Are there researchers who voluntarily share their complete, raw data sets online before even having evaluated the data themselves? Until some time ago, this was unthinkable. Even today, many scientists shy away from permissive data sharing before their results are published—helping strengthen their professional reputation. Magdeburg psychologist Professor Michael Hanke from the Otto von Guericke University Magdeburg has now embarked on a different route altogether with Dr. Jörg Stadler from the Leibniz Institute for Neurobiology and colleagues. They will publish the most comprehensive set of raw brain imaging data on natural language processing in the inaugural issue of the new open-access journal Scientific Data of the Nature Publishing Group. It is already freely available for analyses from the website http://www.studyforrest.org.
„We have received funds from the Federal Ministry of Education and Research to collect data. Now we see it as our duty to maximize the impact from this research for society,“ Hanke explains, whose project was funded in the framework of a German-US-American Collaboration within the Bernstein Network of Computational Neuroscience. The brain researchers will now receive professional acknowledgements through citations of their data article.
This open science approach has the advantage of accelerating progress in science. Competing research labs can simultaneously work on a subject without obstructing other scientists’ research plans through delaying the publication of data sets. Also, when scientists are asked to share data, they do not need to laboriously reconstruct past data collections—some inquirires are made years after the first publication—since the raw data have already been prepared for sharing. This saves time and cost, which can be used to further scientific developments.
The published Magdeburg data set focuses on the processing of acoustic stimuli. In the study, participants listened to an audio movie of the classic feature film Forrest Gump. Meanwhile, their brain activity was measured using functional magnetic resonance imaging (fMRI) as it processed language, music, emotions, memories, and visual imagery. Thus, the recordings do not isolate a single aspect of brain function, but instead reflect the real complexity of information flow in everyday listening experiences. In addition to the fMRI data, the scientists provide comprehensive anatomical descriptions of the participants‘ brains, as well as measurements on breathing and heartbeat. These help indicate the portions of the film when the listener was more excited or relaxed.
With these data, it is possible to study emotion processing during listening experiences—or analyze completely different research questions. Besides Hanke, at least two other research groups in England and Australia are currently evaluating this data. He does not know their specific lines of inquiry, however, there is one thing he is positive about: “professionals from other disciplines—such as engineers—have a very different approach to our data while also possessing the required skills to optimally analyze them for their own use.“ In order to promote such inter-disciplinary research the Magdeburg Center for Behavioral Brain Sciences has sponsored an award of 5000 EUR for the best use of the published data set.
The German-US-American Collaboration “Development of general high-dimenstional models of neuronal representation space” is an international research project in which scientists at Otto von Guericke University Magdeburg, Dartmouth College (USA), and Princeton University (USA) are involved. It is part of the National Bernstein Network Computational Neuroscience in Germany. With this funding initiative, the German Federal Ministry of Education and Research (BMBF) has supported the new discipline of Computational Neuroscience since 2004 with over 180 million Euros. The network is named after the German physiologist Julius Bernstein (1835-1917).
Jun.-Prof. Dr. Michael Hanke
Otto von Guericke University
Institute of Psychology II
Tel: +49 (0)391 67-18481
M. Hanke, F. J. Baumgartner, P. Ibe, F. R. Kaule, S. Pollmann, O. Speck, W. Zinke & J. Stadler (2014): A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data, 1: 140003.