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  THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Hebart, M. N., Contier, O., Teichmann, L., Rockter, A. H., Zheng, C. Y., Kidder, A., et al. (2023). THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. eLife, 12: e82580. doi:10.7554/eLife.82580.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-B24B-7 Version Permalink: https://hdl.handle.net/21.11116/0000-000F-045A-8
Genre: Journal Article

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https://www.biorxiv.org/content/10.1101/2022.07.22.501123v2 (Preprint)
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 Creators:
Hebart, Martin N.1, Author                 
Contier, Oliver1, Author           
Teichmann, Lina2, Author
Rockter, Adam H.2, Author
Zheng, Charles Y.3, Author
Kidder, Alexis2, Author
Corriveau, Anna2, Author
Vaziri-Pashkam, Maryam2, Author
Baker, Chris I.2, Author
Affiliations:
1Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378              
2Laboratory of Brain and Cognition, Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA, ou_persistent22              
3Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA, ou_persistent22              

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Free keywords: Human; Neuroscience
 Abstract: Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

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Language(s): eng - English
 Dates: 2022-08-092023-02-252023-02-272023-04-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.82580
PMID: 36847339
 Degree: -

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Project name : -
Grant ID : ZIA-MH-002909; ZIC-MH002968
Funding program : -
Funding organization : National Institutes of Health (NIH)
Project name : -
Grant ID : StG-2021-101039712
Funding program : -
Funding organization : European Research Council (ERC)
Project name : -
Grant ID : -
Funding program : Max Planck Research Group M.TN.A.NEPF0009
Funding organization : Max-Planck-Gesellschaft
Project name : -
Grant ID : -
Funding program : Max Planck School of Cognition
Funding organization : -
Project name : -
Grant ID : -
Funding program : LOEWE Start Professorship; Tha Adaptive Mind
Funding organization : Hessisches Ministerium für Wissenschaft und Kunst

Source 1

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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 12 Sequence Number: e82580 Start / End Page: - Identifier: Other: URL
ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X