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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 (https://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. The preprint is available [Here].
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Continue reading: Introducing openESM: A database of openly available experience sampling datasets including R/Python interface
Analyzing the Impact of openESM on Experience Sampling Research
The article discussed the newly introduced open-source software openESM, a platform designed to bridge the gap between disjointed experience sampling data repositories. This tool could bring about a significant revolution in research methods, providing an unprecedented insight into daily life. In this context, we will delve into the long-term implications and potential future developments of openESM for the research community.
Long-Term Implications
With openESM making it easier for researchers to collate and harmonize large datasets from multiple sources, experience sampling studies could become more refined and productive in the long run. This tool also stands to enhance the quality of research output, by allowing researchers to examine the robustness, generalizability, and heterogeneity of their studies more efficiently.
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.
Potential Future Developments
Given the scalable architecture of openESM, there is potential for the resource to evolve continually. It is envisaged that an increasing number of researchers will contribute their datasets to the platform, allowing it to grow both in terms of the quantity and the diversity of datasets available. As such, we can expect openESM to become an increasingly valuable resource for cumulative experience sampling research.
Actionable Advice
For those involved in experience sampling research, openESM offers a new, streamlined way to find, download, and analyze relevant data. I recommend learning and becoming comfortable with packages in R and Python to easily select and download datasets from openESM. As the data on the platform grows, consider routinely checking for new datasets that could add depth and nuance to your own research. Furthermore, consider contributing your own datasets to the platform, in order to further enrich the resources available to the research community.
Conclusion
The introduction of openESM is a significant step forward in enhancing the ease and efficiency of experience sampling research. The platform’s potential for growth and evolution assures its relevance for the foreseeable future. Researchers should seize this opportunity to enrich their work and contribute to the growing body of experience sampling data.