Moving From Data to Analysis
In the second year of the PsyTrans Taiwan project, our quantitative research work has moved from data collection toward analysis and research output.
Through our participation in the Global Psychedelic Survey 2025 (GPS 2025), we are working with a large-scale, cross-cultural dataset that includes information on participants’ demographic backgrounds, psychedelic use histories, motivations, contextual factors, psychological states, and subjective experiences.
Why GPS 2025 Matters for Quantitative Research
The scale and richness of GPS 2025 make it especially valuable for exploratory analysis. Because the dataset includes a wide range of variables across different cultural contexts, it allows researchers to examine not only individual factors, but also more complex relationships among patterns of use, mental health background, set and setting, motivations, and reported outcomes.
This kind of dataset is particularly useful for studying psychedelic experiences, where outcomes are rarely shaped by one factor alone. Instead, they often emerge from interactions between personal history, intention, social context, substance-related factors, and the environment in which use takes place.
Launching AI and Machine Learning Analysis
To support this work, PsyTrans Taiwan has launched an AI and machine learning–based big data analysis project, led by Tsung-Ren (Tren) Huang, Associate Professor in the Department of Psychology, Graduate Institute of Brain and Mind Sciences, and Institute of Applied Mathematical Sciences at National Taiwan University.
With the support of high-performance computing, the project will use multivariable modeling to explore system-level interactions within the GPS 2025 dataset. Rather than treating psychedelic experiences as the result of a single factor, this approach examines how multiple dimensions may interact with one another, including use patterns, psychological background, set and setting, motivations, and outcome measures.
Interpreting Patterns with Care
AI-based analysis can help identify patterns in large and complex datasets. However, these patterns do not automatically explain causality, nor do they replace careful interpretation.
This is especially important in cross-cultural research, where the same survey response may carry different meanings across linguistic, social, and cultural contexts. For this reason, PsyTrans Taiwan’s quantitative work combines computational analysis with methodological reflection and attention to cultural context.
Connecting Analysis to International Publication
In parallel, PsyTrans Taiwan is working closely with the GPS international team on the development of a GPS 2025 data paper. This connects our quantitative data work directly to international publication and broader scholarly exchange.
Through this work, PsyTrans Taiwan aims to contribute not only to the analysis of psychedelic survey data, but also to the development of more culturally responsive and methodologically rigorous approaches to global psychedelic research.
