cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11143167
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Dubois, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Dubois, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.5281/zenodo.11135837
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.5281/zenodo.11135838
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.5281/zenodo.11147060
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.5281/zenodo.11147061
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.5281/zenodo.11136535
title: Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23

description: This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23. 

            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 4Sea project co-financed by Monaco Explorations (n°2022/11) as part of the Indian Ocean Mission.
DOI: 10.15468/dl.gpwe35
title: Occurrence Download

description: A dataset containing 124965 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON(((5.598907470703125 43.481822852999905,5.511016845703125 43.509717009288075,5.400552749633789 43.51127322324609,5.412139892578125 43.54257572246922,5.4547119140625 43.563476493226595,5.480804443359375 43.61122246320032,5.483551025390625 43.6499881760459,5.503549575805664 43.66644361490711,5.598907470703125 43.66985832954972,5.69091796875 43.70362249839005,5.763702392578124 43.661911057260674,5.79254150390625 43.625141236940564,5.804901123046875 43.601278509333646,5.799407958984375 43.5734266934657,5.76507568359375 43.52565074189047,5.71563720703125 43.52166770320007,5.598907470703125 43.481822852999905)))"
 ]
}

 The dataset includes 124965 records from 379 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132661-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x7m3ko
title: Occurrence Download

description: A dataset containing 296 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-98.9268266043036 25.55734941612112,-98.90107683900855 25.53271873218418,-98.86384503955244 25.519823620304866,-98.7969682237545 25.548121681481483,-98.79600531041366 25.548264527905005,-98.76850364669538 25.554886944346666,-98.73581181703132 25.57060439090394,-98.71537558275897 25.594636920229405,-98.71527408196052 25.6304596221287,-98.73740283378949 25.67916144111436,-98.80854329645933 25.69440503146724,-98.86074413058294 25.684471177436937,-98.90954858893485 25.65627116762897,-98.92610813260714 25.626711581908925,-98.9268266043036 25.55734941612112)))",
 "HasCoordinate is true",
 "TaxonKey is Animalia",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 296 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013675-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xsn845
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aristida kenyensis Henrard"
 ]
}

 The dataset includes 127 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012492-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wrdjhm
title: Occurrence Download

description: A dataset containing 10607 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Cyanocitta stelleri (Gmelin, 1788)"
 ]
}

 The dataset includes 10607 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013679-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxgc6h
title: Occurrence Download

description: A dataset containing 1423 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Anacampseros albidiflora Poelln., Anacampseros arachnoides (Haw.) Sims, Anacampseros baeseckei Dinter ex Poelln., Anacampseros bayeriana S.A.Hammer, Anacampseros comptonii Pillans, Anacampseros decapitata Burgoyne & J.van Thiel, Anacampseros filamentosa (Haw.) Sims, Anacampseros hillii G.Will., Anacampseros lanceolata (Haw.) Sw., Anacampseros marlothii Poelln., Anacampseros pisina G.Will., Anacampseros retusa Poelln., Anacampseros rufescens (Harv.) Sw., Anacampseros scopata G.Will., Anacampseros subnuda Poelln., Anacampseros telephiastrum DC., Anacampseros vespertina Thulin, Avonia albissima (Marloth) G.D.Rowley, Avonia dinteri (Schinz) G.D.Rowley, Avonia gariepensis G.Will., Avonia herreana (Poelln.) G.D.Rowley, Avonia mallei G.Will., Avonia papyracea (E.Mey. ex Fenzl) G.D.Rowley, Avonia perplexa G.Will., Avonia prominens (G.Will.) G.Will., Avonia quinaria (E.Mey. ex Fenzl) G.D.Rowley, Avonia recurvata (Schönland) G.D.Rowley, Avonia rhodesica (N.E.Br.) G.D.Rowley, Avonia ruschii (Dinter & Poelln.) G.D.Rowley, Avonia ustulata (E.Mey. ex Fenzl) G.D.Rowley, Avonia vanthielii G.Will., Avonia variabilis (Poelln.) G.Will.)"
 ]
}

 The dataset includes 1423 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0110314-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tacdgt
title: Occurrence Download

description: A dataset containing 1500 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 11.4408359019673",
 "DecimalLongitude is less than 11.4438954551517",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 1500 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012512-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vmwsek
title: Occurrence Download

description: A dataset containing 117156 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Barbatula Linck, 1790"
 ]
}

 The dataset includes 117156 records from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0091361-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gcmfau
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Pitcairnia carinata Mez",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013688-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.