Hey friends, my new paper was just published in JAMA Psychiatry. I draw on biological species classification to sketch a new framework for psychiatric nosology.
Brief summary follows below.
Full text link: jamanetwork.com/journals/jam...
🧪 #PsychSciSky #MentalHealth
I'm not involved in organizing, but it's great to see how much this field has grown over the past few years and how truly interdisciplinary meta science for methods research has become
Save the date: 1st Interdisciplinary Symposium on Meta Science for Methods Research (MSMR 2026)
How can we improve research on research methods?
📍 Zurich, Switzerland (+ livestream for attendance)
📅 August 31 – September 1, 2026
🔗 crsuzh.pages.uzh.ch/msmr/
"if you don’t feel warm and fuzzy inside when seeing it, you simply do not have a soul" - agreed, what a nice blogpost, thank you!
Poster for the talk by Björn Siepe on OpenESM database. The poster contains further information about the event along with the QR code for registration.
🔊 ❗We are excited to be collaborating with Björn Siepe @bsiepe.bsky.social for a new free upcoming talk on openESM! The talk will be “Introducing openESM: A Database of Openly Available Experience Sampling Datasets.” Check out the flyer for more details.
✨Register now: forms.gle/2W2Mhh5V2vvY...
*Thanks to my collaborators @fbartos.bsky.social, @boulesteixlaure.bsky.social, @danielheck.bsky.social, @aaronpeikert.bsky.social, Alexandra Sarafoglou & Samuel Pawel.
Diagram showing four phases of methodological research (Theory, Exploration, Systematic Comparison, Evidence Synthesis) with an arrow indicating that preregistration usefulness increases from early to late phases. Each phase lists its aim, elements, outcome, and an example from factor retention research.
Does it make sense to preregister simulation studies?
This question has sparked a lot of debate.
▶️We* work through the why, when, and how
▶️We discuss different phases of methodological research to clarify where preregistration might (or might not) add value
📝 Preprint: doi.org/10.31234/osf...
Congratulations! :)
Excited to share a new call for papers for a special issue in Psychometrika focused on Data Intensive Methods in Psychometrics that I'll be guest editing with @kyliegorney.bsky.social, @jmbh.bsky.social, @leonievogelsmeier.bsky.social, and Ben Domingue: www.psychometricsociety.org/post/call-sp...
After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data.
If you share a single thing of my lab this year, please make it this competition.
eiko-fried.com/warn-d-machi...
sure!
To combine some of the answers: I use Todoist, Notion, and Obsidian, all free. Todoist for fast capture of ToDos (works extremely well on mobile), Notion for project management, and Obsidian for longer form content/notes/drafts. Might be overkill to use 3 tools, but this separation works well for me
This sounds like a very interesting and ambitious project! Good luck with finishing it
Thanks for the shout-out! Here's a summary of the database. I'm sure that many of the datasets could be useful for teaching multilevel modeling (but we have no therapy RCT data, unfortunately)
bsky.app/profile/bsie...
🚨Model Checking for Vector Autoregressive Models 🚨
In a new preprint, @joranjongerling.bsky.social, @bsiepe.bsky.social, @sachaepskamp.bsky.social, Lourens Waldorp and I provide a tutorial on model checking for Vector Autoregressive (VAR) models: osf.io/preprints/ps...
From my master’s thesis to my first PhD project — excited to share that this work (together with @jordanrvl.bsky.social, @ginettelafit.bsky.social, Anja Franziska Ernst, Josip Razum, Eva Ceulemans, and @bringmannlaura.bsky.social) is now published in AMPPS!
Link: doi.org/10.1177/2515...
While it's certainly becoming less common, it's still a thing in Germany and Austria. From anecdotal evidence, this is particularly the case in medicine
Makes sense, thx! I'll make a note to link docs & metadata more clearly
For now, the best way to obtain the time series relevant to you is probably to download all datasets (still manageable) and filter them yourself. In the long term, with more data, we will work to enable more advanced filtering
Amazing, that's great to hear! Feel free to let me know if you or hack participants have any feedback
Thank you!
Yes, that refers to the maximum (see here: openesmdata.org/docs/data/#n...). The number of observations in my first post (> 740k) refers to actual non-missing observations.
We only used "time points" for brevity/consistency, but I agree this could be confusing & I'll likely change it
I see, that makes sense! I'll note it down on our list for future improvements
Simulation studies have a conflict of interest problem. The same team:
- develops a new method
- designs a simulation study to evaluate it
However, the new method has to show good performance to get published.
We propose living synthetic benchmarks to address the issue (doi.org/10.48550/arX...).
Thanks for exploring openESM!
Do you mean the dates and locations at which data were collected for each dataset? If so, this information has not yet been included because it was often not clearly available. However, we do intend to add more metadata on the details of data collection in the future
Another idea could be to write a consortium paper. For instance, everyone who contributes data could be included in a comprehensive paper on the database. I'm very curious to hear other ideas besides those relating to funding and awards
I also hope that DBs can achieve that! I'm also still unsure how to best incentivize sharing & documentation, both as a scientific community in general, and as a DB maintainer in particular. I suppose that the broad adoption of DBs would considerably increase citations of datasets, which could help
Thank you for sharing, Shirley! :)
While study-level CIs differ, this made no difference for our overall results & pooled effect, so we kept this visualization
Thank you!
We used a 2-step aggregation approach to get study-level effects & CIs here (as recommended in the package we used), but also provide an alternative visualization with shrinkage-based estimates & CIs in the online supplement
We are deeply grateful to everyone who shares their ESM data.
Thanks to @jmbh.bsky.social, @matzekloft.bsky.social, @anabelbuechner.bsky.social, @yongzhangzzz.bsky.social, @eikofried.bsky.social, @danielheck.bsky.social for collaborating on this huge effort - we look forward to your feedback!
A figure showing the software components of the openESM database in two large boxes: Front-end (on the left), and back-end (on the right). The front-end contains documentation and the website with search and is written with html and javascript. The backend contains GitHub repositories with code and metadata that serve the website. They are related to Zenodo repositories with data and metadata, and our helper software packages in R and Python. We use TSV files for ESM data and JSON files for metadata storage.
Further details:
▶️Search and filter datasets on openesmdata.org
▶️Auto-generate R/Python code
▶️(Meta-)data are stored on Zenodo with DOIs
▶️Metadata and software on GitHub enable community contributions
▶️Contribution guidelines allow further database extensions so that openESM can continue to grow