4th Symposium on Big Data and Research Syntheses in Psychology with a special focus on Machine Learning & Open Science
You are invited to participate in the 4th symposium on big data and research syntheses in psychology to be held in Frankfurt am Main, Germany (May 8–10, 2023).
We are pleased to collaborate with Europe’s Journal of Psychology (EJOP):Selected contributions from the symposium will be invited for full-text submission to a special thematic section of EJOP!
The 2023 edition builds on the success of our prior events:
- Research Synthesis and Big Data in Psychology, May 17-21, 2021, online
- Research Synthesis incl. Pre-Conference Symposium: Big Data in Psychology, May 27-31, 2019, Dubrovnik, Croatia
- Twin Conference Research Synthesis / Big Data in Psychology 2018, June 7-9, 2018, Trier, Germany
The symposium will address the question:
Psychological research in times of big data: How can machine learning and open science help to cope with the information overload?
What big data and research syntheses have in common is the challenge of dealing with large, novel, and ever-growing amounts of data in psychology. On the one hand, data from social media, as well as sensor data, are of growing interest in psychology. On the other hand, science is producing more publications and at a higher rate, making it increasingly difficult to keep track of the current state of knowledge.
In recent years, machine learning has become a key tool to address these challenges. Methodological advancements along with freely available software, as well as advances in data storage and computational capacities, make it possible to deal with data of large volume, velocity, and variety. There are also promising applications for summarizing scientific evidence, for example, through automation in screening a large and dynamically growing body of literature.
At the same time, open science plays an important role: On the one hand, to create more efficient workflows by sharing data, methodological documentation, technical tools and analysis scripts within science. Especially when applying machine learning methods, transparent documentation and provision of the analysis code are indispensable for their reproducibility. On the other hand, to make scientific evidence accessible and usable by communicating it to the outside world in a way that is understandable to laypersons.
The symposium is scheduled to take place in Frankfurt am Main, Germany, in May 2023 and will last three days. On the first day, introductions to open science and machine learning will provide the basics to the audience. The second day addresses different applications of machine learning, with a focus on the areas of sensor data and social media, as well as automation of research syntheses. Finally, the third day is devoted to the importance and purposeful practices of open science: To improve workflows within science and make findings more available and understandable to the world beyond academia.