cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/sooouv
title: Replication Data for: Elite Retention Strategy in Equatorial Guinea, 1979-2023

description: Replication data for: Matthews, Austin S., and Ana Lúcia Sá. "Elite Retention Strategy in Equatorial Guinea, 1979-2023.
DOI: 10.7910/dvn/wlfk5z
title: Data Sharing Statements on Clinical Trials.gov

description: ClinicalTrials registration data for 2,040 interventional studies with a location in the United States first posted to ClinicalTrials.gov from 01 January 2018 to 06 June 2018.
DOI: 10.7910/dvn/xxzmda
title: A12_20950.JPG

description: Link to OCHRE database: &lt;a href = ''http://pi.lib.uchicago.edu/1001/org/ochre/b137a4cd-f899-4673-a32c-639983b6156b'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/b137a4cd-f899-4673-a32c-639983b6156b
DOI: 10.7910/dvn/zl15oo
title: Senate Unpassed Legislation 1855, Docket 18373, SC1/series 231, Petition of Mary H. Smith

description: &lt;p&gt;Petition subject: For the removal of Edward Greeley Loring &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:11858505"&gt;http://nrs.harvard.edu/urn-3:FHCL:11858505&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Massachusetts &lt;/p&gt; &lt;p&gt;Legislator, committee, or address that the petition was sent to: Nehemiah P. Smith, Millbury; committee on federal relations &lt;/p&gt; &lt;p&gt;Selected signatures:&lt;ol&gt;&lt;li&gt;Mary H. Smith&lt;/li&gt;&lt;li&gt;Mary Newton&lt;/li&gt;&lt;li&gt;Ann Gardner&lt;/li&gt;&lt;/ol&gt; &lt;/p&gt; &lt;p&gt;Actions taken on dates: 1855-02-09 &lt;/p&gt; &lt;p&gt;Legislative action: Received in the House and referred to the committee on federal relations and sent for concurrence and received in the Senate on February 9, 1855 and concurred &lt;/p&gt; &lt;p&gt;Total signatures: 118 &lt;/p&gt; &lt;p&gt;Legislative action summary: Received, referred, sent, received, concurred &lt;/p&gt; &lt;p&gt;Female signatures: 118 &lt;/p&gt; &lt;p&gt;Female only signatures: Yes &lt;/p&gt; &lt;p&gt;Identifications of signatories: [females], ["others"] &lt;/p&gt; &lt;p&gt;Prayer format was &lt;a href="http://en.wikipedia.org/wiki/Printing"&gt;printed&lt;/a&gt; vs. &lt;a href="http://en.wikipedia.org/wiki/Manuscript"&gt;manuscript&lt;/a&gt;: Printed &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: no additional documents &lt;/p&gt; &lt;p&gt;Additional archivist notes: High amount of "mrs." &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: Senate Unpassed 1855, Docket 18373 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;
DOI: 10.7910/dvn/zlrspz
title: A98_9482.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/f0c7723b-1b51-a7a0-17fb-90bb5eab5881'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/f0c7723b-1b51-a7a0-17fb-90bb5eab5881
DOI: 10.7910/dvn/5zvxw1
title: Replication Data for Framing the Origins of COVID-19

description: Conspiracy theories have flourished about the origins of a novel coronavirus (SARS-CoV-2) that causes an acute respiratory syndrome (coronavirus disease 2019 [COVID-19]) in humans. This article reports the results from a study that evaluates the impact of exposure to framed messages about the origins of COVID-19. We tested four hypotheses: two focusing on its origins as either zoonotic or human-engineered and two concerning the impacts of origin beliefs on the desire to penalize China or support increased funding for biomedical research. The results accentuate the importance of finding ways to combat the spread of misinformation and conspiracy theories related to this global pandemic.
DOI: 10.7910/dvn/nwc9cb
title: Why Hospitals Hire Tobacco Lobbyists

description: Replication files for "Why Hospitals Hire Tobacco Lobbyists: Conflicts of Interests Among Lobbyists' Clients."
DOI: 10.7910/dvn/sb3kf
title: Passed Resolves; Resolves 1845, c.131, SC1/series 228, Petition of Henry W. Williams

description: &lt;p&gt;Petition subject: Annexation of Texas &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:10670586"&gt;http://nrs.harvard.edu/urn-3:FHCL:10670586&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Boston &lt;/p&gt; &lt;p&gt;Legislator, committee, or address that the petition was sent to: Charles Francis Adams, Suffolk; committee on the annexation of Texas &lt;/p&gt; &lt;p&gt;Selected signatures:&lt;ol&gt;&lt;li&gt;Henry W. Williams&lt;/li&gt;&lt;li&gt;William F. Channing&lt;/li&gt;&lt;li&gt;Francis Jackson&lt;/li&gt;&lt;li&gt;Wendell Phillips&lt;/li&gt;&lt;li&gt;William Lloyd Garrison&lt;/li&gt;&lt;li&gt;Robert F. Wallcut&lt;/li&gt;&lt;li&gt;George C. Leach&lt;/li&gt;&lt;/ol&gt; &lt;/p&gt; &lt;p&gt;Actions taken on dates: 1845-02-03,1845-02-03 &lt;/p&gt; &lt;p&gt;Legislative action: Received in the Senate on February 3, 1845 and referred to the committee on the annexation of Texas and received in the House on February 3, 1845 and concurred &lt;/p&gt; &lt;p&gt;Total signatures: 20 &lt;/p&gt; &lt;p&gt;Legislative action summary: Received, referred, received, concurred &lt;/p&gt; &lt;p&gt;Legal voter signatures (males not identified as non-legal): 20 &lt;/p&gt; &lt;p&gt;Female only signatures: No &lt;/p&gt; &lt;p&gt;Identifications of signatories: inhabitants, [males of color], ["others"] &lt;/p&gt; &lt;p&gt;Prayer format was &lt;a href="http://en.wikipedia.org/wiki/Printing"&gt;printed&lt;/a&gt; vs. &lt;a href="http://en.wikipedia.org/wiki/Manuscript"&gt;manuscript&lt;/a&gt;: Printed &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: additional documents available &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: Resolves 1845, c.131, passed March 26, 1845 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;
DOI: 10.7910/dvn/szpam7
title: Online Appendix for "The Disadvantage of Nuclear Superiority"

description: When crises occur between nuclear-armed states, do relative nuclear capabilities impact the outcome? Existing literature offers no consensus about nuclear superiority’s effect on crisis victory, but this paper demonstrates that this effect depends on the size of the disparity between states' nuclear arsenals. While superiority is correlated with victory in crises between states with similarly sized nuclear arsenals, superiority provides no advantage in asymmetric crises. Because a vastly inferior state risks annihilation in a nuclear conflict, it will acquiesce to an opponent’s demands before the crisis occurs, unless backing down implies an existential threat as well. Given an asymmetric crisis has emerged, therefore, the inferior side will be willing to bid up the risk of nuclear war, deterring superior opponents. Using quantitative analyses of crisis data, this paper shows the positive association between nuclear superiority and crisis victory decreases as the disparity between competing states’ arsenals increases.
DOI: 10.7910/dvn/x2rgrx
title: Democratization in Conflict Research: How Conceptualization affects Operationalization and Testing Outcomes

description: Using the debate over democratization and conflict, we demonstrate how the connection between conceptualization and operationalization can play a decisive role in the testing of falsifiable hypotheses. We discuss seven different operationalizations of regime change based on three different conceptualizations of democracy. Although we find high correlations between different measures of democracy, when they are used to capture regime change the correlations drop precipitously. In multivariate estimations of the effect of regime change on a range of conflict variables, we generate widely disparate results, providing no consistent support that democratization affects conflict. We thus demonstrate that decisions about conceptualization and subsequent operationalization have decisive impact on the inference we produce. In contrast, our controls for the effect of institutionalized democracy consistently show a negative relationship between joint democracy and conflict. Finally, autocratic regime change seems to be more robustly correlated with a range of conflict behaviors than heretofore recognized in this literature.
DOI: 10.7910/dvn/xzjp2j
title: Replication Data for: Pass the Buck If You Can: How Partisan Competition Triggers Attribution Bias in Multilevel Democracies

description: Data and replication code for "Pass the Buck If You Can: How Partisan Competition Triggers Attribution Bias in Multilevel Democracies"
DOI: 10.7910/dvn/ylymyv
title: Journal of spectrohelioscopic observations № 42

description: Journal of spectrohelioscopic observations made at Tashkent Astronomical Observatory during 14.12.1945 - 27.03.1946.
DOI: 10.34740/kaggle/dsv/12047524
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.7910/dvn/9oolq7
title: Replication Data for: Voter Buying: Shaping the Electorate through Clientelism

description: Studies of clientelism typically assume that political machines distribute rewards to persuade or mobilize the existing electorate. We argue that rewards not only influence actions of the electorate, but can also shape its composition. Across the world, machines employ "voter buying" to import outsiders into their districts. Voter buying demonstrates how clientelism can underpin electoral fraud, and offers an explanation of why machines deliver rewards when they cannot monitor vote choices. Our analyses suggest that voter buying dramatically influences municipal elections in Brazil. A regression discontinuity design suggests that voter audits -- which undermined voter buying -- decreased the electorate by 12 percentage points and reduced the likelihood of mayoral reelection by 18 percentage points. Consistent with voter buying, these effects are significantly greater in municipalities with large voter inflows, and where neighboring municipalities had large voter outflows. Findings are robust to an alternative research design using a different dataset.
description: This dataset underwent an independent verification process that replicated the tables and figures in the main article and those included in supplementary materials on which primary findings are contingent. The verification process was carried out by the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill.
<br>
The associated article has been awarded Open Materials and Open Data Badges. Learn more about the Open Practice Badges from the Center for Open Science.<br>
DOI: 10.15468/dl.umwywq
title: Occurrence Download

description: A dataset containing 1848 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((23.99706 35.8657,23.489 35.68425,23.19678 34.89418,25.49215 34.16383,26.39487 34.67643,26.599 35.29337,26.52189 35.50204,25.96392 35.71071,25.61009 35.7969,23.99706 35.8657))",
 "OccurrenceStatus is Present",
 "TaxonKey is Araneae"
 ]
}

 The dataset includes 1848 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005715-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5255/ukda-sn-9259-1
title: Understanding Society: Innovation Panel, Linked Credit Reference Agency (CRA) Dataset, 2012-2018: Secure Access
title: Understanding Society: Innovation Panel: Waves 1- , 2008-: Controlled/Secure Access
title: United Kingdom Household Longitudinal Study; UKHLS; CRA

description: &lt;p&gt;&lt;span style="font-style: italic;"&gt;Understanding Society&lt;/span&gt;, (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.&lt;/p&gt;&lt;p&gt;The &lt;span style="font-style: italic;"&gt;Understanding Society Innovation Panel&lt;/span&gt; is designed for experimental and methodological research relevant to longitudinal surveys. As far as practical its design, content, and data collection procedures are similar to the main stage Understanding Society survey. It is a multi-topic household survey representative of the population of Great Britain. Data collection takes place annually using computer assisted personal interviewing (CAPI), web surveys and telephone interviewing (CATI) to a small extent. &lt;/p&gt;&lt;p&gt;For details of the main Understanding Society study, please see study numbers 6614 and 6676.&lt;/p&gt; 
&lt;p&gt;The &lt;span style="font-style: italic;"&gt;Understanding Society: Innovation Panel, Linked Credit Reference Agency (CRA) Dataset, 2012-2018: Secure Access&lt;/span&gt; study contains, for study participants who provided consent, a file containing credit histories collected by a Credit Reference Agency (CRA) before being linked to their study records by the Financial Conduct Authority (FCA).&lt;/p&gt;
&lt;p&gt;The data in this study can be linked through the pidp variable to one of the main Understanding Society Innovation Panel datasets: SN 6849 (End User Licence), SN 7083 (Special Licence) or SN 7332 (Secure Access). Further details on those studies can be found on the &lt;a href="https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000053#!/access-data" target="_blank"&gt;Understanding Society series&lt;/a&gt;&lt;a href="https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000053#!/access-data"&gt;&lt;/a&gt; webpage.&lt;/p&gt;&lt;p&gt;For all individuals with a valid consent to CRA linkage collected in Wave 9 of the IP, the data file contains detailed information on credit items held by individuals. The monthly dataset that has been linked to the main survey sample covers a period of six years (2012-2018). A credit item is a (typically financial) product taken out by an individual. Credit items contained in the CRA data include current accounts, mortgages, credit items (e.g. personal loans, motor finance, credit cards, retail credit and subprime credit such as payday loans) as well as some household bills (e.g. mobile phones, gas and electric bills but not council taxes).&lt;/p&gt;
&lt;p&gt;See documentation for further details.&lt;/p&gt;
DOI: 10.5281/zenodo.18413276
title: Kyiv secondary (resale) residential real estate

description: The dataset represents apartment listings collected from the Kyiv secondary housing market. Each row corresponds to a single apartment offered for sale, containing pricing, location, physical characteristics of the property, and descriptive metadata.

Domain: Real estate / Housing marketLocation: Kyiv, UkraineMarket segment: Secondary (resale) residential real estateObservations: 14,066 property listingsFeatures: 20 variables

 

Feature Description

1. Identification





idUnique identifier of the real estate listing.





2. Pricing Information





priceTotal listing price of the apartment (numeric).The currency is specified separately.




price_smPrice per square meter.Useful for normalizing prices across different apartment sizes.




currencyCurrency in which the price is listed.Typical values:





USD




UAH







3. Location Data





addressStreet address of the property (text).




complexName of the residential complex, if applicable.Often missing (NaN) for older buildings.




hist_districtHistorical or informal district name (e.g., neighborhoods commonly used by residents).




admin_districtOfficial administrative district of Kyiv (e.g., Solomianskyi, Pecherskyi).




cityCity name (constant value: Київ).





4. Property Description





descriptionFree-text listing description provided by the seller or agent.May include:





Renovation details




View, layout, or floor plan




Marketing phrases




Commission notes





This field is useful for NLP tasks such as text classification or sentiment analysis.



5. Apartment Characteristics





roomsNumber of rooms in the apartment.




area_totalTotal area of the apartment (square meters).




area_livingLiving area (square meters).




area_kitchenKitchen area (square meters).





6. Building Characteristics





stockFloor on which the apartment is located.




stock_totalTotal number of floors in the building.




yearYear the building was constructed.May contain missing values for older or unknown buildings.




projectBuilding project type or series (e.g., typical Soviet-era series, special project).




wallType of building material / construction technologyExamples:





монолітно-каркасний (monolithic frame)




цегляний будинок (brick)




утеплена панель (insulated panel)







7. Data Quality / Flags





bad_proposalBinary indicator flag:





0 — normal listing




1 — potentially problematic or low-quality listing





This field can be used to filter unreliable or suspicious data points.



Data Characteristics and Notes





The dataset contains missing values, especially in:





complex




year




project






Prices are listed in multiple currencies, requiring normalization for analysis.




Text fields (description, address) are in Ukrainian and Russian, which should be considered for NLP preprocessing.




The dataset reflects real-world market heterogeneity, including:





Old Soviet buildings




Modern residential complexes




Wide price dispersion across districts
DOI: 10.5281/zenodo.18413277
title: Kyiv secondary (resale) residential real estate

description: The dataset represents apartment listings collected from the Kyiv secondary housing market. Each row corresponds to a single apartment offered for sale, containing pricing, location, physical characteristics of the property, and descriptive metadata.

Domain: Real estate / Housing marketLocation: Kyiv, UkraineMarket segment: Secondary (resale) residential real estateObservations: 14,066 property listingsFeatures: 20 variables

 

Feature Description

1. Identification





idUnique identifier of the real estate listing.





2. Pricing Information





priceTotal listing price of the apartment (numeric).The currency is specified separately.




price_smPrice per square meter.Useful for normalizing prices across different apartment sizes.




currencyCurrency in which the price is listed.Typical values:





USD




UAH







3. Location Data





addressStreet address of the property (text).




complexName of the residential complex, if applicable.Often missing (NaN) for older buildings.




hist_districtHistorical or informal district name (e.g., neighborhoods commonly used by residents).




admin_districtOfficial administrative district of Kyiv (e.g., Solomianskyi, Pecherskyi).




cityCity name (constant value: Київ).





4. Property Description





descriptionFree-text listing description provided by the seller or agent.May include:





Renovation details




View, layout, or floor plan




Marketing phrases




Commission notes





This field is useful for NLP tasks such as text classification or sentiment analysis.



5. Apartment Characteristics





roomsNumber of rooms in the apartment.




area_totalTotal area of the apartment (square meters).




area_livingLiving area (square meters).




area_kitchenKitchen area (square meters).





6. Building Characteristics





stockFloor on which the apartment is located.




stock_totalTotal number of floors in the building.




yearYear the building was constructed.May contain missing values for older or unknown buildings.




projectBuilding project type or series (e.g., typical Soviet-era series, special project).




wallType of building material / construction technologyExamples:





монолітно-каркасний (monolithic frame)




цегляний будинок (brick)




утеплена панель (insulated panel)







7. Data Quality / Flags





bad_proposalBinary indicator flag:





0 — normal listing




1 — potentially problematic or low-quality listing





This field can be used to filter unreliable or suspicious data points.



Data Characteristics and Notes





The dataset contains missing values, especially in:





complex




year




project






Prices are listed in multiple currencies, requiring normalization for analysis.




Text fields (description, address) are in Ukrainian and Russian, which should be considered for NLP preprocessing.




The dataset reflects real-world market heterogeneity, including:





Old Soviet buildings




Modern residential complexes




Wide price dispersion across districts
DOI: 10.15468/dl.6u6z2r
title: Occurrence Download

description: A dataset containing 16417 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aedes, Anopheles, Culex)",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 16417 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005714-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data2655
title: Replication data for: "When oil companies race to the bottom: Environmental enforcement in theory and corporate impunity in practice in the Peruvian Amazon"

description: This table compiles structured metadata for each recorded environmental impact event. The dataset integrates quantitative measures (e.g., pollutant volumes, affected surface areas), categorical variables (e.g., event type, containment and remediation status), and procedural information (e.g., enforcement actions, resolution codes, timelines). Each row represents a unique impact, harmonized across official enforcement records (OEFA) and participatory monitoring sources. Geographic coordinates are standardized, and duplicate entries were removed through cross-validation. The table includes identifiers, source reliability checks, temporal markers, spatial descriptors, and compliance outcomes, enabling reproducibility and comparative analysis of environmental incidents and associated accountability processes
DOI: 10.15468/dl.5smyty
title: Occurrence Download

description: A dataset containing 626 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Holodiscus argenteus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 626 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005716-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.