cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24416/uu01-769pd8
title: Microstructural data and trace element concentration measurements of recrystallised zircons

description: We have examined the crystal structure and and composition of recrystallised zircons from the Jack Hills, Australia and the island of Harris & Lewis, Scotland. Data has been collected with scanning electron microscopy (SEM) and electron probe micro analysis (EMPA) in order to resolve the mechanisms responsible for recrystallisation microstructures found in these zircons. The dataset is organised in folders per technique: cathodoluminescence (CL), electron backscatter diffraction (EBSD), backscatter diffraction (BSE), forescatter diffraction (FSE) and electron probe micro analysis (EMPA).

The data is provided in 5 subfolders. Detailed information about the files in these subfolders as well as information on how the data is processed is given in the explanatory file readme.txt.
DOI: 10.15468/dl.cge2e8
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 8edbbde0-055e-11d8-b850-b8a03c50a862",
 "TaxonKey is Micronycteris megalotis"
 ]
}

 The dataset includes 26 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005457-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18595468
title: 3D-TREES-PAN: A LiDAR Point Cloud Dataset for Forest Structure Instance-Level Tree Panoptic Segmentation

description: This repository includes the key results and datasets used in the study "3D-TREES-PAN: A LiDAR Point Cloud Dataset for Forest Structure Instance-Level Tree Panoptic Segmentation". A selection of datasets is available here, certain larger data files are available upon request from the corresponding author due to their size. This collection provides comprehensive resources for the development, evaluation, and benchmarking of panoptic segmentation methods in forest environments. Currently, asociated article currently "Under Review"
DOI: 10.15468/dl.xfsyxc
title: Occurrence Download

description: A dataset containing 1014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magonia pubescens",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 1014 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005458-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sy22sy
title: Occurrence Download

description: A dataset containing 8029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Indigofera hirsuta",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 8029 records from 306 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005459-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pu782d
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lithophyllum johansenii"
 ]
}

 The dataset includes 31 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005460-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13357578
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.2derey
title: Occurrence Download

description: A dataset containing 2760 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is January",
 "OccurrenceStatus is Present",
 "TaxonKey is Balaenoptera musculus"
 ]
}

 The dataset includes 2760 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000801-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.1.3653.0163
title: BLANCO SÍO-LÓPEZ, Cristina - Yale Law School - "Constitutional Identity and Founding Moments" (Panel Discussant) - 'Symposium on Founding Moments in Constitutionalism' - DOI: 10.13140/2.1.3653.0163
DOI: 10.15468/dl.h28vs9
title: Occurrence Download

description: A dataset containing 2 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 (Anaxyrus americanus)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032334-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.guua4x
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleusine indica",
 "Country is Albania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032335-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uem93g
title: Occurrence Download

description: A dataset containing 508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Clematis simensis",
 "HasCoordinate is true",
 "Geometry POLYGON ((-17.66889 -35.11691, 51.34702 -35.11691, 51.34702 37.62, -17.66889 37.62, -17.66889 -35.11691))"
 ]
}

 The dataset includes 508 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032337-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48366/r1432016
title: Comparative Analysis of Educational AI and Technology Interventions in Engineering and STEM Learning (2025)

description: This table presents a structured comparison of five 2025 research studies focused on AI and technology interventions in engineering and STEM education. It summarizes key aspects such as research problems, methods, tools used (e.g., ChatGPT, VR), subjects, countries, and outcomes.
DOI: 10.15468/dl.nrwk6c
title: Occurrence Download

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

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032336-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.647vtu
title: Occurrence Download

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

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032338-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28502159
title: Nectar sugar concentration contributes to structuring bumble bee and plant interactions

description: This folder contains the data in Lun et al. 2025 "Nectar sugar concentration contributes to structuring bumble bee and plant interactions"
DOI: 10.6084/m9.figshare.28502159.v1
title: Nectar sugar concentration contributes to structuring bumble bee and plant interactions

description: This folder contains the data in Lun et al. 2025 "Nectar sugar concentration contributes to structuring bumble bee and plant interactions"
DOI: 10.6084/m9.figshare.29623265
title: Eukaryotic Microbiome (18S V7-V9) from Shale Gas Soil: OTU Sequences and Taxonomic Profiles.

description: This dataset comprises processed 18S rRNA amplicon sequencing data targeting the V7-V9 region (primers 18S11b/18S2a) of eukaryotic microorganisms in soil from shale gas operations. Includes:OTU representative sequences (FASTA) clustered at 98.65% identity.Sample metadata: sequencing statistics (read counts/base counts).Taxonomic profiles: Absolute and relative abundance tables (phylum to genus).Enables assessment of shale gas extraction impacts on soil microeukaryotes.
DOI: 10.6084/m9.figshare.29623265.v2
title: Eukaryotic Microbiome (18S V7-V9) from Shale Gas Soil: OTU Sequences and Taxonomic Profiles.

description: This dataset comprises processed 18S rRNA amplicon sequencing data targeting the V7-V9 region (primers 18S11b/18S2a) of eukaryotic microorganisms in soil from shale gas operations. Includes:OTU representative sequences (FASTA) clustered at 98.65% identity.Sample metadata: sequencing statistics (read counts/base counts).Taxonomic profiles: Absolute and relative abundance tables (phylum to genus).Enables assessment of shale gas extraction impacts on soil microeukaryotes.
DOI: 10.6084/m9.figshare.29623265.v1
title: Eukaryotic Microbiome (18S V7-V9) from Shale Gas Soil: OTU Sequences and Taxonomic Profiles.

description: This dataset comprises processed 18S rRNA amplicon sequencing data targeting the V7-V9 region (primers 18S11b/18S2a) of eukaryotic microorganisms in soil from shale gas operations. Includes: OTU representative sequences (FASTA) clustered at 98.65% identity. Sample metadata: sequencing statistics (read counts/base counts). Taxonomic profiles: Absolute and relative abundance tables (phylum to genus).Community analysis: Alpha diversity indices (e.g. Shannon, Simpson, Chao1).Enables assessment of shale gas extraction impacts on soil microeukaryotes.
DOI: 10.15468/dl.3hr9te
title: Occurrence Download

description: A dataset containing 205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nasturtium officinale",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 205 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032341-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2284qy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleusine indica",
 "Country is Antigua and Barbuda",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032340-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fexnn4
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pandanus rigidifolius"
 ]
}

 The dataset includes 23 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032343-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u96jem
title: Occurrence Download

description: A dataset containing 30323 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Helictotrichon sedenense"
 ]
}

 The dataset includes 30323 records from 211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032339-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29622509
title: KANG2P data

description: Genomic prediction has emerged as a powerful tool with applications spanning human disease risk assessment and plant/animal breeding. Kolmogorov–Arnold networks (KANs) have risen as a potential substitute for multilayer perceptrons within dense fully connected networks. Substantial efforts have been dedicated to incorporating KANs into diverse deep learning frameworks in computer vision and natural language processing. However, the integration of KANs into deep learning models for genomic prediction remains unexplored. Here, we present KANG2P, a novel method designed to enhance genomic prediction accuracy. We extensively evaluated KANG2P on diverse datasets, including ALS and Parkinson's disease, as well as maize and rice datasets. In classification and regression tasks, KANG2P demonstrated superior performance compared to existing methods. By integrating both genotype and predicted expression data, KANG2P leverages a unique architecture to better capture complex genetic patterns. We also propose an explainable approach for spline functions to identify disease-related functional genes. Our results highlight KANG2P's potential to advance research and applications in both biomedical and agricultural fields.
DOI: 10.6084/m9.figshare.29622509.v2
title: KANG2P data

description: Genomic prediction has emerged as a powerful tool with applications spanning human disease risk assessment and plant/animal breeding. Kolmogorov–Arnold networks (KANs) have risen as a potential substitute for multilayer perceptrons within dense fully connected networks. Substantial efforts have been dedicated to incorporating KANs into diverse deep learning frameworks in computer vision and natural language processing. However, the integration of KANs into deep learning models for genomic prediction remains unexplored. Here, we present KANG2P, a novel method designed to enhance genomic prediction accuracy. We extensively evaluated KANG2P on diverse datasets, including ALS and Parkinson's disease, as well as maize and rice datasets. In classification and regression tasks, KANG2P demonstrated superior performance compared to existing methods. By integrating both genotype and predicted expression data, KANG2P leverages a unique architecture to better capture complex genetic patterns. We also propose an explainable approach for spline functions to identify disease-related functional genes. Our results highlight KANG2P's potential to advance research and applications in both biomedical and agricultural fields.
DOI: 10.6084/m9.figshare.29622509.v1
title: KANG2P data

description: Genomic prediction has emerged as a powerful tool with applications spanning human disease risk assessment and plant/animal breeding. Kolmogorov–Arnold networks (KANs) have risen as a potential substitute for multilayer perceptrons within dense fully connected networks. Substantial efforts have been dedicated to incorporating KANs into diverse deep learning frameworks in computer vision and natural language processing. However, the integration of KANs into deep learning models for genomic prediction remains unexplored. Here, we present KANG2P, a novel method designed to enhance genomic prediction accuracy. We extensively evaluated KANG2P on diverse datasets, including ALS and Parkinson's disease, as well as maize and rice datasets. In classification and regression tasks, KANG2P demonstrated superior performance compared to existing methods. By integrating both genotype and predicted expression data, KANG2P leverages a unique architecture to better capture complex genetic patterns. We also propose an explainable approach for spline functions to identify disease-related functional genes. Our results highlight KANG2P's potential to advance research and applications in both biomedical and agricultural fields.
DOI: 10.5281/zenodo.16326468
title: Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms

description: This dataset supports the study “Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.” It includes curated protein sequences from 1,758 virus species across 18 families, analyzed for the SPHAK method. SPHAK implements a unified framework to estimate virus family and cross-host transmission risk by identifying host-specific sequence patterns. This dataset provides the foundation for reproducible modeling of host spillover events across both animal and plant viruses.
DOI: 10.5281/zenodo.16326467
title: Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms

description: This dataset supports the study “Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.” It includes curated protein sequences from 1,758 virus species across 18 families, analyzed for the SPHAK method. SPHAK implements a unified framework to estimate virus family and cross-host transmission risk by identifying host-specific sequence patterns. This dataset provides the foundation for reproducible modeling of host spillover events across both animal and plant viruses.
DOI: 10.5281/zenodo.16353277
title: tes_final_final

description: some final test
DOI: 10.5281/zenodo.16352932
title: test final 3

description: test final 3
DOI: 10.15468/dl.txzh5b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "ScientificName" : [
 "is Ricinus communis"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032275-250717081556266/datasets/export for details.
DOI: 10.15468/dl.kn58eg
title: Occurrence Download

description: A dataset containing 105148 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 (Anaxyrus americanus)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 105148 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032332-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c8ajym
title: Occurrence Download

description: A dataset containing 192581 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lamium amplexicaule"
 ]
}

 The dataset includes 192581 records from 1502 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032261-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0169766.10fu
title: SH0169766.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169767.10fu
title: SH0169767.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169768.10fu
title: SH0169768.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.