ArticleFrontiers in neuroinformatics2016
Handling Metadata in a Neurophysiology Laboratory.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond.Scientific data · 2025Article
- Mirror Neurons in Monkey Frontal and Parietal Areas.Scientific data · 2025Article
- Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO).Scientific data · 2025Article
- Neurophysiological recordings from parietal areas of macaque brain during an instructed-delay reaching task.Scientific data · 2024Article
- A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets.Cell reports methods · 2024Article
- Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation.Frontiers in neuroinformatics · 2024Article
- Data management strategy for a collaborative research center.GigaScience · 2022Article
- Electrophysiological dataset from macaque visual cortical area MST in response to a novel motion stimulus.Scientific data · 2022Article
- Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics".Frontiers in neuroinformatics · 2022Review
- Decoding Neuropathic Pain: Can We Predict Fluctuations of Propagation Speed in Stimulated Peripheral Nerve?Frontiers in computational neuroscience · 2022Article
- A State-of-the-Art of Functional Scaffolds for 3D Nervous Tissue Regeneration.Frontiers in bioengineering and biotechnology · 2021Review
- Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength.Frontiers in neuroscience · 2021Article
- The SONATA data format for efficient description of large-scale network models.PLoS computational biology · 2020Article
- Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis.Frontiers in neuroinformatics · 2020Article
- Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits.PLoS computational biology · 2019Article
- Scientist and data architect collaborate to curate and archive an inner ear electrophysiology data collection.PloS one · 2019Article
- odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments.Frontiers in neuroinformatics · 2019Article
- Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task.Scientific data · 2018Article
- VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output.Frontiers in neuroinformatics · 2018Article
- Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures.Frontiers in neuroinformatics · 2018Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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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