cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/ple-b50-elh
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/px6-367-de9
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/q3t-xz4-zj3
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8b8-1q5-6mf
title: †Heteraster d'Orbigny, 1853

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mdh-tqb-dk2
title: Aves

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/561-s1n-t67
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f42-dzx-8tf
title: Dicerorhinus Gloger, 1841

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1rj-pk8-yg8
title: †Pseudorbitoides trechmanni Douvillé, 1922

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f1j-23m-rnd
title: Glossus Poli, 1795

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/k5x-4y0-0wh
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5yy-rxq-nfn
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/dz0-6ph-g7w
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9mj-f1z-8a4
title: Unio Philipsson, 1788

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j78-r59-bav
title: Modiolus Lamarck, 1799

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d50-dhs-wqs
title: Argilloecia (Argilloecia) Sars, 1866

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/nns-9cm-x36
title: †Miogypsina Sacco, 1893

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sb8-e34-fj9
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g2l-jyk-pdb
title: †Ovalastrea d'Orbigny, 1849

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pk0-y64-rpr
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/b9s-lxz-hlq
title: †Belemnitida

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fz9-3yj-r8m
title: †Pseudorbitoides trechmanni Douvillé, 1922

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1fv-k1k-kdt
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jb2-g12-bym
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/93k-j7b-72k
title: Gastropoda Cuvier, 1795

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.d59236
title: Occurrence Download

description: A dataset containing 15487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Asio clamator)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 15487 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082848-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15480115
title: Air quality monitoring stations data of Hong Kong EPD

description: Pollutant Concentration at individual general and roadside Air Quality Monitoring stations in 10-16 April 2025.

Available at DATA.GOV.HK via https://data.gov.hk/en-data/dataset/hk-epd-airteam-past24hr-pc-of-individual-air-quality-monitoring-stations
DOI: 10.5281/zenodo.15480116
title: Air quality monitoring stations data of Hong Kong EPD

description: Pollutant Concentration at individual general and roadside Air Quality Monitoring stations in 10-16 April 2025.

Available at DATA.GOV.HK via https://data.gov.hk/en-data/dataset/hk-epd-airteam-past24hr-pc-of-individual-air-quality-monitoring-stations
DOI: 10.5258/soton/wp00789
title: Modelled gridded population estimates for KasaÃ¯ Province in the Democratic Republic of Congo version 4.2

description: This data release provides gridded population estimates (at a spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Kasaï Province in the Democratic Republic of Congo (DRC), including estimates for various age-sex groups. The project team utilized Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP), which were collected during anti-malarial campaigns across the DRC. Due to the absence of recent PDRS data for Kasaï, we used data from the neighboring province of Kwilu to train our model and made grid-level predictions for Kasaï, using geospatial covariates specific to Kasaï. The modelling was done using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, remain.
DOI: 10.5281/zenodo.15433599
title: Thermoforming Line Forming & Heating Station Process Parameters

description: This is a csv file that  contains forming and heating station process parameters adjusted during the production of various refrigerator models
DOI: 10.5281/zenodo.15433600
title: Thermoforming Line Forming & Heating Station Process Parameters

description: This is a csv file that  contains forming and heating station process parameters adjusted during the production of various refrigerator models
DOI: 10.4121/61be5dd9-7880-48dc-bacf-36afbc3033ee
title: Data and scripts underlying the publication: Quantifying the Spatial Scales of Animal Clusters Using Density Surfaces

description: <strong>Supplementary scripts to the publication "Quantifying the spatial scales of animal clustering using Density Surfaces"</strong><br>We implement a method to quantify the degree of clustering of point location data at different spatial scales, which uses Kernel Density Estimation to construct a density function from the underlying point-location data. We build upon this method to automatically detect cluster diameters using smoothing kernels that better represent the perception neighbourhood of animals.More details can be found in the manuscript.<br>These scripts construct the artificial data sets and results in the figures in the main text of the manuscript. <br><strong>data_generator.py</strong>This file contains the functions to construct the artificial data sets, as well as visualization tools to plot the point sets.Running the main() function:1. constructs all artificial data sets2. creates visualizations of all generated and real-life datasets, saves them as .pdf files, and shows them on-screen<br><strong>metric_calculator.py</strong>This file contains the functions to calculate the metric described in the manuscript, as well as to compute Ripley's K function and the Radial Distribution Function.Running the main() function:1. generates the metric functions for all artificial and real-life data sets2. creates visualizations of all generated metric functions, saves them as .pdf files, and shows them on-screen3. prints the found relevant spatial scales, and their metric values, in the terminal<br><strong>elephant.pickle</strong>This file contains the real-world dataset of elephant locations to be used in metric_calculator.pyThe original data was collected in March 2014 in the Tsavo National Parks, Kenya. We use a subset of the original data set, consisting of location data of 24 elephants obtained from an aerial image that were manually taken by human observers upon spotting the animals. The aerial image was manually processed into spatial data by placing a point on the approximate centre point of each animal in the image, and projected onto a 100x100 xy-plane.The data is serialized and de-serialized using the native Python package "pickle". The data format used by pickle is Python-specific.<br>To perform the experiments:1. Ensure you have a functioning Python3 installation.2. Install the required packages using pip: - numpy - matplotlib - scipy - scikit-learn3. Run the main() function in data_generator.py to generate the artificial datasets4. Run the main() function in metric_calculator.py to generate the metric functions and figures<br>
DOI: 10.4121/61be5dd9-7880-48dc-bacf-36afbc3033ee.v1
title: Data and scripts underlying the publication: Quantifying the Spatial Scales of Animal Clusters Using Density Surfaces

description: <strong>Supplementary scripts to the publication "Quantifying the spatial scales of animal clustering using Density Surfaces"</strong><br>We implement a method to quantify the degree of clustering of point location data at different spatial scales, which uses Kernel Density Estimation to construct a density function from the underlying point-location data. We build upon this method to automatically detect cluster diameters using smoothing kernels that better represent the perception neighbourhood of animals.More details can be found in the manuscript.<br>These scripts construct the artificial data sets and results in the figures in the main text of the manuscript. <br><strong>data_generator.py</strong>This file contains the functions to construct the artificial data sets, as well as visualization tools to plot the point sets.Running the main() function:1. constructs all artificial data sets2. creates visualizations of all generated and real-life datasets, saves them as .pdf files, and shows them on-screen<br><strong>metric_calculator.py</strong>This file contains the functions to calculate the metric described in the manuscript, as well as to compute Ripley's K function and the Radial Distribution Function.Running the main() function:1. generates the metric functions for all artificial and real-life data sets2. creates visualizations of all generated metric functions, saves them as .pdf files, and shows them on-screen3. prints the found relevant spatial scales, and their metric values, in the terminal<br><strong>elephant.pickle</strong>This file contains the real-world dataset of elephant locations to be used in metric_calculator.pyThe original data was collected in March 2014 in the Tsavo National Parks, Kenya. We use a subset of the original data set, consisting of location data of 24 elephants obtained from an aerial image that were manually taken by human observers upon spotting the animals. The aerial image was manually processed into spatial data by placing a point on the approximate centre point of each animal in the image, and projected onto a 100x100 xy-plane.The data is serialized and de-serialized using the native Python package "pickle". The data format used by pickle is Python-specific.<br>To perform the experiments:1. Ensure you have a functioning Python3 installation.2. Install the required packages using pip: - numpy - matplotlib - scipy - scikit-learn3. Run the main() function in data_generator.py to generate the artificial datasets4. Run the main() function in metric_calculator.py to generate the metric functions and figures<br>
DOI: 10.15468/dl.ztvp7s
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is PHL.15_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Tarsius syrichta (Linnaeus, 1758)"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005085-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12736661
title: VHP4Safety Compound Wikidata Triples

description: Export as a CSV file with triples from the VHP4Safety Compound Wiki.
DOI: 10.5281/zenodo.14835777
title: VHP4Safety Compound Wikidata Triples

description: Export as a CSV file with triples from the VHP4Safety Compound Wiki.
DOI: 10.5281/zenodo.15535453
title: VHP4Safety Compound Wikidata Triples

description: Export as a CSV file with triples from the VHP4Safety Compound Wiki.
DOI: 10.5281/zenodo.8397840
title: VHP4Safety Compound Wikidata Triples

description: Export as a CSV file with triples from the VHP4Safety Compound Wiki.
DOI: 10.15468/dl.g57pu5
title: Occurrence Download

description: A dataset containing 11595486 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is China"
 ]
}

 The dataset includes 11595486 records from 5080 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005074-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7849b
title: Occurrence Download

description: A dataset containing 1173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera chrysantha Turcz.",
 "Year is greater than or equal to 1900",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1173 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020779-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wbqnhm
title: Occurrence Download

description: A dataset containing 1843567308 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation"
 ]
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1843567308 records from 15211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0192536-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ckvgqt
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melia dubia Hiern, 1875"
 ]
}

 The dataset includes 101 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017522-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/kwee-4819
title: "A Multisensory EEG Dataset for Emotion Elicitation by Audiovisual-Haptic Stimuli"

description: "This dataset contains a multisensory emotional-EEG dataset comprising visual, auditory, and haptic stimuli along with EEG recordings.Each folder, named by [ParticipantID], corresponds to the EEG recordings of a single participant under a specific experimental condition. Each folder contains four .mat files representing EEG signals in four stimulation modes and their corresponding emotion labels."
DOI: 10.15468/dl.tf34a5
title: Occurrence Download

description: A dataset containing 2435 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Anopheles sinensis Wiedemann, 1828)"
 ]
}

 The dataset includes 2435 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017524-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4uqy5r
title: Occurrence Download

description: A dataset containing 7341 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((16.710572357727624 48.00975667378594, 16.840448402241634 48.00173815843217, 16.852301404051374 48.089538511446094, 16.722203538846724 48.097573010124314, 16.710572357727624 48.00975667378594))"
 ]
}

 The dataset includes 7341 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017523-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ctfkte
title: Occurrence Download

description: A dataset containing 3003 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 Chordeiles gundlachii Lawrence, 1857",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 3003 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017525-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/e947-h186
title: "CodyDroid-Model1-deepseek-coder-5.9M-kexer"

description: "The CodyDroid Evaluation Dataset is curated to benchmark the Android SDK code generation capabilities of two lightweight language models: CodyDroid Model-1 (deepseek-coder-5.9M-kexer) and CodyDroid Model-2 (codegen-175K-mono-java). Each entry in the dataset consists of a natural language description of an Android development task, paired with code outputs from both models, a reference solution crafted by a developer, and a detailed qualitative analysis. The tasks span typical Android programming challenges involving SDK usage, Jetpack libraries, UI handling, and system services. The dataset is designed to support comparative evaluation, model fine-tuning, and error analysis in the context of mobile app development, offering a unique resource for studying LLM performance in constrained-code and domain-specific scenarios."
DOI: 10.5281/zenodo.15348386
title: Dual-Radar Synthetic Dataset for Static Short-Range Object Detection

description: This dataset provides synthetic radar data generated using a raytracing-based simulation of a static short-range scenario. The simulated scene includes multiple objects in a semi-structured environment, such as a human and a forklift. Two radar sensors (Radar A and Radar B) observe the scene from different positions and angles, allowing for multi-perspective evaluation and sensor fusion analysis.

The dataset includes:



Radar range-Doppler data for Radar A and Radar B (stored in configA.h5 and configB.h5)

Python scripts for dataset generation and processing (generate_radar_dataset.py, radar_utils.py)

Ground truth object information in yolo format for detection tasks

Configuration parameters for radar signal processing, antenna setup, interference modeling, and sensor placement


The radar parameters in configB differ between Radar A and B in FFT size, resolution, and tilt angles, providing realistic variation for multi-sensor perception research. The dataset is suitable for training and evaluating radar-based object detection models (e.g., YOLO) and dual-radar fusion techniques.
DOI: 10.5281/zenodo.15348387
title: Dual-Radar Synthetic Dataset for Static Short-Range Object Detection

description: This dataset provides synthetic radar data generated using a raytracing-based simulation of a static short-range scenario. The simulated scene includes multiple objects in a semi-structured environment, such as a human and a forklift. Two radar sensors (Radar A and Radar B) observe the scene from different positions and angles, allowing for multi-perspective evaluation and sensor fusion analysis.

The dataset includes:



Radar range-Doppler data for Radar A and Radar B (stored in configA.h5 and configB.h5)

Python scripts for dataset generation and processing (generate_radar_dataset.py, radar_utils.py)

Ground truth object information in yolo format for detection tasks

Configuration parameters for radar signal processing, antenna setup, interference modeling, and sensor placement


The radar parameters in configB differ between Radar A and B in FFT size, resolution, and tilt angles, providing realistic variation for multi-sensor perception research. The dataset is suitable for training and evaluating radar-based object detection models (e.g., YOLO) and dual-radar fusion techniques.
DOI: 10.5281/zenodo.15347274
title: Supplementary data for "Satellite Residual-Convolutional Neural Network Heating (Res_CNNH) Algorithm for Retrieving Latent Heat between Rain Top and Cloud Top"

description: This dataset is the supplementary data for "Satellite Residual-Convolutional Neural Network Heating (Res_CNNH) Algorithm for Retrieving Latent Heat between Rain Top and Cloud Top". The dataset contains Res_CNNH and CNNH retrievals of WRF and GPM DPR, CTH from Himawari-8 satellite. This work utilized the Advanced Research WRF model Version 3.4 to perform cloud simulations in the study area (31°N - 51°N,107°E - 131°E). The WRF model simulation used two nested domains, with internal and external spatial resolutions of 3km and 9km, respectively, and the output data have a total of 55 vertical layers (vertical resolution is 0.25km below 5km). LH at each layer was derived from the calculations of water phase transition among associated hydrometeors based on WRF standard outputs. 

Contact: For further questions please contact Xuanye Xu (xxy_0402@mail.ustc.edu.cn).
DOI: 10.5281/zenodo.15347273
title: Supplementary data for "Satellite Residual-Convolutional Neural Network Heating (Res_CNNH) Algorithm for Retrieving Latent Heat between Rain Top and Cloud Top"

description: This dataset is the supplementary data for "Satellite Residual-Convolutional Neural Network Heating (Res_CNNH) Algorithm for Retrieving Latent Heat between Rain Top and Cloud Top". The dataset contains Res_CNNH and CNNH retrievals of WRF and GPM DPR, CTH from Himawari-8 satellite. This work utilized the Advanced Research WRF model Version 3.4 to perform cloud simulations in the study area (31°N - 51°N,107°E - 131°E). The WRF model simulation used two nested domains, with internal and external spatial resolutions of 3km and 9km, respectively, and the output data have a total of 55 vertical layers (vertical resolution is 0.25km below 5km). LH at each layer was derived from the calculations of water phase transition among associated hydrometeors based on WRF standard outputs. 

Contact: For further questions please contact Xuanye Xu (xxy_0402@mail.ustc.edu.cn).
DOI: 10.15468/dl.uhskhj
title: Occurrence Download

description: A dataset containing 5452853 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "OccurrenceStatus is Present",
 "Year 2000-2025"
 ]
}

 The dataset includes 5452853 records from 3353 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017508-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.