cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17614045
title: Underwater images collected by a Kite surf in Ile-Aux-Cerfs, Mauritius - 2018-04-26

description: This dataset was collected by a Kite surf in Ile-Aux-Cerfs, Mauritius - 2018-04-26. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.5281/zenodo.17613841
title: Underwater images collected by a Kite surf in Ile-Aux-Cerfs, Mauritius - 2018-04-26

description: This dataset was collected by a Kite surf in Ile-Aux-Cerfs, Mauritius - 2018-04-26. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.15468/dl.pjygsp
title: Occurrence Download

description: A dataset containing 24319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hoplias malabaricus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 24319 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034688-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.awkzya
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.3688 23.39913, -107.2188 23.39913, -107.2188 23.54913, -107.3688 23.54913, -107.3688 23.39913))"
 ]
}

 The dataset includes 59 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034689-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2b55qq
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hoplosternum littorale",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034690-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13738243
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.17613833
title: MitoEM 2.0: A Benchmark of Hard Mitochondria Segmentation Datasets

description: MitoEM 2.0 is a collection of eight high-complexity 3D mitochondria segmentation datasets curated across diverse cell types, tissues, imaging modalities, and biological systems. The dataset focuses on biologically challenging regions characterized by dense packing, thin-neck morphologies, hyperfused networks, and ambiguous boundaries. Each dataset contains raw volumes and expert-verified ground-truth instance masks organized in nnU-Net–compatible folders. Additional descriptive files include dataset.json, split.json, difficulty metrics, and metadata.csv summarizing system, organ/tissue, cell type, voxel resolution, and imaging modality. This dataset serves as a standardized benchmark for evaluating 3D mitochondria instance segmentation algorithms.
DOI: 10.15468/dl.uvjb23
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.2188 23.39913, -107.0688 23.39913, -107.0688 23.54913, -107.2188 23.54913, -107.2188 23.39913))"
 ]
}

 The dataset includes 20 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034691-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.25rm4m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Horabagrus brachysoma",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034692-251025141854904/datasets/export for details.
DOI: 10.15468/dl.kvn4ng
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.0688 23.39913, -106.9188 23.39913, -106.9188 23.54913, -107.0688 23.54913, -107.0688 23.39913))"
 ]
}

 The dataset includes 108 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034693-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5phmeh
title: Occurrence Download

description: A dataset containing 21951 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Polypedates braueri"
 ]
}

 The dataset includes 21951 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034694-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17614106
title: DataSet When Trust is Not Enough: The Paradox of User Experience and Security in Building E-Wallet Loyalty

description: Dataset for When Trust is Not Enough: The Paradox of User Experience and Security in Building E-Wallet Loyalty
DOI: 10.5281/zenodo.17614107
title: DataSet When Trust is Not Enough: The Paradox of User Experience and Security in Building E-Wallet Loyalty

description: Dataset for When Trust is Not Enough: The Paradox of User Experience and Security in Building E-Wallet Loyalty
DOI: 10.5281/zenodo.17614095
title: Form analysis system: An EEG study of object, Greeble, and word recognition

description: The whole experiment procedure code please see the exp_hz_presskey1118 file. The ERP pre-processing pipline presented in the ERP_preprocessing files. For the time-frequency analysis and the representional similarity analysis code, please see the TF_RSA.m file.
DOI: 10.5281/zenodo.17614094
title: Form analysis system: An EEG study of object, Greeble, and word recognition

description: The whole experiment procedure code please see the exp_hz_presskey1118 file. The ERP pre-processing pipline presented in the ERP_preprocessing files. For the time-frequency analysis and the representional similarity analysis code, please see the TF_RSA.m file.
DOI: 10.15468/dl.t4f5wk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hucho hucho",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034695-251025141854904/datasets/export for details.
DOI: 10.15468/dl.mk3gnc
title: Occurrence Download

description: A dataset containing 270 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-106.9188 23.39913, -106.7688 23.39913, -106.7688 23.54913, -106.9188 23.54913, -106.9188 23.39913))"
 ]
}

 The dataset includes 270 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034696-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.untvgk
title: Occurrence Download

description: A dataset containing 76428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Africa, Oceania, Europe, North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Arundo donax"
 ]
}

 The dataset includes 76428 records from 739 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034677-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.774she
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Huso huso",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034697-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17614102
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.5281/zenodo.17614065
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27. 

            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.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 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!
DOI: 10.15468/dl.b3293g
title: Occurrence Download

description: A dataset containing 9234 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-106.7688 23.39913, -106.6188 23.39913, -106.6188 23.54913, -106.7688 23.54913, -106.7688 23.39913))"
 ]
}

 The dataset includes 9234 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034698-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57783/5wkt-y348
title: LibreChat-TEST
DOI: 10.15468/dl.jyyeqy
title: Occurrence Download

description: A dataset containing 1734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Living Specimen, Occurrence evidence, Human Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Coffea canephora"
 ]
}

 The dataset includes 1734 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034699-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7939/r3-vf51-cs35
title: Personality, academic behaviour, and mental health variables from a college student sample

description: This data was collected to examine the relationships among variables personality, academic behaviour, and mental health variables (perfectionism, avoidant personality disorder symptoms, academic behaviours, stress, self-esteem, demographic variables). It was collected in a college student setting in a large university in Western Canada.
DOI: 10.5281/zenodo.17614139
title: FIShBOT Archive 2025-11-15

description: This dataset is a static snapshot of the FIShBOT data product before new data are aggregated. This version of fishbot is archived for reproducibility and is not updated. The data are aggregated from the FIShBOT system, which collects and processes oceanographic from fishery dependent and independent sources.
DOI: 10.5281/zenodo.17614140
title: FIShBOT Archive 2025-11-15

description: This dataset is a static snapshot of the FIShBOT data product before new data are aggregated. This version of fishbot is archived for reproducibility and is not updated. The data are aggregated from the FIShBOT system, which collects and processes oceanographic from fishery dependent and independent sources.
DOI: 10.5281/zenodo.17614132
title: The experimental date for the paper "Entanglement-enhanced quantum lock-in detection achieving Heisenberg scaling"
DOI: 10.5281/zenodo.17614133
title: The experimental date for the paper "Entanglement-enhanced quantum lock-in detection achieving Heisenberg scaling"
DOI: 10.15468/dl.dc3m7v
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hybognathus hankinsoni",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034701-251025141854904/datasets/export for details.
DOI: 10.15468/dl.gh45kb
title: Occurrence Download

description: A dataset containing 17584 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-106.6188 23.39913, -106.4688 23.39913, -106.4688 23.54913, -106.6188 23.54913, -106.6188 23.39913))"
 ]
}

 The dataset includes 17584 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034702-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.