.com/r-nvsu/events/313201432
#RStats #Git #GitHub #RStudio #VersionControl #ReproducibleResearch #RCommunity
We need more scientists giving feedback here:
🇪🇺 European Open Digital Ecosystems: Call for Feedback
⏰ Deadline: Feb 3, 2026
EU Commission collects feedback from the #opensource community to shape strategies for boosting open source. Scientists need to share insights on how open source can boost #science and #innovation.
#OpenScience
#ReproducibleResearch
#GitHub #GitLab #Codeberg #NumFOCUS
#Funding #Grant
We need more scientists giving feedback here:
🇪🇺 European Open Digital Ecosystems: Call for Feedback
⏰ Deadline: Feb 3, 2026
EU Commission collects feedback from the #opensource community to shape strategies for boosting open source. Scientists need to share insights on how open source can boost #science and #innovation.
#OpenScience
#ReproducibleResearch
#GitHub #GitLab #Codeberg #NumFOCUS
#Funding #Grant
Workshop Announcement: Acyclic Diagrams and #Reproducibility?
In this workshop, @matherion will introduce some behavior change theory that can help you design interventions and understand how behavior change happens. #CultureChange is behavior change - let's find out how this change works and what we can learn from health care #research.
#ReproducibleResearch #OpenScience
Foto: Markus Spiske for Unsplash
It's a good time to test your #Guix profile if everything of #NumPy dependents works well
I'm happy to see code and data shared in a recent paper I read. And while I am very supportive of the authors' sharing code, the README is much better as a Markdown file than a PDF called "README.pdf" and the repository should ideally be archived with osf.io or better zenodo.org, which integrates seamlessly with GitHub w/ a DOI minted
Still, sharing this is a HUGE improvement over doing nothing. These are small ways that would improve the reproducibility and accessibility. #ReproducibleResearch
I'm happy to see code and data shared in a recent paper I read. And while I am very supportive of the authors' sharing code, the README is much better as a Markdown file than a PDF called "README.pdf" and the repository should ideally be archived with osf.io or better zenodo.org, which integrates seamlessly with GitHub w/ a DOI minted
Still, sharing this is a HUGE improvement over doing nothing. These are small ways that would improve the reproducibility and accessibility. #ReproducibleResearch
@guix is about to release 1.5.0!
Python team is working on bringing NumPy stack backed with v2.
There is some tension between v1 and v2 having in profile at the same time so plan is make python-numpy default on v2.
Please check your profiles and report
It's a good time to test your #Guix profile if everything of #NumPy dependents works well
@dcnorris I am not so good at graphics, so I stick to story-telling, much like in this blog post:
https://blog.khinsen.net/posts/2025/06/20/computational-reproducibility.html
But your idea of taking inspiration from Maslow's hierarchy is great!
@khinsen Thanks for your encouragement! Whereas I'd at first thought of Maslow's hierarchy as just a basic pattern to follow, I now think there may be a kind of 'isomorphism' here:
Physiologic Needs: any part of the analytic pipeline (from data to results) that isn't fully scripted à la #reproducibleresearch is effectively DEAD.
Safety Needs: scripts not under source control, unstructured analysis data sets (spreadsheets≈crime), and indeed all unnecessarily imperative pipeline elements (e.g., shell scripts instead of makefiles) create an UNSTABLE and UNSAFE environment for analytic work. (Where applicable, safeguarding sensitive data also belongs in this tier.)
Love & Belonging: code reviews, documentation [incl. tests, pace Dijkstra], preregistration, conference presentations, preprints, and peer review at all stages of the work; using and crediting community-contributed software.
Esteem: contributing reusable code, bug reports, bug fixes, etc. to a community of scientific software users; contributing thoroughly reproducible and criticizable analyses in peer review.
Self-Actualization: At this stage, the values achieved have to be one's own, I think. Examples might be achieving technical transcendence by doing the whole project in #CommonLisp, #Guix or (for me) ISO #Prolog 🧘, or formally proving correctness of algorithms. Another example would be that others extend & improve upon your work. Finally, #scicomm could be regarded as a true pinnacle.
Reading a paper this morning https://onlinelibrary.wiley.com/doi/10.1002/pds.4295 that follows Nosek & Errington (2017) https://elifesciences.org/articles/23383.pdf in muddling the distinction between #replicability as a broader scientific aim vs #reproducibility as captured in the narrow (purely 'computational') idea of #reproducibleresearch discussed e.g. in a #Guix context here https://guix.gnu.org/cookbook/en/html_node/Reproducible-Research.html.
This mistake is not uncommon, and leads (in this present paper, at least) to insufficient stress on computational reproducibility as a sine qua non of any higher-order quality attribute.
Implementing #reproducibility into daily #research practices hinges on pilots, projects, experiments and #collaborative efforts.
Do you have an idea on how to foster more #reproducibleResearch? Or are you already implementing a tool, exercise, standard procedure at your workplace?
You are invited to present your projects (or project ideas) during our poster and/or pitch session:
https://reproducibilitynetwork.nl/event/annual-symposium-2026/
Foto Credits: Robert Kroonen
@dcnorris I often get related questions after my talks. My standard answer is that unless you have decent computational reproducibility, you don't really know what exactly people have done, so when it comes to exploring replicability, you have to navigate in fog.
@khinsen A well though-out Maslow's Hierarchy style graphic to convey this idea would be useful to me right now, putting [computational] reproducibility as a basic 'physiologic' need.
https://en.wikipedia.org/wiki/Maslow's_hierarchy_of_needs
I also see this 'feature grid' type of treatment https://co-analysis.github.io/coding-guide/workflow/rap/#code-maturity, which however does not underscore the #reproducibleresearch concept quite as I'd like.
Do you use any graphical summaries of this kind in your talks?
Reading a paper this morning https://onlinelibrary.wiley.com/doi/10.1002/pds.4295 that follows Nosek & Errington (2017) https://elifesciences.org/articles/23383.pdf in muddling the distinction between #replicability as a broader scientific aim vs #reproducibility as captured in the narrow (purely 'computational') idea of #reproducibleresearch discussed e.g. in a #Guix context here https://guix.gnu.org/cookbook/en/html_node/Reproducible-Research.html.
This mistake is not uncommon, and leads (in this present paper, at least) to insufficient stress on computational reproducibility as a sine qua non of any higher-order quality attribute.
![A snippet from §1.2 of 'Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0', a report from the ISPE‐ISPOR Special Task Force on Real World Evidence in Health Care Decision Making.
"Reproducibility is a characteristic of a study or a finding. A reproducible study is one for which independent investigators implementing the same methods in the same data are able to obtain the same results (direct replication [38]). In contrast, a reproducible finding is a higher order target than a reproducible study, which can be tested by conducting multiple studies that evaluate the same question and estimand (target of inference) but use different data and/or apply different methodology or operational decisions (conceptual replication [38])"
Citation [38] here is Nosek & Errington (2017) https://elifesciences.org/articles/23383.pdf.](https://media.scicomm.xyz/media_attachments/files/115/677/521/023/257/746/original/972153bc6c37f83e.png)
