{"doi":"10.1186/s13073-021-00933-8","title":"Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19","abstract":"BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases. METHODS: Three brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain. RESULTS: Quantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis. CONCLUSIONS: Despite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.","journal":"Genome Medicine","year":2021,"id":148921,"datarank":3.8117517801624405,"base_score":4.762173934797756,"endowment":4.762173934797756,"self_citation_contribution":0.7143260902196635,"citation_network_contribution":3.097425689942777,"self_endowment_contribution":0.7143260902196635,"citer_contribution":3.097425689942777,"corpus_percentile":94.12083236636498,"corpus_rank":761,"citation_count":116,"citer_count":100,"citers_with_citation_signal":100,"citers_with_endowment":100,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5296,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":79.1667,"fair_percentile":97.67655151329869,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1197,"name":"Hao-Chih Lee","orcid":"0000-0002-1538-6175","position":1,"is_corresponding":false},{"id":1200,"name":"Georgios Voloudakis","orcid":"0000-0002-5729-632X","position":2,"is_corresponding":false},{"id":227524,"name":"Shengbao Suo","orcid":"0009-0008-8943-2956","position":3,"is_corresponding":false},{"id":460841,"name":"Behnam Javidfar","orcid":"0000-0003-2754-2742","position":4,"is_corresponding":false},{"id":21481,"name":"Zhiping Shao","orcid":"0009-0006-4889-6637","position":5,"is_corresponding":false},{"id":460844,"name":"Cyril Peter","orcid":"0000-0001-9190-8724","position":6,"is_corresponding":false},{"id":633923,"name":"Wen Zhang","orcid":"0000-0001-7209-2731","position":7,"is_corresponding":false},{"id":1196,"name":"Shan Jiang","orcid":"0000-0002-9791-1593","position":8,"is_corresponding":false},{"id":87010,"name":"André Corvelo","orcid":"0000-0003-0989-7806","position":9,"is_corresponding":false},{"id":635270,"name":"Heather Wargnier","orcid":null,"position":10,"is_corresponding":false},{"id":635271,"name":"Emma Woodoff‐Leith","orcid":null,"position":11,"is_corresponding":false},{"id":308305,"name":"Dushyant P. Purohit","orcid":"0000-0003-4789-0373","position":12,"is_corresponding":false},{"id":635272,"name":"Sadhna Ahuja","orcid":null,"position":13,"is_corresponding":false},{"id":2003,"name":"Nadejda M. Tsankova","orcid":"0000-0002-5333-312X","position":14,"is_corresponding":false},{"id":3115,"name":"Nathalie Jetté","orcid":"0000-0003-1351-5866","position":15,"is_corresponding":false},{"id":1202,"name":"Gabriel E. Hoffman","orcid":"0000-0002-0957-0224","position":16,"is_corresponding":false},{"id":21362,"name":"Schahram Akbarian","orcid":"0000-0001-7700-0891","position":17,"is_corresponding":false},{"id":321137,"name":"Mary Fowkes","orcid":"0000-0003-2699-5555","position":18,"is_corresponding":false},{"id":308308,"name":"John F. Crary","orcid":"0000-0002-0556-293X","position":19,"is_corresponding":false},{"id":31866,"name":"Guo‐Cheng Yuan","orcid":"0000-0002-2283-4714","position":20,"is_corresponding":false},{"id":1203,"name":"Panos Roussos","orcid":"0000-0002-4640-6239","position":21,"is_corresponding":false},{"id":284413,"name":"John F. Fullard","orcid":"0000-0001-9874-2907","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-18T23:42:51.282401Z","pmid":"34281603","pmcid":"PMC8287557","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":62.5,"fair_i":0.0,"fair_r":66.6667,"fair_zscore":1.77,"fair_rationale":{"fair_score":79.17,"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":1.0,"verdict":"yes","evidence":"GSE164485","grounded":true,"rationale":"The dataset is identified by the GEO accession GSE164485, which is a persistent identifier scheme accepted by the rubric.","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":"NCBI GEO","grounded":true,"rationale":"The paper names NCBI GEO as the repository holding the data.","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":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","grounded":true,"rationale":"The data availability statement explicitly names a repository (NCBI GEO) and provides a persistent accession (GSE164485), fulfilling the criteria for a repository record. [majority verdict 'yes' (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":0.0,"verdict":"no","evidence":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41].","grounded":false,"rationale":"The dataset content is stated in running prose only, not in an itemized inventory. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (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":"Fullard JF, Lee H-C, Voloudakis G, Suo S, Javidfar B, Zhiping Shao Z, Peter C, Zhang W, Jiang S, Corvelo A, Wargnier H, Woodoff-Leith E, Purohit DP, Ahuja S, Tsankova NM, Jette N, Hoffman GE, Akbarian S, Fowkes M, Crary JF, Yuan G-C, Roussos P: The landscape of human brain immune response in patients with severe COVID-19. Gene Expression Omnibus. 2021. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","grounded":false,"rationale":"The dataset appears as a full bibliographic entry in the reference list, with a persistent identifier and repository name. [downgraded to 'partial' — 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":62.5,"criteria":[{"key":"a_data_openly_accessible","label":"Access route free of preconditions","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","grounded":true,"rationale":"The paper gives a direct download link to a public repository with no stated precondition, fee, registration, or embargo. [majority verdict 'yes' (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":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","grounded":true,"rationale":"The paper describes the action of downloading the data from GEO but does not state an explicit access-level label such as 'open access' or 'publicly available'.","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":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","grounded":true,"rationale":"The data are deposited in a public repository (GEO) with no mention of controlled access, a Data Access Committee, or any institutional gatekeeper, and the paper does not indicate the data are sensitive or require restricted access.","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 statement about retention or availability timing. [majority verdict 'no' (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":0.0,"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":"The paper does not name any file format for the released data.","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 community-standard metadata checklist, ontology, or schema (e.g., MIAME, MINSEQE, GO) is named for the data. The paper uses standard bioinformatics terms but not an explicit community standard.","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":"No identifier for any external resource (e.g., reference genome build, database accession) is provided. [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":66.67,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.","grounded":true,"rationale":"The paper explicitly states that CC0 (Public Domain Dedication) applies to the data, which is an open standard license. [majority verdict 'yes' (3/5 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":"10x Genomics paired-end sequencing reads were processed and aligned on a pre-mRNA reference genome using cell ranger v3.1.0.","grounded":true,"rationale":"The paper names specific instruments and software versions (cell ranger v3.1.0, Illumina NovaSeq 6000) used to generate the 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":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper does not mention any documentation object (README, data dictionary, codebook) that travels with the data, and the variable definitions are not provided in an article table or appendix beyond the supplementary methods, which describe processing rather than define the dataset's fields.","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":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The paper does not provide a version token, release number, or date for the deposited data. The GEO accession GSE164485 does not include a version suffix.","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":0.5,"verdict":"partial","evidence":"Scripts used in this study are available on GitHub [42]: https://github.com/howchihlee/covid_brain_sc.","grounded":false,"rationale":"The paper provides a machine-resolvable URL to the study's code repository on GitHub. [downgraded to 'partial' — no verifiable quote from the paper]","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":"Supported by the National Institute on Aging, NIH grants R01-AG067025 (to P.R.) and R01-AG065582 (to P.R.) and Mount Sinai COVID-19 seed fund 0285VV12 (to S.A.).","grounded":true,"rationale":"The paper attaches specific grant numbers (R01-AG067025, R01-AG065582, 0285VV12) to named funders (NIH, Mount Sinai).","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":"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.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper does not name any file format for the released data.","gain":8.33,"priority":"important","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 clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Fullard JF, Lee H-C, Voloudakis G, Suo S, Javidfar B, Zhiping Shao Z, Peter C, Zhang W, Jiang S, Corvelo A, Wargnier H, Woodoff-Leith E, Purohit DP, Ahuja S, Tsankova NM, Jette N, Hoffman GE, Akbarian S, Fowkes M, Crary JF, Yuan G-C, Roussos P: The landscape of human brain immune response in patients with severe COVID-19. Gene Expression Omnibus. 2021. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","why":"The dataset appears as a full bibliographic entry in the reference list, with a persistent identifier and repository name. [downgraded to 'partial' — no verifiable quote from the paper]","gain":4.17,"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":"partial","current":0.5,"evidence":"Scripts used in this study are available on GitHub [42]: https://github.com/howchihlee/covid_brain_sc.","why":"The paper provides a machine-resolvable URL to the study's code repository on GitHub. [downgraded to 'partial' — no verifiable quote from the paper]","gain":4.17,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper does not provide a version token, release number, or date for the deposited data. The GEO accession GSE164485 does not include a version suffix.","gain":4.17,"priority":"useful","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":"no","current":0.0,"evidence":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41].","why":"The dataset content is stated in running prose only, not in an itemized inventory. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (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":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","why":"The paper describes the action of downloading the data from GEO but does not state an explicit access-level label such as 'open access' or 'publicly available'.","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 clinical / human-subjects, describe the data with OMOP CDM, CDISC SDTM or HL7 FHIR.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No community-standard metadata checklist, ontology, or schema (e.g., MIAME, MINSEQE, GO) is named for the data. The paper uses standard bioinformatics terms but not an explicit community standard.","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":"no","current":0.0,"evidence":null,"why":"The paper does not mention any documentation object (README, data dictionary, codebook) that travels with the data, and the variable definitions are not provided in an article table or appendix beyond the supplementary methods, which describe processing rather than define the dataset's fields.","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. For sensitive/human clinical / human-subjects data, use a controlled-access repository such as dbGaP or EGA.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"Processed and raw data can be downloaded from NCBI GEO (GSE164485) [41]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164485.","why":"The data are deposited in a public repository (GEO) with no mention of controlled access, a Data Access Committee, or any institutional gatekeeper, and the paper does not indicate the data are sensitive or require restricted access.","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":"No identifier for any external resource (e.g., reference genome build, database accession) is provided. [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":"no","current":0.0,"evidence":null,"why":"No statement about retention or availability timing. [majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["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.","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 clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","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).","Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","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":"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":"2026-07-20T11:02:39.052142Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}