You are invited to participate in the 4th Symposium on Big Data and Research Synthesis in Psychology (with a special focus on Machine Learning & Open Science). The symposium is organized by the Leibniz Institute for Psychology (ZPID) in collaboration with DIPF | Leibniz Institute for Research and Information in Education. It will take place in Frankfurt/Main, Germany from May 8 to May 10, 2023. The submission of abstracts for the Symposium is now open! The deadline for submitting structured abstracts is February 15, 2023.
The call for abstracts is described below, and more information about the submission process can be found on our conference website: www.ressyn-bigdata.org
We are excited to have several keynotes speakers:
- Susanne Bücker, German Sports University, Cologne, DE
- Sandra Matz, Columbia Business School, New York, US
- Clemens Stachl, University of St. Gallen, CH
- Rens van de Schoot, Utrecht University, NL
- Neal Haddaway, Freelance Researcher, SE
- Anita Chasiotis, Leibniz Institute for Psychology (ZPID), Trier, DE
…a live episode of “Everything Hertz” (co-hosted by Daniel Quintana, University of Oslo, NO, and James Heathers, Cipher Skin, Denver, US; https://everythinghertz.com)
…and an introductory machine learning workshop by Smitha Kolan (Co-Founder of Metacent.io; www.youtube.com/c/SmithaKolan)
For more details, please visit our conference website: www.ressyn-bigdata.org
We are looking forward to welcoming you to the Symposium.
The organization team:
André Bittermann, Tanja Burgard, Bettina Leuchtenberg
Leibniz Institute for Psychology (ZPID)
CALL FOR ABSTRACTS
Topics of interest
The symposium addresses the question: “Psychological research in times of big data: How can machine learning and open science help to cope with the information overload?”
We invite submissions from psychology and related fields (e.g., social sciences, education sciences). Presentations can include both applications as well as methodological and technical innovations and tools.
Relevant topics include (but are not limited to):
- machine learning applications for handling big data with relation to open science
- contributions on open syntheses / research syntheses with relation to open science
- studies employing screening automation using machine learning assistance
- contributions addressing the (in)compatibility of machine learning and open science
- comparison/validation of machine learning based screening automation tools
- open science tools and practices in context of big data, research syntheses, information overload
Authors who wish to present at this symposium are invited to submit a structured abstract via the symposium website. Structured abstracts should contain between 250 and 500 words. For the presentation of empirical research, abstracts should encompass information on each of the following headings:
(c) Research question(s) and/or hypothesis/es,
(e) Results/Findings (expected),
(f) Conclusions and implications (expected).
For abstracts addressing methodological issues or demonstration of tools, this structure may be adjusted accordingly.