cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vh9j52
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erica carnea",
 "Country is Estonia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010804-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k2dabc
title: Occurrence Download

description: A dataset containing 6639 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tamarix aphylla",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6639 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010805-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hkfyjn
title: Occurrence Download

description: A dataset containing 1437 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Agave attenuata)"
 ]
}

 The dataset includes 1437 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010806-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17184739
title: Digital city information models to reduce power outages and enhance energy resilience for megacity Shenzhen

description: Extreme weather events are increasing the frequency and duration of urban power outages, threatening critical infrastructure and essential services in cities. This study presents an integrated approach to mitigate outage impacts through coordinated electric vehicle (EV) dispatch and strategic charging station planning, supported by a city information model (CIM). We develop an agent-based CIM for megacity Shenzhen, incorporating 115,544 buildings, 200,000 EVs, and a road network with 22,474 nodes and 36,195 edges, to mitigate power outages under extreme weather conditions. The baseline simulation shows significant spatial variation in outage counts and hours across urban building energy hubs under predefined grid supply thresholds. Two resilience strategies have been proposed, i.e., a vehicle-to-grid (V2G) emergency power dispatch framework, and a graph-based optimization model. The former is to redistribute electricity from available EVs to affected buildings, while the latter is to deploy bidirectional charging stations in high-risk areas to enhance coverage in critical zones. Results indicate that, compared to the baseline scenario, the V2G emergency power dispatch and a graph-based optimization model with additional bidirectional charging stations reduce total power outage counts by 11.1% and 11.3%, and power outage hours by 15.0% and 15.3%, respectively. Supplementary analyses on energy flows and economic costs including government subsidies for V2G participation demonstrate the broad feasibility of the proposed approach. This work provides a data-driven and urban-level strategy with optimal infrastructure deployment and flexible dispatch to improve urban energy resilience under climate-related disruptions.
DOI: 10.5281/zenodo.17184740
title: Digital city information models to reduce power outages and enhance energy resilience for megacity Shenzhen

description: Extreme weather events are increasing the frequency and duration of urban power outages, threatening critical infrastructure and essential services in cities. This study presents an integrated approach to mitigate outage impacts through coordinated electric vehicle (EV) dispatch and strategic charging station planning, supported by a city information model (CIM). We develop an agent-based CIM for megacity Shenzhen, incorporating 115,544 buildings, 200,000 EVs, and a road network with 22,474 nodes and 36,195 edges, to mitigate power outages under extreme weather conditions. The baseline simulation shows significant spatial variation in outage counts and hours across urban building energy hubs under predefined grid supply thresholds. Two resilience strategies have been proposed, i.e., a vehicle-to-grid (V2G) emergency power dispatch framework, and a graph-based optimization model. The former is to redistribute electricity from available EVs to affected buildings, while the latter is to deploy bidirectional charging stations in high-risk areas to enhance coverage in critical zones. Results indicate that, compared to the baseline scenario, the V2G emergency power dispatch and a graph-based optimization model with additional bidirectional charging stations reduce total power outage counts by 11.1% and 11.3%, and power outage hours by 15.0% and 15.3%, respectively. Supplementary analyses on energy flows and economic costs including government subsidies for V2G participation demonstrate the broad feasibility of the proposed approach. This work provides a data-driven and urban-level strategy with optimal infrastructure deployment and flexible dispatch to improve urban energy resilience under climate-related disruptions.
DOI: 10.15468/dl.9yz56c
title: Occurrence Download

description: A dataset containing 167483 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pyrus calleryana",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 167483 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010749-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4w9upf
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Salix rubens J.Presl & C.Presl"
 ]
}

 The dataset includes 75 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008003-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.594jys
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rosa rubiginosa Britton & N.E.Br."
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008004-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yd72pf
title: Occurrence Download

description: A dataset containing 444189 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron canadensis L."
 ]
}

 The dataset includes 444189 records from 2772 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007987-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kt47xi
title: Occurrence Download

description: A dataset containing 245291 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Equisetum arvense L."
 ]
}

 The dataset includes 245291 records from 674 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004325-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uf9ah8
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Characodoma biavicularium (Canu & Bassler, 1929)"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053764-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9a5cy8
title: Occurrence Download

description: A dataset containing 84954 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amaranthus retroflexus L."
 ]
}

 The dataset includes 84954 records from 952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0142751-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dauwne
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Collinsia parvula Rydb."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0142741-230224095556074/datasets/export for details.
DOI: 10.15468/dl.w6pxtt
title: Occurrence Download

description: A dataset listing the 27376 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample)",
 "Country is Brazil",
 "TaxonKey is Ascomycota"
 ]
}

 The dataset's 27376 records were derived from 230 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0142743-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14917842
title: Modeling DTT-based oxidative potential of fine particulate matter on the European and global scales - Supporting data
DOI: 10.5281/zenodo.14917843
title: Modeling DTT-based oxidative potential of fine particulate matter on the European and global scales - Supporting data
DOI: 10.17632/p3k7zntbkp
title: Three-dimensional double negative mechanical metamaterials with tailored anisotropy

description: Compression test data. The file name corresponds to the picture number.
DOI: 10.17632/p3k7zntbkp.1
title: Three-dimensional double negative mechanical metamaterials with tailored anisotropy

description: Compression test data. The file name corresponds to the picture number.
DOI: 10.15468/dl.pjfbz9
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Insecta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-60 -52.5,-52.5 -52.5,-52.5 -45,-60 -45,-60 -52.5))"
 ]
}

 The dataset includes 58 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040030-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/z44-6cp-4qk
title: Bathyvermilia eliasoni (Zibrowius,1970)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.drar75
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-45 -52.5,-37.5 -52.5,-37.5 -45,-45 -45,-45 -52.5))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040031-251009101135966/datasets/export for details.
DOI: 10.57902/d7959s
title: Road Section and Environmental Imaging Dataset: Dalton Highway (Isom Creek Vicinity), Alaska, August 2024.

description: This dataset comprises high-resolution aerial images captured using a Parrot Anafi remote-controlled aircraft along various sections of the Dalton Highway in Alaska during August 2024. The Dalton Highway, a critical transportation corridor in the Arctic, traverses regions prone to permafrost degradation and dynamic environmental changes. The dataset provides detailed visual records of road conditions and the surrounding environment, offering valuable insights for monitoring permafrost subsidence, assessing infrastructure stability, and supporting predictive analytics related to climate impacts on Arctic transportation networks. This dataset contributes to ongoing efforts to expand geospatial data availability in Arctic regions, enhancing research focused on cold-region environments. The aerial imagery, collected at multiple road sections, is georeferenced and systematically cataloged to facilitate longitudinal studies and interdisciplinary research. This resource is intended to support diverse applications, including machine learning model development, environmental monitoring, and the evaluation of road performance under changing climatic conditions.
DOI: 10.57902/d75c7v
title: High Resolution LiDAR Point Cloud data for AI-Integrated Infrastructure Damage Detection in Arctic Road and Bridge Condition Assessment, Disaster Creek, Alaska, 2024.

description: Remote sensing makes it possible to gather data rapidly, accurately, and non-destructively, allowing for access to remote areas in near real-time. LiDAR sensor data were collected along Alaska's Dalton Highway as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Dalton Highway, also referred to as Alaska Route 11, is a remote and vital transportation corridor extending approximately 414 miles from Livengood to Deadhorse near the Arctic Ocean. The highway plays a critical role in facilitating access to the Prudhoe Bay Oil Fields and is a key logistical route for energy development in the region. The survey was conducted in August 2024; the corresponding point cloud data shows the road conditions leading up to the Disaster Creek bridge and some distance after the bridge, offering valuable information for bridge assesment, machine learning applications, terrain analysis, and landscape monitoring.
DOI: 10.15468/dl.ehdtq3
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Insecta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-67.5 -52.5,-60 -52.5,-60 -45,-67.5 -45,-67.5 -52.5))"
 ]
}

 The dataset includes 191 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040033-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57902/d71p4k
title: Geospatial Representation of a Road Segment and Bridge: LiDAR Point Cloud Data from the Dalton Highway (Nutirwik Creek), Alaska, August 2024.

description: Remote sensing makes it possible to gather data rapidly, accurately, and non-destructively, allowing for access to remote areas in near real-time. LiDAR sensor data were collected along Alaska's Dalton Highway as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Dalton Highway, also referred to as Alaska Route 11, is a remote and vital transportation corridor extending approximately 414 miles from Livengood to Deadhorse near the Arctic Ocean. The highway plays a critical role in facilitating access to the Prudhoe Bay Oil Fields and is a key logistical route for energy development in the region. The survey was conducted in August 2024; the corresponding point cloud data shows the road conditions leading up to the Nutirwik Creek bridge and some distance after the bridge, offering valuable information for machine learning applications, terrain analysis, and landscape monitoring.
DOI: 10.15468/dl.vsyd2m
title: Occurrence Download

description: A dataset containing 864 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-15 45,-7.5 45,-7.5 52.5,-15 52.5,-15 45))"
 ]
}

 The dataset includes 864 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040034-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3emb3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-52.5 -52.5,-45 -52.5,-45 -45,-52.5 -45,-52.5 -52.5))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040032-251009101135966/datasets/export for details.
DOI: 10.57902/d7ww2n
title: LiDAR Dataset of a Road and Bridge on the Dalton Highway (Dietrich River) for Predictive Infrastructure Monitoring, August 2024.

description: Remote sensing makes it possible to gather data rapidly, accurately, and non-destructively, allowing for access to remote areas in near real-time. LiDAR sensor data were collected along Alaska's Dalton Highway as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Dalton Highway, also referred to as Alaska Route 11, is a remote and vital transportation corridor extending approximately 414 miles from Livengood to Deadhorse near the Arctic Ocean. The highway plays a critical role in facilitating access to the Prudhoe Bay Oil Fields and is a key logistical route for energy development in the region. The survey was conducted in August 2024; the corresponding point cloud data shows the road conditions leading up to the Dietrich Creek bridge and some distance after the bridge, offering valuable information for machine learning applications, terrain analysis, and landscape monitoring.
DOI: 10.57902/d7s594
title: LiDAR Dataset for Predictive Analytics in Arctic Roadway Resilience: Point Cloud Data Visualizing 72km of the Dalton Highway with Multiple Bridges (Hammon River - Chandalar), 2024

description: Remote sensing makes it possible to gather data rapidly, accurately, and non-destructively, allowing for access to remote areas in near real-time. LiDAR sensor data were collected along Alaska's Dalton Highway as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Dalton Highway, also referred to as Alaska Route 11, is a remote and vital transportation corridor extending approximately 414 miles from Livengood to Deadhorse near the Arctic Ocean. The highway plays a critical role in facilitating access to the Prudhoe Bay Oil Fields and is a key logistical route for energy development in the region. The survey was conducted in August 2024; the corresponding point cloud data start point is about 3.2 km from the Hammon River and shows evidence of road and bridge degradation as well as other features of the environment, offering information for predictive and terrain analytics.
DOI: 10.15468/dl.qqb4zh
title: Occurrence Download

description: A dataset containing 252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cakile lanceolata"
 ]
}

 The dataset includes 252 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040035-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.agriculture.00163
title: List of image datasets of field crop pests and diseases
title: 大田作物病虫害图像数据集列表

description: In recent years, deep learning-based pest and disease image recognition techniques have emerged, and their development is highly dependent on high-quality pest and disease image datasets. However, the current publicly available datasets are deficient in terms of scale, coverage and quality, which limits further breakthroughs in research and practical applications. Therefore, we systematically sorted out the resource sources and data presentations of the existing image datasets of pests and diseases of major field crops, such as rice, wheat, maize, and potato, from data papers, research papers, and data websites. Specifically the datasets collected from the papers contain the paper title, authors, communication information, journal source, crop disease type; dataset name, introduction, collection time, collection location, whether it is publicly available or not, and URL. The datasets collected from the data papers contained the aforementioned information about the dataset and the name of the data website was added. The acquired data were stored in two categories: first, according to the source of acquisition into data papers, websites, and datasets of domestic and foreign research papers; and second, according to the type of crop into rice, wheat, maize, potato, and cotton pest and disease datasets.
description: In recent years, deep learning-based pest and disease image recognition techniques have emerged, and their development is highly dependent on high-quality pest and disease image datasets. However, the current publicly available datasets are deficient in terms of scale, coverage and quality, which limits further breakthroughs in research and practical applications. Therefore, we systematically sorted out the resource sources and data presentations of the existing image datasets of pests and diseases of major field crops, such as rice, wheat, maize, and potato, from data papers, research papers, and data websites. Specifically the datasets collected from the papers contain the paper title, authors, communication information, journal source, crop disease type; dataset name, introduction, collection time, collection location, whether it is publicly available or not, and URL. The datasets collected from the data papers contained the aforementioned information about the dataset and the name of the data website was added. The acquired data were stored in two categories: first, according to the source of acquisition into data papers, websites, and datasets of domestic and foreign research papers; and second, according to the type of crop into rice, wheat, maize, potato, and cotton pest and disease datasets.