cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/vwfht7
title: Monitoring of Oak processionary larvae (Thaumetopoea processionea) in the province of East Flanders, Belgium

description: Monitoring of Oak processionary larvae (Thaumetopoea processionea) in the province of East Flanders, Belgium is a historical occurrence dataset published by the Province East Flanders (Belgium). The dataset is published as a standardized Darwin Core Archive and includes for each occurrence record an occurrenceID, date, location, quantification where available and scientific name. Issues with the dataset can be reported at https://github.com/inbo/data-publication/issuesWe have released this dataset to the public domain under a Creative Commons Attribution License. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be.
DOI: 10.15468/dl.w9pf9c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.754 1.103184, 101.683724 1.097616, 101.615179 1.08105, 101.550054 1.053894, 101.489952 1.016816, 101.436354 0.970731, 101.390579 0.916772, 101.353754 0.85627, 101.326786 0.790713, 101.310337 0.7217170000000001, 101.304814 0.65098, 101.31035 0.580244, 101.326809 0.51125, 101.353786 0.445698, 101.390617 0.385201, 101.436394 0.331249, 101.48999 0.285169, 101.550086 0.248097, 101.615203 0.220946, 101.683737 0.204382, 101.754 0.198816, 101.824263 0.204382, 101.892797 0.220946, 101.957914 0.248097, 102.01801 0.285169, 102.071606 0.331249, 102.117383 0.385201, 102.154214 0.445698, 102.181191 0.51125, 102.19765 0.580244, 102.203186 0.65098, 102.197663 0.7217170000000001, 102.181214 0.790713, 102.154246 0.85627, 102.117421 0.916772, 102.071646 0.970731, 102.018048 1.016816, 101.957946 1.053894, 101.892821 1.08105, 101.824276 1.097616, 101.754 1.103184))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040688-251120083545085/datasets/export for details.
DOI: 10.17026/ar/uxnon8
title: PAN-00166552 - terra sigillata kom type Drag. 37 (EG)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00166552&lt;/p&gt;
description: Modified: 2025-10-08T12:28:21+02:00
DOI: 10.15468/dl.8zdyge
title: Occurrence Download

description: A dataset containing 685 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "GadmGid is BRA.5.370_2",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 685 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040691-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17107419
title: Towards Full Optimisation of Automated Double Electron--Electron Resonance Spectroscopy
DOI: 10.5281/zenodo.17543328
title: Towards Full Optimisation of Automated Double Electron--Electron Resonance Spectroscopy
DOI: 10.15468/dl.xrmqr5
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coralliodrilus statutus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040692-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qk5em9
title: Occurrence Download

description: A dataset containing 1325859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "PublishingOrg is 2853c0e7-c758-4c9c-83a2-7c2d18967fb9"
 ]
}

 The dataset includes 1325859 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040668-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bx95gv
title: Occurrence Download

description: A dataset containing 58183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-105.60044529800969 36.441894091386914, -105.61107822425086 36.44182133349394, -105.62148880921721 36.44007275270362, -105.6312761727594 36.436715718955426, -105.6400635807856 36.43187955335048, -105.64751301610188 36.425750510805116, -105.65333821718113 36.41856456081991, -105.65731567635828 36.410598253195836, -105.6592931752802 36.402158029808525, -105.65919553789496 36.393568402408775, -105.65702739470949 36.38515945773247, -105.6528728711287 36.377254173732695, -105.64689223220648 36.37015603413949, -105.63931563131621 36.364137413265944, -105.63043421695104 36.35942917018169, -105.6205889467027 36.35621184280807, -105.61015753781611 36.35460877033308, -105.59954004766175 36.35468139908384, -105.58914362369678 36.35642694531502, -105.57936699018408 36.359778501071645, -105.5705852476659 36.36460757922787, -105.56313555081648 36.37072900386769, -105.55730420098243 36.37790796524757, -105.55331564192345 36.385868977517774, -105.55132378184891 36.39430640497167, -105.55140598316348 36.40289616151349, -105.55355996535857 36.41130814074665, -105.5577037589093 36.41921890273482, -105.56367873231173 36.42632412977456, -105.57125559471905 36.432350368553735, -105.58014315782796 36.43706560024566, -105.5899995279587 36.44028822297632, -105.60044529800969 36.441894091386914))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 58183 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040646-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h7jm6z
title: Occurrence Download

description: A dataset containing 289205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Continent derived from coordinates, Geodetic datum assumed WGS84, Occurrence status inferred from individual count)",
 "OccurrenceStatus is Present",
 "TaxonKey is Turdus falcklandii"
 ]
}

 The dataset includes 289205 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040609-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15551829
title: AgriPotential dataset

description:  NEWS: the agripotential dataset will be part of ImageCLEF 2026 competition. Please contact us if you're interested in participating 

For more information regarding participation please refer to the following link:

https://www.codabench.org/competitions/12055/ 

NEWS:  an extended version of the dataset is released

Differences:



34 timeframes instead of 11 previously

A more user friendly data format (all .tif with possibility of online data loading)

Sentinel-2 data is not normalized, leaving the choice of normalization to the user


You can find more information on https://github.com/MohammadElSakka/agripotential

***UPDATE: this work was accepted in the conference IEEE CBMI 2025 that will be held in dublin in late october 2025. A final peer-reviewed version will be published soon***

This is the official page for AgriPotential, a publicly available benchmark dataset designed for assessing agricultural potentials using remote sensing data. It integrates 11  Sentinel-2 satellite images from 2019 across 10 spectral bands, covering agricultural regions in the Hérault Department, Southern France. The dataset includes pixel-wise labels representing agricultural potential levels (from Very Low to Very High) across three crop types: viticulture, market gardening, and field crops. Ground truth annotations are derived from the BD Sol - GDPA database and validated by domain experts. The dataset is stored in HDF5 format and supports a range of machine learning tasks, including ordinal classification, regression, segmentation, and spatio-temporal modeling.

This dataset facilitates scalable, data-driven solutions for land suitability analysis, agricultural planning, and sustainable resource management.

On this page you will find a PDF with supplmentary results for the paper.

A tutorial GitHub is also available: https://github.com/MohammadElSakka/agripotential-dataset 

Raw data that was used to make this dataset can be found here (there isn't much to do there apart from downloading it): https://zenodo.org/records/15551802
DOI: 10.5281/zenodo.17787322
title: AgriPotential dataset

description:  NEWS: the agripotential dataset will be part of ImageCLEF 2026 competition. Please contact us if you're interested in participating 

For more information regarding participation please refer to the following link:

https://www.codabench.org/competitions/12055/ 

NEWS:  an extended version of the dataset is released

Differences:



34 timeframes instead of 11 previously

A more user friendly data format (all .tif with possibility of online data loading)

Sentinel-2 data is not normalized, leaving the choice of normalization to the user


You can find more information on https://github.com/MohammadElSakka/agripotential

***UPDATE: this work was accepted in the conference IEEE CBMI 2025 that will be held in dublin in late october 2025. A final peer-reviewed version will be published soon***

This is the official page for AgriPotential, a publicly available benchmark dataset designed for assessing agricultural potentials using remote sensing data. It integrates 11  Sentinel-2 satellite images from 2019 across 10 spectral bands, covering agricultural regions in the Hérault Department, Southern France. The dataset includes pixel-wise labels representing agricultural potential levels (from Very Low to Very High) across three crop types: viticulture, market gardening, and field crops. Ground truth annotations are derived from the BD Sol - GDPA database and validated by domain experts. The dataset is stored in HDF5 format and supports a range of machine learning tasks, including ordinal classification, regression, segmentation, and spatio-temporal modeling.

This dataset facilitates scalable, data-driven solutions for land suitability analysis, agricultural planning, and sustainable resource management.

On this page you will find a PDF with supplmentary results for the paper.

A tutorial GitHub is also available: https://github.com/MohammadElSakka/agripotential-dataset 

Raw data that was used to make this dataset can be found here (there isn't much to do there apart from downloading it): https://zenodo.org/records/15551802
DOI: 10.5281/zenodo.17867550
title: Multi-Hazard Risk Scores by Town - South Africa

description: This file contains the synthetic risk score dataset used to compare multi-hazard risk levels across towns and irrigation contexts. It compiles, for each town and each irrigation level, the average yield and the total and mean multi-hazard risk scores, as well as summary statistics by hazard profile.
DOI: 10.5281/zenodo.17867549
title: Multi-Hazard Risk Scores by Town - South Africa

description: This file contains the synthetic risk score dataset used to compare multi-hazard risk levels across towns and irrigation contexts. It compiles, for each town and each irrigation level, the average yield and the total and mean multi-hazard risk scores, as well as summary statistics by hazard profile.
DOI: 10.5281/zenodo.17867624
title: LINF_300015800

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.17867625
title: LINF_300015800

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.2z5usm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.865 1.106184, 101.794724 1.100616, 101.726179 1.08405, 101.661054 1.056894, 101.600952 1.019816, 101.547354 0.97373, 101.501579 0.919772, 101.464754 0.8592689999999999, 101.437785 0.793713, 101.421337 0.7247169999999999, 101.415813 0.65398, 101.42135 0.583244, 101.437809 0.51425, 101.464786 0.448698, 101.501617 0.388201, 101.547394 0.334249, 101.60099 0.288169, 101.661086 0.251097, 101.726203 0.223946, 101.794737 0.207382, 101.865 0.201816, 101.935263 0.207382, 102.003797 0.223946, 102.068914 0.251097, 102.12901 0.288169, 102.182606 0.334249, 102.228383 0.388201, 102.265214 0.448698, 102.292191 0.51425, 102.30865 0.583244, 102.314187 0.65398, 102.308663 0.7247169999999999, 102.292215 0.793713, 102.265246 0.8592689999999999, 102.228421 0.919772, 102.182646 0.97373, 102.129048 1.019816, 102.068946 1.056894, 102.003821 1.08405, 101.935276 1.100616, 101.865 1.106184))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040694-251120083545085/datasets/export for details.
DOI: 10.15468/dl.d6ehjq
title: Occurrence Download

description: A dataset containing 978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-4999.0",
 "Country is Netherlands",
 "Geometry POLYGON((5.86405 51.82153,6.1535 51.82153,6.1535 51.92508,5.86405 51.92508,5.86405 51.82153))",
 "HasCoordinate is true",
 "TaxonKey is Perdix perdix",
 "Year 1990-2000"
 ]
}

 The dataset includes 978 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040693-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr38985.v2
title: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court, 2022
title: Version 2

description: The data contain records of charges filed against defendants whose cases were filed by United States attorneys in United States district court during fiscal year 2022. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.
description: Datasets: 
DS1: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court, 2022
description: Charges filed against defendants in United States district court during fiscal year 2022.
DOI: 10.3886/icpsr38985
title: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court, 2022
title: Archival Version

description: The data contain records of charges filed against defendants whose cases were filed by United States attorneys in United States district court during fiscal year 2022. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.
description: Datasets: 
DS1: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court, 2022
description: Charges filed against defendants in United States district court during fiscal year 2022.
DOI: 10.15468/dl.4jcxnd
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Astragalus mongolicus)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 32 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040696-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/mgw7yh
title: PAN-00181004 - smooth ware jug type Niederb. 62

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00181004&lt;/p&gt;
description: Modified: 2025-10-15T09:48:18+02:00
DOI: 10.15468/dl.ebk75n
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hygrophila laevis",
 "Continent is Africa"
 ]
}

 The dataset includes 47 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040605-251120083545085/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.28883060
title: Code and data for: Variability, drivers, and utility of genetic diversity-area relationships in terrestrial vertebrates

description: Code and data for: Genetic diversity-area relationships are governed by population structureAuthors: Chloé Schmidt, Sean Hoban, Deborah M. Leigh, Walter Jetz, Colin J. GarrowayCode also available on Github: https://github.com/chloewsch/GDAR<br>File descriptions:1. R scripts: Scripts for data compilation and analysis (in order of use)<br>2. GDAR_env_figs.Rdata: R environment object including data needed to run scripts3. genotypes_files_FS.rds: genotype data saved as a list of genind objects (adegenet package)4. zfst_long_se.rds: a saved R object produced in script 1.<br>5. mammal_traits.csv: trait data for species included in this analysisColumn descriptions:filename: name of file containing genotypesspecies: Genus_speciesindividuals: number of individuals samplednum_sites: number of sites (locations) sampledglobal_fst: estimated global FST (population differentiation)zAC, zAR, zGD: estimated scaling values (z) for allele count (AC), allelic richness (AR), and gene diversity (GD)zACse, zARse, zGDse: standard errors for scaling values (z)class, uid, family, genus, order: other taxonomic informationmass_g, log_mass: species mass in grams, and log10 of this valuerange_area_km2, log_range: species range area in square kilometers, and log10 of this valuemean_HR_km2, log_HR: species mean home range area in square kilometers, and log10 of this valuearea_km2, log_area: area of sample extent (per study) in square kilometers, and log10 of this value<br>6. mpg_test_data.csv: trait data for mammal species from the MacroPopGen database (https://figshare.com/articles/dataset/MacroPopGen_Database_Geo-referenced_population-specific_microsatellite_data_across_the_American_continents/7207514/3?file=23392853)Column descriptions:species_ref: species binomial and MacroPopGen reference IDspecies: Genus_speciesorder, family, genus: other taxonomic informationRefID: MacroPopGen reference IDglobal_fst: global FST value per study/species combinationmass_g, log_mass: species mass in grams, and log10 of this valuerange_area_km2, log_range: species range area in square kilometers, and log10 of this valuemean_HR_km2, log_HR: species mean home range area in square kilometers, and log10 of this valuearea_km2, log_area: area of sample extent (per study) in square kilometers, and log10 of this value