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A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli
Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in the context of scenarios involving continuous sensory streams, such as speech and music listening. Over the past 10 years or so, novel analytic frameworks for analysing the neural processi...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516115/ https://www.ncbi.nlm.nih.gov/pubmed/37744463 |
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author | Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo |
author_facet | Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo |
author_sort | Di Liberto, Giovanni M. |
collection | PubMed |
description | Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in the context of scenarios involving continuous sensory streams, such as speech and music listening. Over the past 10 years or so, novel analytic frameworks for analysing the neural processing of continuous sensory streams combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. As a result, numerous data formats and analysis toolkits are available, with limited or no compatibility between studies. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. The framework has been designed to interface easily with existing toolboxes (e.g., EelBrain, NapLib, MNE, mTRF-Toolbox). We present guidelines by taking both the user view (how to load and rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share). Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. In doing so, we aim to facilitate data sharing and promote transparent research practices, while also making the process as straightforward and accessible as possible for all users. |
format | Online Article Text |
id | pubmed-10516115 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-105161152023-09-23 A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo ArXiv Article Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in the context of scenarios involving continuous sensory streams, such as speech and music listening. Over the past 10 years or so, novel analytic frameworks for analysing the neural processing of continuous sensory streams combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. As a result, numerous data formats and analysis toolkits are available, with limited or no compatibility between studies. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. The framework has been designed to interface easily with existing toolboxes (e.g., EelBrain, NapLib, MNE, mTRF-Toolbox). We present guidelines by taking both the user view (how to load and rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share). Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. In doing so, we aim to facilitate data sharing and promote transparent research practices, while also making the process as straightforward and accessible as possible for all users. Cornell University 2023-09-19 /pmc/articles/PMC10516115/ /pubmed/37744463 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title | A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title_full | A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title_fullStr | A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title_full_unstemmed | A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title_short | A standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
title_sort | standardised open science framework for sharing and re-analysing neural data acquired to continuous sensory stimuli |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516115/ https://www.ncbi.nlm.nih.gov/pubmed/37744463 |
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