{"doi":"10.1002/hbm.26472","title":"Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ <scp>resting‐state fMRI</scp> datasets","abstract":"Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.","journal":"Human Brain Mapping","year":2023,"id":317773,"datarank":0.7669022437303494,"base_score":4.304065093204169,"endowment":4.304065093204169,"self_citation_contribution":0.6456097639806255,"citation_network_contribution":0.12129247974972392,"self_endowment_contribution":0.6456097639806255,"citer_contribution":0.12129247974972392,"corpus_percentile":73.7835538021196,"corpus_rank":3390,"citation_count":73,"citer_count":13,"citers_with_citation_signal":6,"citers_with_endowment":6,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5371,"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":16.6667,"fair_percentile":34.85172730051972,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":227749,"name":"Zening Fu","orcid":"0000-0002-1591-4900","position":1,"is_corresponding":false},{"id":254825,"name":"Ashkan Faghiri","orcid":"0000-0003-1807-6815","position":2,"is_corresponding":false},{"id":559136,"name":"Marlena Duda","orcid":"0000-0003-2369-2225","position":3,"is_corresponding":false},{"id":1024040,"name":"Jun Chen","orcid":"0000-0003-4272-2653","position":4,"is_corresponding":false},{"id":256996,"name":"Srinivas Rachakonda","orcid":null,"position":5,"is_corresponding":false},{"id":673927,"name":"Thomas P. DeRamus","orcid":"0000-0002-5774-2297","position":6,"is_corresponding":false},{"id":227759,"name":"Peter Kochunov","orcid":"0000-0003-3656-4281","position":7,"is_corresponding":false},{"id":109345,"name":"Bhim M. Adhikari","orcid":"0000-0003-1164-0989","position":8,"is_corresponding":false},{"id":523634,"name":"Ayşenil Belger","orcid":"0000-0003-2687-1966","position":9,"is_corresponding":false},{"id":107800,"name":"Judith M. Ford","orcid":"0000-0002-6500-6548","position":10,"is_corresponding":false},{"id":289811,"name":"Daniel H. Mathalon","orcid":"0000-0001-6090-4974","position":11,"is_corresponding":false},{"id":236953,"name":"Godfrey D. Pearlson","orcid":"0000-0002-7525-5185","position":12,"is_corresponding":false},{"id":226064,"name":"Steven G. Potkin","orcid":"0000-0003-1028-1013","position":13,"is_corresponding":false},{"id":236942,"name":"Adrian Preda","orcid":"0000-0003-3373-2438","position":14,"is_corresponding":false},{"id":90656,"name":"Jessica A. Turner","orcid":"0000-0003-0076-8434","position":15,"is_corresponding":false},{"id":236952,"name":"Theo G.M. van Erp","orcid":"0000-0002-2465-2797","position":16,"is_corresponding":false},{"id":233025,"name":"Juan Bustillo","orcid":"0000-0001-8730-8152","position":17,"is_corresponding":false},{"id":236933,"name":"Kun Yang","orcid":"0000-0002-1060-8082","position":18,"is_corresponding":false},{"id":1024775,"name":"K Ishizuka","orcid":null,"position":19,"is_corresponding":false},{"id":315438,"name":"Andréia V. Faria","orcid":"0000-0002-1673-002X","position":20,"is_corresponding":false},{"id":33854,"name":"Akira Sawa","orcid":"0000-0003-1401-3008","position":21,"is_corresponding":false},{"id":651174,"name":"Kent E. Hutchison","orcid":"0000-0002-4805-9277","position":22,"is_corresponding":false},{"id":227760,"name":"Elizabeth Osuch","orcid":"0000-0001-5946-1862","position":23,"is_corresponding":false},{"id":628323,"name":"Jean Théberge","orcid":"0000-0001-7578-4469","position":24,"is_corresponding":false},{"id":355626,"name":"Chris Abbott","orcid":"0000-0001-6884-2464","position":25,"is_corresponding":false},{"id":235047,"name":"Bryon A. Mueller","orcid":"0000-0003-2429-0391","position":26,"is_corresponding":false},{"id":1024776,"name":"D. Zhi","orcid":null,"position":27,"is_corresponding":false},{"id":230037,"name":"Chuanjun Zhuo","orcid":"0000-0002-3793-550X","position":28,"is_corresponding":false},{"id":436267,"name":"Sha Liu","orcid":"0000-0002-6710-8126","position":29,"is_corresponding":false},{"id":660180,"name":"Yong Xu","orcid":"0000-0001-9679-2273","position":30,"is_corresponding":false},{"id":1024041,"name":"Muhammad Salman","orcid":"0000-0002-5818-1364","position":31,"is_corresponding":false},{"id":301189,"name":"Jingyu Liu","orcid":"0000-0002-1724-7523","position":32,"is_corresponding":false},{"id":227748,"name":"Yuhui Du","orcid":"0000-0002-0079-8177","position":33,"is_corresponding":false},{"id":227750,"name":"Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you","orcid":"0000-0001-6837-5966","position":34,"is_corresponding":false},{"id":428411,"name":"Tülay Adalı","orcid":"0000-0003-0594-2796","position":35,"is_corresponding":false},{"id":227761,"name":"Vince D. Calhoun","orcid":"0000-0001-9058-0747","position":36,"is_corresponding":false},{"id":254824,"name":"Armin Iraji","orcid":"0000-0002-0605-593X","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":null,"created_at":"2026-07-19T01:06:55.884740Z","pmid":"37787573","pmcid":"PMC10619392","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":22.2222,"fair_a":25.0,"fair_i":20.0,"fair_r":41.6667,"fair_zscore":-0.7039,"fair_rationale":{"fair_score":16.67,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":22.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","grounded":false,"rationale":"The identifier given is a web URL, not a persistent identifier scheme (DOI, Handle, ARK, repository accession). [downgraded to 'no' — no verifiable quote from the paper]","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":0.0,"verdict":"no","evidence":"https://trendscenter.org/data/","grounded":false,"rationale":"The data are hosted on a project website (trendscenter.org), not a named repository from the curated list. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (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":"The full list of datasets and resources for obtaining further details on each can be found in Supporting Information S1. The links for publicly available datasets and contact information for private datasets are included in Supporting Information S1.","grounded":true,"rationale":"The statement points to supplementary information within the article, not to a repository record (Colavizza category 2). [majority verdict 'partial' (4/5 passes agreed)]","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":"Figure 2a displays the composite views of the 105 selected ICNs and their average functional connectivity.","grounded":true,"rationale":"The dataset is described in running prose and a figure caption, not in an itemised inventory (section, table, or list). [majority verdict 'partial' (4/5 passes agreed)]","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":0.0,"verdict":"no","evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","grounded":false,"rationale":"The dataset identifier (URL) appears only in body text, not in the reference list. [downgraded to 'no' — no verifiable quote from the paper]","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":25.0,"criteria":[{"key":"a_data_openly_accessible","label":"Access route free of preconditions","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","grounded":false,"rationale":"The data are stated to be available at a URL with no precondition, so the route is unconditional. [downgraded to 'partial' — no verifiable quote from the paper]","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":0.0,"verdict":"no","evidence":"available for access at https://trendscenter.org/data/","grounded":false,"rationale":"The paper describes the action of accessing the data at a URL, but does not use an explicit access-level label from the standard vocabulary. [downgraded to 'no' — no verifiable quote from the paper]","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":null,"grounded":false,"rationale":"The paper's own data (the template) are publicly available with no stated gatekeeper; the original sensitive data are not this study's own product.","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":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No sentence in the paper states how long the data will be available or commit to a retention period.","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":20.0,"criteria":[{"key":"i_open_nonproprietary_format","label":"Open file format","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No file format for the released data is mentioned in the text.","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":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No data or metadata community standard (e.g., MIAME, BIDS, ontology) is named for the study's own data.","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":1.0,"verdict":"yes","evidence":"UK Biobank (Littlejohns et al., 2020) Resource under Application Number 49636.","grounded":true,"rationale":"The paper provides an application number (identifier) for the UK Biobank resource, which is a third-party data source used in the study. [majority verdict 'yes' (3/5 passes agreed)]","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":41.67,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No licence or terms document is named for the data; the CC-BY-NC-ND licence applies only to the article.","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":"ICA analysis was performed using the Group ICA of FMRI Toolbox (GIFT) v4.0c package","grounded":true,"rationale":"The paper names specific software tools (GIFT, FSL, SPM) used to produce the data. [majority verdict 'yes' (4/5 passes agreed)]","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":"Supporting Information S4 contains the axial view of each ICN.","grounded":true,"rationale":"The definitions of the ICNs are provided inside the article (supporting information), not in a separate documentation object shipped with the data. [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":"NeuroMark_fMRI_2.0 single- and multi-model order templates","grounded":true,"rationale":"The template is labelled with a version token (2.0), identifying the specific snapshot.","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":"All the code used in this study could be made available by contacting the corresponding author (A. Iraji).","grounded":true,"rationale":"The only offer is a discretionary request to a person, which is not a machine-resolvable locator.","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":1.0,"verdict":"yes","evidence":"National Institutes of Health, Grant/Award Numbers: 1U24RR021992, 1U24RR025736, R01EB006841, R01EB020407, R01MH117107, R01MH118695, R01MH123610","grounded":true,"rationale":"Award numbers are given for named funders. [majority verdict 'yes' (3/5 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":"f_dataset_pid","dimension":"F","label":"Persistent identifier for the data","action":"Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","why":"The identifier given is a web URL, not a persistent identifier scheme (DOI, Handle, ARK, repository accession). [downgraded to 'no' — no verifiable quote from the paper]","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":"no","current":0.0,"evidence":"https://trendscenter.org/data/","why":"The data are hosted on a project website (trendscenter.org), not a named repository from the curated list. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (4/5 passes agreed)]","gain":16.67,"priority":"essential","scored":true},{"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":null,"why":"No licence or terms document is named for the data; the CC-BY-NC-ND licence applies only to the article.","gain":16.67,"priority":"essential","scored":true},{"key":"a_data_openly_accessible","dimension":"A","label":"Access route free of preconditions","action":"Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","why":"The data are stated to be available at a URL with no precondition, so the route is unconditional. [downgraded to 'partial' — no verifiable quote from the paper]","gain":8.33,"priority":"essential","scored":true},{"key":"f_dataset_cited","dimension":"F","label":"Dataset formally cited","action":"Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"NeuroMark_fMRI_2.0 single- and multi-model order templates, which are available for access at https://trendscenter.org/data/","why":"The dataset identifier (URL) appears only in body text, not in the reference list. [downgraded to 'no' — no verifiable quote from the paper]","gain":8.33,"priority":"important","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":null,"why":"No file format for the released data is mentioned in the text.","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":"All the code used in this study could be made available by contacting the corresponding author (A. Iraji).","why":"The only offer is a discretionary request to a person, which is not a machine-resolvable locator.","gain":8.33,"priority":"important","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":"The full list of datasets and resources for obtaining further details on each can be found in Supporting Information S1. The links for publicly available datasets and contact information for private datasets are included in Supporting Information S1.","why":"The statement points to supplementary information within the article, not to a repository record (Colavizza category 2). [majority verdict 'partial' (4/5 passes agreed)]","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":"Figure 2a displays the composite views of the 105 selected ICNs and their average functional connectivity.","why":"The dataset is described in running prose and a figure caption, not in an itemised inventory (section, table, or list). [majority verdict 'partial' (4/5 passes agreed)]","gain":0.0,"priority":"essential","scored":false},{"key":"a_access_conditions_stated","dimension":"A","label":"Access level labelled","action":"State the access level in words, using the standard vocabulary: 'These data are open access' / 'These data are controlled access'. A reader — and a harvester — should not have to infer the access level from the presence of a download link.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"available for access at https://trendscenter.org/data/","why":"The paper describes the action of accessing the data at a URL, but does not use an explicit access-level label from the standard vocabulary. [downgraded to 'no' — no verifiable quote from the paper]","gain":0.0,"priority":"important","scored":false},{"key":"i_community_standard_vocabulary","dimension":"I","label":"Community standard / vocabulary","action":"Adopt and NAME your domain's data standard — the minimum-information checklist, metadata schema, or ontology your community uses (MIAME/MINSEQE, ISA-Tab, BIDS, an OBO ontology, HL7 FHIR/OMOP) — and say which one you followed. A reporting checklist standardises your paper; it does nothing for your data. In neuroimaging, describe the data with BIDS, NIfTI or DICOM.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No data or metadata community standard (e.g., MIAME, BIDS, ontology) is named for the study's own data.","gain":0.0,"priority":"important","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":"Supporting Information S4 contains the axial view of each ICN.","why":"The definitions of the ICNs are provided inside the article (supporting information), not in a separate documentation object shipped with the data. [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":null,"why":"The paper's own data (the template) are publicly available with no stated gatekeeper; the original sensitive data are not this study's own product.","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":"no","current":0.0,"evidence":null,"why":"No sentence in the paper states how long the data will be available or commit to a retention period.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","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.","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.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:11:04.371503Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}