THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
- National Institute of Mental Health, United States
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.
Data availability
All parts of the THINGS-data collection are freely available on scientific data repositories. We provide the raw MRI (https://openneuro.org/datasets/ds004192) and raw MEG (https://openneuro.org/datasets/ds004212) datasets in BIDS format98 on OpenNeuro109. In addition to these raw datasets, we provide the raw and preprocessed MEG data as well as the raw and derivative MRI data on Figshare110 (https://doi.org/10.25452/figshare.plus.c.6161151). The MEG data derivatives include preprocessed and epoched data that are compatible with MNE-python and CoSMoMVPA in MATLAB. The MRI data derivatives include single trial response estimates, category-selective and retinotopic regions of interest, cortical flatmaps, independent component based noise regressors, voxel-wise noise ceilings, and estimates of subject specific retinotopic parameters. In addition, we included the preprocessed and epoched eyetracking data that were recorded during the MEG experiment in the OpenNeuro repository. The behavioral triplet odd-one-out dataset can be accessed on OSF (https://osf.io/f5rn6/, https://doi.org/10.17605/OSF.IO/F5RN6).
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THINGS-fMRIOpenNeuro doi:10.18112/openneuro.ds004192.v1.0.5.
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THINGS-MEGOpenNeuro doi:10.18112/openneuro.ds004212.v2.0.0.
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THINGS-odd-one-outOpen Science Foundation doi:10.17605/OSF.IO/F5RN6.
Article and author information
Author details
Maryam Vaziri-Pashkam
Funding
National Institutes of Health (ZIA-MH-002909)
- Martin N Hebart
- Lina Teichmann
- Adam H Rockter
- Alexis Kidder
- Anna Corriveau
- Maryam Vaziri-Pashkam
- Chris I Baker
National Institutes of Health (ZIC-MH002968)
- Charles Y Zheng
Max-Planck-Gesellschaft (Max Planck Research Group M.TN.A.NEPF0009)
- Martin N Hebart
- Oliver Contier
European Research Council (Starting Grant StG-2021-101039712)
- Martin N Hebart
Hessisches Ministerium für Wissenschaft und Kunst (LOEWE Start Professorship)
- Martin N Hebart
Max Planck School of Cognition
- Oliver Contier
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Morgan Barense, University of Toronto, Canada
Ethics
Human subjects: All research participants for the fMRI and MEG studies provided informed consent in participation and data sharing, and they received financial compensation for taking part in the respective studies. The research was approved by the NIH Institutional Review Board as part of the study protocol 93-M-0170 (NCT00001360).All research participants taking part in the online behavioral study provided informed consent for the participation in the study. The online study was conducted in accordance with all relevant ethical regulations and approved by the NIH Office of Human Research Subject Protection (OHSRP).
Version history
- Preprint posted: July 23, 2022 (view preprint)
- Received: August 9, 2022
- Accepted: February 25, 2023
- Accepted Manuscript published: February 27, 2023 (version 1)
- Version of Record published: March 24, 2023 (version 2)
- Version of Record updated: March 31, 2023 (version 3)
- Version of Record updated: April 11, 2023 (version 4)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
eLife 12:e82580.
https://doi.org/10.7554/eLife.82580
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