.png){kind=icon}
.png){kind=icon}
Return
Introducing openESM: A database of openly available experience sampling datasets
.png){kind=icon}
OA
AI
DOI：10.3758/s13428-026-03112-y.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Experience sampling via mobile devices enables unprecedented insights into daily life. However, individual studies often cannot answer research questions conclusively, and open data are scattered across repositories in different formats. This impedes research into robustness, generalizability, and heterogeneity. We address this issue by introducing openESM, an open-source database of openly available experience sampling datasets in a harmonized format. The growing database currently comprises 60 datasets with more than 16,000 participants and more than 740,000 observations. Metadata can be searched via our website ( openesmdata.org ) to select and download datasets via packages in R and Python. We demonstrate the potential of openESM through an analysis of within-person correlations of positive and negative affect in 39 datasets, providing evidence for a large negative momentary correlation ( $$-0.49$$ , 95% CI: [ $$-0.54$$ , $$-0.42$$ ]). We end by discussing the design principles that will allow openESM to become a continuously evolving community resource for cumulative experience sampling research.
Keywords:
Experience sampling
Daily life
Database
Time series
Generalizability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
689
Citations:
3.6W
View more organizations