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PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS
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 pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539619/ https://www.ncbi.nlm.nih.gov/pubmed/37780458 http://dx.doi.org/10.3389/fninf.2023.1250260 |
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author | Konradi, Peter Troglio, Alina Pérez Garriga, Ariadna Pérez Martín, Aarón Röhrig, Rainer Namer, Barbara Kutafina, Ekaterina |
author_facet | Konradi, Peter Troglio, Alina Pérez Garriga, Ariadna Pérez Martín, Aarón Röhrig, Rainer Namer, Barbara Kutafina, Ekaterina |
author_sort | Konradi, Peter |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10539619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105396192023-09-30 PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS Konradi, Peter Troglio, Alina Pérez Garriga, Ariadna Pérez Martín, Aarón Röhrig, Rainer Namer, Barbara Kutafina, Ekaterina Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2023-09-14 /pmc/articles/PMC10539619/ /pubmed/37780458 http://dx.doi.org/10.3389/fninf.2023.1250260 Text en Copyright © 2023 Konradi, Troglio, Pérez Garriga, Pérez Martín, Röhrig, Namer and Kutafina. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Neuroscience Konradi, Peter Troglio, Alina Pérez Garriga, Ariadna Pérez Martín, Aarón Röhrig, Rainer Namer, Barbara Kutafina, Ekaterina PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title | PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title_full | PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title_fullStr | PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title_full_unstemmed | PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title_short | PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS |
title_sort | pydapsys: an open-source library for accessing electrophysiology data recorded with dapsys |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539619/ https://www.ncbi.nlm.nih.gov/pubmed/37780458 http://dx.doi.org/10.3389/fninf.2023.1250260 |
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