cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6sfthj
title: Occurrence Download

description: A dataset containing 2152915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Capreolus capreolus (Linnaeus, 1758))",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 2152915 records from 4024 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015687-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84fmud
title: Occurrence Download

description: A dataset containing 981024 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cygnus columbianus (Ord, 1815)"
 ]
}

 The dataset includes 981024 records from 455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012971-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15274947
title: Günzburg Obere Apotheke

description: Historical questionnaire/s  1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen]

Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot  be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht  veröffentlicht werden dürfen).
DOI: 10.5281/zenodo.15274948
title: Günzburg Obere Apotheke

description: Historical questionnaire/s  1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen]

Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot  be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht  veröffentlicht werden dürfen).
DOI: 10.15468/dl.x46sva
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lebedodes Holland, 1893"
 ]
}

 The dataset includes 59 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001334-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16958954
title: MAKTABGACHA TA'LIM YOSHIDAGI  BOLALAR UCHUN HAVO VA QUYOSH VANNALARI
DOI: 10.5281/zenodo.16958955
title: MAKTABGACHA TA'LIM YOSHIDAGI  BOLALAR UCHUN HAVO VA QUYOSH VANNALARI
DOI: 10.15468/dl.dvyawv
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049202-250811113504898/datasets/export for details.
DOI: 10.15468/dl.h2rpn2
title: Occurrence Download

description: A dataset containing 6059 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calligonum L."
 ]
}

 The dataset includes 6059 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003911-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8u2z3
title: Occurrence Download

description: A dataset containing 556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Corylus avellana var. avellana, Cota tinctoria subsp. australis (R.Fern.) Oberpr. & Greuter, Cotoneaster froebelii J.Fryer, Cotoneaster hedegaardii J.Fryer & B.Hylmö, Cotoneaster insculptus Diels)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 556 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003917-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7jr6g
title: Occurrence Download

description: A dataset containing 0 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 Kalimeris altaica (Willd.) Nees ex Fisch., C.A.Mey. & Avé-Lall.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009769-250525065834625/datasets/export for details.
DOI: 10.15468/dl.yk2t72
title: Occurrence Download

description: A dataset containing 17590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Lontra canadensis (Schreber, 1777)"
 ]
}

 The dataset includes 17590 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007091-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.abxdzs
title: Occurrence Download

description: A dataset containing 381 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Lontra felina (Molina, 1782)"
 ]
}

 The dataset includes 381 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007093-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zvd466
title: Occurrence Download

description: A dataset containing 1000 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Lontra longicaudis (Olfers, 1818)"
 ]
}

 The dataset includes 1000 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007092-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.j00240.00050
title: Sound2Center
title: Sound2Center

description: The adaptive masking component is the cornerstone of the proposed algorithm，enabling segmented processing of coal mine images. While dividing each image into smaller blocks，the module operates by sequentially applying a mask to the edge and corner pixels of each block, deliberately excluding the central pixels. Subsequently once the masking process is complete， the mask integration module is employed. This module is responsible for fusing the neural network’s output with the masked areas to reconstruct a coherent and denoised image. Furthermore， this module incorporates a quality evaluation mechanism to assess the effectiveness of the integration. The final component of the algorithm is an adaptive integrated loss function， which guides the model during training. This loss function is specifically designed to address the unique challenges of coal mine image denoising， including complex noise patterns and the need to preserve subtle image details. Additionally，by incorporating the original noisy image， the loss function increases the model’s sensitivity to signal changes，enhancing its adaptability across various denoising scenarios and noise conditions.
description: The adaptive masking component is the cornerstone of the proposed algorithm，enabling segmented processing of coal mine images. While dividing each image into smaller blocks，the module operates by sequentially applying a mask to the edge and corner pixels of each block, deliberately excluding the central pixels. Subsequently once the masking process is complete， the mask integration module is employed. This module is responsible for fusing the neural network’s output with the masked areas to reconstruct a coherent and denoised image. Furthermore， this module incorporates a quality evaluation mechanism to assess the effectiveness of the integration. The final component of the algorithm is an adaptive integrated loss function， which guides the model during training. This loss function is specifically designed to address the unique challenges of coal mine image denoising， including complex noise patterns and the need to preserve subtle image details. Additionally，by incorporating the original noisy image， the loss function increases the model’s sensitivity to signal changes，enhancing its adaptability across various denoising scenarios and noise conditions.
DOI: 10.57760/sciencedb.31545
title: A 10-m Vector Dataset of Mangrove Species Distribution in China
title: 10米分辨率中国红树林物种分布矢量数据集

description: This dataset provides a 10-m spatial-resolution vector map of mangrove species distribution along the Chinese coast, produced under an Environment-Enhanced Remote Sensing (EERS) framework. EERS performs feature-level fusion of single-date Sentinel-2 spectral and textural features with long-term climatological and oceanographic variables from WorldClim and Bio-ORACLE. Spectral separability among species was quantified across 36 monthly composites (2018–2020) using the Jeffries–Matusita distance, identifying June 2019 as the most discriminative window. Lightweight, interpretable classifiers were evaluated with spatially blocked validation to map 26 mangrove species merged into 18 classes. Relative to a remote-sensing-only baseline, overall precision increased by 16.7% and AUC-ROC improved from 0.803 to 0.900. Mapping results indicate dominance by Avicennia marina (31.6%), Aegiceras corniculatum (17.0%), and Kandelia obovata (16.8%). Introduced species (Sonneratia apetala and Laguncularia racemosa) together account for ~14%, while threatened taxa such as Nypa fruticans cover &lt;0.05%. Delivered as a vector dataset with an effective 10-m mapping resolution, it offers a current baseline for biodiversity assessment, invasion monitoring, and climate-informed coastal ecosystem management.
description: This dataset provides a 10-m spatial-resolution vector map of mangrove species distribution along the Chinese coast, produced under an Environment-Enhanced Remote Sensing (EERS) framework. EERS performs feature-level fusion of single-date Sentinel-2 spectral and textural features with long-term climatological and oceanographic variables from WorldClim and Bio-ORACLE. Spectral separability among species was quantified across 36 monthly composites (2018–2020) using the Jeffries–Matusita distance, identifying June 2019 as the most discriminative window. Lightweight, interpretable classifiers were evaluated with spatially blocked validation to map 26 mangrove species merged into 18 classes. Relative to a remote-sensing-only baseline, overall precision increased by 16.7% and AUC-ROC improved from 0.803 to 0.900. Mapping results indicate dominance by Avicennia marina (31.6%), Aegiceras corniculatum (17.0%), and Kandelia obovata (16.8%). Introduced species (Sonneratia apetala and Laguncularia racemosa) together account for ~14%, while threatened taxa such as Nypa fruticans cover &lt;0.05%. Delivered as a vector dataset with an effective 10-m mapping resolution, it offers a current baseline for biodiversity assessment, invasion monitoring, and climate-informed coastal ecosystem management.
DOI: 10.57760/sciencedb.31534
title: Annual Mean Wind Speed Raster Data of Xiluodu Reservoir Area (2001-2020)
title: 2001-2020年溪洛渡库区年平均风速栅格数据

description: This dataset is a raster dataset of average wind speed in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s. 
description: This dataset is a raster dataset of average wind speed in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s.
DOI: 10.57760/sciencedb.31521
title: 2001-2020 Longpan Reservoir Area Vegetation Net Primary Productivity Raster Data
title: 2001-2020年龙盘库区植被净初级生产力栅格数据

description: This dataset is the vegetation net primary productivity raster data of Longpan Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
description: This dataset is the vegetation net primary productivity raster data of Longpan Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
DOI: 10.57760/sciencedb.31570
title: Annual SPEI Raster Data of Xiluodu Reservoir Area (2001-2020)
title: 2001-2020年溪洛渡库区年度SPEI栅格数据

description: This dataset is the annual SPEI raster dataset for the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km. 
description: This dataset is the annual SPEI raster dataset for the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km.
DOI: 10.57760/sciencedb.31572
title: Annual SPEI Raster Data of Longpan Reservoir Area (2001-2020)
title: 2001-2020年龙盘库区年度SPEI栅格数据

description: This dataset is the annual SPEI raster dataset for the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km. 
description: This dataset is the annual SPEI raster dataset for the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km.
DOI: 10.15468/dl.fv6y7j
title: Occurrence Download

description: A dataset containing 3908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-191.43860 19.06773,-24.56140 19.06773,-24.56140 85.93227,-191.43860 85.93227,-191.43860 19.06773))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Zapus hudsonius",
 "Year 1970-2020"
 ]
}

 The dataset includes 3908 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029960-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.31569
title: Annual Mean Wind Speed Raster Data of Longpan Reservoir Area (2001-2020)
title: 2001-2020年龙盘库区年平均风速栅格数据

description: This dataset is a raster dataset of average wind speed in the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s. 
description: This dataset is a raster dataset of average wind speed in the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s.
DOI: 10.15468/dl.cqcvnc
title: Occurrence Download

description: A dataset containing 946 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coprophanaeus pluto"
 ]
}

 The dataset includes 946 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029961-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.31528
title: Annual Precipitation Raster Data of Xiluodu Reservoir Area (2001-2020)
title: 2001-2020年溪洛渡库区年降水量栅格数据

description: This dataset is a raster dataset of precipitation in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1km and units of 0.1mm. 
description: This dataset is a raster dataset of precipitation in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1km and units of 0.1mm.
DOI: 10.57760/sciencedb.31530
title: Annual Precipitation Raster Data of Longpan Reservoir Area (2001-2020)
title: 2001-2020年龙盘库区年降水量栅格数据

description: This dataset is a raster dataset of precipitation in Longpan Reservoir Area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of 0.1mm. 
description: This dataset is a raster dataset of precipitation in Longpan Reservoir Area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of 0.1mm.