Find research datasets worth reusing

Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27. 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 <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 30.33 GB of MP4 files, which were trimmed into 11170 frames (at 2997/1000 fps). The frames are georeferenced. 20.01% of these extracted images are useful and 79.99% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 89.09 %, Q2: 10.14 %, Q5: 0.76 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 3.0 m and 30.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.701 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 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 <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 36.7 GB of MP4 files, which were trimmed into 14131 frames (at 2997/1000 fps). The frames are georeferenced. 91.9% of these extracted images are useful and 8.1% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 98.17 %, Q2: 1.48 %, Q5: 0.35 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.701 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23

This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23. 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 <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 37.08 GB of MP4 files, which were trimmed into 13322 frames (at 2997/1000 fps). The frames are georeferenced. 99.84% of these extracted images are useful and 0.16% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 86.94 %, Q2: 12.95 %, Q5: 0.11 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.109 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 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 <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 31.04 GB of MP4 files, which were trimmed into 11523 frames (at 2997/1000 fps). The frames are georeferenced. 98.7% of these extracted images are useful and 1.3% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 98.93 %, Q2: 1.02 %, Q5: 0.05 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.538 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23

This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2023-11-23. 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 <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper. <h2>Image acquisition</h2> This session has 28.72 GB of MP4 files, which were trimmed into 10432 frames (at 2997/1000 fps). The frames are georeferenced. 99.75% of these extracted images are useful and 0.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 98.23 %, Q2: 1.55 %, Q5: 0.23 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.087 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

opencc-by-4.0May 2024View details →

SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)

SunspotsYoloDataset is a set of 1690+380+128 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (<a href="https://vaonis.com/stellina">https://vaonis.com/stellina</a>) and a Vespera smart telescope (<a href="https://vaonis.com/vespera">https://vaonis.com/vespera</a>). SunspotsYoloDataset  can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public. SunspotsYoloDataset is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (<a href="https://www.makesense.ai">https://www.makesense.ai</a>). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels. For more details about the dataset, please contact the author: olivier.parisot@list.lu . For more information about Luxembourg of Science and Technology (LIST), please consult: <a href="https://www.list.lu">https://www.list.lu</a> .

opencc-by-4.0Mar 2024View details →

Metzger and Ozpolat 2024 - raw imaging files for 40S group

Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper. https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024  Publication: https://www.sciencedirect.com/science/article/pii/S0012160624001246

opencc-by-4.0Mar 2024View details →

Metzger and Ozpolat 2024 - raw imaging files for 60S group

Original imaging files for the publication by Metzger and Özpolat 2024. See the GitHub page for details and a link to the paper. https://github.com/BDuyguOzpolat/Platynereis-GermRegen-Metzger-et-al-2024

opencc-by-4.0Apr 2024View details →

Improved visualization of oral microbial consortia- Associated images

These images are associated to the paper "Improved visualization of oral microbial consortia" published in the Journal of Dental Research. Sample author: Tabita Ramirez Puebla Images show microbial consortia from human tongue dorsum biofilm. Imaged in a confocal microscope (Zeiss LSM 780) with a spectral detector (32 channels). Objective: Plan-Apochromat 63X; N.A. 1.4; Oil  Pixel size: 0.07 um x 0.07 um Image size (pixels): 2048 x 2048 Optical section : 1 micron   File descriptions TIFF files in Image5D format. Files resulted from linear unmixing performed with Zeiss ZEN algorithm (ZEN Black) or using the non-linear least-squares function in MATLAB. Individual fluorophore and autofluorescence channels are presented.   Fig2A_5D Fig2C_5D Fig4_zstack_5D (zstack with 12 optical slices) jpg files of pseudocolored images Fig2A_jpeg Fig2B_jpeg Fig2C_jpeg Fig2D_jpeg Fig3A_jpeg_stack_RGB_tif (stack of 257 optical slices RGB images in tif format) Fig3A_Montage20x13_jpeg (Montage of 257 optical slices) Fig3B_jpeg Fig3C_jpeg Fig3D_jpeg Fig4A_xy (view of xy plane) Fig4A_xz_orthogonal (view of xy plane -> orthogonal representation of 12 optical slices) Fig4A_yz_orthogonal (view of yz plane -> orthogonal representation of 12 optical slices) Fig4B_jpeg Fig4C_jpeg Representative Zeiss .czi (raw files from LSM780 confocal microscope) Fig2A_raw (original czi file) Fig2C_raw (original czi file)

Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"

This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.

Images of "Iglu" shelter at Caraz in Peru

Seven images (no. 1-7) of "Iglu" shelter and fragments, located at Jirón Los Alisos with Avenida 9 de Octubre in Caraz, 02167 Peru. The data set includes a list of the photographies with descriptions. The photographies show the remaining parts of the humanitarian shelter "Iglu", a joint relief operation of Bayer AG with the German Red Cross Societies in the aftermath of the Peruvian earthquake of 31st May 1970.

IODP Expedition 360 Scanning electron microscope images

Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.

IODP Expedition 360 Core composite images

A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.

IODP Expedition 360 Whole-round core section images

Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.

IODP Expedition 360 Section-half images

Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.

Jacobaea vulgaris and meadow image classification dataset (binary)

<h3>General Information</h3> Instances in the Jacobaea vulgaris class: 895 Instances in the Meadow class: 9141 Image sizes from 77x77 to 817x817 pixels on three color channels (RGB)   <h3>Data Generation and Source</h3> The images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg. The multicopter flew in a height of circa 11 meters and took pictures with a ground resolution of approximately 3,18 mm/pixel. Additional information about the process of image generation for this dataset are to be found in the relevant papers written by P. Zacharias: 1) <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf" target="_blank" rel="noopener">UAV-basiertes Grünland-Monitoring und Schadpflanzenkartierung mit offenen Geodaten</a> [p. 45–53] and 2)  <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf" target="_blank" rel="noopener">UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene</a>. Additionally, to the images of Jacobaea vulgaris taken by the UAV, the dataset includes images of Jacobaea vulgaris plants from the internet (included in the total 895 images; e.g. images 'jkk0523.jpg', 'jkk0527.jpg'). Furthermore, some of the images of the Jacobaea vulgaris plants have been rotated, further cropped or a filter has been applied. The exact number of augmentations made is unknown. As there are augmented images included in the datasets -which makes the dataset useful for training and validation- a use of the dataset for testing purposes is not recommended due to the risk of data leakage. <h3>Data License</h3> The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.

IODP Expedition 360 Thin section images

Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.

IODP Expedition 360 Closeup images

Close-up images taken by digital cameras as requested by the science party, typically when the section-half image is not sufficient. Close-up photographs of the areas of interest may be taken from whole-round sections, pieces, or section halves in sediments and rock.

Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_17

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221023_SYC-aldabra-arm06_UAV-02_17 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 277 Median height: 70 meters Survey area: 389618.75 hectares Survey from: 2022:10:23 09:11:33 to: 2022:10:23 09:27:32 "

Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221020 - 02_2

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221020 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221020_SYC-aldabra-arm01_UAV-02_2 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 266 Median height: 70 meters Survey area: 9.92 hectares Survey from: 2022:10:20 09:50:29 to: 2022:10:20 10:04:39 "