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Dustyn","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5994-7341"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/00jmfr291","schemeUri":"https://ror.org","name":"University of Michigan","affiliationIdentifierScheme":"ROR"}],"name":"Romero, Roberto","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4448-5121"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Pique-Regi, Roger","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1262-2275"}]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Xu, Yi","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/04teye511","schemeUri":"https://ror.org","name":"Pontificia Universidad Católica de Chile","affiliationIdentifierScheme":"ROR"}],"name":"Galaz, Jose","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01yc7t268","schemeUri":"https://ror.org","name":"Washington University in St. Louis","affiliationIdentifierScheme":"ROR"}],"name":"Badhan, Riddhishrree","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Kareus, Eva","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-6365-4194"}]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Lee, Seungbaek","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2259-3869"}]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Ino, Hajime","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Niesen, Sarah","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/002pd6e78","schemeUri":"https://ror.org","name":"Massachusetts General Hospital","affiliationIdentifierScheme":"ROR"}],"name":"Monticolo, Francesco","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/002pd6e78","schemeUri":"https://ror.org","name":"Massachusetts General Hospital","affiliationIdentifierScheme":"ROR"}],"name":"Shu, Jian","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/05dq2gs74","schemeUri":"https://ror.org","name":"Vanderbilt University Medical Center","affiliationIdentifierScheme":"ROR"}],"name":"Myers, Luke","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1132-2550"}]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Wylie, Kristine","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Bhatti, Gaurav","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Tarca, Adi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1712-7588"}]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Gomez-Lopez, Nardhy","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3406-5262"}]}],"titles":[{"title":"A multi-omic atlas reveals distinct immune landscapes in preterm and term chronic placental inflammation"}],"publisher":{"publisherIdentifierScheme":"ROR","schemeUri":"https://ror.org/","name":"Dryad","publisherIdentifier":"https://ror.org/00x6h5n95"},"container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Placenta","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Preterm birth","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Reproductive immunology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Levenson, Dustyn","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5994-7341"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/00jmfr291","schemeUri":"https://ror.org","name":"University of Michigan","affiliationIdentifierScheme":"ROR"}],"name":"Romero, Roberto","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4448-5121"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Pique-Regi, Roger","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1262-2275"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Xu, Yi","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/04teye511","schemeUri":"https://ror.org","name":"Pontificia Universidad Católica de Chile","affiliationIdentifierScheme":"ROR"}],"name":"Galaz, Jose","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01yc7t268","schemeUri":"https://ror.org","name":"Washington University in St. Louis","affiliationIdentifierScheme":"ROR"}],"name":"Badhan, Riddhishrree","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Kareus, Eva","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-6365-4194"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Lee, Seungbaek","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2259-3869"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Ino, Hajime","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Niesen, Sarah","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/002pd6e78","schemeUri":"https://ror.org","name":"Massachusetts General Hospital","affiliationIdentifierScheme":"ROR"}],"name":"Monticolo, Francesco","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/002pd6e78","schemeUri":"https://ror.org","name":"Massachusetts General Hospital","affiliationIdentifierScheme":"ROR"}],"name":"Shu, Jian","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/05dq2gs74","schemeUri":"https://ror.org","name":"Vanderbilt University Medical Center","affiliationIdentifierScheme":"ROR"}],"name":"Myers, Luke","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1132-2550"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Wylie, Kristine","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Bhatti, Gaurav","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01070mq45","schemeUri":"https://ror.org","name":"Wayne State University","affiliationIdentifierScheme":"ROR"}],"name":"Tarca, Adi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1712-7588"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"name":"Washington University School of Medicine"}],"name":"Gomez-Lopez, Nardhy","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3406-5262"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2025-12-18T23:02:16Z","dateType":"Created"},{"date":"2026-08-28T12:04:40Z","dateType":"Submitted"},{"date":"2026-09-25T00:00:00Z","dateType":"Issued"},{"date":"2026-09-25T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/scitranslmed.aee9049","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["9817037775 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Chronic placental inflammation (CPI) comprises a spectrum of chronic\n inflammatory lesions associated with preterm birth (PTB), yet its cellular\n and molecular determinants across gestation remain poorly defined. Here,\n we constructed an integrated multi-omic atlas of CPI by profiling\n placental and extraplacental tissues from preterm and term pregnancies\n using imaging mass cytometry, single-cell RNA sequencing, spatial\n transcriptomics, TCR repertoire mining, and DNA methylation analysis. CPI\n emerged as a gestational age-dependent immune continuum rather than a\n uniform inflammatory state. Preterm CPI exhibited unrestricted\n infiltration of cytotoxic CD8⁺ T cells, activated macrophages, and NK\n cells forming self-amplifying inflammatory circuits in the extraplacental\n membranes. In contrast, term CPI exhibited spatially confined immune\n niches enriched for homeostatic myeloid cells and regulatory T cells,\n preserving maternal–fetal tolerance despite persistent inflammation.\n Multi-omic integration revealed active contributions from the developing\n fetal immune system in preterm CPI, including macrophages, T cells, and\n mast cells of fetal origin that reshaped maternal–fetal immune\n communication. Cell–cell communication analyses uncovered pro-inflammatory\n signaling programs in preterm CPI and resolution-phase pathways in term\n CPI, while TCR repertoire profiling identified memory clonotypes with\n viral epitope similarity, consistent with antigen-agnostic activation.\n Genome-wide DNA methylation analysis revealed CPI-associated epigenetic\n remodeling linked to developmental and neurocognitive pathways in PTB.\n Together, this atlas defines the cellular, spatial, and transcriptional\n architecture of CPI and explains how chronic inflammation drives\n prematurity and offspring injury, or alternatively remains compatible with\n term delivery, providing a framework for translational biomarker discovery\n and therapeutic intervention development."},{"descriptionType":"TechnicalInfo","description":"# A multi-omic atlas reveals distinct immune landscapes in preterm and\n term chronic placental inflammation Dataset DOI:\n [10.5061/dryad.np5hqc084](https://doi.org/10.5061/dryad.np5hqc084) ##\n Description of the data and file structure Placental de-identified\n specimens were collected under WSU/NICHD/PRB protocols (WSU IRB#s:\n 110605MP2F, 61217MP2F, and 031318MP2F). The collection and processing of\n these specimens were funded by the former Perinatology Research Branch\n (PRB), Division of Intramural Research, *Eunice Kennedy Shriver* National\n Institute of Child Health and Human Development, National Institutes of\n Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS).\n This funding was provided with federal funds from the NICHD/NIH/DHHS under\n Contract No. HHSN275201300006C. Before sample collection, written informed\n consent was provided by all participants.  ### Files and variables ####\n File: IMC_Segmented_AllSamples.xlsx **Description:** Segmented cellular\n expression data of all samples analyzed by imaging mass cytometry (IMC). n\n = 4 patients who delivered preterm without chronic placental inflammation\n (CPI), 4 patients who delivered preterm with CPI, 6 patients who delivered\n at term without CPI, and 6 patients who delivered at term with CPI. IMC\n includes 1 extraplacental membrane ROI and 1 placenta ROI per patient. \n ##### Variables * cellId: unique numerical identifier for each segmented\n cell * area: area of segmented cells             * mask.0.s.perimeter:\n perimeter of segmented cells * mask.0.s.radius.mean: mean radius value\n estimate of segmented cells               * mask.0.s.radius.sd: standard\n deviation of radius value estimate of segmented cells   *\n mask.0.s.radius.min: minimum radius value estimate of segmented\n cells          * mask.0.s.radius.max: maximum radius value estimate of\n segmented cells        * mask.0.m.cx: spatial x coordinate of segmented\n cells       * mask.0.m.cy: spatial y coordinate of segmented cells       *\n mask.0.m.majoraxis: major axis value of segmented cells             *\n mask.0.m.eccentricity: eccentricity of segmented cells   * mask.0.m.theta:\n orientation angle of segmented cells      * name: denotes the specific\n region of interest (ROI) where segmented cells are from *\n mask.80ArArArAr80Di_ArAr80.b.mean: mean expression value for Ar80 channel\n * mask.aSMA.alpha.smooth.muscle.actin.141PrPr141Di_Pr141.b.mean: mean\n expression value for aSMA channel * mask.CD105.158GdGd158Di_Gd158.b.mean:\n mean expression value for CD105 channel *\n mask.CD11b.Mac.1.164DyDy164Di_Dy164.b.mean: mean expression value for\n CD11b channel * mask.CD137.4.1BB.172YbYb172Di_Yb172.b.mean: mean\n expression value for CD137 channel * mask.CD14.144NdNd144Di_Nd144.b.mean:\n mean expression value for CD14 channel *\n mask.CD15.SSEA.1.171YbYb171Di_Yb171.b.mean: mean expression value for CD15\n channel * mask.CD16.146NdNd146Di_Nd146.b.mean: mean expression value for\n CD16 channel * mask.CD163.147SmSm147Di_Sm147.b.mean: mean expression value\n for CD163 channel * mask.CD194.CCR4.149SmSm149Di_Sm149.b.mean: mean\n expression value for CD194 channel * mask.CD20.161DyDy161Di_Dy161.b.mean:\n mean expression value for CD20 channel *\n mask.CD206.MRC1.167ErEr167Di_Er167.b.mean: mean expression value for CD206\n channel * mask.CD223.LAG.3.153EuEu153Di_Eu153.b.mean: mean expression\n value for CD223 channel * mask.CD25.IL2Ra.175LuLu175Di_Lu175.b.mean: mean\n expression value for CD25 channel *\n mask.CD274.PD.L1.150NdNd150Di_Nd150.b.mean: mean expression value for\n CD274 channel * mask.CD279.PD.1.165HoHo165Di_Ho165.b.mean: mean expression\n value for CD279 channel * mask.CD28.160GdGd160Di_Gd160.b.mean: mean\n expression value for CD28 channel * mask.CD3.170ErEr170Di_Er170.b.mean:\n mean expression value for CD3 channel *\n mask.CD31.PECAM.1.151EuEu151Di_Eu151.b.mean: mean expression value for\n CD31 channel * mask.CD366.TIM.3.154SmSm154Di_Sm154.b.mean: mean expression\n value for CD366 channel * mask.CD4.156GdGd156Di_Gd156.b.mean: mean\n expression value for CD4 channel * mask.CD45.159TbTb159Di_Tb159.b.mean:\n mean expression value for CD45 channel *\n mask.CD45RA.166ErEr166Di_Er166.b.mean: mean expression value for CD45RA\n channel * mask.CD45RO.173YbYb173Di_Yb173.b.mean: mean expression value for\n CD45RO channel * mask.CD56.NCAM.163DyDy163Di_Dy163.b.mean: mean expression\n value for CD56 channel * mask.CD66b.152SmSm152Di_Sm152.b.mean: mean\n expression value for CD66b channel * mask.CD68.148NdNd148Di_Nd148.b.mean:\n mean expression value for CD68 channel *\n mask.CD69.169TmTm169Di_Tm169.b.mean: mean expression value for CD69\n channel * mask.CD8a.162DyDy162Di_Dy162.b.mean: mean expression value for\n CD8a channel * mask.FOXP3.155GdGd155Di_Gd155.b.mean: mean expression value\n for FOXP3 channel * mask.Granzyme.B.142NdNd142Di_Nd142.b.mean: mean\n expression value for Granzyme B channel *\n mask.HLA.DR.174YbYb174Di_Yb174.b.mean: mean expression value for HLA-DR\n channel * mask.Ki.67.168ErEr168Di_Er168.b.mean: mean expression value for\n Ki67 channel * mask.NA1.191IrIr191Di_Ir191.b.mean: mean expression value\n for NA1 channel * mask.NA2.193IrIr193Di_Ir193.b.mean: mean expression\n value for NA2 channel * mask.PanCK.176YbYb176Di_Yb176.b.mean: mean\n expression value for PanCK channel *\n mask.T.bet.TBX21.145NdNd145Di_Nd145.b.mean: mean expression value for Tbet\n channel * mask.Vimentin.143NdNd143Di_Nd143.b.mean: mean expression value\n for Vimentin channel * ID: unique string identifier for each patient from\n which tissue samples derived * Tissue: tissue from which segmented cells\n derived, either fetal (extraplacental) membrane or placenta * X.Delivery:\n delivery status of patient, either preterm or term delivery * type: CPI\n status of patient, either control = no CPI or case = CPI #### File:\n IMC_ExtraplacentalMembrane_Raw.zip **Description:** Folder with\n sub-folders containing the raw image files for each sample analyzed by\n imaging mass cytometry. Each sub-folder contains\n \"_metaltag.tiff\" files for each acquired channel, and a\n \\_mask.tiff\" for the cell mask.  n = 4 patients who delivered preterm\n without CPI, 4 patients who delivered preterm with CPI, 6 patients who\n delivered at term without CPI, and 6 patients who delivered at term with\n CPI. IMC includes 1 extraplacental membrane ROI and 1 placenta ROI per\n patient.  #### File: IMC_Placenta_Raw.zip **Description:** Folder with\n sub-folders containing the raw image files for each sample analyzed by\n imaging mass cytometry. Each sub-folder contains\n \"_metaltag.tiff\" files for each acquired channel, and a\n \\_mask.tiff\" for the cell mask. n = 4 patients who delivered preterm\n without CPI, 4 patients who delivered preterm with CPI, 6 patients who\n delivered at term without CPI, and 6 patients who delivered at term with\n CPI. IMC includes 1 extraplacental membrane ROI and 1 placenta ROI per\n patient.  #### File: scRNA-seq_ExtraplacentalMembrane.zip\n **Description:** Seurat objects for all extraplacental membrane samples\n analyzed by single-cell RNA sequencing.\n “scRNA_extraplacentalmembranes_allcells.rds” contains all annotated cell\n clusters, while “scRNA_extraplacentalmembranes_immunecellsonly.rds”\n contains subclustered immune cells only. n = 4 patients who delivered\n preterm without CPI, 2 patients who delivered preterm with CPI.\n **Variables** * nCount_RNA: count value of RNA molecules in each cell *\n nFeature_RNA: count value of number of genes in each cell   * Library:\n unique character identifier for pooled library * NUM.READS: number of\n reads per cell * NUM.SNPS: number of SNPs detected per cell        * EXP:\n synonymous with Library * Origin: denotes origin of sample, M = maternal\n and F = fetal   * Condition: delivery status of patient, sPTL = preterm\n labor           * Group: CPI status of patient, Control_Preterm = preterm\n labor with no CPI and CPI_Preterm = preterm labor with CPI * Location: all\n CAM (extraplacental membranes) * NEW_BARCODE: unique sequence of\n oligonucleotides that identifies each cell * percent.mt: percentage of\n mitochondrial DNA per cell * RNA_snn_res.0.5: number of identified cell\n clusters with resolution parameter set to 0.5 #### File:\n spatial_ExtraplacentalMembranes.zip **Description:** Spatial seurat\n objects for all extraplacental membrane samples analyzed by Visium spatial\n transcriptomics, including the high-resolution tissue\n images. “spatial_extraplacentalmembranes_controlsamples.RData” contains\n all control samples, while\n “spatial_extraplacentalmembranes_CPIsamples.RData” contains all samples\n with chronic placental inflammation. n = 7 patients who delivered preterm\n without CPI, 8 patients who delivered preterm with CPI. **Variables** *\n orig.ident: numerical identifier that specifies unique spots *\n nCount_Spatial: count value of captured RNA per spot * nFeature_Spatial:\n count value of number of genes per spot * percent_mito: percentage of\n mitochondrial genes per spot * nCount_SCT: sum of all SCT-transformed gene\n expression values per spot * nFeature_SCT: count of genes that have\n non-zero SCT-transformed values per spot #### File: scRNA-seq_Placenta.zip\n **Description:** Seurat objects for all placenta samples analyzed by\n single-cell RNA sequencing. “scRNA_placenta_allcells.rds” contains all\n annotated cell clusters, while “scRNA_placenta_immunecellsonly.rds”\n contains subclustered immune cells only. n = 20 patients who delivered\n preterm without CPI, 15 patients who delivered preterm with CPI, 11\n patients who delivered at term without CPI, and 9 patients who delivered\n at term with CPI. **Variables** * nCount_RNA: count value of RNA molecules\n in each cell * nFeature_RNA: count value of number of genes in each cell  \n * Library: unique character identifier for pooled library * NUM.READS:\n number of reads per cell * NUM.SNPS: number of SNPs detected per\n cell             * EXP: synonymous with Library  * Origin2: denotes origin\n of sample, M = maternal and F = fetal * NEW_BARCODE: unique sequence of\n oligonucleotides that identifies each cell * percent.mt: percentage of\n mitochondrial DNA per cell * RNA_snn_res.0.5: number of identified cell\n clusters with resolution parameter set to 0.5 * CCA_Group: identifies\n group that each patient belongs to, Control_Preterm = preterm labor\n without CPI, CCA_Preterm = preterm labor with CPI, Control_Term = term\n labor without CPI, and CCA_Term = term labor with CPI * CCA_Case:\n identifies CCA status of patient, Control = no CPI and CCA = CPI *\n CCA_Term: identifies delivery status of patient, either preterm or term ##\n Code/software R, RStudio ## Access information Other publicly accessible\n locations of the data: * dpGaP ID: phs001886.v6 ## Human subjects data All\n human subjects provided 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It works in double precision without intermediate ASCII files, and agrees with the Fortran codes to 6 to 7 significant digits."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.18842738","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-03-03T00:05:40Z","registered":"2026-03-03T00:05:41Z","published":null,"updated":"2026-10-13T02:25:32Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.18829197","type":"dois","attributes":{"doi":"10.5281/zenodo.18829197","identifiers":[{"identifier":"oai:zenodo.org:18829197","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/00jmfr291","name":"University of Michigan","affiliationIdentifierScheme":"ROR"}],"givenName":"Jeroen","familyName":"Ritsema","name":"Ritsema, Jeroen","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-4287-7639"}]},{"nameType":"Personal","affiliation":[{"name":"University of Oxford Exeter College"},{"affiliationIdentifier":"https://ror.org/052gg0110","name":"University of Oxford","affiliationIdentifierScheme":"ROR"}],"givenName":"Paula","familyName":"Koelemeijer","name":"Koelemeijer, Paula","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0001-5153-3040"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/019wvm592","name":"Australian National University","affiliationIdentifierScheme":"ROR"}],"givenName":"Siavash","familyName":"Ghelichkhan","name":"Ghelichkhan, Siavash","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-1316-3170"}]}],"titles":[{"title":"srts: tomographic filtering of geodynamic models"}],"publisher":{"name":"Zenodo"},"container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-03-02","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"SoftwareSourceCode","resourceTypeGeneral":"Software","citeproc":"article","bibtex":"misc","ris":"COMP","resourceType":""},"relatedIdentifiers":[{"relationType":"IsSupplementTo","resourceTypeGeneral":"Software","relatedIdentifier":"https://github.com/g-adopt/srts/tree/v0.1.0","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.18829196","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v0.1.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rights":"MIT License","rightsIdentifier":"mit"}],"descriptions":[{"descriptionType":"Abstract","description":"srts is a Python package that applies the resolution operators of the S12RTS, S20RTS and S40RTS shear-velocity models to geodynamic models, following the method of Ritsema et al. (2007). A geodynamic model is expanded in spherical harmonics, projected onto the 21-knot cubic spline depth basis of the tomographic models, and multiplied by the model resolution matrix. The result is the structure that the tomographic inversion would recover from the geodynamic model, given the data coverage and damping of the real inversion.\u003c/p\u003e\u003cp\u003eThe package is a Python implementation of the Fortran tomographic filtering codes of Jeroen Ritsema and Paula Koelemeijer. It works in double precision without intermediate ASCII files, and agrees with the Fortran codes to 6 to 7 significant digits."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.18829197","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-03-02T01:06:37Z","registered":"2026-03-02T01:06:38Z","published":null,"updated":"2026-10-13T02:21:02Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5061/dryad.51c59zwd4","type":"dois","attributes":{"doi":"10.5061/dryad.51c59zwd4","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/02gp35s66","schemeUri":"https://ror.org","name":"Universidade Federal da Integração 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Franco","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01zwq4y59","schemeUri":"https://ror.org","name":"Universidade Estadual de Santa Cruz","affiliationIdentifierScheme":"ROR"}],"name":"Solé, Mirco","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/02263ky35","schemeUri":"https://ror.org","name":"Universidade Federal do Amazonas","affiliationIdentifierScheme":"ROR"}],"name":"Menin, Marcelo","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/00987cb86","schemeUri":"https://ror.org","name":"Universidade Estadual Paulista (Unesp)","affiliationIdentifierScheme":"ROR"}],"name":"Rossa-Feres, Denise","nameIdentifiers":[]}],"titles":[{"title":"Data from: Explaining the anuran beta diversity by pond-living tadpoles: the role of dispersal limitation and environmental gradients through multiple scales"}],"publisher":{"publisherIdentifierScheme":"ROR","schemeUri":"https://ror.org/","name":"Dryad","publisherIdentifier":"https://ror.org/00x6h5n95"},"container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Community ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Species diversity","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/02gp35s66","schemeUri":"https://ror.org","name":"Universidade Federal da Integração Latino-Americana","affiliationIdentifierScheme":"ROR"}],"name":"Garey, Michel","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7482-792X"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/00jmfr291","schemeUri":"https://ror.org","name":"University of Michigan","affiliationIdentifierScheme":"ROR"}],"name":"Gonçalves-Souza, Thiago","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2023-03-23T00:03:41Z","dateType":"Created"},{"date":"2023-09-26T19:19:15Z","dateType":"Submitted"},{"date":"2026-10-12T00:00:00Z","dateType":"Issued"},{"date":"2026-10-12T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ddi.13781","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["276573 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Aim: We employed a large-scale standardized protocol to tease apart the\n niche- and dispersal-based processes driving beta diversity patterns of\n pond-living tadpoles across multiple scales. Specifically, we addressed\n the following questions: (i) what roles do pond-level features, climate,\n and spatial variables play in anuran beta diversity across geographical\n scales? (ii) What are the effects of environmental and spatial factors on\n endemic and widespread species? Location: ponds were distributed across\n 5,000,000 km2 in five Brazilian biomes. Methods: We sampled tadpoles in\n five biomes in Brazil, ranging from three to eight locations, and eight to\n 61 ponds sampled per location. We used partial Redundancy Analyses in\n combination with variation partitioning to assess the relative influence\n of environmental and spatial variables on beta-diversity. We performed\n these analyses using fixed grain and varying geographical extent\n considering three scales: metacommunity, biome, and subcontinental scales.\n Results: We revealed that increasing the spatial extent diminishes the\n contribution of niche-based processes while dispersal-based processes gain\n relevance for beta-diversity. We verified that spatially structured\n environmental variation was the most important driver of anuran\n beta-diversity, highlighting that species track environmental gradients\n that are spatial structured over broad spatial scales. At the regional\n scale, the spatial component is as important as spatially structured\n environmental variation, and the environmental component was more\n important at the metacommunity scale. We found that endemic and widespread\n species responded equally to environmental variables, but the spatially\n structured environmental variation was more important to widespread\n species while the pure spatial component was more important to endemic\n species. Main conclusions: Our results reveal that spatial scale is\n crucial to comprehend niche- and dispersal-based processes that generate\n beta-diversity patterns. Our findings highlight that comparing endemic and\n widespread species respond in an analogous way to niche- and\n dispersal-based processes, even though they are under different\n environmental conditions."},{"descriptionType":"TechnicalInfo","description":"# Data from: Explaining the anuran beta diversity by pond-living tadpoles:\n the role of dispersal limitation and environmental gradients through\n multiple scales Dataset DOI:\n [https://doi.org/10.5061/dryad.51c59zwd4](https://doi.org/https://doi.org/10.5061/dryad.51c59zwd4) ## Description of the data and file structure The data are from the species composition in 358 water bodies and the structural environmental variables of these water bodies. ### Files and variables #### File: comm_all_sites.csv **Description:** Matrix of amphibian species presence/absence data (i.e., species composition) for each sampled water body, considering the entire dataset. The first column contains the code corresponding to each sampled water body, while the remaining columns contain the species recorded. ##### Variables * site code = water body code * Remaining columns are names of species; the values are abundance #### File: comm_restr.csv **Description:** Matrix of amphibian species presence/absence data (i.e., species composition) per pond for species with restricted distribution. The first column contains the code corresponding to each sampled water body, while the remaining columns contain the species recorded. ##### Variables * site code = water body code * Remaining columns are names of species; the values are abundance #### File: comm_wide.csv **Description:** Matrix of amphibian species presence/absence data (i.e., species composition) per pond for species with wide distribution. The first column contains the code corresponding to each sampled water body, while the remaining columns contain the species recorded. ##### Variables * site code = water body code * Remaining columns are names of species; the values are abundance #### File: env_restr.csv **Description:** Local environmental variables sampled in each water body where species with restricted distribution occurred. ##### Variables * site_code: water body code * Biome: Brazilian biomes.  We used the following classification: tropical moist broadleaf forests (here called Amazon), tropical and subtropical moist broadleaf forests (here called Atlantic rain forest), xeric shrublands (here called Caatinga), savanna (here called Cerrado) and flooded grasslands (here called Pantanal). * Location: metacommunity name * Latitude: in decimal degrees. * Longitude: in decimal degrees. * hidroperiod:  Categorical variable classified as permanent (i.e., never dries out) or temporary (i.e., dries out at some point during the year). * depth: Continuous quantitative variable representing the maximum depth of the water body, measured in meters. * MV_none: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area without vegetation. * MV_herbaceous: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by herbaceous vegetation * MV_erect_herb: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by upright herbaceous vegetation * MV_shrub: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by shrub vegetation * MV_trees: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by arboreal vegetation * MV_typha: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by *Typha* spp. * canopy cover: A continuous quantitative variable measured as a percentage. The value represents canopy cover over the water body; thus, higher values indicate greater canopy closure over the water body. * fish: A categorical variable indicating the presence (yes) or absence (no) of predatory fish in the water body. #### File: env_all_sites.csv **Description:** Local environmental variables sampled in each water body considering all database ##### Variables * site_code: water body code * Biome: Brazilian biomes.  We used the following classification: tropical moist broadleaf forests (here called Amazon), tropical and subtropical moist broadleaf forests (here called Atlantic rain forest), xeric shrublands (here called Caatinga), savanna (here called Cerrado) and flooded grasslands (here called Pantanal). * Location: metacommunity name * Latitude: in decimal degrees. * Longitude: in decimal degrees. * hidroperiod:  Categorical variable classified as permanent (i.e., never dries out) or temporary (i.e., dries out at some point during the year). * depth: Continuous quantitative variable representing the maximum depth of the water body, measured in meters. * MV_none: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area without vegetation. * MV_herbaceous: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by herbaceous vegetation * MV_erect_herb: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by upright herbaceous vegetation * MV_shrub: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by shrub vegetation * MV_trees: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by arboreal vegetation * MV_typha: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by *Typha* spp. * canopy cover: A continuous quantitative variable measured as a percentage. The value represents canopy cover over the water body; thus, higher values indicate greater canopy closure over the water body. * fish: A categorical variable indicating the presence (yes) or absence (no) of predatory fish in the water body. #### File: env_wide.csv **Description:** Local environmental variables sampled in each water body where species with wide distribution occurred ##### Variables * site_code: water body code * Biome: Brazilian biomes. We used the following classification: tropical moist broadleaf forests (here called Amazon), tropical and subtropical moist broadleaf forests (here called Atlantic rain forest), xeric shrublands (here called Caatinga), savanna (here called Cerrado) and flooded grasslands (here called Pantanal). * Location: metacommunity name * Latitude: in decimal degrees. * Longitude: in decimal degrees. * hidroperiod:  Categorical variable classified as permanent (i.e., never dries out) or temporary (i.e., dries out at some point during the year). * depth: Continuous quantitative variable representing the maximum depth of the water body, measured in meters. * MV_none: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area without vegetation. * MV_herbaceous: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by herbaceous vegetation * MV_erect_herb: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by upright herbaceous vegetation * MV_shrub: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by shrub vegetation * MV_trees: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by arboreal vegetation * MV_typha: A quantitative variable measured as a decimal, ranging from 0 (absence) to 1 (the entire marginal area covered by this category), representing the proportion of the water body's marginal area covered by *Typha* spp. * canopy cover: A continuous quantitative variable measured as a percentage. The value represents canopy cover over the water body; thus, higher values indicate greater canopy closure over the water body. * fish: A categorical variable indicating the presence (yes) or absence (no) of predatory fish in the water body. ## Code/software The spreadsheets are in csv format and can be opened in any spreadsheet editor. Data were analyzed using R software. ## Access information Other publicly accessible locations of the data: * n/a Data was derived from the following sources: * n/a"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","funderIdentifier":"https://ror.org/03swz6y49","awardNumber":"422537/2016-0"},{"funderIdentifierType":"ROR","funderName":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","funderIdentifier":"https://ror.org/03swz6y49","awardNumber":"301306/2018-4"},{"funderIdentifierType":"ROR","funderName":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","funderIdentifier":"https://ror.org/03swz6y49","awardNumber":"563075/2010-4"},{"funderIdentifierType":"ROR","funderName":"Fundação de Amparo à Pesquisa do Estado de São Paulo","funderIdentifier":"https://ror.org/02ddkpn78","awardNumber":"2010/523217"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.51c59zwd4","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":2,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-12T21:50:45Z","registered":"2026-10-12T21:50:46Z","published":null,"updated":"2026-10-12T21:50:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5281/zenodo.23324610","type":"dois","attributes":{"doi":"10.5281/zenodo.23324610","identifiers":[{"identifier":"oai:zenodo.org:23324610","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/0161xgx34","name":"Université de 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Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"\u0026lt;h3\u0026gt;LocalView is a database co-created by \u0026lt;a href:\"mailto:sbarari@g.harvard.edu\"\u0026gt;Soubhik Barari\u0026lt;/a\u0026gt;, \u0026lt;a href=\"mailto:tsimko@umich.edu\"\u0026gt;Tyler Simko\u0026lt;/a\u0026gt;, and \u0026lt;a href=\"mailto:mpreece@mit.edu\"\u0026gt;Mason Reece\u0026lt;/a\u0026gt; to advance the study of local government in the United States.\u0026lt;/h3\u0026gt;\u0026lt;br\u0026gt;\n\nIt is the \u0026lt;i\u0026gt;largest existing dataset of local government public meetings\u0026lt;/i\u0026gt;— the central policy-making process in American local government — as they are captured on video. \u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\n\nTo get started, select the file(s) that you'd like for your use case based on the year that the meeting took place. Note: we are no longer supporting file formats other than \u0026lt;code\u0026gt;.parquet\u0026lt;/code\u0026gt; for space considerations. \u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\n\nFor potential use cases or further guidance on downloading the data in bulk, visit the companion website: \u0026lt;a href=\"https://www.localview.net\"\u0026gt;localview.net\u0026lt;/a\u0026gt;.\n\n\u0026lt;h1\u0026gt;Change Log\u0026lt;/h1\u0026gt;\n\n\u0026lt;p\u0026gt;Scraping, parsing, identifying, and merging together meetings\ninvolves a large number of non-trivial decisions, many of which need to\nbe adjusted over time particularly as the YouTube API changes.\u0026lt;/p\u0026gt;\n\n\u0026lt;p\u0026gt;As such, when such decisions notably deviate from process or the\noutputs documented in the \u0026lt;a href=\"https://soubhikbarari.com/writings/localview.pdf\"\u0026gt;first version of\nthis database\u0026lt;/a\u0026gt;, it will be logged here.\u0026lt;/p\u0026gt;\n\n\u0026lt;div id=\"version-3.0-2026-05\" class=\"section level2\"\u0026gt;\n \u0026lt;h2\u0026gt;Version 3.0 (2026-05)\u0026lt;/h2\u0026gt;\n\n \u0026lt;ul\u0026gt;\n \u0026lt;li\u0026gt;\n Data updated until November 2025. Moving forward, data will be automatically updated to always be six months behind present.\n \u0026lt;/li\u0026gt;\n\n \u0026lt;li\u0026gt;\n Some new channels have been added. Please reach out to us via email if there are new local meeting channels not featured that we should include.\n \u0026lt;/li\u0026gt;\n\n \u0026lt;li\u0026gt;\n Codebook has been updated and simplified. ACS related columns have been removed.\n \u0026lt;/li\u0026gt;\n\n \u0026lt;li\u0026gt;\n Data format has been modified to separate transcripts from metadata\n \u0026lt;ul\u0026gt;\n \u0026lt;li\u0026gt;\n First, download \u0026lt;code\u0026gt;meta_localview.parquet\u0026lt;/code\u0026gt;, then filter to the channels and/or videos of interest.\n \u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\n Then, merge in transcripts as desired from \u0026lt;code\u0026gt;transcripts_localview_*.zip\u0026lt;/code\u0026gt;. Transcript file names are identified as \"\u0026lt;code\u0026gt;channel_id\u0026lt;/code\u0026gt;__\u0026lt;code\u0026gt;id\u0026lt;/code\u0026gt;.txt.gz\".\n \u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\n The transcript files have been separated into two parts only because of Dataverse size constraints, otherwise they are identical.\n \u0026lt;/li\u0026gt;\n \u0026lt;/ul\u0026gt;\n \u0026lt;/li\u0026gt;\n \u0026lt;/ul\u0026gt;\n\u0026lt;/div\u0026gt;\n\n\u0026lt;div id=\"version-2.0-2023-10\" class=\"section level2\"\u0026gt;\n\u0026lt;h2\u0026gt;Version 2.0 (2023-10)\u0026lt;/h2\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Data updated up until September 2023.\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;~10,000 new videos added, all belonging to existing channels in\ndatabase.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Change in data format:\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;channelType\u0026lt;/code\u0026gt; column changed to\n\u0026lt;code\u0026gt;channel_type\u0026lt;/code\u0026gt;.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;ST_FIPS\u0026lt;/code\u0026gt; correctly padded to be 7 characters (2 digit\nstate code + 5 digit place FIPS code).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;videos with no caption available from YouTube are explicitly marked\nas “\u0026lt;No caption available\u0026gt;” in \u0026lt;code\u0026gt;caption_text\u0026lt;/code\u0026gt;.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;caption_text_cleaned\u0026lt;/code\u0026gt; is actually consistently cleaned\n(previously stray timestamps/pause markers in some entries).\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_2018_*\u0026lt;/code\u0026gt; columns now prefixed as\n\u0026lt;code\u0026gt;acs_18\u0026lt;/code\u0026gt;.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;additional ACS variables now available for each place:\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_median_gross_rent\u0026lt;/code\u0026gt;: Median gross rent in FIPS\nplace.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_median_hh_inc\u0026lt;/code\u0026gt;: Median household income in FIPS\nplace.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_median_age\u0026lt;/code\u0026gt;: Median age in FIPS place.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_amind\u0026lt;/code\u0026gt;: American Indian population in FIPS\nplace.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_asian\u0026lt;/code\u0026gt;: Asian population in FIPS place.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;acs_18_nhapi\u0026lt;/code\u0026gt;: Native Hawaiian or Pacific Islander\npopulation in FIPS place.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;\u0026lt;code\u0026gt;census_2015_*\u0026lt;/code\u0026gt; columns removed for redundancy.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;to avoid confusion and possible conflicts, \u0026lt;code\u0026gt;.json\u0026lt;/code\u0026gt; and\n\u0026lt;code\u0026gt;.csv\u0026lt;/code\u0026gt; file formats eliminated in favor of\n\u0026lt;code\u0026gt;.parquet\u0026lt;/code\u0026gt; format.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;municipal voteshare (\u0026lt;code\u0026gt;_dem2pv\u0026lt;/code\u0026gt;) variables have been\nremoved from the public use files for a number of reasons: (1) high\ndegree of missingness, (2) no columns estimates available, (3) potential\nsensitivity\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;The matching process of videos to ST-FIPS and government types is as\nfollows:\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;videos are matched to channels’ previous ST-FIPS codes and/or\ngovernment types \u0026lt;em\u0026gt;if\u0026lt;/em\u0026gt; there is an umambiguous match, otherwise a\n(1) series of regex matches with video title/description are used to\nattempt to match video to the government and (2) state/place names are\nextracted from each video’s title/description/caption and used to match\nto an ST-FIPS entity \u0026lt;em\u0026gt;if\u0026lt;/em\u0026gt; an unambiguous match; only identified\nvideos are then uploaded to the database.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;to identify the date of the meeting that the video captures, we\nfirst try to extract the date from the title, otherwise we try to\nextract the date from the description, otherwise it is discarded.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;/div\u0026gt;\n\u0026lt;div id=\"version-1.0-2023-02\" class=\"section 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