{"doi":"10.1038/s41597-021-00976-x","title":"A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images","abstract":"Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.","journal":"Scientific Data","year":2021,"id":213041,"datarank":1.775622476793493,"base_score":3.7612001156935624,"endowment":3.7612001156935624,"self_citation_contribution":0.5641800173540344,"citation_network_contribution":1.2114424594394584,"self_endowment_contribution":0.5641800173540344,"citer_contribution":1.2114424594394584,"corpus_percentile":88.13336427632088,"corpus_rank":1535,"citation_count":42,"citer_count":42,"citers_with_citation_signal":28,"citers_with_endowment":28,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9311,"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":75.0,"fair_percentile":95.6282482421278,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":485996,"name":"Asterios Toutios","orcid":"0000-0003-3193-2241","position":1,"is_corresponding":false},{"id":548815,"name":"Yannick Bliesener","orcid":"0000-0001-5436-1918","position":2,"is_corresponding":false},{"id":478980,"name":"Ye Tian","orcid":"0000-0002-8559-4404","position":3,"is_corresponding":false},{"id":804901,"name":"Sajan Goud Lingala","orcid":null,"position":4,"is_corresponding":false},{"id":585422,"name":"Colin Vaz","orcid":"0000-0002-5709-7953","position":5,"is_corresponding":false},{"id":804273,"name":"Tanner Sorensen","orcid":"0000-0002-3111-9974","position":6,"is_corresponding":false},{"id":804274,"name":"Miran Oh","orcid":"0000-0002-7371-9820","position":7,"is_corresponding":false},{"id":804275,"name":"Sarah Harper","orcid":"0000-0002-8453-9454","position":8,"is_corresponding":false},{"id":804276,"name":"Weiyi Chen","orcid":"0000-0001-5116-8645","position":9,"is_corresponding":false},{"id":777201,"name":"Yoonjeong Lee","orcid":"0000-0003-1323-049X","position":10,"is_corresponding":false},{"id":804277,"name":"Johannes Töger","orcid":"0000-0002-3365-7282","position":11,"is_corresponding":false},{"id":804902,"name":"Mairym Lloréns Monteserin","orcid":null,"position":12,"is_corresponding":false},{"id":760324,"name":"Caitlin Smith","orcid":null,"position":13,"is_corresponding":false},{"id":580865,"name":"Bianca Godinez","orcid":null,"position":14,"is_corresponding":false},{"id":589486,"name":"Louis Goldstein","orcid":null,"position":15,"is_corresponding":false},{"id":589238,"name":"Dani Byrd","orcid":"0000-0003-3319-5871","position":16,"is_corresponding":false},{"id":280605,"name":"Krishna S. Nayak","orcid":"0000-0001-5735-3550","position":17,"is_corresponding":false},{"id":263280,"name":"Shrikanth Narayanan","orcid":"0000-0002-1052-6204","position":18,"is_corresponding":false},{"id":280606,"name":"Yongwan Lim","orcid":"0000-0003-0070-0034","position":0,"is_corresponding":true}],"reference_count":75,"raw_metadata":null,"created_at":"2026-07-18T23:52:31.378994Z","pmid":"34285240","pmcid":"PMC8292336","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":94.4444,"fair_a":81.25,"fair_i":80.0,"fair_r":58.3333,"fair_zscore":1.6051,"fair_rationale":{"fair_score":75.0,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":94.44,"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/10.6084/m9.figshare.13725546.v1","grounded":true,"rationale":"The paper provides a DOI for the dataset in the reference list, which is a persistent identifier 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":1.0,"verdict":"yes","evidence":"This dataset is publicly available in figshare52.","grounded":true,"rationale":"Figshare is a recognised data repository listed in re3data and FAIRsharing.","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":"This dataset is publicly available in figshare52.","grounded":true,"rationale":"The statement points to a repository record (figshare) with a DOI, matching Colavizza category 3.","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":1.0,"verdict":"yes","evidence":"Data Records","grounded":true,"rationale":"The paper contains a dedicated 'Data Records' section with a table (Table 5) and itemised folder structure, serving as an itemised inventory.","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":"52. 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[downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (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":"Raw RT-MRI data are provided in the vendor-agnostic MRD format (previously known as ISMRMRD, https://ismrmrd.github.io/)53","grounded":true,"rationale":"ISMRMRD/MRD is a community standard for raw MRI data, registered in FAIRsharing.","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":"https://ismrmrd.github.io/","grounded":true,"rationale":"The paper provides a URL for the MRD format (ISMRMRD), which is a resource identifier for a standard used by the data. [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":58.33,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No reuse license is stated for the data; the CC BY license applies only to the article, and the MIT license applies only to the code.","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":"All data were collected using a commercial 1.5 Tesla MRI scanner (Signa Excite, GE Healthcare, Waukesha, WI)","grounded":true,"rationale":"The paper names the specific instrument (GE Signa Excite) used to produce 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":0.5,"verdict":"partial","evidence":"Demographic information for each participant is contained in XLSX format in Subjects.xlsx. 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'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 reuse license is stated for the data; the CC BY license applies only to the article, and the MIT license applies only to the code.","gain":16.67,"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":"52. Lim, Y. et al. A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images. figshare https://doi.org/10.6084/m9.figshare.13725546.v1 (2021).","why":"The dataset appears as a reference-list entry with a DOI, satisfying data citation principles. [downgraded to 'partial' — no verifiable quote from the paper]","gain":4.17,"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":"partial","current":0.5,"evidence":"HDF5 format","why":"The paper states that reconstructed image data are in HDF5 format, which is an open, community-standard format. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (3/5 passes agreed)]","gain":4.17,"priority":"important","scored":true},{"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":"Demographic information for each participant is contained in XLSX format in Subjects.xlsx. 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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":"This dataset is publicly available in figshare52.","why":"The data are from human subjects but are publicly available with no gatekeeper named; the paper does not specify any institutional or personal gatekeeper.","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":"This dataset is publicly available in figshare52.","why":"The paper states the dataset is available now but does not commit to any retention period or persistence.","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.","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.","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.","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.","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."],"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":"2026-07-20T11:24:28.692580Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}