cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/gssy63
title: Modelaje de la distribución potencial de 10 especies de anfibios y reptiles en riesgo de extinción con escasos registros de presencia (ANONAU)

description: Se desarrollarán 10 modelos de distribución potencial correspondientes a cinco especies de anfibios (Plectrohyla charadricola, Chiropterotriton magnipes, C. multidentatus, C. priscus y Lithobates johni) y cinco especies de reptiles (Anolis naufragus, Crotalus transversus, Lepidophyma gaigeae, L. micropholis y L. occulor). Todas las especies consideradas se encuentran incluidas dentro de categorías de riesgo de la NOM-059-SEMARNAT-2010 y la mayoría son especies microendémicas (área de distribución menor a 5000 km2). Estas especies tienen la cualidad de presentar entre 4 y 32 localidades de presencia debido a que son de hábitos crípticos, raras o distribuidas en regiones poco exploradas. Los modelos de distribución potencial serán construidos empleando MAXENT 3.3.2, el cual es un algoritmo que emplea el principio de Máxima Entropía para expresa la probabilidad de distribución de una especie como una función de las variables ambientales y que ha sido descrito como el de mejor desempeño y poder predictivo cuando se trabaja con muestras pequeñas. Se empleará una técnica de construcción y evaluación de modelos de distribución potencial novedosa, desarrollada por Pearson et al. (2007), basada en una partición tipo Jackknife diseñada específicamente para obtener la distribución de modelos de distribución potencial en situaciones donde el número de localidades de presencia es bajo (&lt;25 localidades), el cual ha generado resultados aceptables cuando ha sido aplicado a especies microendémicas o en riesgo de extinción.

Considerando la información actual sobre la distribución de las especies incluidas dentro del presente proyecto, su estatus de conservación, la escasez de registros de presencia de las misma y la disponibilidad de algoritmos que pueden ser empleados en la generación de modelos de distribución potencial de especies raras y en peligro de extinción, se considera viable, útil y necesario obtener un modelaje de las especies consideradas en el presente proyecto que incluya todas las localidades (históricas y actuales) con un algoritmo, métodos de partición y evaluación adecuados a la naturaleza de los datos disponibles. Los modelos generados constituirá una valiosa herramienta que permitirá: evaluar el riesgo de extinción actual de la especie y la generación de estrategias de conservación adecuadas del hábitat.
Reino: 1
Filo: 1
Clase: 1
Orden: 1
Familia: 1
Género: 1
Especie: 1
DOI: 10.15468/dl.2f9vtv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Aetomylaeus bovinus",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen, Material sample)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000746-250402121839773/datasets/export for details.
DOI: 10.15468/hmytta
title: Modelaje de la distribución potencial de 10 especies de anfibios y reptiles en riesgo de extinción con escasos registros de presencia (CHIMUL)

description: Se desarrollarán 10 modelos de distribución potencial correspondientes a cinco especies de anfibios (Plectrohyla charadricola, Chiropterotriton magnipes, C. multidentatus, C. priscus y Lithobates johni) y cinco especies de reptiles (Anolis naufragus, Crotalus transversus, Lepidophyma gaigeae, L. micropholis y L. occulor). Todas las especies consideradas se encuentran incluidas dentro de categorías de riesgo de la NOM-059-SEMARNAT-2010 y la mayoría son especies microendémicas (área de distribución menor a 5000 km2). Estas especies tienen la cualidad de presentar entre 4 y 32 localidades de presencia debido a que son de hábitos crípticos, raras o distribuidas en regiones poco exploradas. Los modelos de distribución potencial serán construidos empleando MAXENT 3.3.2, el cual es un algoritmo que emplea el principio de Máxima Entropía para expresa la probabilidad de distribución de una especie como una función de las variables ambientales y que ha sido descrito como el de mejor desempeño y poder predictivo cuando se trabaja con muestras pequeñas. Se empleará una técnica de construcción y evaluación de modelos de distribución potencial novedosa, desarrollada por Pearson et al. (2007), basada en una partición tipo Jackknife diseñada específicamente para obtener la distribución de modelos de distribución potencial en situaciones donde el número de localidades de presencia es bajo (&lt;25 localidades), el cual ha generado resultados aceptables cuando ha sido aplicado a especies microendémicas o en riesgo de extinción.

Considerando la información actual sobre la distribución de las especies incluidas dentro del presente proyecto, su estatus de conservación, la escasez de registros de presencia de las misma y la disponibilidad de algoritmos que pueden ser empleados en la generación de modelos de distribución potencial de especies raras y en peligro de extinción, se considera viable, útil y necesario obtener un modelaje de las especies consideradas en el presente proyecto que incluya todas las localidades (históricas y actuales) con un algoritmo, métodos de partición y evaluación adecuados a la naturaleza de los datos disponibles. Los modelos generados constituirá una valiosa herramienta que permitirá: evaluar el riesgo de extinción actual de la especie y la generación de estrategias de conservación adecuadas del hábitat.
Reino: 1
Filo: 1
Clase: 1
Orden: 1
Familia: 1
Género: 1
Especie: 1
DOI: 10.15468/dl.pwmm73
title: Occurrence Download

description: A dataset listing the 663 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "Geometry POLYGON((-42.78069 -8.82213,-42.70547 -8.85916,-42.52959 -8.86032,-42.32593 -8.72725,-42.31783 -8.58376,-42.42429 -8.67286,-42.5724 -8.48656,-42.70547 -8.50507,-42.78069 -8.82213))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 663 records were derived from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000741-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ath72h
title: Occurrence Download

description: A dataset listing the 207607 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 207607 records were derived from 2894 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000740-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48320/cbe63819-a5d8-401f-8150-27c3468ccf01
title: Support for Democracy Among the Population of Ukraine (2004, 2013-2025)
DOI: 10.5281/zenodo.17293675
title: Support for Democracy Among the Population of Ukraine (2004, 2013-2025) – Merged data from nationwide public opinion surveys from May 2004 to October 2025

description: Version 1.3 includes data collected in October 2025.
Version 1.2 includes weighting coefficients and extended list of results (aggregated percentages from 18 surveys).
Version 1.1 includes data collected in August 2025.

The dataset includes data collected from a series of public opinion surveys conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Social Monitoring Centre (in 2004), the Kyiv International Institute of Sociology (in 2019 and October 2025), and the Razumkov Centre sociological service from May 2004 to August 2025. In total, the dataset includes 9 nationwide public opinion surveys. A total of 17675 adult respondents participated.

The dataset includes a question that reveals the attitudes of Ukrainians towards the democratic regime before and during the Russian invasion. In addition, the main social and demographic characteristics of the respondents were added to the dataset: gender, age, macro-region of residence, type of settlement, education, and financial situation.

The years 2015-2018, 2020, and 2021 are omitted from the dataset, as the question of attitudes towards the democratic regime was not raised in these years. 

Composition of macro-regions:
West – Volyn, Zakarpattya, Ivano-Frankivsk, Lviv, Rivne, Ternopil and Chernivtsi regions;
Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy and Chernihiv regions, as well as the city of Kyiv;
South – Zaporizhzhya, Mykolayiv, Odesa, Kherson regions and Crimea;
East – Dnipropetrovsk, Donetsk, Luhansk and Kharkiv regions.

This dataset contains merged survey data from 2004 to 2025 (9 surveys) in SPSS (.sav) format. It has been exported to an Excel file. The contents of the corresponding XLSX file should be identical to the original SAV file. The methodology of all included surveys and the questionnaire are presented in the "Documentation" (PDF file) in English. The dynamics of respondents' answers by year, summarised by the Institute of Sociology of the National Academy of Sciences of Ukraine, is presented in the Excel file "Results" in English.
description: Published on Discuss Data, https://discuss-data.net/dataset/cbe63819-a5d8-401f-8150-27c3468ccf01/
Dataset creation: October 2025
description: Method(s) of data collection: Public Opinion PollMethod(s) of data analysis: Descriptive Statistics
DOI: 10.5281/zenodo.17293676
title: Support for Democracy Among the Population of Ukraine (2004, 2013-2025) – Merged data from nationwide public opinion surveys from May 2004 to October 2025

description: Version 1.3 includes data collected in October 2025.
Version 1.2 includes weighting coefficients and extended list of results (aggregated percentages from 18 surveys).
Version 1.1 includes data collected in August 2025.

The dataset includes data collected from a series of public opinion surveys conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Social Monitoring Centre (in 2004), the Kyiv International Institute of Sociology (in 2019 and October 2025), and the Razumkov Centre sociological service from May 2004 to August 2025. In total, the dataset includes 9 nationwide public opinion surveys. A total of 17675 adult respondents participated.

The dataset includes a question that reveals the attitudes of Ukrainians towards the democratic regime before and during the Russian invasion. In addition, the main social and demographic characteristics of the respondents were added to the dataset: gender, age, macro-region of residence, type of settlement, education, and financial situation.

The years 2015-2018, 2020, and 2021 are omitted from the dataset, as the question of attitudes towards the democratic regime was not raised in these years. 

Composition of macro-regions:
West – Volyn, Zakarpattya, Ivano-Frankivsk, Lviv, Rivne, Ternopil and Chernivtsi regions;
Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy and Chernihiv regions, as well as the city of Kyiv;
South – Zaporizhzhya, Mykolayiv, Odesa, Kherson regions and Crimea;
East – Dnipropetrovsk, Donetsk, Luhansk and Kharkiv regions.

This dataset contains merged survey data from 2004 to 2025 (9 surveys) in SPSS (.sav) format. It has been exported to an Excel file. The contents of the corresponding XLSX file should be identical to the original SAV file. The methodology of all included surveys and the questionnaire are presented in the "Documentation" (PDF file) in English. The dynamics of respondents' answers by year, summarised by the Institute of Sociology of the National Academy of Sciences of Ukraine, is presented in the Excel file "Results" in English.
description: Published on Discuss Data, https://discuss-data.net/dataset/cbe63819-a5d8-401f-8150-27c3468ccf01/
Dataset creation: October 2025
description: Method(s) of data collection: Public Opinion PollMethod(s) of data analysis: Descriptive Statistics
DOI: 10.15468/dl.e5dptz
title: Occurrence Download

description: A dataset containing 460 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 Aedes camptorhynchus (Thomson, 1869)"
 ]
}

 The dataset includes 460 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033621-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25810/xfy6-c856
title: FIRED Chile Nov 2000 - July 2024

description: This is event-level polygons for the fire event delineation (FIRED) product for CHILE from November 2000  to July 2024. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v061/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (https://github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixels and 5 days. Daily polygons are included and the event identification numbers are the same for both files, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the associated paper for more details on the methods and more:

Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498
DOI: 10.15468/dl.5sepb7
title: Occurrence Download

description: A dataset containing 13371 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 Aedes canadensis (Theobald, 1901)"
 ]
}

 The dataset includes 13371 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033622-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/41a788a9638831d7eb03d0c30a4cacf6
title: Recruitment data from 1997 to 2021 for mussels, barnacles and rockweeds from an LTREB project in the Gulf of Maine, USA

description: Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on recruitment of mussels, barnacles and fucoid seaweeds in the 60 experimental plots from 1997 to 2021. Earlier versions of the data prior to 2013 can be found in Ecological Archives (E090-039 and E096-274). This EDI version includes corrections of errors in the versions in Ecological Archives. GPS coordinates of all 60 plots can be found in the EDI data package with data on densities and percent cover (https://doi.org/10.6073/pasta/04bf8647c5bb39025aeeecae2e31d404). Research was funded by NSF's LTREB program.
DOI: 10.15468/dl.yddhdq
title: Occurrence Download

description: A dataset containing 303472 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "PublishingOrg is 2a7e3080-28a9-11dd-97cd-b8a03c50a862",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 303472 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033591-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.469pjs
title: Occurrence Download

description: A dataset containing 108184 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Niger"
 ]
}

 The dataset includes 108184 records from 310 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033587-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3s42jv
title: Occurrence Download

description: A dataset containing 922 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 Aedes cantans (Meigen, 1818)"
 ]
}

 The dataset includes 922 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033624-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aap39m
title: Occurrence Download

description: A dataset containing 5203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Fossil, Living Specimen, Occurrence evidence)",
 "Continent is one of (Africa, Asia, Antarctica, Oceania, Europe, North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, February, March, April, May, June, July, August, October, November, December, September)",
 "TaxonKey is Thunnus South, 1845"
 ]
}

 The dataset includes 5203 records from 139 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033623-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48366/r789786
title: Exploring Hyperspectral Data for Vegetation and Resource Mapping: New and Old Generation Technologies in Action

description: This collection of studies explores the diverse applications of hyperspectral remote sensing in vegetation management, resource mapping, and agricultural monitoring. Topics include vegetation management by Catchment Management Authorities using Hyperion imagery, uranium mapping, leaf nitrogen assessment, and comparisons of new-generation hyperspectral sensors like ISS DESIS with traditional multispectral data. The research introduces advanced techniques for hyperspectral pansharpening and component substitution, offering valuable insights and methodologies for researchers, remote sensing professionals, and students in environmental management, resource exploration, and precision agriculture.
DOI: 10.25810/jwzt-3271
title: FIRED Cayman Islands Nov 2000 - July 2024

description: This is event-level polygons for the fire event delineation (FIRED) product for CAYMAN ISLANDS from November 2000  to July 2024. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v061/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (https://github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixels and 5 days. Daily polygons are included and the event identification numbers are the same for both files, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the associated paper for more details on the methods and more:

Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498
DOI: 10.25810/w8xc-nr81
title: FIRED Cabo Verde Nov 2000 - July 2024

description: This is event-level polygons for the fire event delineation (FIRED) product for CABO VERDE from November 2000  to July 2024. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v061/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (https://github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 1 pixels and 5 days. Daily polygons are included and the event identification numbers are the same for both files, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the associated paper for more details on the methods and more:

Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498
DOI: 10.15468/dl.e4prx2
title: Occurrence Download

description: A dataset containing 1081 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 Aedes cantator (Coquillett, 1903)"
 ]
}

 The dataset includes 1081 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033626-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drwttg
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Centris scopipes Friese, 1900"
 ]
}

 The dataset includes 189 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033627-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.