{"doi":"10.7554/elife.82580","title":"THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior","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":"eLife","year":2023,"id":315472,"datarank":2.666057820944948,"base_score":5.062595033026967,"endowment":5.062595033026967,"self_citation_contribution":0.7593892549540452,"citation_network_contribution":1.9066685659909028,"self_endowment_contribution":0.7593892549540452,"citer_contribution":1.9066685659909028,"corpus_percentile":91.80010830045642,"corpus_rank":1061,"citation_count":157,"citer_count":100,"citers_with_citation_signal":78,"citers_with_endowment":78,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9383,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":56.25,"fair_percentile":71.90461632528279,"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":139,"raw_metadata":null,"created_at":"2026-07-19T01:06:25.560098Z","pmid":"36847339","pmcid":"PMC10038662","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":66.6667,"fair_a":81.25,"fair_i":40.0,"fair_r":37.5,"fair_zscore":0.8629,"fair_rationale":{"fair_score":56.25,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":66.67,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"https://doi.org/10.18112/openneuro.ds004192.v1.0.5","grounded":true,"rationale":"The paper gives DOIs for the datasets, which are persistent identifiers in the DOI scheme. [majority verdict 'yes' (3/5 passes agreed)]","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"We provide the raw MRI ... datasets in BIDS format on OpenNeuro","grounded":false,"rationale":"OpenNeuro is a named data repository (listed in re3data/FAIRsharing) that holds the study's data. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories. We provide the raw MRI ( https://doi.org/10.18112/openneuro.ds004192.v1.0.5 ) and raw MEG ( https://doi.org/10.18112/openneuro.ds004212.v2.0.0 ) datasets in BIDS format on OpenNeuro ( Markiewicz et al., 2021 ). In addition to these raw datasets, we provide the raw and preprocessed MEG data as well as the raw and derivative MRI data on Figshare ( Thelwall and Kousha, 2016 ) at https://doi.org/10.25452/figshare.plus.c.6161151 . The behavioral triplet odd-one-out dataset can be accessed on OSF ( https://osf.io/f5rn6/ ).","grounded":false,"rationale":"The data availability statement points to multiple repository records with DOIs and accessions, which is Colavizza category 3. [downgraded to 'partial' — no verifiable quote from the paper]","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"THINGS-data comprises MEG, fMRI and behavioral responses to large samples of object images taken from the THINGS database.","grounded":true,"rationale":"The dataset's content and extent are described in running prose only, without an itemised inventory (section, table, or list) of files or variables.","anchors":["RDA-F2-01M — 'Rich metadata is provided to allow discovery' (priority Essential)","FsF-F2-01M — F-UJI: 'Metadata includes descriptive core elements to support data findability'","FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data'"],"scored":false,"signal":null},{"key":"f_dataset_cited","label":"Dataset formally cited","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"Hebart MN, Contier O, Teichmann L, Rockter AH, Zheng CY, Kidder A, Corriveau A, Vaziri-Pashkam M, Baker CI. 2022. THINGS-fMRI. OpenNeuro.","grounded":true,"rationale":"The dataset appears as a reference-list entry in the Associated Data Citations section, which is a bibliographic entry for the data. [majority verdict 'yes' (4/5 passes agreed)]","anchors":["FORCE11 Joint Declaration of Data Citation Principles (2014) — data should be cited as a first-","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes","FsF-F3-01M — F-UJI: 'Metadata includes the identifier of the data it describes'"],"scored":true,"signal":null}]},"A":{"name":"Accessible","score":81.25,"criteria":[{"key":"a_data_openly_accessible","label":"Access route free of preconditions","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories.","grounded":true,"rationale":"The paper states that the data are freely available with no stated precondition (no embargo, registration, or application required).","anchors":["RDA-A1.1-01D — 'Data is accessible through a free access protocol'","FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data'","NSTC Desirable Characteristics of Data Repositories (2022) — 'Free and Easy Access'"],"scored":true,"signal":null},{"key":"a_access_conditions_stated","label":"Access level labelled","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories.","grounded":true,"rationale":"The paper states that the data are 'freely available', which is a synonym for 'open access' and thus an explicit access-level label.","anchors":["FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data'","RDA-A1-01M — metadata contains information to enable the user to get access to the data","COAR Controlled Vocabularies — Access Rights v1.0 (open / embargoed / restricted / metadata-onl"],"scored":false,"signal":null},{"key":"a_controlled_access_for_sensitive","label":"Gatekeeper for sensitive data","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":"The research was approved by the NIH Institutional Review Board as part of the study protocol 93 M-0170 ( NCT00001360 ).","grounded":true,"rationale":"The paper mentions IRB approval, but does not name any gatekeeper for access to the data; the data are openly available without restriction.","anchors":["NIH Genomic Data Sharing Policy (NOT-OD-14-124) — controlled-access via a Data Access Committee","RDA-A1.2-01D — 'Data is accessible through an access protocol that supports authentication and ","NIH DMS Policy Element 5 (NOT-OD-21-014) — Access, Distribution, or Reuse Considerations (conse"],"scored":false,"signal":null},{"key":"a_timeline_retention","label":"Availability timing & retention","kind":"llm","weight":0.5,"fraction":0.5,"verdict":"partial","evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories.","grounded":true,"rationale":"The paper indicates that the data are available now, but does not specify how long they will persist, so it is an availability-timing statement only. [majority verdict 'partial' (3/5 passes agreed)]","anchors":["NIH DMS Plan Element 4 (NOT-OD-21-014) — Data Preservation, Access, and Associated Timelines","NSTC Desirable Characteristics (2022), Organizational Infrastructure: 'Retention Policy'","RDA-A2-01M — 'Metadata is guaranteed to remain available after data is no longer available'"],"scored":false,"signal":null}]},"I":{"name":"Interoperable","score":40.0,"criteria":[{"key":"i_open_nonproprietary_format","label":"Open file format","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":"BIDS format","grounded":false,"rationale":"BIDS is a specification, not a file format; the paper does not name an open file format (e.g., NIfTI, FIF, CSV) for the released data. [majority verdict 'no' (3/5 passes agreed)]","anchors":["FsF-R1.3-02D — F-UJI: 'Data is available in a file format recommended by the target research co","RDA-R1.3-02D — data is expressed in a machine-understandable community standard","RDA-I1-01D — data uses a knowledge representation expressed in a standardised format"],"scored":true,"signal":null},{"key":"i_community_standard_vocabulary","label":"Community standard / vocabulary","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"datasets in BIDS format ( Gorgolewski et al., 2016 )","grounded":true,"rationale":"BIDS is a community data standard registered in FAIRsharing. [majority verdict 'yes' (3/5 passes agreed)]","anchors":["RDA-R1.3-01M — 'Metadata complies with a community standard' (priority Essential)","RDA-R1.3-01D — 'Data complies with a community standard'","RDA-I2-01M — '(Meta)data use vocabularies that follow FAIR principles'"],"scored":false,"signal":null},{"key":"i_qualified_references","label":"Identifiers for the resources the data depend on","kind":"llm","weight":0.5,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper does not provide any identifier (DOI, accession, RRID, etc.) for an external resource that the data depend on or derive from.","anchors":["RDA-I3-01M — '(meta)data include references to other (meta)data'","RDA-I3-03M — 'metadata includes qualified references to other metadata'","FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'"],"scored":false,"signal":null}]},"R":{"name":"Reusable","score":37.5,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The work is made available under the Creative Commons CC0 public domain dedication.","grounded":false,"rationale":"The CC0 license is stated for the article, not explicitly for the data; the data availability statement does not mention a license for the data. [majority verdict 'no' (4/5 passes agreed)]","anchors":["RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu","RDA-R1.1-02M — 'Metadata refers to a standard reuse licence'","RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'"],"scored":true,"signal":null},{"key":"r_provenance_methods","label":"Provenance of the data","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"3 Tesla Siemens Magnetom Prisma scanner","grounded":true,"rationale":"The paper names the specific instrument (Siemens Magnetom Prisma scanner) used to acquire the fMRI data.","anchors":["RDA-R1.2-01M — 'Metadata includes provenance information according to community- specific standa","FsF-R1.2-01M — F-UJI: 'Metadata includes provenance information about data creation or generati","W3C PROV-O (W3C Recommendation, 2013) — the entity/activity/agent model of provenance"],"scored":false,"signal":null},{"key":"r_documentation_codebook","label":"Documentation / codebook","kind":"llm","weight":1.0,"fraction":0.5,"verdict":"partial","evidence":"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.","grounded":true,"rationale":"The paper lists what is included in the deposits but does not name a dedicated documentation object (README, codebook, schema) that travels with the data; the definitions are inside the article. [majority verdict 'partial' (3/5 passes agreed)]","anchors":["RDA-R1-01M — '(Meta)data are richly described with a plurality of accurate and relevant attribu","FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data'","NIH DMS Policy Element 3 (NOT-OD-21-014) — Standards (documentation and metadata to accompany t"],"scored":false,"signal":null},{"key":"r_versioning","label":"Snapshot identified","kind":"llm","weight":0.5,"fraction":1.0,"verdict":"yes","evidence":"ds004192.v1.0.5","grounded":true,"rationale":"The dataset identifier includes a version token (v1.0.5) in the DOI, which specifies the snapshot of the data.","anchors":["DataCite Metadata Schema 4.6 — the 'Version' property","RDA-R1.2-01M — provenance information (which version was used is provenance)","NSTC Desirable Characteristics of Data Repositories (2022) — 'Provenance', 'Retention Policy'"],"scored":true,"signal":null},{"key":"x_code_availability","label":"Analysis code available","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper does not contain a code availability statement or any locator for the study's own code in the provided text.","anchors":["NIH DMS Policy Element 2 (NOT-OD-21-014) — 'Related Tools, Software and/or Code'","FAIR4RS Principles v1.0 (Chue Hong et al., 2022; RDA/FORCE11/ReSA) — FAIR Principles for Resear","FORCE11 Software Citation Principles (Smith, Katz & Niemeyer, 2016, PeerJ CS 2:e86)"],"scored":true,"signal":null},{"key":"x_funding_attribution","label":"Funder and award number","kind":"llm","weight":0.5,"fraction":0.5,"verdict":"partial","evidence":"National Institute of Mental Health, National Institutes of Health","grounded":true,"rationale":"A funder (NIMH) is named but no award/grant number is provided. [majority verdict 'partial' (4/5 passes agreed)]","anchors":["DataCite Metadata Schema 4.6 — 'FundingReference' property (funderName, funderIdentifier, award","Crossref Funder Registry — canonical funder identifiers for funding metadata","RDA-F2-01M — rich metadata provided to allow discovery (funding is part of the descriptive reco"],"scored":true,"signal":null}]}},"actions":[{"key":"r_reuse_license","dimension":"R","label":"Reuse licence","action":"Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The work is made available under the Creative Commons CC0 public domain dedication.","why":"The CC0 license is stated for the article, not explicitly for the data; the data availability statement does not mention a license for the data. [majority verdict 'no' (4/5 passes agreed)]","gain":16.67,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"We provide the raw MRI ... datasets in BIDS format on OpenNeuro","why":"OpenNeuro is a named data repository (listed in re3data/FAIRsharing) that holds the study's data. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","gain":8.33,"priority":"essential","scored":true},{"key":"i_open_nonproprietary_format","dimension":"I","label":"Open file format","action":"Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"BIDS format","why":"BIDS is a specification, not a file format; the paper does not name an open file format (e.g., NIfTI, FIF, CSV) for the released data. [majority verdict 'no' (3/5 passes agreed)]","gain":8.33,"priority":"important","scored":true},{"key":"x_code_availability","dimension":"R","label":"Analysis code available","action":"Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper does not contain a code availability statement or any locator for the study's own code in the provided text.","gain":8.33,"priority":"important","scored":true},{"key":"x_funding_attribution","dimension":"R","label":"Funder and award number","action":"State the funder AND the award number in the paper, and put them in the dataset's FundingReference metadata. A funder name alone cannot be linked back to the award, so the funding provenance of the data is lost the moment the paper is indexed.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"National Institute of Mental Health, National Institutes of Health","why":"A funder (NIMH) is named but no award/grant number is provided. [majority verdict 'partial' (4/5 passes agreed)]","gain":2.08,"priority":"useful","scored":true},{"key":"f_data_availability_statement","dimension":"F","label":"Data-availability statement","action":"Replace the statement with the repository template: name the repository and give the accession or DOI (Colavizza category 3). This is the only DAS class associated with a measured citation advantage; 'available on reasonable request' and 'within the article' are not.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories. We provide the raw MRI ( https://doi.org/10.18112/openneuro.ds004192.v1.0.5 ) and raw MEG ( https://doi.org/10.18112/openneuro.ds004212.v2.0.0 ) datasets in BIDS format on OpenNeuro ( Markiewicz et al., 2021 ). In addition to these raw datasets, we provide the raw and preprocessed MEG data as well as the raw and derivative MRI data on Figshare ( Thelwall and Kousha, 2016 ) at https://doi.org/10.25452/figshare.plus.c.6161151 . The behavioral triplet odd-one-out dataset can be accessed on OSF ( https://osf.io/f5rn6/ ).","why":"The data availability statement points to multiple repository records with DOIs and accessions, which is Colavizza category 3. [downgraded to 'partial' — no verifiable quote from the paper]","gain":0.0,"priority":"essential","scored":false},{"key":"f_discovery_metadata","dimension":"F","label":"Description of the dataset as an object","action":"Add a 'Data Records' section: itemise every file in the deposit and every variable or sample it holds, with counts and units. Describe the dataset as an object in its own right, not as a by-product of the findings — this is what makes it discoverable to someone who is not looking for your paper.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"THINGS-data comprises MEG, fMRI and behavioral responses to large samples of object images taken from the THINGS database.","why":"The dataset's content and extent are described in running prose only, without an itemised inventory (section, table, or list) of files or variables.","gain":0.0,"priority":"essential","scored":false},{"key":"r_documentation_codebook","dimension":"R","label":"Documentation / codebook","action":"Ship a README and a data dictionary IN the deposit — every file, every variable, its units, its allowed values, its missing-value codes. It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"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.","why":"The paper lists what is included in the deposits but does not name a dedicated documentation object (README, codebook, schema) that travels with the data; the definitions are inside the article. [majority verdict 'partial' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"a_controlled_access_for_sensitive","dimension":"A","label":"Gatekeeper for sensitive data","action":"Route sensitive data through an institutional gatekeeper — deposit in a controlled- access repository (dbGaP, EGA) with a Data Access Committee and a published DUA — rather than through the corresponding author's inbox. An author-gated dataset dies with the author's email address, and 'on reasonable request' has been shown repeatedly not to yield data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The research was approved by the NIH Institutional Review Board as part of the study protocol 93 M-0170 ( NCT00001360 ).","why":"The paper mentions IRB approval, but does not name any gatekeeper for access to the data; the data are openly available without restriction.","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper does not provide any identifier (DOI, accession, RRID, etc.) for an external resource that the data depend on or derive from.","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"All parts of the THINGS-data collection are freely available on scientific data repositories.","why":"The paper indicates that the data are available now, but does not specify how long they will persist, so it is an availability-timing statement only. [majority verdict 'partial' (3/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","State the funder AND the award number in the paper, and put them in the dataset's FundingReference metadata. A funder name alone cannot be linked back to the award, so the funding provenance of the data is lost the moment the paper is indexed."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T10:58:01.545915Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}