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PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS

Authors :
Peter Konradi
Alina Troglio
Ariadna Pérez Garriga
Aarón Pérez Martín
Rainer Röhrig
Barbara Namer
Ekaterina Kutafina
Source :
Frontiers in Neuroinformatics, Vol 17 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed, or otherwise unavailable. To ensure FAIR data management, strategies should be established to enable long-term, independent, and unified access to data in proprietary formats. In this work, we demonstrate PyDapsys, a solution to gain open access to data that was acquired using the proprietary recording system DAPSYS. PyDapsys enables us to open the recorded files directly in Python and saves them as NIX files, commonly used for open research in the electrophysiology domain. Thus, PyDapsys secures efficient and open access to existing and prospective data. The manuscript demonstrates the complete process of reverse engineering a proprietary electrophysiological format on the example of microneurography data collected for studies on pain and itch signaling in peripheral neural fibers.

Details

Language :
English
ISSN :
16625196 and 84094389
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.555265de2f554abe8409438991f09225
Document Type :
article
Full Text :
https://doi.org/10.3389/fninf.2023.1250260