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Practical approaches to research data management and reproducibility


Advances in neuroscience technology and methodology have dramatically increased our abilities to generate data with unprecedented volume and complexity. The growing complexity of experimental paradigms or models pose high demands on data management to ensure reproducibility. As we use more and more powerful experimental, analytical, and modeling techniques, we also require sophisticated methods supporting data handling, reproducibility, and collaboration. Although various tools have started to emerge that address some of these challenges, we must ask how these tools are best combined synergistically to form complete digitized and documented workflows for data acquisition and analysis.

This workshop will present practical examples of methods and tools that enable the researcher to keep track and stay in control of their data and analysis workflows.
Lyuba Zehl and Hiroaki Wagatsuma will demonstrate for different domains of neurophysiology how metadata and data of highly complex experiments can be organized and integrated to enable automated and reproducible data processing. Lyuba Zehl will address the challenges faced by data producers and software developers when integrating data from different neurophysiological domains. HIroaki Wagatsuma will show how metadata and data of highly diverse experiments can be organized using the odML and NIX formats to enable automated and reproducible data processing. Elodie Legouée will present the Neural Activity Resource as a tool developed by the Human Brain Project to organize experimental and simulated neuronal activity data.
Julia Sprenger will demonstrate guidelines and best practices on how to orchestrate analysis workflows using the Elephant and Neo Python libraries in order to perform structured, comprehensible, yet flexible analysis of neuronal activity data. Johannes Köster will show how Snakemake, a workflow management system consisting of a text-based workflow specification language, can be used to easily document, execute, and reproduce data analyses, including the parallelized execution of tasks on high-performance computing systems. Sharon Crook will present NeuroML as a standard for describing computational models and talk about optimizing and testing models against experimental data. Roman Moucek will present the EEGBase platform for annotation and sharing of EEG data. Michael Hanke will demonstrate the decentralized approach of DataLad for collecting, managing and sharing datasets. Christian Garbers will present the GIN services for managing, versioning, collaborative sharing and publication of research data.

Finally, in a joint session several of the presenters will give a hands-on tutorial of combining tools for reproducible workflows and efficient collaboration, and provide the opportunity for workshop participants to directly explore possibilities how the tools can benefit their own work.

The need to enhance reproducibility of research is increasingly being recognized in the scientific community. This workshop introduces tools to efficiently manage data workflows for reproducible and collaborative research.