Evidence map›Paper›PMID 37744463›Full record

ArticleArXiv2024

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli.

Giovanni M Di Liberto, Aaron Nidiffer, Michael J Crosse, Nathaniel J Zuk, Stephanie Haro, Giorgia Cantisani, Martin M Winchester, Aoife Igoe, Ross McCrann, Satwik Chandra and 2 more

Abstract readPreprint
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Giovanni M Di LibertoSchool of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience.
Aaron NidifferDept Biomedical Engineering, Dept Neuroscience, Del Monte Institute for Neuroscience, Center for Visual Science, University of Rochester, NY, USA.
Michael J CrosseSegotia, Galway, Ireland.
Nathaniel J ZukDepartment of Psychology, Nottingham Trent University, Nottingham, UK.
Stephanie HaroHuman Health and Performance Systems, MIT Lincoln Laboratory, Lexington, Massachusetts, USA.
Giorgia CantisaniLaboratoire des systémes perceptifs, Département d'études cognitives, ENS, PSL University, CNRS, 75005 Paris, France.
Martin M WinchesterSchool of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience.
Aoife IgoeSchool of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience.
Ross McCrannSchool of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience.
Satwik ChandraSchool of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience.
Edmund C LalorDept Biomedical Engineering, Dept Neuroscience, Del Monte Institute for Neuroscience, Center for Visual Science, University of Rochester, NY, USA.
Giacomo BaruzzoDepartment of Information Engineering, University of Padova, Padova, Italy.

Funding

TRAINING FOR SPEECH AND HEARING SCIENCEST32DC000038 · NIDCD · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Gwenaelle S Geleoc · 1992 to 2026
$21.8M
NIDCD NIH HHS T32 DC000038
6 · The paper itself

Abstract

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks 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. 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. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.

Indexed as

CNDCNSPContinuous stimuliData sharingECoGEEGMEG

Identifiers

PMID37744463
PMCPMC10516115

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.