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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
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.
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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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