cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11165542
title: Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2024-04-19

description: This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2024-04-19. 

            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.11165854
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-04-26

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-04-26. 

            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.

            

            
 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.11166347
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-04-26

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-04-26. 

            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.

            

            
 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.14928673
title: Data for Variable Frozen Grid(VFG) Algorithm
DOI: 10.5281/zenodo.14928674
title: Data for Variable Frozen Grid(VFG) Algorithm
DOI: 10.5281/zenodo.4444279
title: Memes, Radicalisation, and the Promotion of Violence on Chan Sites - Images

description: This dataset is part of the work called 'Memes, Radicalisation, and the Promotion of Violence on Chan Sites' by Blyth Crawford, Florence Keen, and Guillermo Suarez-Tangil presented as part of ICWSM-2021 (The International AAAI Conference on Web and Social Media).
DOI: 10.5281/zenodo.4444280
title: Images Chans

description: Images from different boards
DOI: 10.5281/zenodo.4669960
title: Memes, Radicalisation, and the Promotion of Violence on Chan Sites - Images

description: This dataset is part of the work called 'Memes, Radicalisation, and the Promotion of Violence on Chan Sites' by Blyth Crawford, Florence Keen, and Guillermo Suarez-Tangil presented as part of ICWSM-2021 (The International AAAI Conference on Web and Social Media).
DOI: 10.5281/zenodo.14928672
title: EMPLOYING THE TRILINGUAL QURAN AS A TOOL TO IMPROVE  VOCABULARY SKILLS AMONG TENTH-GRADE STUDENTS AT  BABUL KHAER ISLAMIC BOARDING SCHOOL
DOI: 10.5281/zenodo.14928671
title: EMPLOYING THE TRILINGUAL QURAN AS A TOOL TO IMPROVE  VOCABULARY SKILLS AMONG TENTH-GRADE STUDENTS AT  BABUL KHAER ISLAMIC BOARDING SCHOOL
DOI: 10.5281/zenodo.14922537
title: PVdata

description: 1. The Original GE image is the Google Earth image we obtained, and the TIFF image we provide contains both location information and spatial coverage information.2. Samples and labels are the training image and validation image, as well as their labels. Before training, the training image and validation image are randomly generated, as shown in the code VOC_ annotation. py.3. Vector data is the vector version corresponding to TIFF data, with specific numbering as shown in https://doi.org/10.5281/zenodo.10684793.
DOI: 10.5281/zenodo.14922538
title: PVdata

description: 1. The Original GE image is the Google Earth image we obtained, and the TIFF image we provide contains both location information and spatial coverage information.2. Samples and labels are the training image and validation image, as well as their labels. Before training, the training image and validation image are randomly generated, as shown in the code VOC_ annotation. py.3. Vector data is the vector version corresponding to TIFF data, with specific numbering as shown in https://doi.org/10.5281/zenodo.10684793.
DOI: 10.5281/zenodo.14672253
title: SWIR_volume_estimation

description: The dataset consists of grayscale images containing up to four circular objects (samples) per image. The dataset follows a structured naming convention, where filenames encode the thickness of the material or the depth of holes present in the samples. Each image file follows the naming pattern cc-cc-cc-cc_0001.jpeg, where cc represents a two-digit number specifying either the material thickness or the hole depth in millimeters. The four cc values correspond to specific positions in the image: the first value represents the top-left object, the second value represents the top-right object, the third value represents the bottom-right object, and the fourth value represents the bottom-left object. If no object is present in a specific position, XX is used instead of a numerical value. The final _0001 is an incrementing counter for multiple images of the same set of objects, increasing with each occurrence.

The numbers in the filenames correspond to sample specifications listed in an accompanying dataset index. The handwritten notes provided define the measurements as follows: 21 corresponds to 0.58 mm, 22 corresponds to 1.20 mm, 23 corresponds to 1.58 mm, 24 corresponds to 2.17 mm, 25 corresponds to 2.57 mm, 26 corresponds to 3.22 mm, 27 corresponds to 3.58 mm, 28 corresponds to 4.16 mm, and 29 corresponds to 4.57 mm. Similarly, hole specifications include 00 corresponding to a 5 mm hole, 01 corresponding to a 4.5 mm hole, going lineary by 0.5 mm decrease to vanule 09 corresponding to a 0.5 mm hole, and 10 corresponding to no hole.

Each image displays up to four dark circular shapes representing material samples, some of which may have central holes of varying diameters. The grayscale shading might indicate variations in depth, surface texture, or material density. This dataset can be used for image processing and segmentation tasks, optical analysis of material properties, and machine learning applications related to material classification. If any additional metadata is required, it can be extracted from the naming conventions.
DOI: 10.5281/zenodo.14672254
title: SWIR_volume_estimation

description: The dataset consists of grayscale images containing up to four circular objects (samples) per image. The dataset follows a structured naming convention, where filenames encode the thickness of the material or the depth of holes present in the samples. Each image file follows the naming pattern cc-cc-cc-cc_0001.jpeg, where cc represents a two-digit number specifying either the material thickness or the hole depth in millimeters. The four cc values correspond to specific positions in the image: the first value represents the top-left object, the second value represents the top-right object, the third value represents the bottom-right object, and the fourth value represents the bottom-left object. If no object is present in a specific position, XX is used instead of a numerical value. The final _0001 is an incrementing counter for multiple images of the same set of objects, increasing with each occurrence.

The numbers in the filenames correspond to sample specifications listed in an accompanying dataset index. The handwritten notes provided define the measurements as follows: 21 corresponds to 0.58 mm, 22 corresponds to 1.20 mm, 23 corresponds to 1.58 mm, 24 corresponds to 2.17 mm, 25 corresponds to 2.57 mm, 26 corresponds to 3.22 mm, 27 corresponds to 3.58 mm, 28 corresponds to 4.16 mm, and 29 corresponds to 4.57 mm. Similarly, hole specifications include 00 corresponding to a 5 mm hole, 01 corresponding to a 4.5 mm hole, going lineary by 0.5 mm decrease to vanule 09 corresponding to a 0.5 mm hole, and 10 corresponding to no hole.

Each image displays up to four dark circular shapes representing material samples, some of which may have central holes of varying diameters. The grayscale shading might indicate variations in depth, surface texture, or material density. This dataset can be used for image processing and segmentation tasks, optical analysis of material properties, and machine learning applications related to material classification. If any additional metadata is required, it can be extracted from the naming conventions.
DOI: 10.5281/zenodo.14899388
title: Crowd_movement_time_series

description: This dataset consists of JSON files that store information about the location and state of moving agents (representing people) in a defined space. Each JSON file corresponds to a specific time frame in a sequence, allowing for tracking agents across multiple frames. The dataset is structured to facilitate motion analysis, behavior recognition, and other spatial-temporal studies of human movement. Each file follows the naming pattern frame_000001.json, frame_000002.json, and so on, where frame_xxxxxx.json represents a specific timestamped frame in the sequence, and the numbering increments sequentially for each successive frame.

Each JSON file contains a list of agents present in that frame, with each agent’s data represented as an object with several attributes. The Name attribute serves as a unique identifier for the agent, such as BP_Agent_C_0 or BP_Agent_C_1, and remains consistent across frames, allowing tracking over time. The Size attribute is a floating-point value representing the physical size of the agent in the simulation, while the Speed attribute indicates the agent’s movement speed. The State attribute defines the behavioral state of the agent, which can be Idle if the agent is stationary, Walking if moving at a normal pace, Running if moving at an accelerated pace, or Fighting if engaged in combat or conflict.

The position of each agent is described using two sets of coordinates: GroundWS (World Space Position - Ground Level) and HeadWS (World Space Position - Head Level). Each of these contains X, Y, and Z values representing the agent's location at ground level and head level, respectively. Each frame may contain a varying number of agents, depending on their presence in the scene. The consistency of the Name attribute across frames allows for temporal tracking of individual agents’ movement and behavior over time.

This dataset is suitable for motion tracking and behavioral analysis, crowd simulation studies, AI-based movement prediction models, and augmented reality or game development applications. By leveraging the structured naming conventions and JSON data format, researchers and developers can efficiently process, analyze, and visualize human movement patterns over time.
DOI: 10.5281/zenodo.14899389
title: Crowd_movement_time_series

description: This dataset consists of JSON files that store information about the location and state of moving agents (representing people) in a defined space. Each JSON file corresponds to a specific time frame in a sequence, allowing for tracking agents across multiple frames. The dataset is structured to facilitate motion analysis, behavior recognition, and other spatial-temporal studies of human movement. Each file follows the naming pattern frame_000001.json, frame_000002.json, and so on, where frame_xxxxxx.json represents a specific timestamped frame in the sequence, and the numbering increments sequentially for each successive frame.

Each JSON file contains a list of agents present in that frame, with each agent’s data represented as an object with several attributes. The Name attribute serves as a unique identifier for the agent, such as BP_Agent_C_0 or BP_Agent_C_1, and remains consistent across frames, allowing tracking over time. The Size attribute is a floating-point value representing the physical size of the agent in the simulation, while the Speed attribute indicates the agent’s movement speed. The State attribute defines the behavioral state of the agent, which can be Idle if the agent is stationary, Walking if moving at a normal pace, Running if moving at an accelerated pace, or Fighting if engaged in combat or conflict.

The position of each agent is described using two sets of coordinates: GroundWS (World Space Position - Ground Level) and HeadWS (World Space Position - Head Level). Each of these contains X, Y, and Z values representing the agent's location at ground level and head level, respectively. Each frame may contain a varying number of agents, depending on their presence in the scene. The consistency of the Name attribute across frames allows for temporal tracking of individual agents’ movement and behavior over time.

This dataset is suitable for motion tracking and behavioral analysis, crowd simulation studies, AI-based movement prediction models, and augmented reality or game development applications. By leveraging the structured naming conventions and JSON data format, researchers and developers can efficiently process, analyze, and visualize human movement patterns over time.
DOI: 10.5281/zenodo.14928789
title: Health literacy and asthma control
DOI: 10.5281/zenodo.14928790
title: Health literacy and asthma control
DOI: 10.15468/dl.45j89b
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tragelaphus imberbis (Blyth, 1869)",
 "Year 1981-2010"
 ]
}

 The dataset includes 52 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000657-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gmed8d
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Egypt",
 "TaxonKey is Capparis spinosa var. aegyptia (Lam.) Boiss."
 ]
}

 The dataset includes 34 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000656-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.663jz3
title: Occurrence Download

description: A dataset containing 2830935 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ITA",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 2830935 records from 439 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000649-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cs6gcm
title: Occurrence Download

description: A dataset listing the 4030678 species recorded in GBIF matching the query:
{ }

 The dataset's 4030678 records were derived from 71335 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000651-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14735676
title: High-resolution coastal topobathymetries

description: The dataset includes time-series of Cala Millor beach topographic and bathymetric surveys.Two primary data products are provided: Very high-resolution irregular-gridded datasets (grid-as-point) with a best point spacing of ~1 m (shapefiles), and interpolated gridded datasets with a spatial resolution of 10 m (NetCDF). Topographic surveys and most shallow waters were surveyed using RTK-GNSS, and multibeam echo-sounder was used for the remaining depths.
DOI: 10.5281/zenodo.14735677
title: High-resolution coastal topobathymetries

description: The dataset includes a time series of Cala Millor beach topographic and bathymetric surveys, gridded on a 10 m grid. Topographic surveys and most shallow waters were surveyed using RTK-GNSS, and multibeam echo-sounder was used for the remaining depths.
DOI: 10.5281/zenodo.14756596
title: High-resolution coastal topobathymetries

description: The dataset includes a time series of Cala Millor beach topographic and bathymetric surveys, gridded on a 10 m grid. Topographic surveys and most shallow waters were surveyed using RTK-GNSS, and multibeam echo-sounder was used for the remaining depths.
DOI: 10.5281/zenodo.14809584
title: High-resolution coastal topobathymetries

description: The dataset includes time-series of Cala Millor beach topographic and bathymetric surveys.Two primary data products are provided: Very high-resolution irregular-gridded datasets (grid-as-point) with a best point spacing of ~1 m (shapefiles), and interpolated gridded datasets with a spatial resolution of 10 m (NetCDF). Topographic surveys and most shallow waters were surveyed using RTK-GNSS, and multibeam echo-sounder was used for the remaining depths.