cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dhv2fj
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bubulcus ibis",
 "Year 2025-2025"
 ]
}

 The dataset includes 62 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035119-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b5c9t2
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hemiramphus praematurus"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035120-250802193616735/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/12727447
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.gvu6uw
title: Occurrence Download

description: A dataset containing 69437 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ESP.15_1",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 69437 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035114-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y3j9f6
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ADR2788"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035121-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16754569
title: SCOOP
DOI: 10.5281/zenodo.16754568
title: SCOOP
DOI: 10.15468/dl.rt9h6r
title: Occurrence Download

description: A dataset containing 290 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scolecenchelys gymnota"
 ]
}

 The dataset includes 290 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035122-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pvvsev
title: Occurrence Download

description: A dataset containing 4511 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hakea decurrens"
 ]
}

 The dataset includes 4511 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035123-250802193616735/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.29847869.v2
title: Supplementary Data

description: MeHg accumulation in contamianted rice paddies and the microbial players behind it.
DOI: 10.5281/zenodo.16789581
title: Historical tree phenology data across contrasting sites in the Congo Basin

description: Indroduction

We present a unique dataset of historical tropical tree phenology observations at two sites from different bioclimatic regions across the Congo Basin. We cover both the Atlantic Mayombe forest and the tropical forest in the central Congo Basin. To our knowledge this is complete extant historical (1937 - 1957) phenology data across the Congo basin. The data contains 10 million observations of 876 species, across 6339 individuals, and phenology metrics including leaf, flowering, and fruiting phenology. The data were recovered through expert transcription and validated community science based crowdsourcing. These data may provide a reference baseline and key information on how tree species are responding to a changing climate.

Main data files



PhenoDRC.rds: This is an R data serialization file. Contains a structured dataset, holding the main phenological data for the DRC.

PhenoDRC.tar.gz: This is a compressed archive file of the data contained in the PhenoDRC.rds file as a flat csv file - for compatibility.

PhenoDRC_life_history.rds: This R data file contains specific data related to the life history traits, separate from the primary phenological observations.

Pierlot_1966_inventories.csv: Contains the inventory data of sampling plots described in Pierlot 1966.


Appendix data



Yangambi_observation_schedule.csv: The observation schedule as run by the observers at Yangambi.

Capon_1947.pdf: A copy of Capon 1947 describing the phenology research at Yangambi.

yangambi_validation_annotations.rds: Validation data as an R data serialization file.

yangambi_validation_data.xlsx: Validation data in the Excel format - for compatibility.


Acknowledgements

Historical phenological data recovery was supported by the Institut National pour l'Étude et la Recherche Agronomiques (INERA) at Yangambi. Initial digitization was funded through the COBIMFO project funded by the Belgian Science Policy Office (Belspo; contract no. SD/AR/01A). Additional, postdoctoral funding was granted by the BOF Special Research Fund, Ghent University, Belgium, the Belgian Science Policy office COBECORE project (BELSPO; grant BR/175/A3/COBECORE), the European Union Marie Skłodowska-Curie Action (project number 797668) and the NSF Macrosystems Biology programme (Award Numbers: EF-1065029). Consolidation and post-processing of the data was privately funded by BlueGreen Labs (BV).
DOI: 10.15468/dl.aruev8
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Muraenichthys gymnotus"
 ]
}

 The dataset includes 43 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035124-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.88g2nu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ADY0961"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035125-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dnpc9b
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAJ8783"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035126-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15350518
title: anablepsoides_urophthalmus_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.15350519
title: anablepsoides_urophthalmus_zanfona_1.fasta.gz
DOI: 10.15468/dl.8w9kx7
title: Occurrence Download

description: A dataset containing 8893 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversity weekend observations, Recorder-Lux database"
 ]
}

 The dataset includes 8893 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018049-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14229872
title: Mozilla CORE issue report dataset

description: Dataset of issues reports from the CORE project containing 522.355 reports from the start of the project until 27-11-2024
DOI: 10.15468/dl.dh3zbv
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Sierra Leone",
 "TaxonKey is Archaea"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018051-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1310095401-eng
title: Number of tests for select diagnostic services at medical and diagnostic laboratories, by type of billing and provincial insurance coverage

description: Number of services delivered for Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, and ultrasounds by type of billing and patient provincial insurance coverage.
DOI: 10.15468/dl.tgf2fu
title: Occurrence Download

description: A dataset containing 6537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Hipposideros diadema (É.Geoffroy Saint-Hilaire, 1813)"
 ]
}

 The dataset includes 6537 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018053-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a79smj
title: Occurrence Download

description: A dataset containing 464964 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Côte d’Ivoire",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 464964 records from 413 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018027-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b5kepv
title: Occurrence Download

description: A dataset containing 234112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Carnivora"
 ]
}

 The dataset includes 234112 records from 720 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018025-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bmbsry
title: Occurrence Download

description: A dataset containing 3135587 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 3135587 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018042-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8152783
title: Demo datasets for PhenoLearn

description: This Zenodo record contains two test datasets (Birds and Littorina) used in the paper:

PhenoLearn: A user-friendly Toolkit for Image Annotation and Deep Learning-Based Phenotyping for Biological Datasets

Authors: Yichen He, Christopher R. Cooney, Steve Maddock, Gavin H. Thomas

 

PhenoLearn is a graphical and script-based toolkit designed to help biologists annotate and analyse biological images using deep learning. This dataset includes two test cases: one of bird specimen images for semantic segmentation, and another of marine snail (Littorina) images for landmark detection. These datasets are used to demonstrate the PhenoLearn workflow in the accompanying paper.

Dataset Structure

Bird Dataset



train/ — 120 bird specimen images for annotation and model training.

pred/ — 100 images for prediction and testing.

seg_train.csv — Pixel-wise segmentations (CSV format with RLE or polygon masks).

name_file_pred — Filenames corresponding to prediction images.


Littorina Dataset



train/ — 120 snail images for training landmark prediction models.

pred/ — 100 snail images for model testing.

pts_train.csv — Ground-truth landmark coordinates for training images.

name_file_pred — Prediction image filenames for evaluation.


How to Use These Datasets

Workflow Instructions (via PhenoLearn)





Download the dataset folders.




Use PhenoLearn to load seg_train.csv (segmentation) or pts_train.csv (landmark) to view and edit annotations.




Train segmentation or landmark prediction models directly via PhenoLearn's training module, or export data for external tools.




Use name_file_pred to match predictions with ground-truth for evaluation.



See the full tutorial and usage guide in the https://github.com/EchanHe/PhenoLearn.
DOI: 10.5281/zenodo.8152784
title: Demo datasets for PhenoLearn

description: This Zenodo record contains two test datasets (Birds and Littorina) used in the paper:

PhenoLearn: A user-friendly Toolkit for Image Annotation and Deep Learning-Based Phenotyping for Biological Datasets

Authors: Yichen He, Christopher R. Cooney, Steve Maddock, Gavin H. Thomas

 

PhenoLearn is a graphical and script-based toolkit designed to help biologists annotate and analyse biological images using deep learning. This dataset includes two test cases: one of bird specimen images for semantic segmentation, and another of marine snail (Littorina) images for landmark detection. These datasets are used to demonstrate the PhenoLearn workflow in the accompanying paper.

Dataset Structure

Bird Dataset



train/ — 120 bird specimen images for annotation and model training.

pred/ — 100 images for prediction and testing.

seg_train.csv — Pixel-wise segmentations (CSV format with RLE or polygon masks).

name_file_pred — Filenames corresponding to prediction images.


Littorina Dataset



train/ — 120 snail images for training landmark prediction models.

pred/ — 100 snail images for model testing.

pts_train.csv — Ground-truth landmark coordinates for training images.

name_file_pred — Prediction image filenames for evaluation.


How to Use These Datasets

Workflow Instructions (via PhenoLearn)





Download the dataset folders.




Use PhenoLearn to load seg_train.csv (segmentation) or pts_train.csv (landmark) to view and edit annotations.




Train segmentation or landmark prediction models directly via PhenoLearn's training module, or export data for external tools.




Use name_file_pred to match predictions with ground-truth for evaluation.



See the full tutorial and usage guide in the https://github.com/EchanHe/PhenoLearn.
DOI: 10.5281/zenodo.15350375
title: Overlooked factors in vector control: Addressing biological compromises in pyrethroid-resistant Aedes aegypti
DOI: 10.5281/zenodo.15350376
title: Overlooked factors in vector control: Addressing biological compromises in pyrethroid-resistant Aedes aegypti
DOI: 10.15468/dl.7xfccr
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT "year", GBIF_EEARGCode( 1000, decimalLatitude, decimalLongitude, COALESCE(coordinateUncertaintyInMeters, 1000) ) AS eeaCellCode, classKey, class, familyKey, family, COUNT(*) AS occurrences, MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters, MIN(GBIF_TemporalUncertainty(eventDate, null)) AS minTemporalUncertainty FROM occurrence WHERE occurrenceStatus = 'PRESENT' AND countrycode = 'BE' AND "year" &gt;= 1900 AND hasCoordinate = TRUE AND familyKey IS NOT NULL AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') AND (LOWER(identificationVerificationStatus) NOT IN ( 'unverified', 'unvalidated', 'not validated', 'under validation', 'not able to validate', 'control could not be conclusive due to insufficient knowledge', 'uncertain', 'unconfirmed', 'unconfirmed - not reviewed', 'validation requested' ) OR identificationVerificationStatus IS NULL)AND familyKey IN (5666)AND COALESCE(coordinateUncertaintyInMeters, 1000) &lt;= 10000GROUP BY "year", eeaCellCode, classKey, class, familyKey, family ORDER BY "year" DESC, eeaCellCode ASC, familyKey ASC;

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018047-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1310095401-fra
title: Nombre de tests pour certains services de diagnostic à laboratoires médicaux et d'analyses diagnostiques, selon le type de facturation et couverture d'assurance provinciale

description: Nombre de services fournis pour l'imagerie par résonance magnétique (IRM), la tomodensitométrie (TDM) et les échographies selon le type de facturation et couverture d'assurance provinciale du patient.
DOI: 10.21227/v8ag-gp51
title: "Optimization Deep Neural Network Test Case through Abductive Learning"

description: "The rapid advancement of deep neural network (DNN) models has enabled their widespread application across various domains, including face recognition and natural language processing. However, data-driven DNN models are prone to erroneous behavior when inadequately trained, necessitating extensive predictive labeling of test data to identify and mitigate defects. Manual labeling, however, remains both labor-intensive and inefficient. To address this limitation, several automated test predictions labeling methods have been proposed. These approaches, however, often result in a high rate of mislabeling, thereby limiting improvements in classification performance during model retraining.To address these challenges, this study proposes a Deep Neural Network Test Case Optimization Method based on Abductive Learning. This method integrates domain knowledge with logical reasoning to optimize the labeling of incorrectly predicted samples and utilizes abductively labeled data to retrain DNN models, thereby reducing both human effort and time requirements. Experiments were conducted on four deep learning test sets using four classical neural network models and compared against eight baseline methods. The proposed method achieved a fault detection rate of 96.77%, surpassing baseline methods by up to 13.46%. Furthermore, the labeling accuracy within the selected test case subset reached a maximum of 98.99%."
DOI: 10.15468/dl.bytvgm
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Sargassum vulgare C.Agardh",
 "Year 2010-2020"
 ]
}

 The dataset includes 234 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018058-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqwydc
title: Occurrence Download

description: A dataset listing the 824 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.95231 35.32385,-2.97074 35.30803,-2.97328 35.28494,-2.95612 35.26471,-2.93197 35.26523,-2.89739 35.27846,-2.95231 35.32385))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 824 records were derived from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018055-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cdmzfs
title: Occurrence Download

description: A dataset containing 164670 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (IDN, PNG)",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, DD)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 164670 records from 397 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018012-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cy2v3v
title: Occurrence Download

description: A dataset containing 105819 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Glycine Willd."
 ]
}

 The dataset includes 105819 records from 419 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001491-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.89hjzg
title: Occurrence Download

description: A dataset containing 5937689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Helophilus latifrons Loew, 1863, Bombus flavidus flavidus, Paracorymbia fulva (Degeer, 1775), Cycloneda sanguinea (Linnaeus, 1763), Stenurella bifasciata (Müller, 1776), Callophrys muiri (Hy.Edwards, 1881), Anthrenus lepidus LeConte, 1854, Adela flammeusella Chambers, 1876, Hylephila phyleus phyleus, Copestylum violaceum (Say, 1829), Pholisora catullus (Fabricius, 1793), Callophrys dumetorum (Boisduval, 1852), Myathropa florea (Linnaeus, 1758), Syrphus opinator Osten Sacken, 1877, Toxomerus occidentalis Curran, 1922, Bombus vancouverensis vancouverensis, Argynnis coronis, Chalybion californicum (de Saussure, 1867), Heliothodes diminutiva Grote, 1873, Strymon acadica (Edwards, 1862), Tharsalea gorgon, Harrisina americana (Guérin-Méneville, 1832), Bembix americana Fabricius, 1793, Cerceris fumipennis Say, 1837, Vanessa atalanta rubria (Fruhstorfer, 1909), Lycaena phlaeas (Linnaeus, 1761), Mitoura nelsoni (Boisduval, 1869), Maniola jurtina (Linnaeus, 1758), Thymelicus lineola (Ochsenheimer, 1808), Tharsalea xanthoides, Palpada alhambra (Hull, 1925), Paradejeania rutilioides (Jaennicke, 1867), Ctenucha virginica Esper, 1794, Nemognatha scutellaris LeConte, 1853, Stenurella melanura (Linnaeus, 1758), Eristalis transversa (Wiedemann, 1830), Lycaena hypophlaeas (Boisduval, 1852), Eristalis hirta Loew, 1866, Platycheirus trichopus (Thomson, 1869), Trichodes ornatus Say, 1823, Philanthus gibbosus (Fabricius, 1775), Helophilus fasciatus Walker, 1849, Cisseps fulvicollis Hübner, 1818, Pseudovadonia livida (Fabricius, 1776), Dolichovespula arenaria (Fabricius, 1775), Philanthus bilunatus Cresson, 1865, Sphex ichneumoneus (Linnaeus, 1758), Epargyreus clarus (Cramer, 1775), Eremnophila aureonotata (Cameron, 1888), Allograpta obliqua (Say, 1823))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5937689 records from 5442 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011877-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/b74-2vy-0et
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pfn-mzx-pfv
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hky-j07-196
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ykh-m23-9w6
title: Chelonibia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1g1-0g2-zs0
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.