{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T13:53:36Z","timestamp":1718200416166},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,21]]},"abstract":"<jats:p>openMNGlab is an open-source software framework for data analysis, tailored for the specific needs of microneurography \u2013 a type of electrophysiological technique particularly important for research on peripheral neural fibers coding. Currently, openMNGlab loads data from Spike2 and Dapsys, which are two major data acquisition solutions. By building on top of the Neo software, openMNGlab can be easily extended to handle the most common electrophysiological data formats. Furthermore, it provides methods for data visualization, fiber tracking, and a modular feature database to extract features for data analysis and machine learning.<\/jats:p>","DOI":"10.3233\/shti210556","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T11:45:21Z","timestamp":1632224721000},"source":"Crossref","is-referenced-by-count":3,"title":["openMNGlab: Data Analysis Framework for Microneurography \u2013 A Technical Report"],"prefix":"10.3233","author":[{"given":"Fabian","family":"Schlebusch","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany"}]},{"given":"Frederic","family":"Kehrein","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany"}]},{"given":"Rainer","family":"R\u00f6hrig","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany"}]},{"given":"Barbara","family":"Namer","sequence":"additional","affiliation":[{"name":"Junior Research Group Neuroscience, Interdisciplinary Center for Clinical Research Within the Faculty of Medicine, RWTH Aachen University, Aachen, Germany"}]},{"given":"Ekaterina","family":"Kutafina","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany"},{"name":"Faculty of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2021: Digital Medicine: Recognize \u2013 Understand \u2013 Heal"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:30:33Z","timestamp":1635168633000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,21]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210556","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,21]]}}}