Abstract
Computational tools can transform the manner by which neuroscientists perform their experiments. More than helping researchers to manage the complexity of experimental data, these tools can increase the value of experiments by enabling reproducibility and supporting the sharing and reuse of data. Despite the remarkable advances made in the Neuroinformatics field in recent years, there is still a lack of open-source computational tools to cope with the heterogeneity and volume of neuroscientific data and the related metadata that needs to be collected during an experiment and stored for posterior analysis. In this work, we present the Neuroscience Experiments System (NES), a free software to assist researchers in data collecting routines of clinical, electrophysiological, and behavioral experiments. NES enables researchers to efficiently perform the management of their experimental data in a secure and user-friendly environment, providing a unified repository for the experimental data of an entire research group. Furthermore, its modular software architecture is aligned with several initiatives of the neuroscience community and promotes standardized data formats for experiments and analysis reporting.
Highlights
The overlap between neuroscience and informatics has been growing rapidly in the recent years, collection and organization of experimental data are still frequently done manually
Python is an open-source programming language that has become one of the most popular programming language used in neuroscience systems
To the best of our knowledge, there are no other open-source software tools which provide facilities to record the data and metadata involved in all steps of a neuroscience experiment
Summary
The overlap between neuroscience and informatics has been growing rapidly in the recent years, collection and organization of experimental data are still frequently done manually. There is a wide variability in the types of data that are collected, from the form and behavior of individual neurons to measures of brain functioning This large quantity and variety of information requires a type of database that is especially designed for this purpose. The provenance information of raw data is too often lost or, when digitized, ends up as text files or spread-sheets without a standardized structure (Koslow, 2000). Within this context, the reproducibility of experiments—a core scientific principle—and the reuse of data may be seriously compromised. Efforts to develop best practices must be made on four foundational principles—Findability, Accessibility, Interoperability and Reusability (FAIR), as described by Wilkinson et al in the FAIR Guiding Principles for scientific data management and stewardship (Abrams et al, 2021)
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