{"doi":"10.1101/2022.07.22.501123","title":"THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior","abstract":"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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":297736,"datarank":0.5461926099550327,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.2166089233545997,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.2166089233545997,"corpus_percentile":64.35367834764446,"corpus_rank":4609,"citation_count":8,"citer_count":7,"citers_with_citation_signal":7,"citers_with_endowment":7,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9408,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":986269,"name":"Oliver Contier","orcid":"0000-0002-2983-4709","position":1,"is_corresponding":false},{"id":260385,"name":"Lina Teichmann","orcid":"0000-0002-8040-5686","position":2,"is_corresponding":false},{"id":986270,"name":"Adam H Rockter","orcid":"0000-0002-2446-717X","position":3,"is_corresponding":false},{"id":233515,"name":"Charles Zheng","orcid":"0000-0003-3427-0845","position":4,"is_corresponding":false},{"id":964199,"name":"Alexis Kidder","orcid":"0000-0002-2198-231X","position":5,"is_corresponding":false},{"id":890353,"name":"Anna Corriveau","orcid":"0000-0003-3122-7198","position":6,"is_corresponding":false},{"id":497978,"name":"Maryam Vaziri-Pashkam","orcid":"0000-0003-1830-2501","position":7,"is_corresponding":false},{"id":233517,"name":"Chris I. Baker","orcid":"0000-0001-6861-8964","position":8,"is_corresponding":false},{"id":233514,"name":"Martin N. Hebart","orcid":"0000-0001-7257-428X","position":0,"is_corresponding":true}],"reference_count":120,"raw_metadata":null,"created_at":"2026-07-19T00:31:31.270564Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}