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

description: A dataset containing 310 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Afrixalus aureus, Afrixalus delicatus, Afrixalus fornasini, Afrixalus knysnae, Afrixalus spinifrons, Amietia delalandii, Amietia fuscigula, Amietia hymenopus, Amietia poyntoni, Amietia vandijki, Amietia vertebralis, Anhydrophryne hewitti, Anhydrophryne ngongoniensis, Anhydrophryne rattrayi, Arthroleptella atermina, Arthroleptella bicolor, Arthroleptella draconella, Arthroleptella drewesii, Arthroleptella kogelbergensis, Arthroleptella landdrosia, Arthroleptella lightfooti, Arthroleptella rugosa, Arthroleptella subvoce, Arthroleptella villiersi, Arthroleptis stenodactylus, Arthroleptis wageri, Arthroleptis wahlbergii, Breviceps acutirostris, Breviceps adspersus, Breviceps bagginsi, Breviceps branchi, Breviceps carruthersi, Breviceps fuscus, Breviceps gibbosus, Breviceps macrops, Breviceps montanus, Breviceps mossambicus, Breviceps namaquensis, Breviceps passmorei, Breviceps pentheri, Breviceps rosei, Breviceps sopranus, Breviceps sylvestris, Breviceps verrucosus, Cacosternum aggestum, Cacosternum australis, Cacosternum boettgeri, Cacosternum capense, Cacosternum karooicum, Cacosternum namaquense, Cacosternum nanogularum, Cacosternum nanum, Cacosternum parvum, Cacosternum platys, Cacosternum rhythmum, Cacosternum striatum, Cacosternum thorini, Capensibufo deceptus, Capensibufo magistratus, Capensibufo rosei, Capensibufo selenophos, Capensibufo tradouwi, Chiromantis xerampelina, Hadromophryne natalensis, Heleophryne depressa, Heleophryne hewitti, Heleophryne orientalis, Heleophryne purcelli, Heleophryne regis, Heleophryne rosei, Hemisus guineensis, Hemisus guttatus, Hemisus marmoratus, Hildebrandtia ornata, Hyperolius argus, Hyperolius horstockii, Hyperolius marmoratus, Hyperolius pickersgilli, Hyperolius poweri, Hyperolius pusillus, Hyperolius semidiscus, Hyperolius tuberilinguis, Kassina senegalensis, Leptopelis mossambicus, Leptopelis natalensis, Leptopelis xenodactylus, Microbatrachella capensis, Natalobatrachus bonebergi, Phlyctimantis maculatus, Phrynobatrachus acridoides, Phrynobatrachus mababiensis, Phrynobatrachus natalensis, Phrynomantis annectens, Phrynomantis bifasciatus, Poyntonia paludicola, Poyntonophrynus fenoulheti, Poyntonophrynus vertebralis, Ptychadena anchietae, Ptychadena mossambica, Ptychadena nilotica)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 310 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019182-250827131500795/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.28183181
title: Table S2

description: The means and standard deviations of the emotional judgments for arousal and valence (<i>N</i> =3 2), provided by the participants across the two experimental conditions. Discrepancies between the expressed and perceived emotional dimensions are highlighted in bold.
DOI: 10.6084/m9.figshare.28183181.v2
title: Table S2

description: The means and standard deviations of the emotional judgments for arousal and valence (<i>N</i> =3 2), provided by the participants across the two experimental conditions. Discrepancies between the expressed and perceived emotional dimensions are highlighted in bold.
DOI: 10.6084/m9.figshare.28183181.v1
title: Table S2

description: The means and standard deviations of the emotional judgments for arousal and valence, provided by the participants across the two listening conditions. Discrepancies between the expressed and perceived emotional dimensions are highlighted in bold.
DOI: 10.5281/zenodo.16948908
title: Tripeptide Database for 'Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models'

description: Tripeptide Database for 'Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models'
DOI: 10.5281/zenodo.16948909
title: Tripeptide Database for 'Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models'

description: Tripeptide Database for 'Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models'
DOI: 10.15468/dl.889f93
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Benthopanope eucratoides"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019184-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4hqqpa
title: Occurrence Download

description: A dataset containing 26764 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Carex muricata"
 ]
}

 The dataset includes 26764 records from 545 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019181-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e237642
title: Mozambique USAID Education Data

description: Two USAID-funded project's data are included in this folder, covering the period from 2013 to 2017. The projects are: 1) Aprender a Ler (ApaL) and 2) Eu Leio. Across the projects the folder contains the following iles and numbers of each: codebooks (2), consent (0), data files (7), instruments (1) and reports (2).
DOI: 10.3886/e237642v1
title: Mozambique USAID Education Data

description: Two USAID-funded project's data are included in this folder, covering the period from 2013 to 2017. The projects are: 1) Aprender a Ler (ApaL) and 2) Eu Leio. Across the projects the folder contains the following iles and numbers of each: codebooks (2), consent (0), data files (7), instruments (1) and reports (2).
DOI: 10.25318/1310096301-fra
title: Estimations de la survie nette, prédite sur une durée de cinq ans, normalisées selon l'âge et tous âges confondus, pour certains cancers, par sexe, 2019 à 2021

description: Estimations nationales (excluant le Québec) de la survie nette après cinq ans pour 31 types de cancer. La survie nette désigne la probabilité de survie qui serait observée dans l’hypothèse où le cancer à l’étude serait la seule cause de décès possible. La survie prédite fournit une estimation plus à jour de la survie en se référant exclusivement à la survie caractérisant les cas de cancer au cours d'une période récente.
DOI: 10.15468/dl.czsesj
title: Occurrence Download

description: A dataset containing 562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Indigofera caerulea",
 "Geometry POLYGON ((-17.62504 -34.81917, 51.13387 -34.81917, 51.13387 37.34999, -17.62504 37.34999, -17.62504 -34.81917))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 562 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019190-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e7cftg
title: Occurrence Download

description: A dataset containing 232 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hydrolagus mirabilis"
 ]
}

 The dataset includes 232 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019187-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d49w32
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tragopogon miscellus"
 ]
}

 The dataset includes 82 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019186-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8u3qpd
title: Occurrence Download

description: A dataset containing 10920 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "CoordinateUncertaintyInMeters 0.0-10.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Picea",
 "Year 2024-2024"
 ]
}

 The dataset includes 10920 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019189-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17638/datacat.liverpool.ac.uk/3038
title: The biomechanics of working dog locomotion I: Steady-state trotting

description: The biomechanics of steady-state locomotion in different breeds of working dog is understudied, despite widespread use of these animals in multiple industries. It is unknown how kinematic and kinetic parameters vary between breeds and how these variations are potentially related to inter-breed variations in morphology. Here, gross morphology and trotting locomotion within a cohort of 27 Labrador Retrievers (“Labradors”), shepherd breeds (“Shepherds”) and spaniel breeds (“Spaniels”) were compared using motion capture, force plates and biomechanical modelling. Evidence for slight positive allometric scaling of limb and body lengths was found between the breeds, with relatively longer lengths seen in Shepherds compared to Spaniels. Significant between-breed differences in raw spatiotemporal parameters were found with the larger Shepherds trotting with greater velocities, stride lengths and ground reaction forces than the smaller breeds, although many of these factors scaled with isometry with respect to body mass when accounting for variations in trotting speed. However, gait cycle times and stride lengths do not scale isometrically with body size, which, taken together with significant differences in flexion-extension joint angles and moments, suggest dynamic similarity during trotting is unlikely between these breeds. Overall, these findings highlight specific differences in the biomechanics of steady-state trotting locomotion between working dog breeds despite their somewhat geometrically similar gross body proportions. This suggests that not only should locomotion databases for individual breeds exist for future canine research, but also that breed-specific considerations should be adopted to maximise the health and welfare of these dogs in working practices, such as load-carrying tasks.
DOI: 10.6084/m9.figshare.30031534
title: Caregiver Focus group discussions transcribed data_2023.docx

description: The study focused on the centring caregiver voices on the caregiving needs of children with Neurodevelopmental Disorders in Low-resource Settings. Three focus group discussions were conducted in Lusaka. This dataset consists of transcripts from the qualitative recorded interviews used for this study.
DOI: 10.6084/m9.figshare.30031534.v1
title: Caregiver Focus group discussions transcribed data_2023.docx

description: The study focused on the centring caregiver voices on the caregiving needs of children with Neurodevelopmental Disorders in Low-resource Settings. Three focus group discussions were conducted in Lusaka. This dataset consists of transcripts from the qualitative recorded interviews used for this study.
DOI: 10.25625/cmqy0q/xdrhmo
title: lemur_output_val_PriMAT.tar

description: lemur validation videos with PriMAT bounding box predictions
DOI: 10.25625/cmqy0q/fuixou
title: lemur_images_train_IndividualID.tar

description: 782 images to train to classify individual lemurs
DOI: 10.25625/cmqy0q/zh2ykh
title: macaque_images_train.tar

description: 500 images for training (actually 500 images pairs within a few frames of difference). In PriMAT we only used the first image of each pair.
DOI: 10.25625/cmqy0q/aakbat
title: lemur_output_val_DLC.tar

description: 12 validation videos with predicted keypoints by DeepLabCut
DOI: 10.25625/cmqy0q/rj2smg
title: Models.tar

description: Models for tracking and individual ID
DOI: 10.25625/cmqy0q/7xzly3
title: lemur_images_train.tar

description: 500 images with annotated lemurs and feeding boxes
DOI: 10.25625/cmqy0q/t9vt0t
title: lemur_videos_eval.tar

description: 12 validation videos of length 12 seconds of redfronted lemurs with labels.
DOI: 10.25625/cmqy0q/yuhmdg
title: lemur_videos_test.tar

description: 12 test videos of length 12 seconds of redfronted lemurs with labels.
DOI: 10.6084/m9.figshare.30031423.v1
title: ControlCity

description: code and data of ControlCity
DOI: 10.25625/cmqy0q/damkl8
title: macaque_videos_test.tar

description: 12 test sequences for tracking macaques in the wild
DOI: 10.25625/cmqy0q
title: Data for: "PriMAT: Robust multi-animal tracking for primates in the wild"

description: Training images for tracking lemurs and macaques (500 each) with their respective labels, training images for identifying lemurs with labels. Validation and test video sequences for tracking lemurs and macaques (24 sequences x 12s for each species). Video output of our model and a DeepLabCut model on the validation sequences of the lemurs. Video output of our model for the macaques. Model weights for tracking and identification models.
DOI: 10.25625/cmqy0q/tlnydp
title: macaque_videos_eval.tar

description: 12 validation sequences for tracking macaques in the wild
DOI: 10.5281/zenodo.17020133
title: Source data: Interdomain assembly between the fungal tRNA ligase adenylyltransferase and kinase domain
DOI: 10.5281/zenodo.17020134
title: Source data: Interdomain assembly between the fungal tRNA ligase adenylyltransferase and kinase domain
DOI: 10.5281/zenodo.17037087
title: Elderly Canteens of Nanjing
DOI: 10.5281/zenodo.17037088
title: Elderly Canteens of Nanjing
DOI: 10.6084/m9.figshare.29630672
title: Multivariate Disaggregation Modeling of Air Pollutants: A Case-Study of PM2.5, PM10 and Ozone Prediction in Portugal and Italy

description: Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM 2.5, PM<sub>10</sub> and ozone levels in Portugal and Italy, improving spatial resolution from 0.1° (10 km) to 0.02° (2 km), and revealing dependencies among pollutants. Our framework provides a robust foundation for analyzing complex pollutant interactions, offering valuable insights for decision-makers seeking to address air pollution and its impacts.
DOI: 10.6084/m9.figshare.29630672.v2
title: Multivariate Disaggregation Modeling of Air Pollutants: A Case-Study of PM2.5, PM10 and Ozone Prediction in Portugal and Italy

description: Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM 2.5, PM<sub>10</sub> and ozone levels in Portugal and Italy, improving spatial resolution from 0.1° (10 km) to 0.02° (2 km), and revealing dependencies among pollutants. Our framework provides a robust foundation for analyzing complex pollutant interactions, offering valuable insights for decision-makers seeking to address air pollution and its impacts.
DOI: 10.6084/m9.figshare.29630672.v1
title: Multivariate disaggregation modeling of air pollutants: a case-study of PM2.5, PM10 and ozone prediction in Portugal and Italy

description: Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM 2.5, PM<sub>10</sub> and ozone levels in Portugal and Italy, improving spatial resolution from 0.1° (10 km) to 0.02° (2 km), and revealing dependencies among pollutants. Our framework provides a robust foundation for analyzing complex pollutant interactions, offering valuable insights for decision-makers seeking to address air pollution and its impacts.
DOI: 10.15468/dl.8q3tzh
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scolymus hispanicus",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019193-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n39bcp
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eragrostis cilianensis major, Eragrostis cilianensis megastachya, Eragrostis cilianensis starosselskyi, Eragrostis cilianensis starosselskyi, Eragrostis cilianensis subbiloba, Eragrostis cilianensis thyrsiflora, Eragrostis cilianensis cilianensis, Eragrostis cilianensis leersioides, Eragrostis cilianensis major, Eragrostis cilianensis minoroides, Eragrostis cilianensis starosselskyi, Eragrostis cilianensis subbiloba, Eragrostis cilianensis thyrsiflora, Eragrostis costata, Eragrostis costata, Eragrostis eragrostis, Eragrostis eragrostis, Eragrostis eragrostis megastachya, Eragrostis eragrostis megastachya, Eragrostis flexuosa, Eragrostis leersioides, Eragrostis leersioides, Eragrostis leersioides leersioides, Eragrostis major, Eragrostis major subbiloba, Eragrostis major conferta, Eragrostis major subbiloba, Eragrostis megalostachya, Eragrostis megastachya, Eragrostis megastachya nana, Eragrostis megastachya thessalonica, Eragrostis megastachya cilianensis, Eragrostis megastachya compacta, Eragrostis megastachya nana, Eragrostis megastachya thyrsiflora, Eragrostis megastachya acutiuscula, Eragrostis megastachya cilianensis, Eragrostis megastachya compacta, Eragrostis megastachya densiflora, Eragrostis megastachya elongata, Eragrostis megastachya leersioides, Eragrostis megastachya microstachya, Eragrostis megastachya multiflora, Eragrostis megastachya parvispicula, Eragrostis megastachya paucispicula, Eragrostis megastachya thyrsiflora, Eragrostis megastachya verrucosa, Eragrostis minor major, Eragrostis minor megastycha, Eragrostis minor major)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 163 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019192-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.