cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.h563vn
title: Occurrence Download

description: A dataset containing 241339 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Ceropegia occidentalis, Mentzelia aspera, Chenopodium wahlii, Euphorbia trachysperma, Myrica californica, Polygonum afyonicum, Polygonum cappadocicum, Polygonum longipes, Chenopodium papulosum, Euphorbia jaegeri, Kitaibelia balansae, Valerianella turcica, Microseris pygmaea, Garhadiolus hamosus, Albizia antunesiana, Neurada al-eisawii, Prismatocarpus cordifolius, Wahlenbergia caryophylloides, Wahlenbergia distincta, Wahlenbergia ecklonii, Wahlenbergia graniticola, Hemigenia tomentosa, Ptilotus royceanus, Ptilotus disparilis, Thysanotus pyramidalis, Arctostaphylos bakeri, Arctostaphylos luciana, Arctostaphylos pallida, Bupleurum dianthifolium, Bupleurum lycaonicum, Ophrys cicmiriana, Chenopodium incognitum, Polygonum maritimum, Ptilotus daphne, Polygonum urnigerum, Euphorbia canuti, Euphorbia chamaepeplus, Euphorbia chersonesa, Euphorbia cumbrae, Euphorbia inermis, Euphorbia maconochieana, Euphorbia magdalenae, Euphorbia nutans, Euphorbia ocellata, Euphorbia officinarum, Euphorbia phillipsiae, Euphorbia platyphyllos, Euphorbia racemosa, Euphorbia schizoloba, Euphorbia schlechtendalii, Hemigenia glabrescens, Hemigenia ramosissima, Ophrys ferrum-equinum, Ophrys soller, Thysanotus wangariensis, Thysanotus baueri, Thysanotus brevifolius, Elatine campylosperma, Elatine heterandra, Paeonia californica, Ophrys peteri, Moehringia minutiflora, Chenopodium sandersii, Linaria orbensis, Myrica hartwegii, Polygonum papillosum, Epacris longiflora, Asteridea pulverulenta, Psacalium peltigerum, Pegolettia baccharidifolia, Cornus mas, Wahlenbergia oxyphylla, Wahlenbergia subrosulata, Wahlenbergia tenuis, Wahlenbergia dichotoma, Wahlenbergia madagascariensis, Tripogon montanus, Ferula pisidica, Ferula ermenekense, Ptilotus blackii, Helianthemum mansanetianum, Euphorbia altissima, Linaria gharbensis, Linaria grandiflora, Cymbalaria glutinosa, Helianthemum angustatum, Helianthemum germanicopolitanum, Helianthemum ventosum, Brachyscome mittagongensis, Brachyscome decipiens, Pulsatilla occidentalis, Hemigenia buccinata, Euphorbia arguta, Euphorbia armstrongiana, Euphorbia falcata, Euphorbia gayi, Euphorbia terracina, Euphorbia virgata, Ophrys carquierannensis, Ephedra distachya)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 241339 records from 1672 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051438-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zwnu3f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Adenocarpus ombriosus Ceballos & Ortuño)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000336-250226093636041/datasets/export for details.
DOI: 10.15468/dl.pm3r3r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Adenocarpus ombriosus Ceballos & Ortuño)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000337-250226093636041/datasets/export for details.
DOI: 10.15468/dl.gx4s4k
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Adenocarpus ombriosus Ceballos & Ortuño)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000338-250226093636041/datasets/export for details.
DOI: 10.15468/dl.sk2agv
title: Occurrence Download

description: A dataset containing 104 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coffea tsirananae J.-F.Leroy"
 ]
}

 The dataset includes 104 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000339-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bj6h96
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oligochaeta minima (Boiss.) Briq."
 ]
}

 The dataset includes 70 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000343-250226093636041/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.28497578
title: Evaluation of thermal comfort in urban commercial space with Vision Language Models-based agent model

description: Thermal comfort in urban commercial spaces plays a crucial role in influencing both business performance and public well-being. Traditional evaluation methods, which typically depend on field surveys and expert assessments, are often time-consuming and labor-intensive. This study introduces a novel proxy evaluation method based on the Vision Language Models (VLMs) to assess thermal comfort in commercial spaces. Specifically, we developed a VLMs-based agent system (ChatGPT-4o) that simulates eight distinct roles with diverse demographic backgrounds to assess the thermal comfort of Harbin Central Street’s commercial district through visual analysis. To validate the method’s effectiveness, we compared the VLMs assessment results with evaluations from 200 professional volunteers based on a 25% sample (n=143). Additionally, we conducted spatial distribution and thematic analyses to examine evaluation patterns and identify the key factors influencing thermal comfort. The findings reveal the following: First, the VLMs assessments showed a significant correlation with human expert evaluations (r=0.602, p&lt;0.001), confirming the feasibility of the VLMs-based thermal comfort assessment method; Second, the eight role proxies demonstrated notable heterogeneity in thermal comfort evaluations, highlighting the VLMs’ ability to accurately capture perceptual differences across social groups; Finally, spatial analysis revealed that, although evaluation differences exist among roles, the eastern region generally experiences higher thermal comfort than the western and central regions, with shade and vegetation being the most commonly identified influencing factors. This study assesses the effectiveness of VLMs-based proxies in urban thermal comfort evaluation. It demonstrates their ability to capture perceptual differences across social groups while offering a more efficient alternative to traditional assessment methods. This innovative approach opens new avenues for research and provides practical pathways for rapid, comprehensive, and multi-perspective thermal comfort assessments in urban commercial spaces.
DOI: 10.6084/m9.figshare.28497578.v1
title: Evaluation of thermal comfort in urban commercial space with Vision Language Models-based agent model

description: Thermal comfort in urban commercial spaces plays a crucial role in influencing both business performance and public well-being. Traditional evaluation methods, which typically depend on field surveys and expert assessments, are often time-consuming and labor-intensive. This study introduces a novel proxy evaluation method based on the Vision Language Models (VLMs) to assess thermal comfort in commercial spaces. Specifically, we developed a VLMs-based agent system (ChatGPT-4o) that simulates eight distinct roles with diverse demographic backgrounds to assess the thermal comfort of Harbin Central Street’s commercial district through visual analysis. To validate the method’s effectiveness, we compared the VLMs assessment results with evaluations from 200 professional volunteers based on a 25% sample (n=143). Additionally, we conducted spatial distribution and thematic analyses to examine evaluation patterns and identify the key factors influencing thermal comfort. The findings reveal the following: First, the VLMs assessments showed a significant correlation with human expert evaluations (r=0.602, p&lt;0.001), confirming the feasibility of the VLMs-based thermal comfort assessment method; Second, the eight role proxies demonstrated notable heterogeneity in thermal comfort evaluations, highlighting the VLMs’ ability to accurately capture perceptual differences across social groups; Finally, spatial analysis revealed that, although evaluation differences exist among roles, the eastern region generally experiences higher thermal comfort than the western and central regions, with shade and vegetation being the most commonly identified influencing factors. This study assesses the effectiveness of VLMs-based proxies in urban thermal comfort evaluation. It demonstrates their ability to capture perceptual differences across social groups while offering a more efficient alternative to traditional assessment methods. This innovative approach opens new avenues for research and provides practical pathways for rapid, comprehensive, and multi-perspective thermal comfort assessments in urban commercial spaces.
DOI: 10.15468/dl.e7nsxs
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oligochaeta minima (Boiss.) Briq."
 ]
}

 The dataset includes 70 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000341-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ppqhf9
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "StateProvince is RIO GRaNdE dO SUL",
 "TaxonKey is Colibri serrirostris (Vieillot, 1816)"
 ]
}

 The dataset includes 17 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000340-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/97da-73eh
title: Measurement of Aerosol_optical_depth at Abashiri

description: Measurement of Aerosol_optical_depth at Abashiri
DOI: 10.15468/dl.kv7gr3
title: Occurrence Download

description: A dataset containing 52736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (Norway, Sweden, Finland)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alces alces (Linnaeus, 1758)",
 "Year 1900-2025"
 ]
}

 The dataset includes 52736 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000345-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/24pq-thux
title: Measurement of Aerosol_optical_depth at Abashiri

description: Measurement of Aerosol_optical_depth at Abashiri
DOI: 10.48597/7nnx-5mpu
title: Measurement of Aerosol_optical_depth at Abashiri

description: Measurement of Aerosol_optical_depth at Abashiri
DOI: 10.15468/dl.agy9hm
title: Occurrence Download

description: A dataset listing the 1607 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Museo Argentino de Ciencias Naturales \"Bernardino Rivadavia\" (MACN). Ameghino National Collection (MACNA)"
 ]
}

 The dataset's 1607 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000335-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vn4kyn
title: Occurrence Download

description: A dataset containing 6038 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cercocarpus ledifolius Nutt."
 ]
}

 The dataset includes 6038 records from 136 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0130121-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g4pe5m
title: Occurrence Download

description: A dataset containing 6746353 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pica pica (Linnaeus, 1758)"
 ]
}

 The dataset includes 6746353 records from 5539 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000324-250226093636041/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.1.2099.0881
title: Water Management in Agriculture (Prelims-final)
DOI: 10.26023/ry3f-vvf3-cq04
title: SOS: Thermistor Harp Data. Version 1.0

description: Thermistor measurements from 8 thermistors vertically spaced 5mm apart on two faces of a "thermistor harp," collected on four different days (3-4 February and 6-7 March 2023) during the Sublimation of Snow (SOS) field project. These data are available in one CSV file with an accompanying PNG image. See the readme file for more detail.
DOI: 10.26023/e1wq-jrnx-8205
title: SOS: Weather Blog. Version 1.0

description: Weather Blog data consists of daily descriptions and discussion of weather systems impacting the Sublimation of Snow (SOS) field project and organized by week. These data consist of one PDF file and one TAR file containing the HTML file and associated imagery. Note that the available weekly data exceeds the actual field project dates (22 Oct 2022 through 1 June 2023). See the readme file for more detail.
DOI: 10.26023/wbfh-x820-fc0c
title: SOS: Stossel Box Weight Data. Version 1.0

description: Stossel Box Weight measurements, from two manual lysimeters, collected during the Sublimation of Snow (SOS) field project from 31 January to 2 March 2023. These measurements are available in CSV format. See the readme file for more detail.
DOI: 10.15468/dl.77umzk
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Orphula pagana"
 ]
}

 The dataset includes 142 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058443-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/j1g9-4b6n-180e
title: PERiLS_2023: NOXP Radar Data. Version 1.0

description: Observations from the mobile NOAA X-POL (NOXP) radar that was deployed at locations around the southeastern United States for three of the PERiLS_2023 deployments. Data are included from IOP3 (24-25 March), IOP4 (31 March to 1 April) and IOP5 (5 April 2023).
DOI: 10.26023/fevr-h7v5-pp0z
title: ESCAPE: NAWX Radar Data. Version 1.0

description: NAWX Radar Data collected onboard the NRC Convair 580 aircraft. These data were collected during the Experiment of Sea Breeze, Convection, Aerosol, Precipitation and Environment (ESCAPE) field campaign from 31 May to 17 June 2022 and are available in NetCDF format.
DOI: 10.15468/dl.uauhtw
title: Occurrence Download

description: A dataset containing 131281 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Observations opportunistes de flore vasculaire, bryophytes, champignons et lichens-Observations opportunistes Flore, Fonge et Bryophytes des salariés et bénévoles de l'association Nature en Occitanie (ex-Nature en Midi-Pyrénées) en Midi-Pyrénées"
 ]
}

 The dataset includes 131281 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020522-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hy48hh
title: Occurrence Download

description: A dataset containing 133279 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is CEN - Herbário da Embrapa Recursos Genéticos e Biotecnologia"
 ]
}

 The dataset includes 133279 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020520-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15205598
title: Bone retouchers MSA Bushman Rock Shelter

description: Database comprising measurements and descriptions of bone surface modifications (taphonomy, used areas) for a set of bone retouchers retrieved from the Middle Stone Age deposits of Bushman Rock Shelter (Limpopo Province, South Africa).
DOI: 10.5281/zenodo.12759739
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-20

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-20. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11174581
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-21

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-21. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1