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      Handling Metadata in a Neurophysiology Laboratory

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          Abstract

          To date, non-reproducibility of neurophysiological research is a matter of intense discussion in the scientific community. A crucial component to enhance reproducibility is to comprehensively collect and store metadata, that is, all information about the experiment, the data, and the applied preprocessing steps on the data, such that they can be accessed and shared in a consistent and simple manner. However, the complexity of experiments, the highly specialized analysis workflows and a lack of knowledge on how to make use of supporting software tools often overburden researchers to perform such a detailed documentation. For this reason, the collected metadata are often incomplete, incomprehensible for outsiders or ambiguous. Based on our research experience in dealing with diverse datasets, we here provide conceptual and technical guidance to overcome the challenges associated with the collection, organization, and storage of metadata in a neurophysiology laboratory. Through the concrete example of managing the metadata of a complex experiment that yields multi-channel recordings from monkeys performing a behavioral motor task, we practically demonstrate the implementation of these approaches and solutions with the intention that they may be generalized to other projects. Moreover, we detail five use cases that demonstrate the resulting benefits of constructing a well-organized metadata collection when processing or analyzing the recorded data, in particular when these are shared between laboratories in a modern scientific collaboration. Finally, we suggest an adaptable workflow to accumulate, structure and store metadata from different sources using, by way of example, the odML metadata framework.

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          Most cited references 33

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          PSYCHOLOGY. Estimating the reproducibility of psychological science.

          Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
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            The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

            Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. The specification of SBML Level 1 is freely available from http://www.sbml.org/
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              Reproducible research in computational science.

               Roger Peng (2011)
              Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                19 July 2016
                2016
                : 10
                Affiliations
                1Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre Jülich, Germany
                2Laboratoire d'informatique Fondamentale, UMR 7279, Centre National de la Recherche Scientifique, Aix-Marseille Université Marseille, France
                3Institut de Neurosciences de la Timone, UMR 7289, Centre National de la Recherche Scientifique, Aix-Marseille Université Marseille, France
                4Department of Biology II, Ludwig-Maximilians-Universität München Martinsried, Germany
                5Institut for Neurobiology, Abteilung Neuroethologie, Eberhard-Karls-Universität Tübingen Tübingen, Germany
                6Institute of Neuroscience and Medicine (INM-6), Jülich Research Centre Jülich, Germany
                7Theoretical Systems Neurobiology, RWTH Aachen University Aachen, Germany
                Author notes

                Edited by: Qingming Luo, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, China

                Reviewed by: Gully A. Burns, USC Information Sciences Institute, USA; Werner Van Geit, École Polytechnique Fédérale de Lausanne, Switzerland

                *Correspondence: Lyuba Zehl l.zehl@ 123456fz-juelich.de
                Article
                10.3389/fninf.2016.00026
                4949266
                27486397
                Copyright © 2016 Zehl, Jaillet, Stoewer, Grewe, Sobolev, Wachtler, Brochier, Riehle, Denker and Grün.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 10, Tables: 2, Equations: 0, References: 40, Pages: 20, Words: 13306
                Categories
                Neuroscience
                Methods

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