cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wy4525
title: Occurrence Download

description: A dataset containing 473 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.37_1",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)"
 ]
}

 The dataset includes 473 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008660-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.thkteg
title: Occurrence Download

description: A dataset containing 883 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Macrobrachium rosenbergii (De Man, 1879)"
 ]
}

 The dataset includes 883 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008659-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m9gcjb
title: Occurrence Download

description: A dataset containing 701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceratonia siliqua L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 701 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008661-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9m83m3
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.37_1",
 "TaxonKey is Procyon lotor (Linnaeus, 1758)"
 ]
}

 The dataset's 2 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008652-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rghmgd
title: Occurrence Download

description: A dataset containing 379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cercidium floridum Benth. ex A.Gray",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 379 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008662-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5343z
title: Occurrence Download

description: A dataset containing 722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cuba",
 "HasCoordinate is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eriocaulaceae"
 ]
}

 The dataset includes 722 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008663-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/9xfy-nrbj-aw10
title: WINTRE-MIX: NRC Convair-580 Flight Summaries. Version 1.0

description: This dataset is comprised of a series of PDF flight summaries that describe the general microphysical characteristics that were recorded along each flight leg for the 9 research flight missions of the WINTRE-MIX NRC Convair-580 aircraft. Each summary includes a written overview of the IOP and orientation of performed flight legs as well as a description of the microphysical characteristics of cloud measured along each transect by the NRC Convair-580. Several figures are provided for each flight leg detailing microphysical parameters such as particle number concentration and mean size distributions measured across these periods. Particle imagery from the 2DS probe is also provided for select periods across all flight legs to demonstrate the cloud composition of the weather systems of focus for each flight. General atmospheric state statistics are also provided in these summaries, as well as any data limitations observed with the parameters provided in each summary.
DOI: 10.15468/dl.g55uw2
title: Occurrence Download

description: A dataset listing the 3684 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "Geometry POLYGON((4.68576 52.29328,5.03217 52.29328,5.03217 52.45904,4.68576 52.45904,4.68576 52.29328))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta",
 "Year 2018-2024"
 ]
}

 The dataset's 3684 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058439-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/afyt-f3gs-880w
title: LIFT_2024: NSSL Mobile Mesonet Quality Controlled (QC) Meteorological Data. Version 1.0

description: Surface meteorological data from the two NOAA/NSSL Mobile Mesonet vehicles that were deployed at locations around the central and southern High Plains of the United States for the Low-level Internal Flow of Tornadoes 2024 (LIFT 2024) campaign.
DOI: 10.15468/dl.ypqs24
title: Occurrence Download

description: A dataset containing 537952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Transancistrus santarosensis, Bryconamericus oroensis, Chaetostoma branickii, Chaetostoma marmorescens, Chaetostoma taczanowskii, Pseudocurimata peruana, Brachyhypopomus occidentalis, Triplophysa edsinica, Enteromius neglectus, Ichthyborus besse, Coptodon ismailiaensis, Oreochromis ismailiaensis, Labeo vulgaris, Garra napata, Luciobarbus numidiensis, Coregonus stigmaticus, Salvelinus lonsdalii, Salvelinus willoughbii, Coregonus vandesius, Nothobranchius milvertzi, Aphyosemion malumbresi, Schistura megalodon, Cyprinus longipectoralis, Garra ethelwynnae, Garra blanfordii, Gymnotus esmeraldas, Hemiancistrus furtivus, Microglanis berbixae, Phenacogaster jancupa, Hyphessobrycon auca, Coregonus maraenoides, Listrura nematopteryx, Odontesthes brevianalis, Garra ignestii, Enteromius arambourgi, Marcusenius annamariae, Labeobarbus ethiopicus, Labeo pellegrini, Garra dembecha, Pelecus cultratus, Alburnus alburnus, Cobitis strumicae, Gobio bulgaricus, Vimba melanops, Cobitis puncticulata, Oxynoemacheilus bureschi, Petroleuciscus smyrnaeus, Luciobarbus capito, Luciobarbus brachycephalus, Capoeta kaput, Salmo danilewskii, Salmo gegarkuni, Rutilus atropatenus, Neogobius melanostomus, Cobitis gladkovi, Barbus tauricus, Salmo labrax, Sabanejewia balcanica, Alburnus chalcoides, Romanogobio macropterus, Neogobius fluviatilis, Mesogobius batrachocephalus, Proterorhinus semilunaris, Vimba tenella, Alburnus schischkovi, Ponticola eurycephalus, Rutilus heckelii, Benthophilus nudus, Ponticola platyrostris, Capoeta sieboldii, Chondrostoma colchicum, Knipowitschia longecaudata, Petroleuciscus borysthenicus, Babka gymnotrachelus, Ponticola ratan, Rutilus frisii, Ponticola syrman, Pungitius platygaster, Alburnoides tzanevi, Alosa braschnikowi, Proterorhinus semipellucidus, Benthophilus mahmudbejovi, Sabanejewia aurata, Anatirostrum profundorum, Benthophilus ctenolepidus, Atherina caspia, Babka macrophthalma, Benthophilus abdurahmanovi, Benthophilus kessleri, Benthophilus leptocephalus, Neogobius caspius, Ponticola goebelii, Rutilus kutum, Syngnathus caspius, Alosa volgensis, Hyrcanogobius bergi, Alosa caspia)",
 "HasCoordinate is true",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 537952 records from 475 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058417-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11180165
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            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.11198516
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            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.11201136
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-13. 

            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.11199961
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-22

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-22. 

            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.11200723
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-22

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-22. 

            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