cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.adv8bj
title: Occurrence Download

description: A dataset containing 95 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "Geometry POLYGON((22.9067 52.68603,24.76584 52.81011,24.58065 53.59174,22.95224 53.57628,22.9067 52.68603))",
 "HasGeospatialIssue is false",
 "TaxonKey is Canis lupus Linnaeus, 1758"
 ]
}

 The dataset includes 95 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031393-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6rfsbr
title: Occurrence Download

description: A dataset containing 40803 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platanus occidentalis L."
 ]
}

 The dataset includes 40803 records from 396 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031391-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2cnb59
title: Occurrence Download

description: A dataset containing 5728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Dioclea Kunth"
 ]
}

 The dataset includes 5728 records from 266 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031394-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xs8xpq
title: Occurrence Download

description: A dataset containing 217501 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Juglans regia L."
 ]
}

 The dataset includes 217501 records from 1994 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031377-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11599369
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.15468/dl.vx94au
title: Occurrence Download

description: A dataset containing 1270 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuscuta monogyna"
 ]
}

 The dataset includes 1270 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009301-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18246968
title: Baccharis genistelloides var. trimera (Less.) Baker. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18246965
title: Baccharis genistelloides var. trimera (Less.) Baker. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.15468/dl.h9jtue
title: Occurrence Download

description: A dataset containing 17551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((28.4559 1.43484,30.42687 1.43484,30.58016 3.75621,28.51663 3.7223,28.4559 1.43484))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 17551 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009302-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4pu2j
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.34.57_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009303-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryhfpc
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abramis brama",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1830",
 "Year is less than or equal to 1834"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009304-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18246970
title: Baccharis genistifolia DC. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18246969
title: Baccharis genistifolia DC. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.25574/30637
title: Chandra X-ray Observatory ObsId 30637

description: Probing the power source in narrow-line AGN Green Pea galaxies
DOI: 10.57967/hf/7531
title: GoogleTrendArchive
DOI: 10.15468/dl.ctcq4q
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.20.31_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 48 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009305-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6dywgj
title: Occurrence Download

description: A dataset containing 368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-64.379 -42.253,-64.098 -42.253,-64.098 -42.183,-64.379 -42.183,-64.379 -42.253))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 368 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009306-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30576818
title: Social Isolation Predictions for County/County-Equivalents Missing Social Isolation Data in the CDC PLACES 2024 Dataset Release

description: Social isolation, described as a state in which an individual lacks social connectedness and personal relationships, was declared an epidemic by the United States Surgeon General in 2023, but nationwide data for county-level isolation prevalence and its most influential social determinants of health (SDOH) is limited. This gap in data and lack of SDOH attribution prevents policy-makers from constructing targeted prevention strategies that directly impact social isolation at the county level. Our study aims to close this gap by linking all available SDOH predictor variables with known social isolation estimates, enabling us to predict isolation estimates for counties that lack them and identify dominant SDOH drivers for each county.<br>We obtained known social isolation data from the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (PLACES) 2024 dataset and county-level SDOH data from the Agency for Healthcare Research and Quality (AHRQ) 2022 dataset. We then merged the two datasets, applied a linear regression model, and pruned predictors using statistical tools, generating a final set of 23 SDOH predictors used to predict social isolation percentages for 727 missing counties/county-equivalents. The contributions for each predictor were also calculated to find the dominant SDOH for each county and each predictor was classified as actionable or non-actionable. Of the 23, 6 were actionable, the most significant of which was broadband access, followed by public-only health insurance coverage and bachelor’s degree attainment.<br>This study completes the CDC PLACES dataset with comprehensive nationwide county-level estimates and identifies the most dominant SDOH drivers. This can inform targeted public health interventions, aligning with PHR’s mission of moving science into policy and practical public health applications. <br>
DOI: 10.6084/m9.figshare.30576818.v1
title: Social Isolation Predictions for County/County-Equivalents Missing Social Isolation Data in the CDC PLACES 2024 Dataset Release

description: Social isolation, described as a state in which an individual lacks social connectedness and personal relationships, was declared an epidemic by the United States Surgeon General in 2023, but nationwide data for county-level isolation prevalence and its most influential social determinants of health (SDOH) is limited. This gap in data and lack of SDOH attribution prevents policy-makers from constructing targeted prevention strategies that directly impact social isolation at the county level. Our study aims to close this gap by linking all available SDOH predictor variables with known social isolation estimates, enabling us to predict isolation estimates for counties that lack them and identify dominant SDOH drivers for each county.<br>We obtained known social isolation data from the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (PLACES) 2024 dataset and county-level SDOH data from the Agency for Healthcare Research and Quality (AHRQ) 2022 dataset. We then merged the two datasets, applied a linear regression model, and pruned predictors using statistical tools, generating a final set of 23 SDOH predictors used to predict social isolation percentages for 727 missing counties/county-equivalents. The contributions for each predictor were also calculated to find the dominant SDOH for each county and each predictor was classified as actionable or non-actionable. Of the 23, 6 were actionable, the most significant of which was broadband access, followed by public-only health insurance coverage and bachelor’s degree attainment.<br>This study completes the CDC PLACES dataset with comprehensive nationwide county-level estimates and identifies the most dominant SDOH drivers. This can inform targeted public health interventions, aligning with PHR’s mission of moving science into policy and practical public health applications. <br>
DOI: 10.6084/m9.figshare.30576818.v2
title: Social Isolation Predictions for County/County-Equivalents Missing Social Isolation Data in the CDC PLACES 2024 Dataset Release

description: Social isolation, described as a state in which an individual lacks social connectedness and personal relationships, was declared an epidemic by the United States Surgeon General in 2023, but nationwide data for county-level isolation prevalence and its most influential social determinants of health (SDOH) is limited. This gap in data and lack of SDOH attribution prevents policy-makers from constructing targeted prevention strategies that directly impact social isolation at the county level. Our study aims to close this gap by linking all available SDOH predictor variables with known social isolation estimates, enabling us to predict isolation estimates for counties that lack them and identify dominant SDOH drivers for each county.<br>We obtained known social isolation data from the Centers for Disease Control and Prevention Population Level Analysis and Community Estimates (PLACES) 2024 dataset and county-level SDOH data from the Agency for Healthcare Research and Quality (AHRQ) 2022 dataset. We then merged the two datasets, applied a linear regression model, and pruned predictors using statistical tools, generating a final set of 23 SDOH predictors used to predict social isolation percentages for 727 missing counties/county-equivalents. The contributions for each predictor were also calculated to find the dominant SDOH for each county and each predictor was classified as actionable or non-actionable. Of the 23, 6 were actionable, the most significant of which was broadband access, followed by public-only health insurance coverage and bachelor’s degree attainment.<br>This study completes the CDC PLACES dataset with comprehensive nationwide county-level estimates and identifies the most dominant SDOH drivers. This can inform targeted public health interventions, aligning with PHR’s mission of moving science into policy and practical public health applications. <br>
DOI: 10.5281/zenodo.18246980
title: Baccharis gibertii Baker in Mart. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18246979
title: Baccharis gibertii Baker in Mart. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.15468/dl.gjfg6m
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Geometry POLYGON((120 39,123 39,123 42,120 42,120 39))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Platalea minor"
 ]
}

 The dataset includes 12 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009308-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25810/rxtv-eb29.93
title: German Newspaper Coverage of Climate Change or Global Warming, 2004-2025 - December 2025

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.15468/dl.z2jjbp
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is The collection of Bathynellacea specimens of MNCN (CSIC) Madrid: microscope slices and DNA extracts",
 "Geometry POLYGON((-125.39349 44.65324,-114.79277 44.65324,-114.79277 49.40013,-125.39349 49.40013,-125.39349 44.65324))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 85 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009309-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18246860
title: Dataset: Cold plasma treatment of Tartary buckwheat (O and N plasma)

description: Dataset: Cold plasma treatment of Tartary buckwheat grain (low pressute O and N cold plasma treatment) - germination and early growth parameters.
DOI: 10.15468/dl.gvq2z9
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.34.58_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 17 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009311-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18246859
title: Dataset: Cold plasma treatment of Tartary buckwheat (O and N plasma)

description: Dataset: Cold plasma treatment of Tartary buckwheat grain (low pressute O and N cold plasma treatment) - germination and early growth parameters.
DOI: 10.5281/zenodo.17569353
title: Chaos meets stochasticity: A variance-based method for Lyapunov exponent estimation

description: Adrián García-Gutiérrez, Carlos Rubio, Diego Domínguez, Deibi López; Chaos meets stochasticity: A variance-based method for Lyapunov exponent estimation. Chaos 1 January 2026; 36 (1): 013126. https://doi.org/10.1063/5.0311209
DOI: 10.5281/zenodo.17483700
title: Chaos meets stochasticity: A variance-based method for Lyapunov exponent estimation

description: Adrián García-Gutiérrez, Carlos Rubio, Diego Domínguez, Deibi López; Chaos meets stochasticity: A variance-based method for Lyapunov exponent estimation. Chaos 1 January 2026; 36 (1): 013126. https://doi.org/10.1063/5.0311209
DOI: 10.15468/dl.5e897e
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abramis brama",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1835",
 "Year is less than or equal to 1839"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009312-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/jygjqg
title: Prairie Fen Biodiversity Project

description: Since 2012, the Prairie Fen Biodiversity Project (PFBP) has collected baseline plant diversity data in prairie fens to investigate drivers of biodiversity in these diverse systems. Prairie fen wetlands are globally vulnerable wetlands that provide habitat for over 35 state (i.e., Michigan) and federally listed species and function at the headwaters for several major watersheds. These at risk habitats are of high conservation concern and are heavily managed at the local, state, and federal level. The Prairie Fen Biodiversity Project began in 2012 as a part of the Master's thesis of Central Michigan University student Rachel A Hackett working with Professor Anna K Monfils. The project was continued and expanded by Clint D Pogue in 2014, and has since been a focus of the graduate students in Monfils' laboratory. The original project focused on plant diversity which has expanded to several listed species and pollinators of prairie fen wetlands. This project has expanded into a Prairie Fen Research Collaborative (https://www.researchgate.net/project/Prairie-Fen-Research-Collaborative) that includes several Central Michigan University professors and studies and scientists at the Michigan Natural Features Inventory. The goal of the Prairie Fen Research Collaborative is to conduct research that addresses knowledge gaps hindering the conservation of prairie fen biodiversity and improve the efficacy of prairie fen research, monitoring, and management. By accruing knowledge and creating new tools, we help a variety of agencies and organizations working to conserve prairie fens for the benefit of unique species and people. Plant species list can be found at http://midwestherbaria.org/portal/projects/index.php?pid=113.
DOI: 10.5281/zenodo.17470752
title: Measurements of Ecuadorian Furipterus

description: This file contain measurements of Ecuadorian Furipterus
DOI: 10.5281/zenodo.17525799
title: Measurements of Ecuadorian Furipterus

description: This file contain measurements of Ecuadorian Furipterus
DOI: 10.5281/zenodo.18246994
title: HAVODAGI CHANG – SOG'LIG'IMIZGA KO'RINMAS TAHDID

description: Ushbu maqolada bugungi kundagi eng dolzarb ekologik muammolardan biri bo‘lgan havodagi chang va havo ifloslanishi masalasi yoritib berilgan. Muallif havodagi changning kelib chiqish sabablari, uning inson salomatligiga, atrof-muhit va ekologik tizimga ko‘rsatadigan salbiy ta’sirlarini chuqur tahlil qiladi.
DOI: 10.5281/zenodo.18246995
title: HAVODAGI CHANG – SOG'LIG'IMIZGA KO'RINMAS TAHDID

description: Ushbu maqolada bugungi kundagi eng dolzarb ekologik muammolardan biri bo‘lgan havodagi chang va havo ifloslanishi masalasi yoritib berilgan. Muallif havodagi changning kelib chiqish sabablari, uning inson salomatligiga, atrof-muhit va ekologik tizimga ko‘rsatadigan salbiy ta’sirlarini chuqur tahlil qiladi.
DOI: 10.6084/m9.figshare.31064782
title: LVIS

description: <b>LVIS (Large Vocabulary Instance Segmentation)</b> is a large-scale, high-quality dataset designed for instance segmentation tasks. Unlike traditional datasets such as COCO, LVIS contains a much larger number of object categories, specifically focusing on long-tail categories, which are underrepresented in most computer vision datasets. LVIS includes 1203 object categories, with over 2 million instances across more than 50,000 images.
DOI: 10.6084/m9.figshare.31064782.v1
title: LVIS

description: <b>LVIS (Large Vocabulary Instance Segmentation)</b> is a large-scale, high-quality dataset designed for instance segmentation tasks. Unlike traditional datasets such as COCO, LVIS contains a much larger number of object categories, specifically focusing on long-tail categories, which are underrepresented in most computer vision datasets. LVIS includes 1203 object categories, with over 2 million instances across more than 50,000 images.