cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26023/tw19-n2gw-zk0f
title: ICE-T: Aerosol Particle Spectrometer with Polarization Detection (APSPD) Data. Version 1.0

description: The APSPD is an optical spectrometer that measures side scattered light from individual aerosol particles that pass through a focused laser beam, in the optical equivalent diameter range from 0.5 to 20 μm.This dataset contains ASPSD data collected during the ICE-T project.
DOI: 10.26023/ph7f-aaw2-800z
title: TORUS_2022: NOAA P-3 Flight Level Data. Version 1.0

description: The flight level navigation and state parameter data in various forms from the NOAA P-3 aircraft that participated in the TORUS_2022 (Targeted Observation by Radars and UAS of Supercells 2022) field season. These data were provided by NOAA.
DOI: 10.26023/51b6-hxyn-1e07
title: LEE: NSSL LMA Raw Station Data. Version 1.0

description: Lightning Mapping Array (LMA) sensors were deployed at pre-designated locations for each IOP in the LEE Field Project. The array consisted of up to 8 GTRI sensors (Units N-X), 8 NSSL sensors (Sites 1-8, symbols a-h) and 1 TTU sensor (r). The network was operated by Georgia Tech Research Institute, National Oceanic and Atmospheric Administration / Office of Atmospheric Research / National Severe Storms Laboratory and OU / CIWRO staff with invaluable assistance from SUNY Oswego faculty and students.This data set includes Level 0 (raw binary) files for each NSSL station.
DOI: 10.5519/qd.tqn3tpgc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (253 records)
DOI: 10.26023/j0mk-8app-kj0t
title: LEE: GTRI LMA Raw Station Data. Version 1.0

description: Lightning Mapping Array (LMA) sensors were deployed at pre-designated locations for each IOP. The array consisted of up to 8 GTRI sensors (Units N-X), 8 NSSL sensors (Sites 1-8) and 1 TTU sensor, as listed in each Level 1 file header. The network was operated by Georgia Tech Research Institute, National Oceanic and Atmospheric Administration / Office of Atmospheric Research / National Severe Storms Laboratory and OU / CIWRO staff with invaluable assistance from SUNY Oswego faculty and students. Data set includes Level 0 (raw binary) files for each GTRI station.
DOI: 10.26023/k47h-xye9-jj0n
title: SAVANT: UIUC Aerosol Lidar Data. Version 1.0

description: UIUC Aerosol Lidar data are included in this dataset for the SAVANT field project time period and area of interest. The lidar measured a backscatter of aerosol in the air. The experiment location was on Crowley Rd in Mahomet Township, Illinois. Details about the instrument, instrument location and data can be found in the readme documentation.
DOI: 10.15468/dl.ume7jd
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Czechia",
 "Geometry POLYGON((13.39206 50.38,13.35646 50.37672,13.33187 50.36245,13.33811 50.34465,13.34985 50.33692,13.39463 50.34723,13.41922 50.34324,13.42839 50.37836,13.41922 50.38375,13.39206 50.38))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anseriformes",
 "Year 2024-2024"
 ]
}

 The dataset includes 172 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058432-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/68df-f1jw-8h04
title: PRECIP: PARSIVEL Disdrometer Data. Version 1.0

description: Data from the Parsivel disdrometer that was deployed on Yonaguni (a Japanese island to the east of Taiwan) for the PRECIP (Prediction of Rainfall Extremes Campaign in the Pacific) campaign from June to August 2022. It was deployed near the CSU SEA-POL dual-polarization radar to obtain drop-size distribution measurements in Mei-yu fronts, thunderstorms, and tropical cyclones.
DOI: 10.15468/dl.xassd5
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Llanolebias stellifer (Thomerson & Turner, 1973)"
 ]
}

 The dataset includes 17 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011068-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndu7gt
title: Occurrence Download

description: A dataset containing 1425 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Echinochloa crus-pavonis (Kunth) Schult.)",
 "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 1425 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011067-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11201114
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08. 

            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.11201176
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08. 

            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.11164474
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-08. 

            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.11165301
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-09

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-09. 

            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: 10.25549/pcra-c14-721
title: Puteshestvennik = The traveller, vol. 7, no. 5 (May 1930)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.xjbcza
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.",
 "Year 1970-2022"
 ]
}

 The dataset includes 66 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0160976-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.px7yym
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((105.08653 20.19387,100.57722 15.29244,97.83242 6.07777,113.90909 -2.54874,133.51479 35.48631,120.18291 45.0931,111.18075 20.64061,109.59584 23.91895,105.08653 20.19387))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Latreilliidae",
 "Year 1969-2023"
 ]
}

 The dataset includes 18 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018513-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yykyuu
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "GadmGid is one of (CHN.31_1, CHN.1_1, CHN.24_1, CHN.15_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Phalaenopsis zhejiangensis (Z.H.Tsi) Schuit., Dendrobium huoshanense Z.Z.Tang & S.J.Cheng, Dendrobium officinale Kimura & Migo, Cymbidium sinense (Andrews) Willd., Cymbidium ensifolium (L.) Sw.)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016198-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-747
title: Puteshestvennik = The traveller, vol. 7, no. 7 (July 1930)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.4jn7me
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-100.62822 18.53052,-98.46209 18.53052,-98.46209 20.11777,-100.62822 20.11777,-100.62822 18.53052))",
 "HasGeospatialIssue is false",
 "TaxonKey is Agave angustifolia Haw."
 ]
}

 The dataset includes 40 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069313-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pv5pcj
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((105.08653 20.19387,100.57722 15.29244,97.83242 6.07777,113.90909 -2.54874,133.51479 35.48631,120.18291 45.0931,111.18075 20.64061,109.59584 23.91895,105.08653 20.19387))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetraliidae",
 "Year 1969-2023"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018488-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-14436
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 4, no. 1 (January 1931)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.dz3hxm
title: Occurrence Download

description: A dataset containing 1427 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "Geometry POLYGON((-131.03883 11.47918,-70.96117 11.47918,-70.96117 35.52082,-131.03883 35.52082,-131.03883 11.47918))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plestiodon brevirostris (Günther, 1860)"
 ]
}

 The dataset includes 1427 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0140845-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-14429
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 3, no. 12 (December 1930)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.nej2tb
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Puerto Rico",
 "TaxonKey is Sphaerodactylus roosevelti Grant, 1931"
 ]
}

 The dataset includes 174 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004548-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bsf43e
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ovis ammon hodgsonii Blyth, 1841"
 ]
}

 The dataset includes 57 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0160991-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-152465
title: Suchasnaia religiinasts = Modern religion, 1930

description: [description english/roman)] S.IA. Volfson. Suchasnaia religiinasts: pavodle matar'ialau dasledvannia u BSSR. (Belaruskaia Akademiia Navuk: Katedra Marksyzmu-Leninizmu, pratsu. vup. 1). Vudanne BAN, 1930. = C.J. Wolfson. Modern religion: according to research in the BeloUSSRn SSR. (Belarusian Academy of Sciences: Department of Marxism-Leninism, 1). Minsk, Belarus: Edition BAN, 1930.
DOI: 10.15468/dl.rz9vkh
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hedera Conti-Jerpe & Freshwater, 2017)",
 "HasCoordinate is true",
 {
 "not" : {
 "EstablishmentMeans" : [
 "is INTRODUCED"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is INVASIVE"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is MANAGED"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is NATURALISED"
 ]
 }
 }
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053556-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.