cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17611/dp/18000491
title: Moment Tensor for October 16, 2018 MW 5.6 (GCMT) CENTRAL MID-ATLANTIC RIDGE
DOI: 10.5281/zenodo.16926391
title: Predictive Modeling  of Soil Types and Their Characteristics - supplementary data

description: The supplementary dataset accompanying a textbook "Cherlinka, Gallay, Dmytruk (2025) Predictive Modeling  of Soil Types and Their Characteristics" contains the geospatial materials used throughout the practical exercises in Parts II and III. The data cover the territory of Slovakia and include raster and vector layers representing environmental covariates, soil samples, and derived model outputs. While some datasets have been modified or simplified for educational purposes, they are based on real, publicly available sources to ensure reproducibility and transferability to similar studies.
DOI: 10.5281/zenodo.16926392
title: Predictive Modeling  of Soil Types and Their Characteristics - supplementary data

description: The supplementary dataset accompanying a textbook "Cherlinka, Gallay, Dmytruk (2025) Predictive Modeling  of Soil Types and Their Characteristics" contains the geospatial materials used throughout the practical exercises in Parts II and III. The data cover the territory of Slovakia and include raster and vector layers representing environmental covariates, soil samples, and derived model outputs. While some datasets have been modified or simplified for educational purposes, they are based on real, publicly available sources to ensure reproducibility and transferability to similar studies.
DOI: 10.17611/dp/18000497
title: Moment Tensor for October 17, 2018 MW 5.7 (GCMT) SANTA CRUZ ISLANDS
DOI: 10.17611/dp/18000503
title: Moment Tensor for October 17, 2018 MW 5.4 (GCMT) SOUTHERN MID-ATLANTIC RIDGE
DOI: 10.17611/dp/18000509
title: Moment Tensor for October 19, 2018 MW 5.5 (GCMT) NEAR NORTH COAST OF IRIAN JAYA
DOI: 10.17611/dp/18000515
title: Moment Tensor for October 21, 2018 MW 5.9 (GCMT) SOUTHWEST INDIAN RIDGE
DOI: 10.17611/dp/18000521
title: Moment Tensor for October 22, 2018 MW 6.5 (GCMT) VANCOUVER ISLAND, CANADA REGION
DOI: 10.17611/dp/18000527
title: Moment Tensor for October 22, 2018 MW 6.8 (GCMT) VANCOUVER ISLAND, CANADA REGION
DOI: 10.17611/dp/18000533
title: Moment Tensor for October 22, 2018 MW 6.5 (GCMT) VANCOUVER ISLAND, CANADA REGION
DOI: 10.17611/dp/18000539
title: Moment Tensor for October 23, 2018 MW 5.8 (GCMT) TAIWAN REGION
DOI: 10.17611/dp/18000545
title: Moment Tensor for October 23, 2018 MW 5.5 (GCMT) SOUTHERN EAST PACIFIC RISE
DOI: 10.17611/dp/18000551
title: Moment Tensor for October 23, 2018 MW 5.8 (GCMT) TAIWAN REGION
DOI: 10.17611/dp/18000557
title: Moment Tensor for October 25, 2018 MW 5.8 (GCMT) NEAR EAST COAST OF HONSHU, JAPAN
DOI: 10.17611/dp/18000563
title: Moment Tensor for October 25, 2018 MW 6.8 (GCMT) IONIAN SEA
DOI: 10.17611/dp/18000569
title: Moment Tensor for October 26, 2018 MW 5.7 (GCMT) HOKKAIDO, JAPAN REGION
DOI: 10.17611/dp/18000575
title: Moment Tensor for October 26, 2018 MW 5.9 (GCMT) MARIANA ISLANDS REGION
DOI: 10.15468/dl.v39wkm
title: Occurrence Download

description: A dataset containing 13156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Astragalus onobrychis"
 ]
}

 The dataset includes 13156 records from 213 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003991-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17611/dp/18000581
title: Moment Tensor for November 06, 2018 MW 5.6 (GCMT) TONGA ISLANDS REGION
DOI: 10.17611/dp/18000587
title: Moment Tensor for November 09, 2018 MW 6.8 (GCMT) JAN MAYEN ISLAND REGION
DOI: 10.17611/dp/18000593
title: Moment Tensor for November 09, 2018 MW 5.6 (GCMT) TRISTAN DA CUNHA REGION
DOI: 10.17611/dp/18000599
title: Moment Tensor for November 09, 2018 MW 5.7 (GCMT) TRISTAN DA CUNHA REGION
DOI: 10.17611/dp/18000605
title: Moment Tensor for November 10, 2018 MW 5.6 (GCMT) NEW BRITAIN REGION, P.N.G.
DOI: 10.17611/dp/18000611
title: Moment Tensor for November 10, 2018 MW 6.1 (GCMT) TONGA ISLANDS
DOI: 10.5281/zenodo.15690838
title: TVC Experiment 2018/19: Soil Vegetation Snow Version 2, land surface model output

description: This dataset contains the raw snow and soil outputs from the Environment and Climate Change Canada (ECCC) Land Surface Model (LSM), Soil Vegetation Snow version 2 (SVS-2; Vionnet et al., 2025) used in the studies of Woolley et al. (2024) and  Montpetit et al. (2025).

A previous version of this dataset, processed and adapted for the study of Woolley et al. (2024) was published.Here the SVS-2 outputs are given in its original format in a NetCDF file as described in the readme file. Codes to read and process this dataset are available at https://github.com/ECCCBen/TVCExp18-19_SWE.

DOI links to citation above are posted below.
DOI: 10.17611/dp/18000617
title: Moment Tensor for November 11, 2018 MW 5.2 (GCMT) HALMAHERA, INDONESIA
DOI: 10.5281/zenodo.15690837
title: TVC Experiment 2018/19: Soil Vegetation Snow Version 2, land surface model output

description: This dataset contains the raw snow and soil outputs from the Environment and Climate Change Canada (ECCC) Land Surface Model (LSM), Soil Vegetation Snow version 2 (SVS-2; Vionnet et al., 2025) used in the studies of Woolley et al. (2024) and  Montpetit et al. (2025).

A previous version of this dataset, processed and adapted for the study of Woolley et al. (2024) was published.Here the SVS-2 outputs are given in its original format in a NetCDF file as described in the readme file. Codes to read and process this dataset are available at https://github.com/ECCCBen/TVCExp18-19_SWE.

DOI links to citation above are posted below.
DOI: 10.17611/dp/18000623
title: Moment Tensor for November 11, 2018 MW 6.2 (GCMT) NORTH ATLANTIC OCEAN
DOI: 10.15468/dl.cmq3dn
title: Occurrence Download

description: A dataset containing 46880494 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "Geometry POLYGON((4.3533 42.84621,7.96371 42.84621,7.96371 46.5885,4.3533 46.5885,4.3533 42.84621))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 46880494 records from 4984 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003938-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nvpxsz
title: Occurrence Download

description: A dataset containing 241021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bolboschoenus maritimus, Bomarea multiflora, Bonnemaisonia hamifera, Bontia daphnoides, Borago officinalis, Bothriochloa bladhii, Bothriochloa pertusa, Bougainvillea glabra, Brachiaria arrecta, Brachiaria brizantha, Brachiaria decumbens, Brachiaria deflexa, Brachiaria dictyoneura, Brachiaria distachya, Brachiaria eminii, Brachiaria eruciformis, Brachiaria humidicola, Brachiaria lata, Brachiaria mutica, Brachiaria plantaginea)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 241021 records from 927 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000921-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15482/usda.adc/28199936
title: Data from: Dogs can detect powdery mildew (Erysiphe necator) in grapevine (Vitis vinifera) leaves

description: Powdery mildew (<i>Erysiphe necator</i>, PM) is a fungus that affects grapes (<i>Vitis spp.)</i> worldwide. Despite its negative impact on vineyard production and economics, there is no fast and economic way to detect it at scale. The aim of this study was to evaluate dogs’ capability to detect grape leaves infected with PM. Three pet dogs were trained to perform a detection task using a three-alternative automated olfactometer. In Experiment 1, dogs were trained to alert to the headspace of a vial containing a PM positive leaf (with visible signs of infection in more than 50% of leaf surface area) and discriminate it between control leaves (e.g., healthy) and leaves inoculated with other fungi. Dogs learned the discrimination task and reached sensitivity and specificity values of 0.96 ± 0.01 (SE). In Experiment 2, we tested dogs’ response rate to the filtered (10µm) headspace, a gauze exposed to the headspace of a PM positive leaf for 24 h, and to a control leaf after 1 and 24h post PM inoculation. Response rate to the filtered headspace and to the inoculated gauze were identical to the response rate to the PM positive leaf (0.96 ± 0.04 SE). Average response rate to leaves right after inoculation and 24 h post inoculation was 0.58 ± 0.10 (SE). Although when averaged across dogs this was above chance, it indicates that longer than 24h post inoculation is needed for proficient detection. Experiment 2 results also suggest dogs were detecting PM specific VOCs that were captured by the gauze and passed through the 10 µm filter, and not to the presence of airborne spores. In Experiment 3, we tested dogs’ ability to discriminate between PM (<i>Erysiphe necator) </i>and a fungus of the same genus (<i>Erysiphe lagerstroemiae). </i>Our results show that dogs did not spontaneously discriminate between these two fungi, even though they were tested on different hosts. This suggests that these two fungi are perceptually similar to dogs. Our results show<i> </i>that dogs are proficient in detecting and discriminating leaves with PM. Future studies are needed to evaluate their use as PM biosensors in vineyards.
DOI: 10.15482/usda.adc/28199936.v1
title: Data from: Dogs can detect powdery mildew (Erysiphe necator) in grapevine (Vitis vinifera) leaves

description: Powdery mildew (<i>Erysiphe necator</i>, PM) is a fungus that affects grapes (<i>Vitis spp.)</i> worldwide. Despite its negative impact on vineyard production and economics, there is no fast and economic way to detect it at scale. The aim of this study was to evaluate dogs’ capability to detect grape leaves infected with PM. Three pet dogs were trained to perform a detection task using a three-alternative automated olfactometer. In Experiment 1, dogs were trained to alert to the headspace of a vial containing a PM positive leaf (with visible signs of infection in more than 50% of leaf surface area) and discriminate it between control leaves (e.g., healthy) and leaves inoculated with other fungi. Dogs learned the discrimination task and reached sensitivity and specificity values of 0.96 ± 0.01 (SE). In Experiment 2, we tested dogs’ response rate to the filtered (10µm) headspace, a gauze exposed to the headspace of a PM positive leaf for 24 h, and to a control leaf after 1 and 24h post PM inoculation. Response rate to the filtered headspace and to the inoculated gauze were identical to the response rate to the PM positive leaf (0.96 ± 0.04 SE). Average response rate to leaves right after inoculation and 24 h post inoculation was 0.58 ± 0.10 (SE). Although when averaged across dogs this was above chance, it indicates that longer than 24h post inoculation is needed for proficient detection. Experiment 2 results also suggest dogs were detecting PM specific VOCs that were captured by the gauze and passed through the 10 µm filter, and not to the presence of airborne spores. In Experiment 3, we tested dogs’ ability to discriminate between PM (<i>Erysiphe necator) </i>and a fungus of the same genus (<i>Erysiphe lagerstroemiae). </i>Our results show that dogs did not spontaneously discriminate between these two fungi, even though they were tested on different hosts. This suggests that these two fungi are perceptually similar to dogs. Our results show<i> </i>that dogs are proficient in detecting and discriminating leaves with PM. Future studies are needed to evaluate their use as PM biosensors in vineyards.
DOI: 10.5281/zenodo.16969253
title: Mixing of natural dust with anthropogenic pollution leads to negative climate potential over the Western Himalayas

description: The dataset provided here pertains to the Dhundi Research Site. Users are requested to acknowledge the data curators before utilizing it.
DOI: 10.5281/zenodo.16969252
title: Mixing of natural dust with anthropogenic pollution leads to negative climate potential over the Western Himalayas

description: The dataset provided here pertains to the Dhundi Research Site. Users are requested to acknowledge the data curators before utilizing it.
DOI: 10.34740/kaggle/dsv/12889255
title: Antutu Benchmark
DOI: 10.15468/dl.pfrq7g
title: Occurrence Download

description: A dataset containing 160059 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyanoderma ruficeps",
 "Country is Chinese Taipei",
 "HasCoordinate is true"
 ]
}

 The dataset includes 160059 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000916-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pcvp6r
title: Occurrence Download

description: A dataset containing 259 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bats of Northern Peninsular Malaysia"
 ]
}

 The dataset includes 259 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000950-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16955662
title: PRYSM & CESM Data Files for Chapter 3 of Heitmann Dissertation
DOI: 10.5281/zenodo.16955663
title: PRYSM & CESM Data Files for Chapter 3 of Heitmann Dissertation
DOI: 10.5281/zenodo.16969357
title: SHAXSDA GENDER MADANIYATNI RIVOJLANTIRISHDA JAHON TAJRIBALARINING QIYOSIY TAHLILI

description: Ushbu maqolada jahon tajribalari asosida shaxsda gender madaniyatni rivojlantirishning qiyosiy tahlili, gender stereotip va rollar o‘zgaruvchan xarakterga egaligi takidlab utilgan. Genderning ijtimoiy konstruksiyasi shaxsning ijtimoiylashuvi jarayonida vujudga keladi. Shuni alohida ta’kidlash kerakki, gender stereotip va rollar o‘zgaruvchan xarakterga ega. Tadqiqotlar shuni ko‘rsatadiki, gender rollar turli tarixiy sharoitlarda turli madaniyatlar ta’siri ostida o‘zgaradi. Shu jumladan, gibrid xarakterdagi ommaviy madaniyat ham shaxsning ijtimoiy rollarini o‘zgartirishga muvaffaq bo‘lmoqda.
DOI: 10.5281/zenodo.16969358
title: SHAXSDA GENDER MADANIYATNI RIVOJLANTIRISHDA JAHON TAJRIBALARINING QIYOSIY TAHLILI

description: Ushbu maqolada jahon tajribalari asosida shaxsda gender madaniyatni rivojlantirishning qiyosiy tahlili, gender stereotip va rollar o‘zgaruvchan xarakterga egaligi takidlab utilgan. Genderning ijtimoiy konstruksiyasi shaxsning ijtimoiylashuvi jarayonida vujudga keladi. Shuni alohida ta’kidlash kerakki, gender stereotip va rollar o‘zgaruvchan xarakterga ega. Tadqiqotlar shuni ko‘rsatadiki, gender rollar turli tarixiy sharoitlarda turli madaniyatlar ta’siri ostida o‘zgaradi. Shu jumladan, gibrid xarakterdagi ommaviy madaniyat ham shaxsning ijtimoiy rollarini o‘zgartirishga muvaffaq bo‘lmoqda.
DOI: 10.5281/zenodo.14026969
title: Data and Code for "Accurate prediction of absolute prokaryotic abundance from DNA concentration"

description: Data and Code for "Accurate prediction of absolute prokaryotic abundance from DNA concentration"
DOI: 10.5281/zenodo.16969374
title: Data and Code for "Accurate prediction of absolute prokaryotic abundance from DNA concentration"

description: Data and Code for "Accurate prediction of absolute prokaryotic abundance from DNA concentration"
DOI: 10.15468/dl.k4hfu3
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Etheostoma lugoi)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 19 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000956-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zkamg2
title: Occurrence Download

description: A dataset containing 204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Olea europaea",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 204 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000957-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2k42m9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Olea europaea",
 "Country is Armenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000954-250827131500795/datasets/export for details.
DOI: 10.15468/dl.4ra477
title: Occurrence Download

description: A dataset containing 20592 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Olea europaea",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20592 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000955-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4w8kwc
title: Occurrence Download

description: A dataset containing 1299269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Nepal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1299269 records from 482 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000879-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gm5ns2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spelaeornis badeigularis",
 "Country is Chinese Taipei",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000958-250827131500795/datasets/export for details.
DOI: 10.15468/dl.caxekr
title: Occurrence Download

description: A dataset containing 21409 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is USA.37_1",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 21409 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000959-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16969383
title: Leuven 3-30-300

description: The 3-30-300 Rule is an urban planning guideline designed to enhance the quality of life in cities by promoting access to nature and green spaces.

It consists of three key principles:



3 Trees: every person should be able to see at least 3 trees from their home, supporting visual contact with nature that can improve mental health and well-being.

30% Canopy: neighbourhoods should maintain at least 30% tree canopy cover, ensuring sufficient green coverage that contributes to cooling urban heat islands, improving air quality, and enhancing biodiversity.

300 Meters: every resident should live within 300 meters of a public green space.





scores-3-30-300.tif
Raster file indicating the overal score on the 3-30-300indicators, ranging from 0 to 3


Visible_tree_count.tif
Indicator for rule '3': raster file indicating per cell the number of trees visibile within a viewing distance of 200m


sum-in_buffer_20.tif
Indiactor for rule '30': raster file indicating the percentage of tree cover within a 200 m buffer


sum_in_buffer_30.tif
Alternative indicator for rule '30": raster file indicating the percentage of tree cover within a 300 m buffer


leuven_groen_osm.tif
Indicator for rule '300': raster file indicating the distance allong the street network to accessible green


leuven_groen_osm_interpolated.tif
Indicator for rule '300': raster file indicating the distance allong the street network to accessible green, interpolated to surrounding cells for overlay with buildings


style_3.sld
An SLD (Styled Layer Descriptor) file fro styling of the '3' indicator


style_30.sld
An SLD (Styled Layer Descriptor) file fro styling of the '30' indicator


style_300_distances.sld
An SLD (Styled Layer Descriptor) file fro styling of the '300' indicator
DOI: 10.5281/zenodo.16969384
title: Leuven 3-30-300

description: The 3-30-300 Rule is an urban planning guideline designed to enhance the quality of life in cities by promoting access to nature and green spaces.

It consists of three key principles:



3 Trees: every person should be able to see at least 3 trees from their home, supporting visual contact with nature that can improve mental health and well-being.

30% Canopy: neighbourhoods should maintain at least 30% tree canopy cover, ensuring sufficient green coverage that contributes to cooling urban heat islands, improving air quality, and enhancing biodiversity.

300 Meters: every resident should live within 300 meters of a public green space.





scores-3-30-300.tif
Raster file indicating the overal score on the 3-30-300indicators, ranging from 0 to 3


Visible_tree_count.tif
Indicator for rule '3': raster file indicating per cell the number of trees visibile within a viewing distance of 200m


sum-in_buffer_20.tif
Indiactor for rule '30': raster file indicating the percentage of tree cover within a 200 m buffer


sum_in_buffer_30.tif
Alternative indicator for rule '30": raster file indicating the percentage of tree cover within a 300 m buffer


leuven_groen_osm.tif
Indicator for rule '300': raster file indicating the distance allong the street network to accessible green


leuven_groen_osm_interpolated.tif
Indicator for rule '300': raster file indicating the distance allong the street network to accessible green, interpolated to surrounding cells for overlay with buildings


style_3.sld
An SLD (Styled Layer Descriptor) file fro styling of the '3' indicator


style_30.sld
An SLD (Styled Layer Descriptor) file fro styling of the '30' indicator


style_300_distances.sld
An SLD (Styled Layer Descriptor) file fro styling of the '300' indicator
DOI: 10.15468/dl.g2qpnh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Olea europaea",
 "Country is Tonga",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000962-250827131500795/datasets/export for details.
DOI: 10.15468/dl.hszkpy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Olea europaea",
 "Country is Sao Tome and Principe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000961-250827131500795/datasets/export for details.