{"data":[{"id":"10.7910/dvn/tt00uu","type":"dois","attributes":{"doi":"10.7910/dvn/tt00uu","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["https://ror.org/04ejags36"],"givenName":"Chloé","familyName":"Mayeur","name":"Mayeur, Chloé","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0507-7774"}]},{"nameType":"Personal","affiliation":["https://ror.org/04ejags36"],"givenName":"Wannes","familyName":"Van Hoof","name":"Van Hoof, Wannes","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0300-9823"}]}],"titles":[{"title":"Citizens' contributions on the online DNA Debate"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2023,"subjects":[{"subject":"Medicine, Health and Life Sciences"},{"subject":"Social Sciences"},{"subject":"Ethics"},{"subject":"Public engagement"},{"subject":"Genomics"}],"contributors":[{"nameType":"Personal","affiliation":["Sciensano"],"givenName":"Chloé","familyName":"Mayeur","name":"Mayeur, Chloé","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2023-02-20","dateType":"Submitted"},{"date":"2023-02-20","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["371339"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.1","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset gathers all anonymised contributions posted by Belgian citizens on the online DNA Debate platform. The DNA Debate was a public engagement initiative organised by Sciensano in 2019-2020 to investigate Belgian citizens' attitudes toward the ethical, legal, and societal issues of the use of genomic information."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/TT00UU","contentUrl":null,"metadataVersion":4,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":41,"downloadCount":4,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-02-20T11:00:30Z","registered":"2023-02-20T11:02:35Z","published":null,"updated":"2026-08-18T08:45:56Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/piobbh","type":"dois","attributes":{"doi":"10.7910/dvn/piobbh","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Bank for International Settlements"],"givenName":"Sebastian","familyName":"Doerr","name":"Doerr, Sebastian","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["École Polytechnique Fédérale de Lausanne"],"givenName":"Andreas","familyName":"Fuster","name":"Fuster, Andreas","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4256-5005"}]}],"titles":[{"title":"Replication Package for: Affordable Housing, Unaffordable Credit? Concentration and High-Cost Lending for Manufactured Homes"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Bank for International Settlements"],"givenName":"Sebastian","familyName":"Doerr","name":"Doerr, Sebastian","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-06","dateType":"Submitted"},{"date":"2026-08-18","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["2097152000","486434514","2097152000","3874741","4868","7128","21056","34037","19982018","39288"],"formats":["application/octet-stream","application/octet-stream","application/octet-stream","application/zip","text/x-stata-syntax","text/x-stata-syntax","text/plain","application/zip","application/zip","text/markdown"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsUri":"https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/PIOBBH","rights":"Custom terms specific to this dataset"}],"descriptions":[{"descriptionType":"Abstract","description":"Replication package for RFS MS 20250210. The package reproduces all figures and tables in the paper and the Online Appendix from public HMDA loan-level data and public county-level sources. All tables are reported in the log files in results/log/ rather than written to separate files. The raw data are supplied as a three-part 7-Zip archive (data_raw.7z.001–.003); the split and the 7-Zip format were agreed with the RFS Data Editor in advance, as individual Dataverse files are limited to 2.5 GB. Derived analysis datasets are not included: they are reconstructed by the code from the supplied raw data. See README.md for full instructions."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/PIOBBH","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-06T15:16:31Z","registered":"2026-08-18T07:48:47Z","published":null,"updated":"2026-08-18T07:48:47Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/apduap","type":"dois","attributes":{"doi":"10.7910/dvn/apduap","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Ghent University"],"givenName":"Olivier","familyName":"Degomme","name":"Olivier Degomme","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4360-0220"}]}],"titles":[{"title":"Data: Economic Costs and Missed Opportunities: Promoting Cervical Cancer Screening in Hard-to-Reach Populations in Belgium, Ecuador, and Portugal"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Medicine, Health and Life Sciences"},{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Ghent University"],"givenName":"Olivier","familyName":"Degomme","name":"Olivier Degomme","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-18","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["20167"],"formats":["text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Cervical cancer remains a major cause of cancer-related mortality, largely driven by insufficient screening coverage. Educational interventions can play a key role in improving uptake, particularly among underscreened populations. However, their implementation costs are seldom included in economic evaluations. This study aimed to: (1) estimate the cost per woman screened through community-based education; (2) identify key barriers to screening uptake; and (3) evaluate the potential gains in uptake and reduced costs per woman screened if those barriers were addressed."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/APDUAP","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T16:39:29Z","registered":"2026-08-18T05:07:43Z","published":null,"updated":"2026-08-18T05:07:43Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/rfa3xz","type":"dois","attributes":{"doi":"10.7910/dvn/rfa3xz","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Federal University of Parana, Brazil"],"givenName":"Adriano","familyName":"Codato","name":"Codato, Adriano","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5015-4273"}]},{"nameType":"Personal","affiliation":["Universidade Federal do Paraná"],"givenName":"Roberta","familyName":"Picussa","name":"Picussa, Roberta","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5400-5136"}]},{"nameType":"Personal","affiliation":["https://ror.org/04bqqa360"],"givenName":"Ednaldo","familyName":"Ribeiro","name":"Ribeiro, Ednaldo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4005-5108"}]}],"titles":[{"title":"Outsider Deputies in Argentina, Brazil, and Chile (2017–2023)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"},{"subject":"Political outsiders"}],"contributors":[{"nameType":"Personal","affiliation":["Federal University of Parana, Brazil"],"givenName":"Adriano","familyName":"Codato","name":"Codato, Adriano","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-06","dateType":"Submitted"},{"date":"2026-08-10","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["130","2107","419","1193","7520","2674","1365","7222","6852","13860","7595","227130","2229","606","11839","7063","204","666","23113","193295","9637","14479","14348"],"formats":["text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/markdown","text/markdown","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/markdown","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/markdown","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","text/tab-separated-values","text/markdown","text/markdown"],"version":"1.1","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset documents the prior political and partisan experience of legislative newcomers in the lower chambers of Argentina, Brazil, and Chile, assembled to examine how, and under what conditions, anti-establishment presidential victories translate into the recruitment of outsider deputies. The unit of analysis is the individual first-term deputy, defined as a legislator who had never previously held elective office at any level of government. The dataset comprises the complete population of such newcomers, 406 cases, rather than a sample; accordingly, the inferential statistics reported in the associated work should be read as measures of the magnitude and stability of observed differences, not as generalizations to a larger population. For each deputy, prior trajectory is coded through dichotomous indicators of political-partisan experience, in which the value 1 denotes the absence of the experience or tie, that is, greater outsiderism. From these indicators two complementary measures are derived: an additive index over the six indicators common to all three countries, comparable across cohorts by construction, and an index estimated through Item Response Theory, appropriate for ranking cases within a single cohort. Coverage spans seven legislative elections, Argentina (2019, 2021, 2023), Brazil (2018, 2022), and Chile (2017, 2021), held between 2017 and 2023. The information was compiled manually from official legislative biographies, electoral-authority records, party registries, institutional pages, and, where necessary, national and regional press."},{"descriptionType":"Other","description":"The additive index (INDICE_ADITIVO_6) is the principal, cross-cohort-comparable measure. The Item Response Theory index is valid only for ranking cases within a given cohort and must not be used to compare mean levels across cohorts: separate per-cohort calibration fixes each cohort's latent mean at zero by identification, so differences in level do not survive estimation. Because five of the six common indicators exhibit differential item functioning across countries, cross-national comparisons should be treated as approximate, and within-country contrasts are privileged throughout. The indicator of party-affiliation tenure was not systematically collected in Argentina and is therefore excluded from the comparable measure; consequently, the seven-indicator index is empty for Argentine cases. A Chile-specific eighth indicator, formal party affiliation, reflects the possibility of running as an independent in that country. The dataset derives from the doctoral dissertation of Roberta Picussa (Universidade Federal do Paraná, 2024) and underpins the associated article by Codato, Picussa, and Ribeiro. Users should consult the CHANGELOG for the data and documentation corrections introduced in version 1.0 and the methodological note for the full account of construction, psychometric diagnostics, and robustness."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/RFA3XZ","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-06T21:20:19Z","registered":"2026-08-11T00:26:29Z","published":null,"updated":"2026-08-18T05:07:15Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/odmok0","type":"dois","attributes":{"doi":"10.7910/dvn/odmok0","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["King Fahd University of Petroleum and Minerals (KFUPM)"],"givenName":"Nuha","familyName":"Albadi","name":"Albadi, Nuha","nameIdentifiers":[]}],"titles":[{"title":"Niswa: A Decade-Long Corpus and Annotated Dataset of Twitter Discourse on Women in Saudi Arabia"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Computer and Information Science"},{"subject":"Social Sciences"},{"subject":"Saudi Arabia; Twitter; Social Media; Arabic; Stance; Topic Classification; Women’s Rights; Social Reforms; Longitudinal Data"}],"contributors":[{"nameType":"Personal","affiliation":["King Fahd University of Petroleum and Minerals (KFUPM)"],"givenName":"Nuha","familyName":"Albadi","name":"Albadi, Nuha","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-18","dateType":"Available"},{"date":"2011-01-10/2021-07-31","dateType":"Other","dateInformation":"Time period covered by the data"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["146838","683245","146436","83704370"],"formats":["application/json","application/json","application/json","application/json"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains a longitudinal collection of women-related Twitter discourse in Saudi Arabia spanning approximately ten years, from January 2011 to July 2021. It comprises a large-scale unlabeled corpus of 1,017,564 tweets and a human-annotated dataset of 5,000 tweets. The annotated dataset is organized into predefined training, validation, and test splits to facilitate standardized model development and evaluation.\n\nThe annotated data provide gold-standard labels for stance and discourse topics. The stance annotation distinguishes between supportive, traditional opposition, sarcastic opposition, negative stereotyping, abusive or hateful, and neutral or irrelevant discourse. The topic annotation is multi-label, allowing each tweet to be associated with one or more topics, including social and cultural issues, driving, work and leadership, legal autonomy, and other topics."}],"geoLocations":[],"fundingReferences":[{"funderName":"King Fahd University of Petroleum and Minerals","awardNumber":"EC2620"}],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/ODMOK0","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T16:07:16Z","registered":"2026-08-18T05:04:45Z","published":null,"updated":"2026-08-18T05:04:45Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/2hi67x","type":"dois","attributes":{"doi":"10.7910/dvn/2hi67x","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Kent"],"givenName":"Robert","familyName":"de Vries","name":"de Vries, Robert","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["King\u0026apos;s College London"],"givenName":"Mark","familyName":"Hill","name":"Hill, Mark","nameIdentifiers":[]},{"nameType":"Personal","givenName":"Laura","familyName":"Ruis","name":"Ruis, Laura","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Replication Data for: Exploring occupational prestige through Large Language Models:large language models: A multi-dimensional approach"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"},{"subject":"Stratification"},{"subject":"Prestige"},{"subject":"LLM"},{"subject":"Large Language Model"},{"subject":"ChatGPT"}],"contributors":[{"nameType":"Personal","affiliation":["University of Kent"],"givenName":"Robert","familyName":"de Vries","name":"de Vries, Robert","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-18","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["10861","4440398","4208444","1286265","4643879","4327515","10645","4566722","8555","4360056","3935481","4891914","5050656","4784922","4530007","4847101","4288608","4614119","5853720","5762221","19806"],"formats":["type/x-r-syntax","text/comma-separated-values","text/tab-separated-values","application/vnd.openxmlformats-officedocument.wordprocessingml.document","text/comma-separated-values","text/comma-separated-values","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","text/comma-separated-values","type/x-r-syntax","text/tab-separated-values","application/vnd.openxmlformats-officedocument.wordprocessingml.document","text/comma-separated-values","text/comma-separated-values","text/tab-separated-values","text/tab-separated-values","text/comma-separated-values","text/comma-separated-values","text/comma-separated-values","text/comma-separated-values","text/tab-separated-values","type/x-r-syntax"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Replication Data for: Exploring occupational prestige through Large Language Models:large language models: A multi-dimensional approach. Published in Research in Social Stratification and Mobility: https://www.sciencedirect.com/science/article/abs/pii/S0276562426000661"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/2HI67X","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T15:20:05Z","registered":"2026-08-18T05:03:35Z","published":null,"updated":"2026-08-18T05:03:35Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/zmqrih","type":"dois","attributes":{"doi":"10.7910/dvn/zmqrih","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["ETH Zürich"],"givenName":"Sarah","familyName":"Gomm","name":"Gomm, Sarah","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2354-6439"}]},{"nameType":"Personal","affiliation":["GESIS - Leibniz-Institute for the Social Sciences"],"givenName":"Franziska","familyName":"Quoß","name":"Quoß, Franziska","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9549-2922"}]}],"titles":[{"title":"Replication Data for: How to measure environmental concern: an IRT evaluation and advancement of established survey scales"},{"titleType":"Subtitle","title":"Published in Environmental Politics (10.1080/09644016.2026.2718610)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["ETH Zürich"],"givenName":"Sarah","familyName":"Gomm","name":"Gomm, Sarah","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2025-12-08","dateType":"Submitted"},{"date":"2026-08-18","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["104927","400377","6285","7320","1230","9158","1498","6068","318","275162","202398","88","395279","7480","207426","679","5083","571","318","6337","12464","870","6170","267170","5617","457185","8988","747","362","736","6383","1052","12613","1498","1498","1498","15822","173631","1265","6037","905","2123","5843","4842","1498","4438","195837","6063","6795","555883","6346","362","1498"],"formats":["type/x-r-syntax","application/octet-stream","application/pdf","application/pdf","application/x-tex","application/pdf","application/x-tex","application/pdf","image/png","image/png","image/png","text/tab-separated-values","image/png","application/x-tex","image/png","image/png","application/pdf","text/tab-separated-values","image/png","application/pdf","application/pdf","image/png","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/pdf","application/pdf","image/png","application/pdf","application/x-tex","image/png","application/x-tex","application/pdf","application/x-tex","application/pdf","application/x-tex","application/x-tex","application/x-tex","application/pdf","image/png","application/x-tex","application/pdf","application/x-tex","application/x-tex","application/pdf","application/pdf","application/x-tex","image/png","image/png","application/pdf","application/pdf","application/pdf","application/pdf","image/png","application/x-tex"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Measuring environmental concern is central to environmental public opinion research, yet the field lacks a comprehensive and consistent approach. Existing measures are limited by a lack of theoretical consensus, scarce validation, outdated wording, and, critically, limited precision at the upper end of the scale. We address these limitations by developing and validating a refined measure, defining environmental concern as people’s affective, cognitive, and conative positions toward human-induced problems and their willingness to solve them. Using Item Response Theory (IRT) on a large, representative Swiss sample (N = 7932), we assess 36 established survey questions to identify validated items with high measurement quality that reflect our core theoretical interest. We select the best-performing items and validate their power to predict pro-environmental behavior and policy support. We propose a new, precise combination of established survey items that significantly improves measurement at high levels of environmental concern and offer practical recommendations for survey researchers."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/ZMQRIH","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-12-08T10:51:13Z","registered":"2026-08-18T05:01:36Z","published":null,"updated":"2026-08-18T05:01:36Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/kcvkyf","type":"dois","attributes":{"doi":"10.7910/dvn/kcvkyf","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Rice University"],"givenName":"Yu-En","familyName":"Wong","name":"Wong, Yu-En","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5578-797X"}]},{"nameType":"Personal","affiliation":["Rice Univesrity"],"givenName":"Songtao","familyName":"Chen","name":"Chen, Songtao","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4206-6131"}]}],"titles":[{"title":"Replication Data for: Cavity-assisted single-shot 𝑇-center spin readout"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Physics"},{"subject":"Quantum Physics"}],"contributors":[{"nameType":"Personal","affiliation":["Rice University"],"givenName":"Songtao","familyName":"Chen","name":"Chen, Songtao","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-18","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1103/r54v-jfbl","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["65232","86736","4001368","961664","1808","2384","1088","4001368","464","32656","1464","2384","2240","17392"],"formats":["application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat","application/matlab-mat"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"High-fidelity spin readout is a crucial component for quantum information processing with optically interfaced solid-state spins. Here, we propose and investigate two theoretical protocols for fast single-shot readout of cavity-coupled single T center electronic spins. For fluorescence-based readout, we selectively couple one of the T center spin-conserving transitions to a single-mode photonic cavity, exploiting the enhancement of the fluorescence emission and cyclicity. For reflection-based readout, we leverage the spin-dependent cavity reflection contrast to generate the qubit readout signal. We show that the cavity reflection approach enables high-fidelity spin readout even when the T center only has a modest cyclicity. With realistic system parameters, such as cavity quality factor 𝑄 = 200,000 and T center optical linewidth 𝛤/2⁢𝜋 = 100 MHz, we calculate a single-shot readout fidelity exceeding 99% within 10 𝜇s for both spin readout protocols."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/KCVKYF","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T15:28:42Z","registered":"2026-08-18T04:59:44Z","published":null,"updated":"2026-08-18T04:59:44Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/lm1odz","type":"dois","attributes":{"doi":"10.7910/dvn/lm1odz","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["ElectionLAB"],"givenName":"Alvaro","familyName":"Ruiz","name":"Ruiz, Alvaro","nameIdentifiers":[]}],"titles":[{"title":"SSMEP: A Harmonized Multilevel Panel of Spanish Elections, 1977–2023"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"},{"subject":"Elections"},{"subject":"Spain"},{"subject":"Political Science"},{"subject":"Electoral Panel"},{"subject":"Congress"},{"subject":"Senate"},{"subject":"Referendum"},{"subject":"Party System"},{"subject":"Electoral Volatility"},{"subject":"Nationalization"}],"contributors":[{"nameType":"Personal","affiliation":["ElectionLAB"],"givenName":"Alvaro","familyName":"Ruiz","name":"Alvaro Ruiz","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["496","1253834","21355","3951","57897","19310009","773930","18831","11192288","10405499","19512","26901"],"formats":["text/tab-separated-values","text/tab-separated-values","text/comma-separated-values","text/markdown","text/tab-separated-values","application/x-gzip","text/tab-separated-values","text/tab-separated-values","application/octet-stream","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"SMEP (Spanish Multilevel Electoral Panel) is a harmonized municipal dataset covering Spain's Congress of Deputies elections (1977–2023, 16 elections), Senate elections (1986–2023, 13 elections), and the four national referendums of Spain's democratic period (1976, 1978, 1986, 2005). It contains 3,180,515 rows across 8,270 historical municipality codes. Unlike the existing reference archive for Spain (Spanish Electoral Archive, SEA), SMEP systematically incorporates the Senate (open-list vote aggregation), the referendums, a party-family harmonization layer verified against academic sources (96.3% vote-weighted coverage), and a year-sensitive territorial crosswalk resolving municipal mergers. The dataset passes a turnout-identity check with zero exceptions across 3.18 million rows and reconstructs the real Congress seat allocation exactly (350/350) across all 16 elections. Full methodology and validation are documented in the accompanying Data Descriptor article."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/LM1ODZ","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T14:41:24Z","registered":"2026-08-18T03:52:19Z","published":null,"updated":"2026-08-18T03:52:19Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/mjr8qi","type":"dois","attributes":{"doi":"10.7910/dvn/mjr8qi","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["MIT"],"givenName":"Yifan","familyName":"Su","name":"Su, Yifan","nameIdentifiers":[]}],"titles":[{"title":"Replication Data for: Time-domain identification of distinct mechanisms for competing charge density waves in a rare-earth tritelluride"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Physics"}],"contributors":[{"nameType":"Personal","givenName":"Yifan","familyName":"Su","name":"Su, Yifan","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-06-02","dateType":"Submitted"},{"date":"2026-07-01","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["249325","183665","245217","216782","170287","247352","196666","170298","170643","196067","22692","169993","213585","210542","170562","217374","170072","186762","229865","23585"],"formats":["text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","text/plain","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"2.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Replication Data for: Time-domain identification of distinct mechanisms for competing charge density waves in a rare-earth tritelluride\n\nFig1, 2, and 3. Fig 4 does not include original experimental data."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/MJR8QI","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-06-02T18:36:24Z","registered":"2026-07-01T23:27:49Z","published":null,"updated":"2026-08-18T03:51:48Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/dvytk6","type":"dois","attributes":{"doi":"10.7910/dvn/dvytk6","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Kyonggi University"],"givenName":"Seungwoo","familyName":"Han","name":"Han, Seungwoo","nameIdentifiers":[]}],"titles":[{"title":"Generational Divergence, Structural Cleavages, and Contingent Optimism in South Korean Unification Attitudes"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2025,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Kyonggi University"],"givenName":"Seungwoo","familyName":"Han","name":"Han, Seungwoo","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2025-08-09","dateType":"Submitted"},{"date":"2025-08-09","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["1796222","24962","875452"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/x-ipynb+json","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"2.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This study examines how generational positioning, structural cleavages, and political events interact to shape Korean attitudes toward unification. This study integrates insights from political sociology, political psychology, and comparative politics to assess domain-specific expectations regarding unification’s potential to reduce economic inequality, mitigate regional disparity, and ease political polarization. Using nationally representative Unification Perception Survey data (2013–2024), regression models reveal consistent generational divergence: older cohorts hold higher, more integrated expectations, whereas younger cohorts express lower and fragmented optimism. Temporal analysis shows a short-lived peak in 2019, following intensive 2018 inter-Korean diplomacy, with counterfactual simulation confirming a statistically and substantively significant event effect. However, optimism quickly regressed absent tangible progress. Findings suggest that sustaining support requires aligning symbolic momentum with credible, domain-specific policy outcomes, especially for younger cohorts, while addressing structural divisions that otherwise erode receptivity to unification discourse."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/DVYTK6","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":8,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-08-09T16:06:34Z","registered":"2025-08-09T16:07:00Z","published":null,"updated":"2026-08-18T03:49:21Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/tqo50f","type":"dois","attributes":{"doi":"10.7910/dvn/tqo50f","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Jomo Kenyatta University of Agriculture \u0026amp; Technology"],"givenName":"Richard","familyName":"Wanzala","name":"Wanzala, Richard","nameIdentifiers":[]}],"titles":[{"title":"Replication Data for: From AI Capabilities to Self-Evolving Business Models: The Sequential Roles of Business Model Innovation and Organizational Adaptation"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Business and Management"},{"subject":"artificial intelligence"}],"contributors":[{"nameType":"Personal","affiliation":["Jomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya"],"givenName":"Richard","familyName":"Wamalwa Wanzala","name":"Richard Wamalwa Wanzala","contributorType":"Producer","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Jomo Kenyatta University of Agriculture \u0026amp; Technology"],"givenName":"Richard","familyName":"Wanzala","name":"Wanzala, Richard","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["74020"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains hypothetical/synthetic panel data constructed for methodological illustration and replication of the empirical framework presented in the manuscript “From AI Capabilities to Self-Evolving Business Models: The Sequential Roles of Business Model Innovation and Organizational Adaptation.” The dataset represents 50 hypothetical firms in China observed annually from 2021 to 2025, yielding 250 firm-year observations. The variables cover artificial intelligence capabilities (AI_CAP), AI-driven automation (AI_AUT), business model innovation (BMI), feedback mechanisms (FEEDBACK), organisational adaptation (ORG_ADAPT), firm performance (PERF), competitive advantage (COMP_ADV), firm size (FSIZE), firm age (FAGE), leverage (LEV), and R\u0026amp;D intensity (RD_INT). The repository includes the panel dataset, descriptive statistics, correlation matrix, variable codebook, and model-ready data with lagged variables. Because the data are synthetic, they should not be interpreted as observations collected from actual firms or as empirical evidence. The dataset is provided solely for methodological demonstration, transparency, and reproducibility of the analytical framework."},{"descriptionType":"Other","description":"This dataset contains the firm-level and firm-year observations used in the study to examine the relationships among artificial intelligence capabilities, AI-driven automation, business model innovation, organisational adaptation, firm performance, and competitive advantage among selected Chinese firms over the 2021–2025 study period. The dataset includes the variables, observations, and derived measures underlying the empirical analyses reported in the manuscript. The data are provided to support research transparency, reproducibility, and verification of the study's empirical findings."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/TQO50F","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T13:36:30Z","registered":"2026-08-18T03:43:03Z","published":null,"updated":"2026-08-18T03:43:03Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/mgstd4","type":"dois","attributes":{"doi":"10.7910/dvn/mgstd4","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Joseph","familyName":"Jones","name":"Jones, Joseph","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Virginia Blank Shapefiles and excel workbooks"},{"titleType":"AlternativeTitle","title":"Virginia Public Mapping Project 1996-2021"},{"titleType":"AlternativeTitle","title":"Virginia Voting precincts 1996-2011"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2025,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","givenName":"Joseph","familyName":"Jones","name":"Jones, Joseph","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2025-05-22","dateType":"Submitted"},{"date":"2025-05-22","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["17402201","854379","17412477","1090136","17481766","940342","1930506","21774798","17523243","3226742","17529766","2161004","655244","933399","1000329","8231337","19971516","844173","2119265","10669","3066405","22342363","2074364","1335626","17463848","1552073","2661600","1274190","18411047","3142564","4333107","2523122","19035076","7997","125793","125813","126782","126770","2472313","19625569","17484109","1274134","858341","17527291","19612397","2453242","19608835","19631624","19745886","1176229","19632083","19754242","19754769","933346","1176937","1586530","19810643","2203963","2258797","1810790","1711880","19726873","19726872","1534096","19729665","19657707","1877542","1924726","19728909","1945061","19601227","19601395","1193076","19524239","1196039","18068018","1333863","18069321","792183","27302150","1896044","19620794","1428334","29001001","1948006","1744670","29633008","1583264","2528279","19049436","928269","1899573","1626932","1000616","1000620","1312413","1345416","17526189","2034617","1493211","1359489","1126158","21353543","21353543","945542","19757860","1232162","19761626","1770819","461780","928477","1232140","1570779","1372740","2772395","1843061","2277434","2783557","1845235","1687950","1687948","773525","19718021","1496399","1831433","19831734","1852906","1966616","17530755","17526274","2969147","17526274","17533840","2866879","2869601","17531233","2414515","2395607","2395148","19722847","18069306","19620651","19051343","19048278","4589772","4723979","19067898","4684495","17540601","2109800","2480796","19070298","19069969","19073714","1267084","19600721","19602619","1791793","19652153","19527120","19541374","1257560","1253802","1264329","644523","29776355","1816599","956901","1564015","2284844","1910417","2299895","19761615","1930890","1769114","1221603","21353543","810220","5386939","5667418","7071341","7028120","716528","1605803","6581469","8197093","8197095","1091419","1629514","1649919","4935944","8368877","8371916","672084","5400668","1625051","1653972","29627896","1654557","2317396","18030787","1440754","1656278","842796","18164382","29724555","18150971","12416707","8368037","8458906","27305707","12267261","27647707","27640473","12719703","15487687","15157896","12598406","8660397","8137301","1086786","7641429","8198306","38859030","17531898","7632360","12585545","7390928","12621648","12651214","1659306","2721851","1664238","7411294","8210298","1142941","2124302","7524218","2127378","7537596","8068923","24751168","4886363","24755747","24768966","7869673","24912770","24915806","7870900","7883745","24931498","7903182","24951548","25343078","5649821","7892799","4981893","24937865","5707595","6215058","5983488","5934269","7856557","7858932","7859030","5408101","790532","1603253","6163088","8332041","828258","665127","7856571","25486498","26116883","19095263","717471","995180","5408101","743048","1059623","1069523","797375","801108","844876","852538","854267","264327","1097687","138686","1097687","222639","757867","1022492","2785873","98185","1099496","119318","1164867","1224308","2129285","2179562","1766598","1771323","1771208","1773735","1816658","1817303","2213177","5885653","5848511","5595642","7855701","5613230","1892342","5623622","5625747","7883964","5768223","8013953","25302323","1627306","6825546","21534400","697589","19153009","7011827","8210285","400914","5979478","223371","19747737","27641608","8224571","5306497","8221739","8209401","8210690","5639429","2159232","26349705","2207505","2208872","8260702","4658147","9254792","19098395","12791255","7041343","12470626","3457120","1621698","1621690","1621331","1621223","8332476","2219927","1487167","19671684","1696956","1778538","1116961","21532178","2058788","19096639","8269448","5559327","21497676","9235568","8272748","789758","2267087","7059828","7051699","25273528","7077633","25273528","7855871","25278490","5967944","5977468","5979493","6912427","19835195","25369597","19693706","6919406","19835391","6922651","19545912","6924762","6625933","7107640","6927857","20362883","7907576","7906818","24979242"],"formats":["application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","text/csv","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.wordprocessingml.document","application/pdf","application/pdf","application/pdf","application/pdf","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zipped-shapefile","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/zip","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/pdf","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/zip","application/zip","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"311.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"shapefiles and their corresponding excel workbooks, it is a collection of shapefiles similar to https://github.com/erikalopresti/virginia-voting-precincts except for the 1990-2009 era"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/MGSTD4","contentUrl":null,"metadataVersion":324,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1004,"downloadCount":455,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-05-22T18:45:55Z","registered":"2025-05-22T18:46:10Z","published":null,"updated":"2026-08-18T03:08:09Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/hhxynt","type":"dois","attributes":{"doi":"10.7910/dvn/hhxynt","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["McLean Hospital/Harvard Medical School"],"givenName":"Staci","familyName":"Gruber","name":"Gruber, Staci","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6921-677X"}]}],"titles":[{"title":"Clinical Change and Cognitive Performance in Middle-Aged to Older Adults Using Medical Cannabis for Up to 12 Months"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Medicine, Health and Life Sciences"},{"subject":"medical cannabis"}],"contributors":[{"nameType":"Personal","affiliation":["McLean Hospital/Harvard Medical School"],"givenName":"Kelly","familyName":"Sagar","name":"Sagar, Kelly","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-14","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["212100"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Longitudinal data for medical cannabis and treatment-as-usual patients at baseline and following 3, 6, and 12 months. Data include demographic information, clinical rating scales, cognitive measures, and information regarding medical cannabis use regimens."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/HHXYNT","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-14T17:48:20Z","registered":"2026-08-18T02:53:37Z","published":null,"updated":"2026-08-18T02:53:37Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/lmqkuq","type":"dois","attributes":{"doi":"10.7910/dvn/lmqkuq","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Mario","familyName":"Menichella","name":"Menichella, Mario","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Ugo","familyName":"Abundo","name":"Abundo, Ugo","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Federico","familyName":"Galli","name":"Galli, Federico","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Guido","familyName":"Parchi","name":"Parchi, Guido","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Supplementary Material for “A Multidimensional Parameter-Space Framework for Low Energy Nuclear Reactions”"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Physics"}],"contributors":[{"nameType":"Personal","givenName":"Mario","familyName":"Menichella","name":"Menichella, Mario","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["28961"],"formats":["application/zip"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This repository contains the source code and numerical datasets used to generate the figures, tables, and illustrative analyses presented in the article “A Multidimensional Parameter-Space Framework for Low Energy Nuclear Reactions”. \nThe files include calculations related to picometric confinement, electron screening, tunneling enhancement, mass-window proximity metrics, qualitative model-parameter mappings, and indicative threshold estimates. \nThe material is provided to support transparency, reproducibility, and independent verification of the results reported in the manuscript. The framework is phenomenological and organizational in nature; the datasets primarily document the computational procedures and illustrative analyses described in the paper."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/LMQKUQ","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T20:20:52Z","registered":"2026-08-18T02:49:02Z","published":null,"updated":"2026-08-18T02:49:02Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/gvruvx","type":"dois","attributes":{"doi":"10.7910/dvn/gvruvx","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Yonsei University"],"name":"Seoyeon Kim","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0001-1307-7686"}]},{"nameType":"Personal","affiliation":["Yonsei University"],"givenName":"Inbok","familyName":"Rhee","name":"Rhee, Inbok","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2081-0652"}]}],"titles":[{"title":"Replication Data for: The Great Divide: The Global North/South Gap in Migration Research"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Yonsei University"],"name":"Inbok Rhee","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-16","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"},{"date":"2026-08-18","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["8418520"],"formats":["application/zip"],"version":"1.1","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Whose migration does political science study? Prior bibliometric work establishes that authorship is concentrated in the Global North. We start there and show that the geography of production maps onto that of the questions the field publishes. Using structural topic modeling on 4,553 SSCI Political Science articles (2002–2024), we confirm that research is steeply concentrated, far more unequally distributed than migration itself (Gini 0.94 versus 0.80), and clustered in a few high-income countries. High-income countries host 69% of the world’s migrants but produce 95% of the research. This divide extends to what gets studied. Among published articles, North-based authors more often study immigration attitudes, welfare chauvinism, and populist parties, South-based authors diaspora politics, labor exploitation, and local integration. Because tests of prominent receiving-society frameworks are concentrated in high-income settings, how far they travel remains largely untested, and origin-country dynamics and migrant experiences stay at the field’s margins."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/GVRUVX","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-16T08:53:07Z","registered":"2026-08-17T20:07:58Z","published":null,"updated":"2026-08-18T02:44:38Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/6yp6fi","type":"dois","attributes":{"doi":"10.7910/dvn/6yp6fi","identifiers":[],"creators":[{"nameType":"Personal","givenName":"THI CHINH","familyName":"VO","name":"VO, THI CHINH","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Replication Data for: Nonlinear relationship between uncertainty and FDI: Dynamic panel threshold analysis"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"}],"contributors":[{"nameType":"Personal","givenName":"THI CHINH","familyName":"VO","name":"VO, THI CHINH","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["286297","33274","13927"],"formats":["text/tab-separated-values","text/x-stata-syntax","text/markdown"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Replication data and code for the paper \"Nonlinear relationship between uncertainty and FDI: Dynamic panel threshold analysis\" by Thi Chinh Vo, Taeyoon Kim and Donghwan An. The study examines whether host-country macroeconomic uncertainty affects foreign direct investment only through its level, or also by changing the relative importance of the standard determinants of FDI. The empirical strategy combines a dynamic interaction model estimated by two-step system GMM with a dynamic panel threshold model in which the entire parameter vector is allowed to differ across two endogenously estimated uncertainty regimes."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/6YP6FI","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T11:19:35Z","registered":"2026-08-18T02:15:53Z","published":null,"updated":"2026-08-18T02:15:53Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/crcvya","type":"dois","attributes":{"doi":"10.7910/dvn/crcvya","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Kelly","familyName":"Dougherty","name":"Dougherty, Kelly","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0005-5889-1832"}]}],"titles":[{"title":"County level analysis comprising public data for Family Medicine-Gen Surg Dyads"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Medicine, Health and Life Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Kelly","familyName":"Dougherty","name":"Dougherty, Kelly","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-02-21","dateType":"Submitted"},{"date":"2026-02-23","dateType":"Available"},{"date":"2026-08-17","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["5297118","795188","5894"],"formats":["text/html","text/tab-separated-values","text/tab-separated-values"],"version":"2.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Linked datasets \n• AMA Masterfile: Physician specialty and county of practice location data\n• County Health Rankings: Age-adjusted premature mortality data\n• Area Health Resource Files: Hospital location and disease-specific death rates\n• EPA Air Quality Index: Percentage of days with poor air quality\n• U.S. Census Data: Poverty rates, health insurance coverage, and population centers within counties\n• Centers for Medicare \u0026amp; Medicaid Services: Geographic inflation-adjusted medical costs and risk scores using hierarchical condition categories"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/CRCVYA","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":8,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-02-21T17:07:08Z","registered":"2026-02-23T17:40:52Z","published":null,"updated":"2026-08-17T23:43:38Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/4dubjx","type":"dois","attributes":{"doi":"10.7910/dvn/4dubjx","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Ada","familyName":"Fang","name":"Fang, Ada","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0003-7957-1905"}]},{"nameType":"Personal","affiliation":["MIT"],"givenName":"Michael","familyName":"Desgagné","name":"Desgagné, Michael","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Harvard University"],"name":"Zaixi Zhang","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Andrew","familyName":"Zhou","name":"Andrew Zhou","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Harvard Medical School"],"givenName":"Joseph","familyName":"Loscalzo","name":"Loscalzo, Joseph","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["MIT"],"givenName":"Bradley","familyName":"Pentelute","name":"Pentelute, Bradley","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7242-801X"}]},{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Marinka","familyName":"Zitnik","name":"Marinka Zitnik","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8530-7228"}]}],"titles":[{"title":"Learning Universal Representations of Intermolecular Interactions with ATOMICA"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2025,"subjects":[{"subject":"Chemistry"},{"subject":"Computer and Information Science"},{"subject":"Medicine, Health and Life Sciences"}],"contributors":[{"nameType":"Personal","affiliation":["Harvard University"],"givenName":"Ada","familyName":"Fang","name":"Fang, Ada","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-04-12","dateType":"Submitted"},{"date":"2025-04-02","dateType":"Available"},{"date":"2026-08-17","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["436969","109686","139775","2605845","227023","1404346699","7089739","2616073958","2792820","53339541","502259276","57193859","324436","105449814","69038123","4590020","194350982","21048537","41289880","132879043","55285687","16785015","12700087","12799626","12764384","11809864","7460707","7517537","11849320","7559505","7521809","7542889","12202176","12779490","10264981","7505514","11764457","12064998","11021341","7580284","11821308","7466861","14328029","4511346","4718642","5092933","4621912","168528","89416322","3868970","1644950","991707","938152","4050298","3807680","4086955","19424871","3930588","963883","3597695","11562277","2460788","982678","11291257","1178391","2759436","667578","115345095","474642","12467132","776376","2116581","222711","562558102","8556041","325883","72962","4589197","4588316","2791199","4589250","2790280","4588105","2790024","4589290","2792355","4579733","4586055","4557436","2791508","2791396","4577118","4588594","2791456","2792157","4588940","2790819","4580488"],"formats":["text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","application/x-gzip","application/x-gzip","application/x-gzip","text/tab-separated-values","application/x-gzip","application/x-gzip","application/x-gzip","text/tab-separated-values","application/x-gzip","application/x-gzip","text/tab-separated-values","text/csv","application/x-gzip","application/x-gzip","application/x-gzip","application/x-gzip","application/x-gzip","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","application/gml+xml","application/gml+xml","application/gml+xml","application/gml+xml","application/gml+xml","text/tab-separated-values","application/x-gzip","application/x-gzip","application/x-gzip","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","application/octet-stream","text/comma-separated-values","application/octet-stream","text/tab-separated-values","application/octet-stream","application/octet-stream","application/octet-stream","text/tab-separated-values","application/octet-stream","application/octet-stream","text/tab-separated-values","application/octet-stream","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values"],"version":"3.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Datasets used in developing \u0026amp; evaluating ATOMICA. See preprint here: https://www.biorxiv.org/content/10.1101/2025.04.02.646906"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/4DUBJX","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":621,"downloadCount":144,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-03-27T16:55:13Z","registered":"2025-04-03T00:59:58Z","published":null,"updated":"2026-08-17T22:19:39Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/e5j8p9","type":"dois","attributes":{"doi":"10.7910/dvn/e5j8p9","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidad de Navarra"],"givenName":"Lizeth Katherine","familyName":"Vilcherrez Pizarro","name":"Vilcherrez Pizarro, Lizeth Katherine","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9932-3007"}]},{"nameType":"Personal","affiliation":["Universidad de Navarra"],"givenName":"Begoña","familyName":"Urien","name":"Urien, Begoña","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7476-6362"}]},{"nameType":"Personal","affiliation":["Universidad de Navarra"],"givenName":"María Ángeles","familyName":"Sotés Elizalde","name":"Sotés Elizalde, María Ángeles","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1008-7640"}]}],"titles":[{"title":"Perfiles de investigación de las universidades públicas peruanas: estructura multivariada y distribución territorial, 2020-2024"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Social Sciences"},{"subject":"Peruvian public universities; scientific production; research profiles; research funding; research human capital; Canon resources; Scopus; Web of Science; principal component analysis; PERMANOVA; territorial distribution; Peru"}],"contributors":[{"nameType":"Personal","affiliation":["Universidad de Navarra"],"givenName":"Lizeth Katherine","familyName":"Vilcherrez Pizarro","name":"Vilcherrez Pizarro, Lizeth Katherine","contributorType":"Producer","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidad de Navarra"],"givenName":"Lizeth Katherine","familyName":"Vilcherrez Pizarro","name":"Vilcherrez Pizarro, Lizeth Katherine","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Created"},{"date":"2026-08-17","dateType":"Submitted"},{"date":"2026-08-17","dateType":"Available"},{"date":"2020-01-01/2024-12-31","dateType":"Other","dateInformation":"Time period covered by the data"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["33193"],"formats":["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","lang":"en","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains the aggregated institutional data used to analyze the research profiles of Peruvian public universities during the 2020–2024 period. The dataset includes five indicators: number of Docentes Investigadores beneficiaries of the Bonificación Especial para el Docente Investigador (BDI), executed institutional funding for research, executed funding from Canon resources, scientific production indexed in Web of Science, and scientific production indexed in Scopus.\n\nThe dataset includes 47 public universities with BDI beneficiaries in each year of the 2020-2024 period. The regional classification comprises four categories: Lima, Costa, Sierra and Selva. Region was not included as an active variable in the Principal Component Analysis (PCA), but was subsequently used as a territorial classification variable and in the PERMANOVA analysis.\n\nThe file provides the university-level indicators accumulated for 2020–2024, annual university-level observations, variable descriptions, PCA eigenvalues, variable contributions and university coordinates on the principal dimensions."},{"descriptionType":"TechnicalInfo","description":"RStudio, 2026.05.0+218"},{"descriptionType":"Methods","description":"Peruvian public universities; public administrative records; Scopus; Web of Science"},{"descriptionType":"Methods","description":"Existing data"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/E5J8P9","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-11T20:42:41Z","registered":"2026-08-17T21:54:09Z","published":null,"updated":"2026-08-17T21:54:09Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/lcuyoj","type":"dois","attributes":{"doi":"10.7910/dvn/lcuyoj","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Settlement Insight Research"],"givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","nameIdentifiers":[]}],"titles":[{"title":"NPDB Medical Malpractice Payments — Aggregated Dataset (2000–2025)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Law"},{"subject":"Social Sciences"},{"subject":"medical malpractice"},{"subject":"NPDB"},{"subject":"healthcare"},{"subject":"litigation"},{"subject":"settlements"},{"subject":"tort reform"},{"subject":"damage caps"},{"subject":"health policy"},{"subject":"United States"},{"subject":"HRSA"},{"subject":"public data"}],"contributors":[{"nameType":"Personal","givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","schemeUri":"https://settlementinsight.com","relatedIdentifier":"/research/medical-malpractice-settlements","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["310","102","2307","829","3347"],"formats":["text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/markdown"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"MIRROR NOTICE: This dataset is a verbatim mirror. The canonical version of record is the Zenodo deposit (isIdenticalTo): https://doi.org/10.5281/zenodo.19495953 — please cite the Zenodo DOI. Full study, methodology and interactive tables: https://settlementinsight.com/research/medical-malpractice-settlements\n\nA structured aggregation of 529,804 medical malpractice payment reports filed with the U.S. National Practitioner Data Bank (NPDB) between 2000 and 2025, totaling $136.4 billion in payments.\nThis dataset aggregates the NPDB Public Use File (published by HRSA) into four CSV tables: by state, by year, by payment bracket, and overall summary. All underlying data is in the public domain; this release provides the processed aggregations used in the Settlement Insight research study.\nKey findings:\nMean payment: $257,531; median: $97,500Massive state variation: IL averages $403K vs. CA at $141K (damage cap effect)Only 2.7% of cases exceed $1M but drive a disproportionate share of dollar volumeAnnual case volume down ~40% since 2000 while average payments nearly doubledFull analysis: https://settlementinsight.com/research/medical-malpractice-settlements\nMethodology: https://settlementinsight.com/methodology"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/LCUYOJ","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:19:05Z","registered":"2026-08-17T21:19:25Z","published":null,"updated":"2026-08-17T21:19:25Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/cptzri","type":"dois","attributes":{"doi":"10.7910/dvn/cptzri","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Settlement Insight Research"],"givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","nameIdentifiers":[]}],"titles":[{"title":"U.S. Treasury Judgment Fund — Aggregated Payments Dataset (2008–2025)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Law"},{"subject":"Social Sciences"},{"subject":"Treasury Judgment Fund"},{"subject":"federal government"},{"subject":"settlements"},{"subject":"litigation"},{"subject":"accountability"},{"subject":"tort claims"},{"subject":"FTCA"},{"subject":"government spending"},{"subject":"public data"},{"subject":"United States"},{"subject":"Bureau of Fiscal Service"}],"contributors":[{"nameType":"Personal","givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","schemeUri":"https://settlementinsight.com","relatedIdentifier":"/research/federal-government-payments","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["94","4299","581","1334","374","1164","592"],"formats":["text/tab-separated-values","text/markdown","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"MIRROR NOTICE: This dataset is a verbatim mirror. The canonical version of record is the Zenodo deposit (isIdenticalTo): https://doi.org/10.5281/zenodo.19495955 — please cite the Zenodo DOI. Full study, methodology and interactive tables: https://settlementinsight.com/research/federal-government-payments\n\nA structured aggregation of 113,969 payments made from the U.S. Treasury Judgment Fund between fiscal year 2008 and 2025, totaling $59.9 billion. The Judgment Fund is the permanent appropriation Congress created in 1956 (31 U.S.C. § 1304) to pay settlements and court judgments against the federal government.\nTo our knowledge, this is the first public systematic aggregation of this data. The underlying records are available via the Bureau of the Fiscal Service payment search but have never been published in aggregated, analysis-ready form.\nKey findings:\nFY2020 payments spiked to $14.2B — ~10x normal volume — with 89% from a single citation categoryTop paying agencies: CMS ($13.6B), DOE ($13.3B), DOI ($6.0B)Federal medical malpractice under FTCA: 2,196 cases, $1.27B; VA is the largest payerLargest single payment: $1.9 billionFull analysis: https://settlementinsight.com/research/federal-government-payments\nMethodology: https://settlementinsight.com/methodology"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/CPTZRI","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:18:38Z","registered":"2026-08-17T21:19:03Z","published":null,"updated":"2026-08-17T21:19:03Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/ph4nz6","type":"dois","attributes":{"doi":"10.7910/dvn/ph4nz6","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Settlement Insight Research"],"givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","nameIdentifiers":[]}],"titles":[{"title":"U.S. CFPB Consumer Complaints — Aggregated Dataset (2011–2026)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Law"},{"subject":"Social Sciences"},{"subject":"CFPB"},{"subject":"consumer complaints"},{"subject":"credit reporting"},{"subject":"Equifax"},{"subject":"Experian"},{"subject":"TransUnion"},{"subject":"credit bureau"},{"subject":"consumer finance"},{"subject":"debt collection"},{"subject":"United States"},{"subject":"public data"}],"contributors":[{"nameType":"Personal","givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","schemeUri":"https://settlementinsight.com","relatedIdentifier":"/research/credit-report-complaints","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["292","1886","706","416","304","676"],"formats":["text/tab-separated-values","text/markdown","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"MIRROR NOTICE: This dataset is a verbatim mirror. The canonical version of record is the Zenodo deposit (isIdenticalTo): https://doi.org/10.5281/zenodo.20575559 — please cite the Zenodo DOI. Full study, methodology and interactive tables: https://settlementinsight.com/research/credit-report-complaints\n\nA structured aggregation of all 15,694,521 consumer complaints in the U.S. Consumer Financial Protection Bureau (CFPB) Consumer Complaint Database, received between December 2011 and June 2026, across 7,945 distinct companies.\nThis release aggregates the public CFPB database into five CSV tables: overall summary, by year, by company, by product, and by state. All underlying data is published by the CFPB as a U.S. Government work; this dataset provides the processed aggregations used in the Settlement Insight research study.\nKey findings:\nThe three national credit bureaus — Equifax, Experian, and TransUnion — together account for 76.7% of all complaints (12,040,506 of 15,694,521).Annual complaint volume grew 32.3×, from 168,273 in 2015 to 5,442,994 in 2025.80.4% of all complaints concern credit reporting.More than half of all complaints in the database were filed in 2024–2025 alone.Full analysis: https://settlementinsight.com/research/credit-report-complaints\nCaveat: A complaint is a consumer submission, not a verified violation; the recent surge is concentrated in credit-reporting disputes, many submitted in bulk via third-party credit-repair services. The 2026 figure is a partial year."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/PH4NZ6","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:18:15Z","registered":"2026-08-17T21:18:37Z","published":null,"updated":"2026-08-17T21:18:37Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/wfdwtx","type":"dois","attributes":{"doi":"10.7910/dvn/wfdwtx","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Settlement Insight Research"],"givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","nameIdentifiers":[]}],"titles":[{"title":"U.S. Settlement, Claim \u0026amp; Enforcement Records — Cross-Source Aggregated Dataset (2000–2025)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Law"},{"subject":"Social Sciences"},{"subject":"settlements"},{"subject":"legal data"},{"subject":"medical malpractice"},{"subject":"federal payments"},{"subject":"municipal claims"},{"subject":"police misconduct"},{"subject":"regulatory enforcement"},{"subject":"tort"},{"subject":"United States"},{"subject":"public data"}],"contributors":[{"nameType":"Personal","givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","schemeUri":"https://settlementinsight.com","relatedIdentifier":"/research/settlement-trends","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["857","190","628","2343","473"],"formats":["text/tab-separated-values","text/tab-separated-values","text/tab-separated-values","text/markdown","text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"MIRROR NOTICE: This dataset is a verbatim mirror. The canonical version of record is the Zenodo deposit (isIdenticalTo): https://doi.org/10.5281/zenodo.20575702 — please cite the Zenodo DOI. Full study, methodology and interactive tables: https://settlementinsight.com/research/settlement-trends\n\nA cross-source aggregation of 3,824,813 settlement, claim, and enforcement records from 14 U.S. public databases (2000–2025), spanning medical malpractice, the federal Treasury Judgment Fund, municipal claims, regulatory penalties, and consumer-protection settlements.\nThis release provides four aggregate CSV tables: overall summary, by source, by year, and by state. All underlying data is published by U.S. government bodies; this dataset provides the processed cross-source aggregations used in the Settlement Insight research study.\nKey findings:\nTracked payouts total $209 billion across the 12 sources with reliable dollar amounts.Annual settlement dollars peaked in 2020–2021 (~$12.7B/yr) versus ~$4.2B/yr in the early 2000s.Record volume is dominated by high-frequency regulatory penalties (mine-safety citations), while dollar volume is concentrated in medical malpractice and federal payments.Data-quality note: Two sources are excluded from all dollar totals — DOJ press-release figures (text-extracted from headlines, unreliable) and Phoenix claim values (placeholder/sentinel figures). Their record counts are retained. Amounts are nominal (not inflation-adjusted).\nFull analysis: https://settlementinsight.com/research/settlement-trends"}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/WFDWTX","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:17:51Z","registered":"2026-08-17T21:18:13Z","published":null,"updated":"2026-08-17T21:18:13Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}},{"id":"10.7910/dvn/me97c8","type":"dois","attributes":{"doi":"10.7910/dvn/me97c8","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Settlement Insight Research"],"givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","nameIdentifiers":[]}],"titles":[{"title":"Settlement Amounts by State — Cross-Source Percentile Benchmarks from 578,245 Public Payout Records (1990–2026)"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2026,"subjects":[{"subject":"Law"},{"subject":"Social Sciences"},{"subject":"settlements"},{"subject":"settlement amounts by state"},{"subject":"percentile benchmarks"},{"subject":"medical malpractice"},{"subject":"NPDB"},{"subject":"municipal claims"},{"subject":"police misconduct settlements"},{"subject":"Prop 65"},{"subject":"open government data"},{"subject":"United States"},{"subject":"public data"}],"contributors":[{"nameType":"Personal","givenName":"Leonard","familyName":"Goldberg","name":"Goldberg, Leonard","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17","dateType":"Available"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","schemeUri":"https://settlementinsight.com","relatedIdentifier":"/research/settlement-amounts-by-state","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["583","3551","3523","2204","340"],"formats":["text/tab-separated-values","text/tab-separated-values","text/markdown","text/tab-separated-values","text/tab-separated-values"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","lang":"en","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"MIRROR NOTICE: This dataset is a verbatim mirror. The canonical version of record is the Zenodo deposit (isIdenticalTo): https://doi.org/10.5281/zenodo.20618226 — please cite the Zenodo DOI. Full study, methodology and interactive tables: https://settlementinsight.com/research/settlement-amounts-by-state\n\nState-level percentile benchmarks (p25, median, p75, p90) for settlement and claim payouts in all 50 US states + DC, computed from 578,245 public payout records across 9 government and municipal data sources, 1990–2026.\nThe dataset combines National Practitioner Data Bank medical-malpractice payment reports (79.5% of records), municipal claim payouts published by New York City, Los Angeles, Chicago, Philadelphia, and Montgomery County (MD), California Prop 65 settlements and judgments, and the FiveThirtyEight police-misconduct settlement dataset. Four CSV tables are provided: percentiles by state, state-by-source composition, national by-source summary, and a national headline summary including all exclusion counts.\nKey figures:\nNational median payout $72,500 (mean $214,059; p25 $15,000; p75 $245,000; p90 $545,000).Pennsylvania shows the highest state median ($195,000; 92.8% med-mal records); New York and California the lowest ($27,500), driven by large volumes of small municipal and consumer claims alongside their malpractice reports.Source composition is disclosed per state because it is the first-order driver of cross-state differences: 76,015 New York City municipal claims (median $10,000) sit next to New York's 60,690 medical-malpractice records (median $145,000).Cleaning rules: two unreliable sources are excluded entirely (DOJ press-release text-extracted amounts and Phoenix sentinel placeholder amounts; 28,627 rows), 3,217,941 rows without a valid 50-state+DC code are excluded via an explicit whitelist (federal payments without a state, territories, military codes, degraded mine-safety state codes, blanks), and all amounts must be positive. Percentiles use Python's statistics.quantiles with the deterministic 'inclusive' method.\nFull analysis: https://settlementinsight.com/research/settlement-amounts-by-state\nCaveat: Rows are individual payment/claim records, not cases or persons; state-to-state comparisons reflect source composition as much as underlying payouts. NPDB amounts are public-use-file range midpoints. These are descriptive statistics of public records — not predictions of individual case values."}],"geoLocations":[],"fundingReferences":[],"url":"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/ME97C8","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:17:30Z","registered":"2026-08-17T21:17:50Z","published":null,"updated":"2026-08-17T21:17:50Z"},"relationships":{"client":{"data":{"id":"gdcc.harvard-dv","type":"clients"}}}}],"meta":{"total":816928,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois/?client-id=gdcc.harvard-dv","next":"https://api.datacite.org/dois?client-id=gdcc.harvard-dv\u0026page%5Bnumber%5D=2\u0026page%5Bsize%5D=25"}}