cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr06599
title: CBS News/New York Times Monthly Poll, June 1994
title: Archival Version

description: This poll is part of a continuing series of monthly surveys
 that solicit public opinion on the presidency and on a range of other
 political and social issues. Respondents were asked whether they
 approved of President Bill Clinton's handling of foreign policy, the
 economy, health care reform, the budget deficit, and crime. Questions
 also covered respondents' opinions regarding how important the crises
 in Haiti, Rwanda, and North Korea were to United States interests and
 whether they approved of Clinton's handling of the situations. The
 issue of spousal abuse/domestic violence was also addressed in detail,
 with specific reference to the O.J. Simpson murder trial. Respondents
 were asked how serious a problem they thought wife abuse was in this
 country, and whether the police treated cases of domestic violence
 seriously enough, or if police were more likely to treat domestic
 violence as just a family matter. Other issues addressed included
 the space exploration program and the use of United States military
 troops in other countries. Background information on respondents
 includes voter registration status, household composition, vote choice
 in the 1992 presidential election, political party, political
 orientation, education, age, sex, race, religious preference, and
family income.
description: A variation of random-digit dialing using primary sampling
 units (PSUs) was employed, consisting of blocks of 100 telephone
 numbers identical through the eighth digit and stratified by
 geographic region, area code, and size of place. Within households,
 respondents were selected using a method developed by Leslie Kish and
 modified by Charles Backstrom and Gerald Hursh (see Backstrom and
 Hursh, SURVEY RESEARCH [Evanston, IL: Northwestern University Press,
1963]).
description: Datasets: 
DS1: CBS News/New York Times Monthly Poll, June 1994
description: Adult population of the United States aged 18 and over
having telephones at home.
description: (1) A weight variable has been included and must be
 used for any analysis. (2) The codebook is provided as a Portable
 Document Format (PDF) file. The PDF file format was developed by Adobe
 Systems Incorporated and can be accessed using PDF reader software,
 such as the Adobe Acrobat Reader. Information on how to obtain a copy
of the Acrobat Reader is provided on the ICPSR Website.
DOI: 10.15468/dl.kd8y7g
title: Occurrence Download

description: A dataset containing 2767 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lobaria pulmonaria (L.) Hoffm.",
 "Geometry POLYGON((-8.00174339329464 54.1998526673513, 0.202120938926891 54.1998526673513, 0.202120938926891 61.2628565887411, -8.00174339329464 61.2628565887411, -8.00174339329464 54.1998526673513))",
 "CoordinateUncertaintyInMeters is 0,101",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2767 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071720-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zf8kz8
title: Occurrence Download

description: A dataset containing 2419 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Equisetum sylvaticum L.",
 "Geometry POLYGON((-8.00174339329464 54.1998526673513, 0.202120938926891 54.1998526673513, 0.202120938926891 61.2628565887411, -8.00174339329464 61.2628565887411, -8.00174339329464 54.1998526673513))",
 "CoordinateUncertaintyInMeters is 0,101",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2419 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071721-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17605/osf.io/9e7pt
title: Municipality-level Healthcare and Long-Term Care Dataset in Japan (2015–2022)

description: This dataset provides a panel data structure of all 1,741 municipalities in Japan over eight fiscal years (2015–2022).
It integrates publicly available statistics related to healthcare, long-term care, and demographic indicators.
Key variables include population density, ageing rate, hospital bed counts, home-care resources, long-term care facilities, and average per capita expenditure on medical and long-term care services.

The dataset is constructed by converting long-term care insurance (LTCI) data originally published at the insurer-level into municipality-level units.
For municipalities that share a single insurer, data have been duplicated accordingly.
All values have been standardised for research use and include consistent units (e.g., per 10,000 people, JPY per capita).

This dataset is intended to support research and education related to regional healthcare and long-term care policy.
It is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). See: https://creativecommons.org/licenses/by/4.0/
Please cite the dataset appropriately when using it in publications or presentations.

For more information, visit the Nana-r Home-visit Nursing Development Center: https://www.nana-dc.jp/
DOI: 10.3886/icpsr06224.v1
title: National Hospital Discharge Survey, 1989
title: Version 1

description: The National Hospital Discharge Survey (NHDS) collects 
 medical and demographic information annually from a sample of hospital 
 discharge records. Variables include the patients' demographic 
 characteristics (sex, age, race, marital status), dates of admission 
 and discharge, status at discharge, final diagnoses, surgical and 
 nonsurgical procedures, dates of surgeries, and sources of payment. 
 Information on hospital characteristics such as bedsize, ownership, and 
region of the country is also included.
description: The redesigned (as of 1988) NHDS sample includes with 
 certainty all hospitals with 1,000 or more beds or 40,000 or more 
 discharges annually. The remaining sample of hospitals is based on a 
 stratified three-stage design. The first stage consisted of selection 
 of 112 primary sampling units (PSUs) that comprise a probability 
 subsample of PSUs used in the 1985-1994 National Health Interview 
 Surveys. The second stage consists of selection of noncertainty 
 hospitals from the sample PSUs. At the third stage, a sample of 
discharges was selected by a systematic random sampling technique.
description: Datasets: 
DS1: National Hospital Discharge Survey, 1989
description: Patient discharges from nonfederal short-stay hospitals 
located in the 50 states and the District of Columbia.
description: Per agreement with NCHS, ICPSR distributes the data 
 file(s) and technical documentation in this collection in their 
 original form as prepared by NCHS. In 1988, the NHDS was redesigned to 
 provide geographic sampling comparability with other surveys conducted 
 by the National Center for Health Statistics (NCHS), to update the 
 sample of hospitals selected into the survey, and to maximize the use 
 of data collected through automated systems. Changes in the survey may 
 affect trend data. That is, some of the differences between NHDS 
 statistics based on the 1965-1987 sample and statistics based on the 
 sample drawn for the new design may be due to sampling error rather 
than changes in hospital utilization.
DOI: 10.5281/zenodo.15755081
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Das Intensivregister (www.intensivregister.de) ist eine digitale Plattform zur Echtzeiterfassung von intensivmedizinischen Behandlungs- und Bettenkapazitäten sowie den Fallzahlen intensivmedizinisch behandelter COVID-19-Patient:innen der etwa 1.300 Akut-Krankenhäuser Deutschlands. Bis 12 Uhr ist die tägliche Meldung laut Verordnung für die Krankenhäuser verpflichtend. Darüber hinaus kann beliebig oft gemeldet werden, sodass Veränderungen in den Kapazitäten aktuell berichtet werden können.
Damit ermöglicht das Intensivregister in der Pandemie sowie darüber hinaus, Engpässe in der intensivmedizinischen Versorgung im regionalen und zeitlichen Vergleich zu erkennen. Es schafft somit eine wertvolle Grundlage zur Reaktion und zur datengestützten Handlungssteuerung in Echtzeit. Zur Aufrechterhaltung der Krankenhausversorgung bietet das DIVI-Intensivregister demnach ein Portal zur Suche freier Intensivbetten und zur Kontaktaufnahme zwischen Fachkolleg*innen, um die gegenseitige Unterstützung bei Behandlungsfragen im intensivmedizinischen Kontext zu erleichtern. Ebenso liefert es eine maßgebliche Informationsgrundlage für staatliche Steuerungs- und Planungsmaßnahmen sowie für die lokale und überregionale Steuerung und Koordinierung der Bettenbelegung.
DOI: 10.3886/icpsr06395.v1
title: Annual Survey of Jails: Jurisdiction-Level and Jail-Level Data, 1992
title: Version 1

description: This collection provides annual data on jail populations 
 across the nation and examines the "spillover" effect on local jails 
 resulting from the dramatic growth in federal and state prison 
 populations. These data permit an assessment of the demands placed on 
 correctional resources and provide a comprehensive picture of the adult 
 correctional system and changes that occur within the system. 
 Information is available on the number of inmates by sex, race, adult 
 or juvenile status, reason being held, and cause of death. Also added 
 in the 1992 survey were variables on citizenship, population movement, 
 and total number of inmate deaths for inmates originally confined to 
 the facility in question who died either at that facility or elsewhere. 
 Also, the 1992 version included a more complete survey of jail programs 
 and a supplemental questionnaire (CJ-5S), which dealt with AIDS-related 
 questions. In addition, information was collected for the first time on 
 drug testing, programs that treated or educated inmates, boot camps, work 
 release, and alternatives to incarceration such as electronic 
 monitoring, house arrest, community service, and weekend or day 
reporting.
description: A stratified random sample of county or city jurisdictions 
 stratified by average daily population (ADP) as reported by the 
 NATIONAL JAIL CENSUS, 1988 (ICPSR 9256) with allocation to the strata 
based on 1988 ADP.
description: Datasets: 
DS0: Study-Level Files
DS1: Jail-Level Data
DS2: Jurisdiction-Level Data
DS3: SAS Data Definition Statements for Jail-Level Data
DS4: SAS Data Definition Statements for Jurisdiction-Level Data
description: All local jails in the United States listed in the NATIONAL 
JAIL CENSUS, 1988 (ICPSR 9256).
description: (1) A "Census Use Only" variable is present in the 
 Jurisdiction-Level Data file, but not in the Jail-Level Data file. (2) The 
 codebooks are provided by ICPSR as a Portable Document Format (PDF) file. 
 The PDF file format was developed by Adobe Systems Incorporated and can 
 be accessed using PDF reader software, such as the Adobe Acrobat Reader. 
 Information on how to obtain a copy of the Acrobat Reader is provided on 
the ICPSR Web site.
DOI: 10.3886/icpsr06316.v1
title: Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest and Offense Data, 1992
title: Version 1

description: This data collection contains county-level counts of arrests 
 and offenses for Part I offenses (murder, rape, robbery, assault, 
 burglary, larceny, auto theft, and arson) and Part II offenses 
 (forgery, fraud, embezzlement, vandalism, weapons violations, sex 
 offenses, drug and alcohol abuse violations, gambling, vagrancy, curfew 
 violations, and runaways). Two sets of county populations are reported: 
 one for total county population and the other for counties reporting 
six months or more of data.
description: Datasets: 
DS0: Study-Level Files
DS1: Arrests, All Ages
DS2: Arrests, Adults
DS3: Arrests, Juveniles
DS4: Crimes Reported
DS5: Codebook for All Parts
DS6: SAS Data Definition Statements for Arrests Data
DS7: SAS Data Definition Statements for Crimes Reported Data
DS8: SPSS Data Definition Statements for Arrests Data
description: County law enforcement agencies in the United States.
description: Data have been aggregated to the county level. Within 
 each county, data for agencies reporting 6 to 11 months of information 
 were weighted to yield 12-month equivalents. Agencies reporting fewer 
 than 6 months of data were excluded from the aggregation. Data from 
 agencies reporting only statewide figures were allocated to counties in 
 proportion to each county's share of the state population (which 
 totaled the populations of those county agencies reporting six months 
or more of data).
DOI: 10.15468/dl.73yyhd
title: Occurrence Download

description: A dataset containing 2605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Emberiza cia Linnaeus, 1767",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 2605 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071722-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15755024
title: Coffee Bean Dataset Resized

description: Description:







This dataset is a resized version of the original Coffee Bean Dataset Resized Version 1, offering high-quality images of roasted coffee beans, meticulously captured to aid in various machine learning and image recognition tasks.

 

Dataset Details:



Roasting Levels: The dataset includes coffee beans roasted at four distinct levels:



Green (Unroasted) Coffee Beans: Laos Typica Bolaven (Coffea arabica)

Lightly Roasted Beans: Laos Typica Bolaven (Coffea arabica)

Medium Roasted Beans: Doi Chaang (Coffea arabica)

Dark Roasted Beans: Brazil Cerrado (Coffea arabica)






Photography Details:



The images were captured using an iPhone 12 Mini, equipped with a 12-megapixel back camera, featuring Ultra-wide and WideCamera capabilities.

The camera setup was designed to ensure a plane parallel to the object’s path during image capture, guaranteeing consistency and clarity.

Photographs were taken under various lighting conditions, including both LED light from a lightbox and natural light, to simulate different real-world environments and enhance the dataset’s versatility.










Download Dataset









Image Specifications:



Format: PNG

Resolution: Each image measures 3024×3032 pixels, ensuring high-resolution details suitable for detailed analysis and machine learning tasks.

Quantity: The dataset comprises a total of 4800 images, divided evenly across the four roasting levels, with 1200 images per level.


Additional Features:



Noise Enhancement: To simulate real-world scenarios, images include enhanced noise by placing each variety of coffee beans in a container. This feature aims to improve the robustness of machine learning models trained using this dataset.

Diverse Settings: The dataset includes images captured in a variety of settings, providing a broad spectrum of inputs to validate and train image recognition systems effectively.


Applications:

This comprehensive dataset is ideal for:



Machine Learning: Training models for image classification, object detection, and other AI tasks.

Quality Control: Automated inspection systems in coffee production and roasting facilities.

Research: Studies related to food processing, quality analysis, and agricultural research.

Educational Purposes: Teaching materials for courses on machine learning, image processing, and computer vision.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15755025
title: Coffee Bean Dataset Resized

description: Description:







This dataset is a resized version of the original Coffee Bean Dataset Resized Version 1, offering high-quality images of roasted coffee beans, meticulously captured to aid in various machine learning and image recognition tasks.

 

Dataset Details:



Roasting Levels: The dataset includes coffee beans roasted at four distinct levels:



Green (Unroasted) Coffee Beans: Laos Typica Bolaven (Coffea arabica)

Lightly Roasted Beans: Laos Typica Bolaven (Coffea arabica)

Medium Roasted Beans: Doi Chaang (Coffea arabica)

Dark Roasted Beans: Brazil Cerrado (Coffea arabica)






Photography Details:



The images were captured using an iPhone 12 Mini, equipped with a 12-megapixel back camera, featuring Ultra-wide and WideCamera capabilities.

The camera setup was designed to ensure a plane parallel to the object’s path during image capture, guaranteeing consistency and clarity.

Photographs were taken under various lighting conditions, including both LED light from a lightbox and natural light, to simulate different real-world environments and enhance the dataset’s versatility.










Download Dataset









Image Specifications:



Format: PNG

Resolution: Each image measures 3024×3032 pixels, ensuring high-resolution details suitable for detailed analysis and machine learning tasks.

Quantity: The dataset comprises a total of 4800 images, divided evenly across the four roasting levels, with 1200 images per level.


Additional Features:



Noise Enhancement: To simulate real-world scenarios, images include enhanced noise by placing each variety of coffee beans in a container. This feature aims to improve the robustness of machine learning models trained using this dataset.

Diverse Settings: The dataset includes images captured in a variety of settings, providing a broad spectrum of inputs to validate and train image recognition systems effectively.


Applications:

This comprehensive dataset is ideal for:



Machine Learning: Training models for image classification, object detection, and other AI tasks.

Quality Control: Automated inspection systems in coffee production and roasting facilities.

Research: Studies related to food processing, quality analysis, and agricultural research.

Educational Purposes: Teaching materials for courses on machine learning, image processing, and computer vision.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15746275
title: Safali-S/Sindhi-News-Headline-Dataset-for-Category-Based-Sentiment-Analysis: Sindhi News Headline Dataset for Category Based Sentiment Analysis

description: This project focuses on Category-based Sentiment Analysis of a dataset containing Sindhi news headlines. The dataset comprises 30,462 headlines collected from various online Sindhi news platforms. Each headline is annotated with two key attributes: its category (such as politics, sports, crime, health, economy, entertainment, science & technology and social.) and its sentiment polarity (positive, negative, or neutral).
The primary objective of this analysis is to understand how sentiment varies across different news categories within Sindhi media. This involves training machine learning and natural language processing (NLP) models to classify the sentiment of each headline, while also exploring the distribution and trends of sentiment within each news category.
The analysis provides insights into:
•	Which categories tend to feature more negative or positive sentiments.
•	The relationship between topical focus and emotional tone in Sindhi journalism.
By combining linguistic features with categorical metadata, this study contributes to both sentiment analysis in low-resource languages and media bias or tone detection in regional news content.
DOI: 10.5281/zenodo.15746276
title: Safali-S/Sindhi-News-Headline-Dataset-for-Category-Based-Sentiment-Analysis: Sindhi News Headline Dataset for Category Based Sentiment Analysis

description: This project focuses on Category-based Sentiment Analysis of a dataset containing Sindhi news headlines. The dataset comprises 30,462 headlines collected from various online Sindhi news platforms. Each headline is annotated with two key attributes: its category (such as politics, sports, crime, health, economy, entertainment, science & technology and social.) and its sentiment polarity (positive, negative, or neutral).
The primary objective of this analysis is to understand how sentiment varies across different news categories within Sindhi media. This involves training machine learning and natural language processing (NLP) models to classify the sentiment of each headline, while also exploring the distribution and trends of sentiment within each news category.
The analysis provides insights into:
•	Which categories tend to feature more negative or positive sentiments.
•	The relationship between topical focus and emotional tone in Sindhi journalism.
By combining linguistic features with categorical metadata, this study contributes to both sentiment analysis in low-resource languages and media bias or tone detection in regional news content.
DOI: 10.5281/zenodo.15755082
title: Kassel Engel-Apotheke am Rathaus (ca. 1924/1947)

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding thehistory of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient dasNachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuchanzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folderwhich cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat beiKalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oderurheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.5281/zenodo.15755083
title: Kassel Engel-Apotheke am Rathaus (ca. 1924/1947)

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding thehistory of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient dasNachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuchanzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folderwhich cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat beiKalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oderurheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.6084/m9.figshare.27923616
title: LiCor_figshare.csv

description: LiCor data from experiment. <br>
DOI: 10.6084/m9.figshare.27923616.v1
title: LiCor_figshare.csv

description: LiCor data from experiment. <br>
DOI: 10.6084/m9.figshare.27617541
title: TR-Flow3D

description: We generate Uniform flow and Beltrami flow using computational fluid dynamics (CFD), and extracting other flow from the Johns Hopkins Turbulence Database.We further extend it to a time-resolved setting, where each sample consists of ten particle frames and nine corresponding ground-truth flow fields.TR-Flow3D is generated by an exhaustive combination of parameters listed in this table, ultimately yielding 19,500 training samples and 1,950 testing samples.
DOI: 10.6084/m9.figshare.27617541.v3
title: TR-Flow3D

description: We generate Uniform flow and Beltrami flow using computational fluid dynamics (CFD), and extracting other flow from the Johns Hopkins Turbulence Database.We further extend it to a time-resolved setting, where each sample consists of ten particle frames and nine corresponding ground-truth flow fields.TR-Flow3D is generated by an exhaustive combination of parameters listed in this table, ultimately yielding 19,500 training samples and 1,950 testing samples.