cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11175435
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06. 

            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.11176463
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06. 

            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.11176533
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06. 

            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.11177403
title: Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06

description: This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06. 

            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: 10.17882/95885
title: Swath bathymetry along a stretch of coast near the etruscan settlement of Pyrgi, Santa Severa, Italy

description: These bathymetric data were produced using the interferometric side-scan sonar onboard the Haliotis Research Vessel, Operated by Genavir, for the French Oceanographic Fleet, in September 2022, during the oceanographic campaign HISOPE (l'Haliotis pour l’Imagerie Sismique d’Orbetello et Pyrgi Etrusques). The investigated area is located in front of the prominent archeological site of Pyrgi, the etruscan harbor of Cerveteri. At the site, next to the sea, is found a series of etruscan temples, surrounded by an etruscan city largely destroyed by coastal erosion. A roman castrum dating back to the 3rd century BCE covers part of the site and is also partly eroded by the sea. Above the castrum is built a fortified medieval village as well as a large renaissance castle (Santa Severa), built over a submerged lava dome (the Massicciata reef). In 2019, foundations of an etruscan monument were discovered 150 m offhshore, over a larger reef located south of the archeological site (the Secche di Macchiatonda). The goal of this campaign was to map the seafloor along this stretch of eroded coastline, in order to determine the location of the coastline in Etruscan time, such as to better understand the geography of the site when the harbor was settled. One of the main questions is why this specific site was chosen to develop a port, despite its exposure to waves. Acquisition took place under stormy conditions, such that only 1.5 day of acquisition was possible over the 7 days planned. Geoswath mapping was conducted under heavy swell conditions along the sandy coast, north of of the castle of Santa Severa, then in front of the sandy coast, south of the castle, and finally over the extensive Secche di Macchiatonda reef in the south. Data were acquired by eng. Quentin Layahe, Genavir, onboard the Haliotis, piloted by Serge Garcia, and post-processed using the software Globe, developed by the IFREMER, by Dr.Gilles Brocard (Archéorient, University of Lyon 2, France) and Alessandro Conforti, research engineer at the CNR (Italian national center for research) at Orosi, Sardinia.
DOI: 10.17882/95997
title: Swath bathymetry of the Gulf of Porto Ercole, Tuscany, Italy

description: These bathymetric data were produced using the interferometric side-scan sonar onboard the Haliotis Research Vessel, Operated by Genavir, for the French Oceanographic Fleet, in October 2022, during the oceanographic campaign HISOPE (l'Haliotis pour l’Imagerie Sismique d’Orbetello et Pyrgi Etrusques). The investigated area is located in front of the tombolo di Feniglia, in the Gulf of Porto Ercole. The goal of the campaign was to image the sedimentary architecture of the Tombolo di Feniglia Acquisition took place from October 1st to October 6th 2022. Data were acquired by eng. Quentin Layahe, Genavir, onboard the Haliotis, piloted by Serge Garcia, and post-processed using the software Globe, developed by the IFREMER, by Dr.Gilles Brocard (Archéorient, University of Lyon 2, France) and Alessandro Conforti, research engineer at the CNR (Italian national center for research) at Orosi, Sardinia.
DOI: 10.15468/dl.nk7bbh
title: Occurrence Download

description: A dataset containing 3425 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Spea hammondii (Baird, 1859))"
 ]
}

 The dataset includes 3425 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018505-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ky96k
title: Occurrence Download

description: A dataset containing 369 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tuber melanosporum Vittad."
 ]
}

 The dataset includes 369 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070043-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15746501
title: Human kidney Xenium spatial transcriptomics
title: Single-Cell Resolution of Cellular Damage Illuminates Disease Progression

description: Supplementary data to article "Single-Cell Resolution of Cellular Damage Illuminates Disease Progression".

The dataset contains:



20250514__090010__AG_Kann_KOELN_12052025.zip - human kidney Xenium Prime 5K spatial transcriptomics for 8 human subjects diagnosed with FSGS and/or MCD. Each subject is represented by 2 adjucent (serial) tissue cylinders.

Sample_&_Experiment_Metadata.xlsx -  tabular patient and sample metadata

Xenium_HE.staining.zip - H&E tissue cylinders staining images

Xenium_HE.stainings_lower.Res.zip  - lower resolution H&E stainings for individual tissue cylinders, images are named by block, patient ID and replicate
DOI: 10.25549/pcra-c14-180373
title: Blahovisnyk = Evangelist, 2001, no. 4 (2001 October-December), whole no. 34

description: [description english/roman)] Blahovisnyk, 2001, no. 4 (2001 zhovtenʹ-hrudenʹ), obshchiy no. 34 = Evangelist, 2001, no. 4 (2001 October-December), whole no. 34.
DOI: 10.5281/zenodo.7868416
title: Agulhas Current O2 Fluxes

description: This file contains ML – random forest constructed oxygen fluxes in the Agulhas Current.
DOI: 10.15468/dl.ydrw89
title: Occurrence Download

description: A dataset containing 264 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abronia graminea (Cope, 1864)",
 "Year 1900-2016"
 ]
}

 The dataset includes 264 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005207-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1052920.10fu
title: SH1052920.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1052921.10fu
title: SH1052921.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1052922.10fu
title: SH1052922.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1052924.10fu
title: SH1052924.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1052925.10fu
title: SH1052925.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.