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

description: A dataset containing 140809 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sciurus carolinensis Gmelin, 1788",
 "StateProvince is one of (Alabama, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Maine, Maryland, Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin)"
 ]
}

 The dataset includes 140809 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016027-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t6x42h
title: Occurrence Download

description: A dataset containing 895429 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "IucnRedListCategory is EN",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 895429 records from 366 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016028-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s5z62s
title: Occurrence Download

description: A dataset containing 3371 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-122.44157908888565 41.97771913891751, -122.45296014998043 41.975683290990666, -122.4635889829785 41.97202862656865, -122.47305643523549 41.966895932234394, -122.48099834867723 41.96048287973854, -122.48710960417958 41.95303636475948, -122.49115583398917 41.94484295093067, -122.49298235419342 41.93621779672481, -122.49251998539681 41.92749249996277, -122.48978755687814 41.91900233377723, -122.48489102155797 41.91107336770102, -122.47801924034853 41.90400996794629, -122.46943661971345 41.89808315249556, -122.45947290116655 41.89352024062966, -122.44851050249953 41.89049618483135, -122.43696989514214 41.88912690790096, -122.42529356840075 41.889464892167034, -122.41392917820436 41.89149718361669, -122.40331250471799 41.895145884465386, -122.39385084941128 41.900271116031, -122.3859074878295 41.906676342702355, -122.37978775956807 41.91411586021166, -122.37572732228408 41.92230417022618, -122.37388302284855 41.93092689128174, -122.3743267473796 41.9396527959821, -122.37704250501382 41.94814651864155, -122.38192688081864 41.956081448287094, -122.38879286505863 41.96315231080809, -122.39737693381035 41.96908695208395, -122.40734912510239 41.973656861422846, -122.41832573124746 41.976686021039484, -122.42988411785527 41.978057731122526, -122.44157908888565 41.97771913891751))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 3371 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005485-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3w9aqa
title: Occurrence Download

description: A dataset containing 23861 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-1.50947 46.67788,-1.50617 46.67555,-1.5065 46.66961,-1.50418 46.66776,-1.49086 46.66551,-1.49017 46.66334,-1.48667 46.66261,-1.48696 46.66089,-1.47703 46.65657,-1.47373 46.65687,-1.47241 46.65461,-1.46651 46.65483,-1.46584 46.65238,-1.46034 46.65286,-1.45834 46.65009,-1.44956 46.64944,-1.44722 46.64624,-1.44131 46.64479,-1.43902 46.63951,-1.43217 46.63502,-1.43487 46.63008,-1.42732 46.6285,-1.42369 46.62507,-1.41929 46.62511,-1.41497 46.61927,-1.40127 46.61516,-1.39853 46.61086,-1.39338 46.61251,-1.38978 46.61743,-1.38106 46.6241,-1.36192 46.6296,-1.35948 46.63327,-1.35368 46.63498,-1.34586 46.64149,-1.33926 46.64395,-1.3322 46.65116,-1.32291 46.65188,-1.32394 46.65503,-1.32711 46.65773,-1.335 46.66047,-1.34212 46.66687,-1.33973 46.66946,-1.34025 46.6724,-1.33463 46.67866,-1.33897 46.67876,-1.33921 46.68146,-1.34074 46.68186,-1.33784 46.68329,-1.33466 46.68254,-1.33307 46.68615,-1.33526 46.69027,-1.33984 46.68959,-1.35069 46.69441,-1.35744 46.69433,-1.36517 46.69086,-1.36952 46.69671,-1.36812 46.69858,-1.38222 46.7083,-1.39092 46.71101,-1.39772 46.70932,-1.39953 46.7108,-1.40491 46.7104,-1.40517 46.71392,-1.39887 46.71513,-1.40423 46.72075,-1.41728 46.71986,-1.41703 46.71583,-1.42465 46.71479,-1.42986 46.70545,-1.43675 46.70402,-1.4426 46.70719,-1.44658 46.7069,-1.44455 46.70203,-1.44759 46.70131,-1.44743 46.69883,-1.45009 46.69724,-1.45894 46.69834,-1.45799 46.69441,-1.46027 46.69268,-1.45984 46.68812,-1.46218 46.68561,-1.46147 46.68354,-1.47068 46.68803,-1.46919 46.68899,-1.47094 46.69439,-1.46839 46.69601,-1.48539 46.69505,-1.48053 46.69237,-1.48323 46.68516,-1.49218 46.68562,-1.4932 46.68143,-1.49184 46.68028,-1.49477 46.67985,-1.49486 46.67674,-1.50571 46.6761,-1.50947 46.67788)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 23861 records from 138 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005486-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17698792
title: Port Container image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models for container detection. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Container (1 class)




Split
Size


Train
2880


Test
823


Validate
411




This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Ignacio Benítez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0  url: https://universe.roboflow.com/de/elven/dataset/13
DOI: 10.5281/zenodo.17698793
title: Port Container image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models for container detection. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Container (1 class)




Split
Size


Train
2880


Test
823


Validate
411




This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Ignacio Benítez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0  url: https://universe.roboflow.com/de/elven/dataset/13
DOI: 10.5281/zenodo.17698328
title: Safety Vest image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models to monitor safety vest usage for ocupational safety in ports. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Vest (2 class)




Split
Size


Train
9233


Test
390


Validate
779




 

This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Francisco Martínez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0  url: https://universe.roboflow.com/roboflow-universe-projects/safety-vests/dataset/7
DOI: 10.5281/zenodo.17698329
title: Safety Vest image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models to monitor safety vest usage for ocupational safety in ports. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Vest (2 class)




Split
Size


Train
9233


Test
390


Validate
779




 

This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Francisco Martínez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0  url: https://universe.roboflow.com/roboflow-universe-projects/safety-vests/dataset/7
DOI: 10.15468/dl.ceqkza
title: Occurrence Download

description: A dataset containing 1298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-122.3322115668985 42.02260046411914, -122.34360586501393 42.02057692414219, -122.35425052436035 42.01693357974585, -122.3637357805827 42.011810781716584, -122.37169678788653 42.00540582119044, -122.37782769038444 41.99796528308637, -122.38189336328675 41.98977550204321, -122.38373837476964 41.98115149799811, -122.38329283550193 41.97242482585585, -122.38057492997966 41.96393081290649, -122.3756900559716 41.95599567763392, -122.36882662975175 41.94892402410075, -122.36024874021416 41.94298718778184, -122.35028595004322 41.93841287284514, -122.3393206433612 41.93537646929635, -122.32777340407904 41.933994373386184, -122.31608697570194 41.93431955878816, -122.30470940040806 41.93633956204199, -122.29407696211399 41.93997695646887, -122.28459756463121 41.94509229711216, -122.2766351618182 41.95148942815571, -122.27049582202118 41.95892295664819, -122.26641595453515 41.967107615099906, -122.26455315217981 41.97572916344093, -122.26498001278132 41.984456420624625, -122.26768119549615 41.99295397029385, -122.27255384844827 42.00089505553185, -122.27941141590952 42.0079741664486, -122.28799070093274 42.013918832250695, -122.29796192838873 42.01850015680225, -122.30894142965799 42.02154168294648, -122.3205064598317 42.02292623456318, -122.3322115668985 42.02260046411914))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 1298 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005488-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17698257
title: Helmet image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models to monitor helmet usage for ocupational safety in ports. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Helmet (2 class)




Split
Size


Train
15490


Test
615


Validate
1171




 

This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Ignacio Benítez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0

  url: https://universe.roboflow.com/dataset-bfndu/ppe-detection-using-cv-metiz/dataset/3
DOI: 10.5281/zenodo.17698258
title: Helmet image data for risk and safety monitoring in port environments

description: This repository contains training, testing, and validation data to train vision models to monitor helmet usage for ocupational safety in ports. The data is provided with assigned splits, and labels are provided in YOLO format, and images are provided with Data Augmentation already applied to them.

Helmet (2 class)




Split
Size


Train
15490


Test
615


Validate
1171




 

This dataset contains data originally published under a CC-BY 4.0 License which is credited below:



Author: Ignacio Benítez
Affiliation: Secmotic Innovatin S.L.

Credits: Model trained based on the next roboflow dataset, completed with other partial datasets:  license: CC BY 4.0

  url: https://universe.roboflow.com/dataset-bfndu/ppe-detection-using-cv-metiz/dataset/3
DOI: 10.15468/dl.rwtmmr
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Living Specimen)",
 "TaxonKey is Indotestudo forstenii"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005487-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/q7k4-fe73
title: "WF-DTS  dataset"

description: "Display of raw data collected for WF-DTS device!There are a total of 8 types of insect data, each with 800 images, for a total of 6400 image data.Tessaratoma papillosa, Plautia fimbriata, Helicoverpa armigera, Holotrichia parallela, Hydrophilus acuminatus, Eurygaster integriceps, Cnaphalocrocis medinalis and Coccinella septempunctata."
DOI: 10.15468/dl.taw57y
title: Occurrence Download

description: A dataset containing 2206720 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "Month is one of (March, April)",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2002-2002"
 ]
}

 The dataset includes 2206720 records from 2054 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005362-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.r43k1
title: Data from: Altitudinal clinal variation in wing form in African Drosophila melanogaster: one cline or many?

description: Geographical patterns of morphological variation have been useful in
 addressing hypotheses about environmental adaptation. In particular,
 latitudinal clines in phenotypes have been studied in a number of
 Drosophila species. Some environmental conditions along latitudinal
 clines—for example, temperature—also vary along altitudinal clines, but
 these have been studied infrequently and it remains unclear whether these
 environmental factors are similar enough for convergence or parallel
 evolution. Most clinal studies in Drosophila have dealt exclusively with
 univariate phenotypes, allowing for the detection of clinal relationships,
 but not for estimating the directions of covariation between them. We
 measured variation in wing shape and size in D. melanogaster derived from
 populations at varying altitudes and latitudes across sub-Saharan Africa.
 Geometric morphometrics allows us to compare shape changes associated with
 latitude and altitude, and manipulating rearing temperature allows us to
 quantify the extent to which thermal plasticity recapitulates clinal
 effects. Comparing effect vectors demonstrates that altitude, latitude,
 and temperature are only partly associated, and that the altitudinal shape
 effect may differ between Eastern and Western Africa. Our results suggest
 that selection responsible for these phenotypic clines may be more complex
 than just thermal adaptation.
description: Data&Scripts_PitchersPoolDworkin2012Data files and analysis scripts are found in the .zip archive. For details, see the ReadMe.txt file.African_Wing_images
DOI: 10.5061/dryad.f5qk7
title: Data from: Targeted capture of complete coding regions across divergent species

description: Despite continued advances in sequencing technologies, there is a need for
 methods that can efficiently sequence large numbers of genes from diverse
 species. One approach to accomplish this is targeted capture (hybrid
 enrichment). While these methods are well established for genome
 resequencing projects, cross-species capture strategies are still being
 developed and generally focus on the capture of conserved regions, rather
 than complete coding regions from specific genes of interest. The
 resulting data is thus useful for phylogenetic studies, but the wealth of
 comparative data that could be used for evolutionary and functional
 studies is lost. Here we design and implement a targeted capture method
 that enables recovery of complete coding regions across broad taxonomic
 scales. Capture probes were designed from multiple reference species and
 extensively tiled in order to facilitate cross-species capture. Using
 novel bioinformatics pipelines we were able to recover nearly all of the
 targeted genes with high completeness from species that were up to 200 myr
 divergent. Increased probe diversity and tiling for a subset of genes had
 a large positive effect on both recovery and completeness. The resulting
 data produced an accurate species tree, but importantly this same data can
 also be applied to studies of molecular evolution and function that will
 allow researchers to ask larger questions in broader phylogenetic
 contexts. Our method demonstrates the utility of cross-species approaches
 for the capture of full length coding sequences, and will substantially
 improve the ability for researchers to conduct large-scale comparative
 studies of molecular evolution and function.
description: Supplementary File 2 -
 Probe Design and SequencesComplete set of probes
 used for cross-species targeted capture and associated
 dataSupplementary File 3 -
 Assembly and Analysis Pipelines and ScriptsCustom pipelines and
 scripts for the assembly and analysis of cross-species targeted capture
 dataSupplementary File 4 -
 Anolis ReferenceReference file used for
 assemblySupplementary File 5 -
 Snake ReferenceReference file used for
 assemblySupplementary File 6 -
 Gekko ReferenceReference file used for
 assembly
DOI: 10.15468/dl.cp99he
title: Occurrence Download

description: A dataset containing 439785 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Global distribution of marine forests [literature records]"
 ]
}

 The dataset includes 439785 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001717-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.m66b10q
title: Data from: Climbing adaptations, locomotory disparity and ecological convergence in ancient stem ‘kangaroos’

description: Living kangaroos, wallabies and rat-kangaroos (Macropodoidea) constitute
 the most ecologically diverse radiation of Australasian marsupials.
 Indeed, even their hallmark bipedal hopping gait has been variously
 modified for bounding, walking, and climbing. However, the origins of this
 locomotory adaptability are uncertain because skeletons of the most
 ancient macropodoids are exceptionally rare. Some of the stratigraphically
 oldest fossils have been attributed to Balbaridae — a clade of potentially
 quadrupedal stem macropodoids that became extinct during the late Miocene.
 Here we undertake the first assessment of balbarid locomotion using 2D
 geometric morphometrics and a correlative multivariate analysis of linear
 measurements. We selected the astragalus and pedal digit IV ungual as
 proxies for primary gait because these elements are preserved in the only
 articulated balbarid skeleton, as well as some unusual early Miocene
 balbarid-like remains that resemble the bones of modern tree-kangaroos.
 Our results show that these fossils manifest character states indicative
 of contrasting locomotory capabilities. Furthermore, predictive modelling
 reveals similarities with extant macropodoids that employ either bipedal
 saltation and/or climbing. We interpret this as evidence for archetypal
 gait versatility, which probably integrated higher-speed hopping with
 slower-speed quadrupedal progression, and varying degrees of scansoriality
 as independent specialisations for life in forest and woodland settings.
description: Den Boer et
 al_SourceCodeDen Boer et
 al_Astragalus-Trochlear_ResultsDen Boer et
 al_Astragalus-Medial_ResultsDen Boer et
 al_Astragalus-Anterior_ResultsDen Boer et
 al_Astragalus-Plantar_ResultsDen Boer et
 al_Astragalus-Lateral_ResultsDen Boer et
 al_Astragalus-Posterior_ResultsDen Boer et
 al_Astragalus-LinearMeasurementsDen Boer et
 al_Ungual-Dorsal_ResultsDen Boer et
 al_Ungual-Lateral_Results
DOI: 10.5061/dryad.89b2c4s
title: Data from: Gliding for a free lunch: biomechanics of foraging flight in Common Swifts (Apus apus)

description: Although the biomechanics of animal flight have been well studied in
 laboratory apparatus such as wind tunnels for many years, the
 applicability of these data to natural flight behaviour has been examined
 in few instances and mostly in the context of long-distance migration.
 Here we use rotational stereo-videography to record the free-flight
 trajectories of foraging common swifts. We find that despite their
 exquisite manoeuvring capabilities, the swifts only rarely performed
 high-acceleration turns. More surprisingly, we also found that despite
 feeding on tiny insects likely moving with ambient flow, the birds adjust
 their air speed to optimize cost of transport over distance. Finally,
 swifts spent only 25% of their time flapping; the majority of time (71%)
 was spent in extended wing gliding during which the average power expended
 for changes in speed or elevation was 0.84 W kg-1 and not significantly
 different from 0. Thus, gliding swifts extracted sufficient environmental
 energy to pay the cost of flight during foraging.
description: Trajectory kinematics
 and behaviour from all tracksTrajectory,
 environmental and behavioral data from Common Swift (Apus apus) flight
 tracksDryadData_v2.mat