cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xvhrd3
title: Occurrence Download

description: A dataset containing 4893 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Natalidae",
 "HasCoordinate is true",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 4893 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008610-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d98sat
title: Occurrence Download

description: A dataset containing 1835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carya glabra (Mill.) Sweet",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 1835 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008611-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xqgq26
title: Occurrence Download

description: A dataset containing 333 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carya laciniosa (F.Michx.) Loudon",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 333 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008612-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25790/bml0cm.170
title: GluGlnGsh3T.zip
DOI: 10.15468/dl.3jm2yv
title: Occurrence Download

description: A dataset containing 2108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carya ovata (Mill.) K.Koch",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 2108 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008613-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4425rg
title: Occurrence Download

description: A dataset containing 893 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Tunisia",
 "Geometry POLYGON((10.29681 36.81324,11.26729 36.81324,11.26729 37.25997,10.29681 37.25997,10.29681 36.81324))",
 "HasCoordinate is true",
 "TaxonKey is Tracheophyta",
 "Year 2014-2024"
 ]
}

 The dataset includes 893 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008614-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bfe7ab
title: Occurrence Download

description: A dataset containing 545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carya texana Buckley",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 545 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008615-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sv5vfd
title: Occurrence Download

description: A dataset containing 1221 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Russian Federation",
 "Geometry POLYGON((48.62116 50.00252,62.98893 50.00252,62.98893 55.24896,48.62116 55.24896,48.62116 50.00252))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Nonea pulla (L.) DC."
 ]
}

 The dataset includes 1221 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008616-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14050745
title: Comparative genomics of different lifestyle fungi in Helotiales (Leotiomycetes) reveals temperature and ecosystem adaptations

description: Paper "Comparative genomics of different lifestyle fungi in Helotiales (Leotiomycetes) reveals temperature and ecosystem adaptations". Supplementary files
DOI: 10.5281/zenodo.14050746
title: Comparative genomics of Helotiales (Leotiomycetes) and in silico analysis of temperature adaptations of plant-associated genes

description: Paper "Comparative genomics of Helotiales (Leotiomycetes) and in silico analysis of temperature adaptations of plant-associated genes". Supplementary files
DOI: 10.5281/zenodo.14251293
title: Comparative genomics of different lifestyle fungi in Helotiales (Leotiomycetes) reveals temperature and ecosystem adaptations

description: Paper "Comparative genomics of different lifestyle fungi in Helotiales (Leotiomycetes) reveals temperature and ecosystem adaptations". Supplementary files
DOI: 10.5281/zenodo.14251294
title: leptopelis_bocagii_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14251295
title: leptopelis_bocagii_zanfona_1.fasta.gz
DOI: 10.15468/dl.4m7mf9
title: Occurrence Download

description: A dataset containing 481 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caryota mitis Lour.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 481 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008617-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.prcy63
title: Occurrence Download

description: A dataset containing 234808 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gentianella Moench"
 ]
}

 The dataset includes 234808 records from 1108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008604-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tf9h7f
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "DatasetKey is one of (Xeno-canto - Bird sounds from around the world, DMNS Bird Collection (Arctos), Museu Paraense Emilio Goeldi - Ornithology Collection - Eggs, Coleção de Aves do Museu Nacional / UFRJ)",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhea americana"
 ]
}

 The dataset includes 63 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058471-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/196m-abfq-j20h
title: SNOWIE: DOW6 Doppler Spectra. Version 1.0

description: Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The frequency of data collection was based on DOW6 scanning and data are usually sampled every 4-6 minutes. The data are in netCDF format.
DOI: 10.26023/yh3g-vhxw-6r0c
title: SNOWIE: DOW7 Doppler Spectra. Version 1.0

description: Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The frequency of data collection was based on DOW7 scanning and data are usually sampled every 4-6 minutes. The data are in netCDF format.
DOI: 10.15468/dl.jjwprc
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Macrobrachium reyesi"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058472-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/q79r-vj1y-b90d
title: SWEX: JSU Mobile Surface Meteorology: Automobile. Version 1.0

description: Surface meteorology parameters measured by sensors mounted to an automobile which was driven on roads within the SWEX (Sundowner Winds Experiment) field project domain and surrounding region from 4 April to 13 May 2022. Data are provided in CSV files zipped by IOP/EOP.
DOI: 10.26023/hyeh-mjdv-9e0y
title: WINTRE-MIX: VAD winds from the DOW6 or CAND. Version 1.0

description: The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data within the tar.gz files are in readable ASCII format.
DOI: 10.5517/ccdc.csd.cc2jl3w4
title: CCDC 2341116: Experimental Crystal Structure Determination

description: Related Article: Avanish Kumar Singh|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.11178001
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08. 

            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.11178709
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08. 

            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: 10.15468/dl.v68va9
title: Occurrence Download

description: A dataset containing 18536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DwcaExtension is http://rs.gbif.org/terms/1.0/Reference",
 "OccurrenceStatus is Present",
 "Year 1801-1901"
 ]
}

 The dataset includes 18536 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005354-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11178844
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08. 

            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.11179570
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08. 

            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