cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/pcra-c14-3044
title: Primiritel' = [Reconciler], vol. 5, no. 6-7 (June-July 1933)

description: [description english/roman)] 12 issues per year
DOI: 10.15468/dl.qynz85
title: Occurrence Download

description: A dataset containing 4387565 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 4387565 records from 6622 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018574-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/pyc4-zsyk-cz0k
title: ESCAPE: Center for Multiscale Applied Sensing (CMAS) Windsond Measurements. Version 1.0

description: Radiosonde Measurements from 164 Sparv Windsonds launched from the Center for Multiscale Applied Sensing (CMAS) mobile observatory at variable locations within a 50-km radius domain around Houston, TX between 29 May 2022 to 27 June 2022, as part of the ESCAPE (Experiment of Sea Breeze, Convection, Aerosol, Precipitation and Environment) and TRACER field campaigns in Houston, TX. These files are available in CSV format.
DOI: 10.26023/shcv-xv1c-d50d
title: PERiLS_2023: Portable In Situ Precipitation Stations (PIPS) Data. Version 1.0

description: Data from the Purdue University Portable In Situ Precipitation Stations (PIPS) that were deployed at locations around the southeastern United States for three PERiLS 2023 deployments. The PERiLS deployments were on 3, 24 and 31 March and 5 April 2023. The PIPS are a set of 6 instrumented probes equipped with conventional surface meteorological instrumentation as well as Parsivel 2 laser disdrometers. Their mission in PERiLS was to collect drop size distribution and other surface observations to characterize the surface microphysical and thermodynamic variability in time and space as the quasi linear convective system (QLCS) moves over the array. During PERiLS 2023, the PIPS were deployed in IOP’s 2-5 in QLCS’s and supercells.
DOI: 10.26023/ya53-3e1g-mr0f
title: ACCLIP: NIER Beechcraft 1900D King Air Data. Version 1.0

description: The one second resolution merger of the data from the instrumentation that was onboard the Korean National Institute of Environmental Research (NIER) Beechcraft 1900D King Air aircraft for the ACCLIP campaign.
DOI: 10.15468/dl.wu4vz2
title: Occurrence Download

description: A dataset containing 376146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eleotris tubularis, Sicydium condotense, Sicydium hildebrandi, Ariopsis simonsi, Sciades dowii, Gillichthys detrusus, Pomadasys empherus, Trinectes opercularis, Trinectes fluviatilis, Atherinella argentea, Lophogobius cristulatus, Sicydium rosenbergii, Trinectes xanthurus, Ariopsis seemanni, Fundulus parvipinnis, Cathorops taylori, Guavina micropus, Acipenser transmontanus, Sicydium salvini, Sicydium fayae, Centropomus robalito, Cathorops raredonae, Lile gracilis, Bathygobius andrei, Cathorops fuerthii, Gobiomorus polylepis, Cathorops steindachneri, Gillichthys mirabilis, Strongylura exilis, Haemulopsis leuciscus, Rhencus macracanthus, Eugerres lineatus, Microgobius miraflorensis, Centropomus nigrescens, Anchoa curta, Microdesmus dorsipunctatus, Chaenomugil proboscideus, Centropomus viridis, Citharichthys gilberti, Achirus mazatlanus, Centropomus medius, Lutjanus jordani, Hyporhamphus gilli, Diapterus brevirostris, Eugerres axillaris, Halichoeres notospilus, Rhonciscus bayanus, Batrachoides pacifici, Cathorops tuyra, Ctenogobius sagittula, Aboma etheostoma, Lampetra ayresii, Cymatogaster aggregata, Lile stolifera, Atherinops affinis, Eucinostomus gracilis, Lutjanus colorado, Lutjanus argentiventris, Eucinostomus currani, Caranx vinctus, Hyporhamphus rosae, Sphoeroides annulatus, Eleotris picta, Caranx caninus, Opisthopterus effulgens, Evorthodus minutus, Dactyloscopus amnis, Batrachoides waltersi, Erotelis armiger, Trinectes fonsecensis, Gobiomorus maculatus, Holtbyrnia latifrons, Allomogurnda papua, Hypostomus piratatu, Fenerbahce devosi, Phenacorhamdia taphorni, Garra poecilura, Hemimyzon taitungensis, Rhinogobius delicatus, Amphilius crassus, Nothobranchius melanospilus, Cyprinella stigmatura, Alburnus mentoides, Clarias werneri, Enteromius kerstenii, Rheocles alaotrensis, Ptychochromoides itasy, Rheocles sikorae, Rheocles wrightae, Pomoxis annularis, Periophthalmus weberi, Fluvitrygon kittipongi, Squalius laietanus, Andinoacara blombergi, Astroblepus whymperi, Hyphessobrycon ecuadoriensis, Paracetopsis bleekeri, Sternopygus arenatus, Astroblepus regani)",
 "HasCoordinate is true",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 376146 records from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058413-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/ytq1-v0s8-fy0y
title: TORUS-LItE Deployment Summary IDV Bundles. Version 1.0

description: This data set contains IDV (Integrated Data Viewer) bundles of key TORUS-LItE deployments. Each bundle includes the positions of all assets operating on a particular day updated at a 1 minute time interval, the radar reflectivity from the nearest WSR-88D, and scanning symbols for remote-sensing instruments. The data are in compressed IDV bundle format (zidv).
DOI: 10.5281/zenodo.11179583
title: Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28. 

            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.11179908
title: Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28. 

            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.11179931
title: Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28. 

            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.11179984
title: Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28. 

            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: 10.15468/dl.vxs5jm
title: Occurrence Download

description: A dataset containing 3141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Benin",
 "DatasetKey is one of (iNaturalist Research-grade Observations, Precious and rare plant species protected in classified forests managed by ONAB, Abundance and diversity of birdlife in the \"Trois Rivières\" forest, Benin, West Africa, Forest plant species inventory of Lama classified forest, Socio-economic survey and inventory of forest species of Djigbé forest, EOD – eBird Observation Dataset, Evaluation et conservation des espèces forestières de la forêt de Lama (Bénin), Census of avian richness of the Pendjari Park in Northen of Benin (Part 1), Census of plant species of Benin (West Africa): Database4, Census of plant species in Benin as part of several research projects)",
 "OccurrenceStatus is Present",
 "TaxonKey is Khaya senegalensis (Desv.) A.Juss.",
 "Year 1984-2025"
 ]
}

 The dataset includes 3141 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005346-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xaw425
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristimantis eremitus (Lynch, 1980)"
 ]
}

 The dataset includes 100 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005347-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11180050
title: Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

description: This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28. 

            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: 10.15468/dl.ufabf6
title: Occurrence Download

description: A dataset containing 773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Neyraudia reynaudiana (Kunth) Keng ex Hitchc.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 773 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011084-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hvxz4z
title: Occurrence Download

description: A dataset containing 2728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Marumba sperchius Ménétriés, 1857"
 ]
}

 The dataset includes 2728 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011086-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxemyp
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "Geometry POLYGON((106.81539 -6.60506,106.83317 -6.60754,106.82926 -6.59631,106.82431 -6.59268,106.81539 -6.60506))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2020-2025"
 ]
}

 The dataset includes 54 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000103-250226125047203/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.