IGOR Symposium: Open Science initiatives in biopsychological research
Chair(s): Ocklenburg, Sebastian (MSH Medical School Hamburg, Germany), Artemenko, Christina (University of Tuebingen)
Presenter(s): Guersoy, Cagatay (ZI Mannheim), Meier, Maria (University of Konstanz), Yang, Yu-Fang (Freie Universität Berlin), Koppold, Alina (UKE Hamburg), Puhlmann, Lara (University of Mainz), Reutter, Mario (University of Würzburg), Klingelhöfer-Jens, Maren (UKE Hamburg)
In this IGOR symposium, we spotlight five open science initiatives in biopsychological research in short talks: First, the resource navigator ARIADNE will be introduced by Cagatay Guersoy (https://igor-biodgps.github.io/ARIADNE). ARIADNE was created to support researchers in the research process. It provides an overview of resources and a step-by-step guide on how to perform a research project in the field of biological psychology. Second, Maria Meier will introduce the Neuroendocrinology Open Data Exchange Standard (NODES) project that aims to facilitate data sharing in psychoneuroendocrinology. The first results of a Delphi study designed to reach an expert consensus will be presented. Third, Yu-Fang Yang will outline that the reproducibility and robustness of results obtained by event-related potentials (ERP) vary with the EEG data preprocessing pipeline. She will provide an overview of recommendations and consequences of preprocessing choices. Fourth, psychophysiological measures often yield non-normally distributed data, prompting researchers to choose from various transformation procedures. Alina Koppold will present on the heterogeneity of data transformation and reporting standards and evaluate their impact on effect sizes and reliability. Fifth, the IGOR sustainability team will highlight how open science practices provide tools that critically contribute to sustainable neuroscientific research practices. Specifically, a comprehensive guide for integrating open science practices for sustainability at every stage of a research project will be presented. A final discussion by Mario Reutter and Maren Klingelhöfer-Jens will address the challenges in applying Open Science practices to the field of biopsychological research.
ARIADNE: A Scientific Navigator to Find Your Way Through the Resource Labyrinth of Psychological Sciences
Hartmann, Helena1; Gürsoy, Çağatay2,3,4,5; Lischke, Alexander6,7; Mueckstein, Marie8,9; Sperl, Matthias F. J.10,11,12; Vogel, Susanne7,13; Yang, Yu-Fang14; Feld, Gordon B.2,3,4,5; Kastrinogiannis, Alexandros15,16; Koppold, Alina15
1Clinical Neurosciences, Department for Neurology and Center for Translational and Behavioral Neuroscience, University Hospital Essen, Essen, Germany; 2Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Ruprecht Karl University of Heidelberg, Mannheim, Germany; 3Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Ruprecht Karl University of Heidelberg, Mannheim, Germany; 4Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Ruprecht Karl University of Heidelberg, Mannheim, Germany; 5Department of Psychology, Ruprecht Karl University of Heidelberg, Heidelberg, Germany; 6Institute of Clinical Psychology and Psychotherapy, Medical School Hamburg, Hamburg, Germany; 7Department of Psychology, Medical School Hamburg, Hamburg, Germany; 8Department of General and Neurocognitive Psychology, International Psychoanalytic University Berlin, Berlin, Germany; 9Department of Psychology, University of Potsdam, Potsdam, Germany; 10Department of Clinical Psychology and Psychotherapy, University of Giessen, Giessen, Germany; 11Center for Mind, Brain and Behavior, Universities of Marburg and Giessen (Research Campus Central Hessen), Marburg, Germany; 12Department of Clinical Psychology and Psychotherapy, University of Siegen, Siegen, Germany; 13ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany; 14Division of Experimental Psychology and Neuropsychology, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; 15Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 16Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
ARIADNE is a living, interactive resource navigator and database (https://igor-biodgps.github.io/ARIADNE) created to help researchers, especially early-career ones, navigate the challenging research process in psychological science. Created to address the lack of comprehensive resource overviews in the field, ARIADNE particularly benefits early-career researchers who face challenges in conducting research. The open-access platform organizes resources across ten sequential research steps: from project initiation and study design, through data collection and analysis, to publication and dissemination. For each step, ARIADNE provides curated tools and resources that emphasize open-access and open-source options, thereby democratizing research capabilities across institutions and countries with varying resource access. The database serves dual functions: (1) offering a structured step-by-step guide for conducting research projects, initially focused on biological psychology and neuroscience but applicable to neighboring disciplines, and (2) presenting a searchable collection of practical resources for each research phase. By prioritizing open-science resources, ARIADNE promotes transparency, fairness, and reproducibility in psychological research while reducing the time-consuming and often frustrating aspects of experiential learning. Developed by members of the DGPs Interest Group for Open and Reproducible Science (IGOR), this navigator addresses common research challenges including resource fragmentation, comparison difficulties, and accessibility barriers. ARIADNE continues to evolve through community contributions, ensuring its relevance and comprehensiveness as research practices and tools develop within psychological science.
Towards more open data in psychoneuroendocrinology: Introducing the Neuroendocrinology Open Data Exchange Standard (NODES) Task Force
Meier, Maria1,2; Task Force, Nodes1; Vinkers, Christiaan H.3,4,5,6,7; Pruessner, Jens C.1,8; Sep, Milou S.C.3,4,5,6
1Neuropsychology, Department of Psychology, University of Konstanz, Germany; 2Physiological Psychology, Department of Psychology, University of Bamberg, Germany; 3Department of Psychiatry, Amsterdam University Medical Centers Location Vrije Universiteit Amsterdam, The Netherlands; 4GGZ in Geest Mental Health Care, Amsterdam, The Netherlands; 5Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands; 6Amsterdam Public Health, Mental Health Program, Amsterdam, The Netherlands; 7Department of Anatomy and Neurosciences, Vrije University, Amsterdam, The Netherlands; 8Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany
Sharing data openly conveys great potential for science, as it facilitates reproducibility and substainability. In the field of psychoneuroendocrinology (PNE), which investigates the links between hormones, behavior and health, sharing data openly is currently not common practice. We believe that this is partly because preparing hormonal data for publication can be time-consuming and challenging, as the correct interpretation relies on contextual factors and technical details of the data acquisition. At the moment, no standard data format in PNE has been established, thus complicating data sharing and reuse. The Task Force Neuroendocrinology Open Data Exchange Standard (NODES) aims to develop a community-driven standard data structure for PNE as well as supportive infrastructure, such as web applications that help restructuring and validating datasets and meta-data prior to publication. NODES intends to clearly define which information is necessary to correctly interpret PNE data while at the same time addressing related practical considerations like accessibility and compatibility. In a first step, a Delphi study will be conducted to reach an expert consesus regarding important open questions concerning the development of a standard data structure. The talk will introduce the NODES Task Force, present first results of the Delphi study and give an outlook on future milestones and plans.
Is There a Standard? An Umbrella Review of EEG Preprocessing Pipelines for ERP Studies
Yang, Yu Fang1; Bublatzky, Florian2; Fischer, Nastassja L.3; Hilger, Kirsten4; Klatt, Laura-Isabelle5; Koppold, Alina6; Kulke, Louisa7; Lischke, Alexander8; Ocklenburg, Sebastian8; Panitz, Christian7; Paul, Katharina9; Reinke, Petunia8; Reutter, Mario4; Weiß, Martin4; Sperl, Matthias F. J.10,11,12
1Freie Universität Berlin, Germany; 2Heidelberg University, Mannheim, Germany; 3Nanyang Technological University (NTU), Singapore; 4University of Würzburg, Würzburg, Germany; 5Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; 6University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 7University of Bremen, Bremen, Germany; 8MSH Medical School Hamburg, Hamburg, Germany; 9University of Hamburg, Hamburg, Germany; 10University of Siegen, Siegen, Germany; 11University of Giessen, Giessen, Germany; 12Universities of Marburg and Giessen (Research Campus Central Hessen), Marburg, Germany
Event-related potentials (ERP) derived from EEG are widely used in cognitive and clinical neuroscience, yet there is no consensus on preprocessing pipelines. This lack of standardization creates a "Garden of Forking Paths", where analytical decisions influence data quality, comparability, and replicability. In recent years, numerous best-practice recommendations and methodological guidelines have emerged to address this issue. Given the growing number of these publications, our umbrella review synthesizes systematic reviews, tutorials, and guidelines to identify common preprocessing practices, sources of variability, and the extent of standardization across different areas—assessing both its benefits and potential limitations for research quality and integrity. By evaluating these recommendations, we provide an overview of the current landscape of preprocessing guidelines and contribute to improving transparency and reproducibility in ERP research. We have completed preregistration and are now beginning the screening process.
From Raw to Refined: How Data Transformations Shape Effect Sizes and Reliability
Koppold, Alina1,10; Bruntsch, Maria1,10; Döhr, Konstantin2; Feld, Gordon3,4; Gerosa, Marta6,21; Hartmann, Helena7; Hilger, Kirsten5; Jentsch, Valerie L.8; Kastrinogiannis, Alexandros1,6; Klingelhöfer-Jens, Maren1; Kroczek, Leon O. H.9; Lonsdorf, Tina B.1,10; Merz, Christian J.8; Miller, Robert11; Meier, Maria12,13; Reutter, Mario5; Sperl, Matthias F. J.14,15,16; Szeska, Christoph17; Ventura-Bort, Carlos17; Vogel, Susanne18,19; Yang, Yu-Fang20
1Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 2Institute for Medical Psychology, University of Lübeck, Lübeck, Germany; 3Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; 4Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; 5Department of Psychology, Würzburg University, Würzburg, Germany; 6Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 7Clinical Neurosciences, Department for Neurology and Center for Translational and Behavioral Neuroscience, University Hospital Essen, Germany; 8Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany; 9Department of Psychology, Clinical Psychology and Psychotherapy, Regensburg University, Regensburg; 10Biological Psychology and Cognitive Neuroscience, Department of Psychology, University of Bielefeld, Bielefeld, Germany; 11Faculty of Psychology, Technische Universität Dresden, Dresden, Germany; 12Department of Psychology, University of Konstanz, Konstanz, Germany; 13Child and Adolescent Psychiatric Research Department, University Psychiatric Clinics Basel (UPK), University of Basel, Switzerland; 14Department of Clinical Psychology and Psychotherapy, University of Siegen, Germany; 15Department of Clinical Psychology and Psychotherapy, University of Giessen, Germany; 16Center for Mind, Brain and Behavior, Universities of Marburg and Giessen (Research Campus Central Hessen), Germany; 17Department of Biological Psychology and Affective Science, Faculty of Human Sciences, University of Potsdam, Potsdam, Germany; 18Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Hamburg, Germany; 19ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany; 20Division of Experimental Psychology and Neuropsychology, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; 21Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin
Psychophysiological measures are commonly used proxies for latent constructs in neuroscience. These measures often yield non-normally distributed data, prompting researchers to choose from various transformation procedures. However, there is limited understanding of transformation practices and their influence on statistical power and reliability across psychophysiological measures and analyses, as well as a lack of guidance for selecting appropriate transformations. The current project aims to (1) review the literature on transformation heterogeneity and reporting standards for multiple outcome measures (e.g., skin conductance, heart rate, electromyography) and (2) assess the impact of different transformation procedures on effect sizes and reliability using empirical and simulated datasets, in the field of human fear conditioning as a case example. Preliminary results emphasize the need for standardized reporting practices and reveal the complexity of transformation choices across measures. Specific transformations enhance reliability and effect sizes by reducing systematic error variance, highlighting the importance of methodological choices. The goal is to provide researchers with a guide for selecting transformations, including test-specific statistical assumptions. The findings will contribute to improving reporting and transformation standards in neuroscience, enhancing robustness and replicability.
There Is No Research On A Dead Planet – Fostering Ecologically Sustainable Open Science Practices In Neuroscience
Ocklenburg, Sebastian1; Koppold, Alina2; Puhlmann, Lara3
1MSH Medical School Hamburg, Germany; 2Universität Bielefeld, Germany; 3Leibniz-Institut für Resilienzforschung Mainz, Germany
The rapidly escalating climate crisis poses an existential threat to human wellbeing. Reducing anthropogenic greenhouse gas emissions must therefore become a primary goal of humanity. At the same time, advancing knowledge on human experience and behaviour through empirical research is likewise essential for wellbeing, but can incur substantial negative impact for the environment. Neuroscientific methods are particularly resource intensive and potentially harmful, from the carbon footprint of MRI scanners to the long-term impact of data centres keeping datasets permanently accessible for scientific reuse. This IGOR talk addresses the resulting tension between scientific research, open science principles, and responsible scientific stewardship in times of the climate crisis. We discuss how sustainable open science practices can be implemented in neuroscience at each step of the research cycle following the ARIADNE framework. Specifically, we suggest to (1) re-place new data with open data, (2) re-fine methods to make them more sustainable, and (3) re-duce carbon emission of testing by precisely determining sample sizes and research protocols beforehand.
Discussion: Future Directions of Open Science in Biopsychological Research
Reutter, Mario1; Klingelhöfer-Jens, Maren2
1Experimental Clinical Psychology, Department of Psychology, University of Würzburg, Würzburg, Germany; 2Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Most scientists agree that Open Science practices are beneficial for the quality of science by providing greater transparency, accessibility, and reproducibility. Yet, they are not as widespread as one might expect from this generally positive evaluation. This is partly because Open Science often involves extra work in practice, along with uncertainties and concerns that range from implementation issues, such as licensing and anonymization challenges, to personal worries about being scooped. In this interactive discussion, we want to gather what opportunities and challenges can be identified and what experiences, hopes, and reservations the audience holds towards Open Science in biological and neuropsychology. Subsequently, we will transfer these insights to the previous talks of the symposium. Finally, we will offer an overarching perspective based on the incentive structure currently attached to Open Science practices and highlight the importance of rewarding Open Science behavior with initiatives like the IGOR prize.
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