PyDapsys:一个用于访问DAPSYS记录的电生理数据的开源库。

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2023-09-14 eCollection Date: 2023-01-01 DOI:10.3389/fninf.2023.1250260
Peter Konradi, Alina Troglio, Ariadna Pérez Garriga, Aarón Pérez Martín, Rainer Röhrig, Barbara Namer, Ekaterina Kutafina
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引用次数: 0

摘要

在神经科学领域,相当多的商业数据采集和处理解决方案依赖于专有的数据存储格式。这通常会导致数据被锁定在只能使用原始软件访问的格式中,这可能会导致互操作性问题。事实上,如果软件变得不受支持、更改或不可用,甚至可能失去数据访问。为了确保FAIR数据管理,应制定策略,以实现对专有格式数据的长期、独立和统一访问。在这项工作中,我们展示了PyDapsys,这是一种获得对使用专有记录系统DAPSYS获取的数据的开放访问的解决方案。PyDapsys使我们能够直接在Python中打开记录的文件,并将其保存为NIX文件,通常用于电生理领域的开放研究。因此,PyDapsys确保了对现有和未来数据的高效和开放访问。该手稿以为研究外周神经纤维中的疼痛和瘙痒信号而收集的显微神经造影数据为例,展示了反向工程专有电生理格式的完整过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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 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.

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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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