{"doi":"10.1111/epi.18487","title":"Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals","abstract":"OBJECTIVE: This article presents the Harvard Electroencephalography Database (HEEDB), a large-scale, deidentified, and standardized electroencephalographic (EEG) resource supporting artificial intelligence-driven and reproducible research in epilepsy and broader clinical neuroscience. METHODS: HEEDB aggregates more than 280 000 EEG recordings from more than 108 000 patients across four Harvard-affiliated hospitals. Data are harmonized using the Brain Imaging Data Structure and hosted on the Brain Data Science Platform. EEG data are linked with clinical notes, International Classification of Diseases, 10th Revision codes, medications, and EEG reports. Deidentification follows Health Insurance Portability and Accountability Act Safe Harbor standards. RESULTS: The database includes routine, epilepsy monitoring unit, and intensive care unit EEGs across all age groups, with 73% linked to deidentified clinical reports and 96% of those matched to recordings. Findings are extracted using expert curation, regular expressions, and medical natural language processing models. Auxiliary data include diagnoses, medications, and hospital course, supporting multimodal analysis. SIGNIFICANCE: HEEDB fills a critical gap in EEG data availability for epilepsy research. By enabling large-scale, privacy-compliant, and clinically relevant analysis, it accelerates the development of diagnostic tools, improves training datasets for machine learning, and promotes data-sharing in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) and National Institutes of Health data policies.","journal":"Epilepsia","year":2025,"id":514318,"datarank":0.34945220671464344,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.019868520114210492,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.019868520114210492,"corpus_percentile":49.62481627601145,"corpus_rank":6513,"citation_count":8,"citer_count":7,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9527,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":62.5,"fair_percentile":81.0149801284011,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":536464,"name":"Jin Jing","orcid":"0000-0002-2415-5854","position":1,"is_corresponding":false},{"id":1265254,"name":"Niels Turley","orcid":null,"position":2,"is_corresponding":false},{"id":1377510,"name":"Callison Alcott","orcid":null,"position":3,"is_corresponding":false},{"id":1377511,"name":"Wan‐Yee Kang","orcid":null,"position":4,"is_corresponding":false},{"id":227178,"name":"Andrew J. Cole","orcid":"0000-0002-0828-826X","position":5,"is_corresponding":false},{"id":424528,"name":"Daniel M. Goldenholz","orcid":"0000-0002-8370-2758","position":6,"is_corresponding":false},{"id":305330,"name":"Alice Lam","orcid":"0000-0001-7754-4637","position":7,"is_corresponding":false},{"id":436218,"name":"Edilberto Amorim","orcid":"0000-0001-6972-5622","position":8,"is_corresponding":false},{"id":485319,"name":"Catherine J. Chu","orcid":"0000-0001-7670-9313","position":9,"is_corresponding":false},{"id":227177,"name":"Sydney S. Cash","orcid":"0000-0002-4557-6391","position":10,"is_corresponding":false},{"id":723459,"name":"Valdery Moura","orcid":"0000-0001-5735-9143","position":11,"is_corresponding":false},{"id":1074947,"name":"Aditya Gupta","orcid":"0000-0002-5243-368X","position":12,"is_corresponding":false},{"id":723458,"name":"Manohar Ghanta","orcid":"0009-0004-8488-3644","position":13,"is_corresponding":false},{"id":1376888,"name":"Bruce D. Nearing","orcid":"0000-0003-3517-0600","position":14,"is_corresponding":false},{"id":670680,"name":"Fábio A. Nascimento","orcid":"0000-0002-7161-6385","position":15,"is_corresponding":false},{"id":306970,"name":"Aaron F. Struck","orcid":"0000-0002-9103-1798","position":16,"is_corresponding":false},{"id":1376889,"name":"Jennifer Kim","orcid":"0000-0001-9129-6158","position":17,"is_corresponding":false},{"id":1311339,"name":"Shadi Sartipi","orcid":"0000-0002-8441-4475","position":18,"is_corresponding":false},{"id":1377512,"name":"Alexandra‐Maria Tauton","orcid":null,"position":19,"is_corresponding":false},{"id":446095,"name":"Marta Fernandes","orcid":"0000-0002-7203-2832","position":20,"is_corresponding":false},{"id":305327,"name":"Haoqi Sun","orcid":"0000-0002-5041-8312","position":21,"is_corresponding":false},{"id":1377513,"name":"Grace Bayas","orcid":null,"position":22,"is_corresponding":false},{"id":1376890,"name":"Kaileigh Gallagher","orcid":"0009-0005-2802-9790","position":23,"is_corresponding":false},{"id":662096,"name":"Joost Wagenaar","orcid":"0000-0003-0837-7120","position":24,"is_corresponding":false},{"id":644407,"name":"Nishant Sinha","orcid":"0000-0002-2090-4889","position":25,"is_corresponding":false},{"id":280796,"name":"Christopher Lee‐Messer","orcid":"0000-0002-2938-6184","position":26,"is_corresponding":false},{"id":1377514,"name":"Christine Tsien Silvers","orcid":null,"position":27,"is_corresponding":false},{"id":1377515,"name":"Bharath Gunapati","orcid":null,"position":28,"is_corresponding":false},{"id":268233,"name":"Jonathan Rosand","orcid":"0000-0002-1014-9138","position":29,"is_corresponding":false},{"id":355482,"name":"Jurriaan M. Peters","orcid":"0000-0002-6725-2814","position":30,"is_corresponding":false},{"id":337308,"name":"Tobias Loddenkemper","orcid":"0000-0003-2074-0674","position":31,"is_corresponding":false},{"id":661352,"name":"Jong Woo Lee","orcid":"0000-0001-5283-7476","position":32,"is_corresponding":false},{"id":485320,"name":"Sahar F. Zafar","orcid":"0000-0001-5252-5376","position":33,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":34,"is_corresponding":false},{"id":1376887,"name":"Chenxi Sun","orcid":"0000-0002-1762-0877","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T02:48:29.165853Z","pmid":"40464151","pmcid":"PMC12455399","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":72.2222,"fair_a":56.25,"fair_i":80.0,"fair_r":58.3333,"fair_zscore":1.1103,"fair_rationale":{"fair_score":62.5,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":72.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"https://bdsp.io/content/harvard-eeg-db/4.1/","grounded":true,"rationale":"The paper provides a URL for the dataset, not a PID scheme identifier.","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":1.0,"verdict":"yes","evidence":"Brain Data Science Platform (BDSP)","grounded":true,"rationale":"The paper names the Brain Data Science Platform as the repository host. [majority verdict 'yes' (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":1.0,"verdict":"yes","evidence":"All data needed to reproduce the results are available at bdsp.io at https://bdsp.io/content/harvard-eeg-db/4.1/","grounded":true,"rationale":"The data availability statement points to a repository record with a URL.","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 2. Hierarchical organization of electroencephalographic (EEG) data in the Brain Imaging Data Structure (BIDS) format.","grounded":false,"rationale":"The paper includes a figure (Figure 2) that itemises the BIDS directory structure of the dataset, serving as an itemised inventory. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (3/5 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":0.5,"verdict":"partial","evidence":"https://bdsp.io/content/harvard‐eeg‐db/3.0/","grounded":true,"rationale":"The dataset identifier appears only in body text, not in the reference list.","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":56.25,"criteria":[{"key":"a_data_openly_accessible","label":"Access route free of preconditions","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"Access to HEEDB is facilitated through the BDSP (bdsp.io), ensuring secure and controlled use of the data. Although the data have been deidentified per HIPAA, HEEDB is classified as “restricted access,” allowing only credentialed researchers who meet specific criteria. Users must sign a DUA committing to data security, prohibiting reidentification, and restricting use to lawful scientific research.","grounded":true,"rationale":"Access requires a DUA and credentialing, so a precondition is stated. [majority verdict 'partial' (4/5 passes agreed)]","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":0.5,"verdict":"partial","evidence":"HEEDB is classified as “restricted access”","grounded":false,"rationale":"The paper explicitly labels the access level as 'restricted access'. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","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":1.0,"verdict":"yes","evidence":"Users must sign a DUA committing to data security, prohibiting reidentification, and restricting use to lawful scientific research.","grounded":true,"rationale":"The paper names a Data Use Agreement and institutional gatekeeper.","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":"HEEDB is an actively maintained and continuously expanding resource.","grounded":true,"rationale":"The paper states the resource is actively maintained but no explicit retention period. [majority verdict 'partial' (2/5 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":80.0,"criteria":[{"key":"i_open_nonproprietary_format","label":"Open file format","kind":"llm","weight":1.0,"fraction":1.0,"verdict":"yes","evidence":"raw EEG data (.edf)","grounded":true,"rationale":"The paper states the EEG data are in .edf format, which is open. [majority verdict 'yes' (4/5 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":"Brain Imaging Data Structure (BIDS)","grounded":true,"rationale":"The paper uses BIDS, a community standard for neuroimaging data. [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 an identifier for any external resource that the data depend on or derive from (e.g., source dataset, reference genome). [majority verdict 'no' (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":58.33,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"Data use agreement (DUA)","grounded":false,"rationale":"The data are governed by a Data Use Agreement, not an open license. [downgraded to 'no' — no verifiable quote from the paper] [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":0.5,"verdict":"partial","evidence":"PHILter","grounded":false,"rationale":"The paper names specific tools like PHILter for deidentification. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (3/5 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":1.0,"verdict":"yes","evidence":"dataset_description.json, participants.tsv, README","grounded":true,"rationale":"The paper states that the dataset includes metadata files like README and dataset_description.json. 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[majority verdict 'yes' (4/5 passes agreed)]","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":1.0,"verdict":"yes","evidence":"https://github.com/bdsp-core/SpikeNet1","grounded":true,"rationale":"The paper provides GitHub URLs for code. [majority verdict 'yes' (3/5 passes agreed)]","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":"RF1AG064312","grounded":true,"rationale":"The paper lists multiple NIH grant numbers. [majority verdict 'yes' (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":"Data use agreement (DUA)","why":"The data are governed by a Data Use Agreement, not an open license. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (4/5 passes agreed)]","gain":16.67,"priority":"essential","scored":true},{"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":"partial","current":0.5,"evidence":"https://bdsp.io/content/harvard-eeg-db/4.1/","why":"The paper provides a URL for the dataset, not a PID scheme identifier.","gain":8.33,"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":"Access to HEEDB is facilitated through the BDSP (bdsp.io), ensuring secure and controlled use of the data. Although the data have been deidentified per HIPAA, HEEDB is classified as “restricted access,” allowing only credentialed researchers who meet specific criteria. Users must sign a DUA committing to data security, prohibiting reidentification, and restricting use to lawful scientific research.","why":"Access requires a DUA and credentialing, so a precondition is stated. [majority verdict 'partial' (4/5 passes agreed)]","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":"partial","current":0.5,"evidence":"https://bdsp.io/content/harvard‐eeg‐db/3.0/","why":"The dataset identifier appears only in body text, not in the reference list.","gain":4.17,"priority":"important","scored":true},{"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 2. Hierarchical organization of electroencephalographic (EEG) data in the Brain Imaging Data Structure (BIDS) format.","why":"The paper includes a figure (Figure 2) that itemises the BIDS directory structure of the dataset, serving as an itemised inventory. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (3/5 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":"partial","current":0.5,"evidence":"HEEDB is classified as “restricted access”","why":"The paper explicitly labels the access level as 'restricted access'. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"r_provenance_methods","dimension":"R","label":"Provenance of the data","action":"Name the instruments, kits, and software — with versions — that produced the data, not just the verbs. 'Reads were aligned' is not provenance; 'aligned with STAR v2.7.9a to GRCh38' is, because someone else can rerun it.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"PHILter","why":"The paper names specific tools like PHILter for deidentification. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (3/5 passes agreed)]","gain":0.0,"priority":"important","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 an identifier for any external resource that the data depend on or derive from (e.g., source dataset, reference genome). [majority verdict 'no' (3/5 passes agreed)]","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":"HEEDB is an actively maintained and continuously expanding resource.","why":"The paper states the resource is actively maintained but no explicit retention period. [majority verdict 'partial' (2/5 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.","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.","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.","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."],"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":"2026-07-20T12:35:46.099675Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}