Björn Siepe
@bsiepe.bsky.social
PhD Student in Psychological Methods (University of Marburg)
Interested in time series, simulation studies & open science
https://bsiepe.github.io
Interested in time series, simulation studies & open science
https://bsiepe.github.io
We built the openESM database:
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python
Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python
Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...
October 22, 2025 at 7:34 PM
We built the openESM database:
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python
Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python
Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...
With all the new people on here (hi!), a delayed summary of our paper on Bayesian estimation and comparison of n=1 gVAR/network models, recently published in PsychMethods: How can we understand whether differences in these popular, but highly parameterized models reflect more than just noise? 🧵
October 29, 2024 at 12:30 PM
With all the new people on here (hi!), a delayed summary of our paper on Bayesian estimation and comparison of n=1 gVAR/network models, recently published in PsychMethods: How can we understand whether differences in these popular, but highly parameterized models reflect more than just noise? 🧵