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Data set for lattice Boltzmann modelling of capillarity, adsorption and fluid retention in simple geometries: do capillary and film water have equal matric suction or not?
No full textThis data set includes the raw data of test cases, postprocessing method and final results for the numerical simulations presented in the article
DEIMS-SDR - Geodata Collection - August 2023
No full textThis record contains the boundaries and centroid/representative coordinates of all sites registered on DEIMS-SDR (www.deims.org). The provided shapefiles include information about the location and extent of sites as well as their names and IDs. This record will be updated periodically to reflect the latest version of the geodata on DEIMS-SDR. The purpose of this record is to provide researchers with a simple way to have access to the geodata of DEIMS-SDR. While we aim for highest accuracy, we cannot guarantee that the provided information is always completely accurate. For the very latest version of geodata, please download the respective coordinates or boundaries from DEIMS.org.
For further information (citation, data licence, disclaimer) please refer to the following pages:
www.deims.org
www.deims.org/about
www.deims.org/terms
www.deims.org/doc
TOAR-II Data User Workshop June/July 2022
No full textThe Tropospheric Ozone Assessment Report (TOAR) is an initiative of the International Global Atmospheric Chemistry (IGAC) project. TOAR-II is the second phase of TOAR. It builds on the successful completion of the first comprehensive assessment on tropospheric ozone and will last from 2020 to 2024. The TOAR-II Data User Workshop 2022 introduced the new TOAR database and its tools for accessing and analysing TOAR data. This publication contains all lectures that were held during the on-site event.
Furthermore, users' workflows and data analysis problems have been tackled in hands-on sessions. The feedback from this workshop will also influence the further development of the TOAR infrastructure. All workshop material including the hands-on codes can be found at https://gitlab.jsc.fz-juelich.de/esde/toar-public/toar-data-user-workshop-2022
Gesäuse-Johnsbachtal - Austria, Bulk density
No full textBulk density data of the Gesäuse-Johnsbachtal sit
TOAR-II Data User Workshop January 2023
No full textThe Tropospheric Ozone Assessment Report (TOAR) is an initiative of the International Global Atmospheric Chemistry (IGAC) project. TOAR-II is the second phase of TOAR. It builds on the successful completion of the first comprehensive assessment on tropospheric ozone and will last from 2020 to 2024. The TOAR-II Data User Workshop 2023 is an extension of the workshop already held in 2022 (http://doi.org/10.34730/f52a5d792fbb42c3b1f1b92333d4d86e) and showed the innovations in development since the previous year. In particular, the newly developed dashboard was presented, and the tools for accessing and analysing data from the TOAR database were explained. This publication contains all lectures that were held during this event.
Furthermore, users' workflows and data analysis problems have been tackled in hands-on sessions. The feedback from this workshop will also influence the further development of the TOAR infrastructure. All workshop material including the hands-on codes can be found at https://gitlab.jsc.fz-juelich.de/esde/toar-public/toar-data-user-workshop-2023
Capturing the Dynamic Processes of Porosity Clogging
No full textThe provided files contain the segmented images (.png) used as input data for the TauFactor Matlab application to calculate the effective diffusivity as a function of the evolving porous geometry. The COMSOL Mulitphysic project file (.mph) contains the steady state simulation of the velocity and chemical components distribution inside the 3D microfluidic reactor. The velocity and component distribution profiles across the porous reaction chamber were extracted and given as .txt-files. Based on the simulated velocity and components distribution, the dimensionless Péclet and Reynolds number as well as the theoretical maximum saturation index (SI) were calculated (.xlsx). In addition, the Raman large area scans are given
CerebNet Segmentation Models for Cerebellar Sug-Segmentation
No full textQuantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).Three files for trained models of the three different slicing directions
TOAR-II workshop, Cologne, March 2023
No full textThis three-day meeting aimed to discuss the status and plans of the TOAR-II Community Special Issue papers and develop ideas for the TOAR-II assessment report. While the workshop provided opportunities to advance discussions within the TOAR-II working groups, the focus was on identifying cross-cutting topics, possible synergies and potential conflicts.
We thank all presenters for their valuable contributions
Capturing the Dynamic Processes of Porosity Clogging
No full textThe provided files contain the segmented images (.png) used as input data for the TauFactor Matlab application to calculate the effective diffusivity as a function of the evolving porous geometry. The COMSOL Mulitphysic project file (.mph) contains the steady state simulation of the velocity and chemical components distribution inside the 3D microfluidic reactor. The velocity and component distribution profiles across the porous reaction chamber were extracted and given as .txt-files. Based on the simulated velocity and components distribution, the dimensionless Péclet and Reynolds number as well as the theoretical maximum saturation index (SI) were calculated (.xlsx). In addition, the Raman large area scans are given
Deep learning based 3d reconstruction for phenotyping of wheat seeds: dataset (with raw images)
No full textWe present a new data set for 3d wheat seed reconstruction, propose a challenge “Wheat Seed 3d Reconstruction Challenge”, and provide baseline methods [1].
The dataset consists of 2964 seeds, split into 2520 seeds for training/validation and 444 for testing. Ground truth data for the test set is not provided, however, test results can be evaluated in the “Wheat Seed 3d Reconstruction Challenge” on https://helmholtz-data-challenges.de/.
Per seed there are:
(0) 36 images captured with a 10-degree increment in the rotation in front of the camera, ensuring a complete 360-degree view
(1) Point cloud, reconstructed from these images with volume carving
(2) 36 projection matrices, projecting point cloud (2) back to corresponding images (1)
(3) Top view taken from another camera
We provide preprocessed versions of (1) and (2): (5) and (6) respectively.
========================
Detailed content:
(0) raw_3d_station_images/ -- .tif -- 1800x1000, 8 bit grayscale
(1) raw_point_clouds/ -- .ply -- N points, N<70000, x-y-z coordinates, ascii format
(2) projection_matrices/ -- .txt -- 36x4x4 floats
(3) 2d_station_images/ -- .png -- 700x700, 24 bit, RGB color
(4) preprocessed_3d_station_images/ -- .tif -- stabelized and cropped to 373x200, 8 bit, grayscale
(5) preprocessed_gt_point_clouds/ --.ply – 2000 points, x-y-z coordinates, ascii format
(6) general_files/ -- additional files, files for competition submission
(1) consists of point clouds, one per seed. (5) consists of point clouds in 36 corresponding poses per seed.
Raw 3d station images (0) were preprocessed, stabilized and resampled, which results in (4). Raw point clouds (1) were preprocessed (resampled) such that each of them contains 2000 points, lying in the fixed set of directions /general_files/directions.csv. Preprocessed point clouds (5) are convertible to triangular mesh using indices of vertex triplets /general_files/triangles.txt. A sphere sampled with these vertices and triangles is in /general_files/fibo_msh.ply.
The test set does not include point clouds, and has 3 views per seed: 0, 120 and 240 degrees.
Zero-padded integers XXXX are seed indices (here depicted 0000 and 0003). Zero-padded integers YYY in rotation_YYY are rotation angles in degrees: 0, 10, .. 350 degrees.
Indices of train and test sets are provided in general_files/indices_train.txt and general_files/indices_test.txt files. Volumes of raw point clouds of the train set are located in general_files/train_gt_volumes.csv.
Point cloud files .ply (1) and (5) were created with open3d python library, the header of the file is:
format ascii 1.0
element vertex ?????
property float x
property float y
property float z
end_header
These .ply are visualizable with, e.g., MeshLab (https://www.meshlab.net/) in windows.
There are 16 files in this data record: seed_dataset.zip, numpy_arrays.zip, raw_3d_station_images_train_part_X.zip with X in [0, 1, .., 12], raw_3d_station_images_test.zip
The file seed_dataset.zip contains the data, where each file is presented separately. The file numpy_arrays.zip contains numerical arrays, aggregating these separate files into multidimensional arrays readable with the python library numpy as np.load('file_name.npy'). raw_3d_station_images_*.zip contain images from turntable setup (0).
Structure of the data after extraction of the numpy_arrays.zip into corresponding directories:
numpy_arrays
├── general_files
│ ├── directions.csv
│ ├── fibo_msh.ply
│ ├── indices_test.txt
│ ├── indices_train.txt
│ ├── train_gt_volumes.csv
│ └── triangles.txt
├── 2d_station_images_test.npy
├── 2d_station_images_train.npy
├── gt_points_train.npy
├── preprocessed_3d_station_images_test.npy
├── preprocessed_3d_station_images_train.npy
├── projection_matrices_test.npy
└── projection_matrices_train.npy
2d_station_images_test.npy (444, 3, 700, 700) uint8 (seed_idx_test, RGB_channel, height, width)
2d_station_images_train.npy (2520, 3, 700, 700) uint8 (seed_idx_train, RGB_channel, height, width)
gt_points_train.npy (2520, 36, 3, 2000) float32 (seed_idx_train, rotation_idx, x-y-z, point_idx)
preprocessed_3d_station_images_test.npy (444, 3, 200, 373) uint8 (seed_idx_test, ~rotation_idx~, height, width)
preprocessed_3d_station_images_train.npy (2520, 36, 200, 373) uint8 (seed_idx_train, rotation_idx, height, width)
projection_matrices_test.npy (444, 36, 4, 4) float32 (seed_idx_test, rotation_idx, row, column)
projection_matrices_train.npy (2520, 36, 4, 4) float32 (seed_idx_train, rotation_idx, row, column)
rotataion_idx [0, 1, .., 35] corresponds to [0, 10, .., 350] degrees.
~rotation_idx~~ [0, 1, 2] corresponds to [0, 120, 240] degrees.
seed_idx_test and seed_idx_train are in following order: general_files/indices_test.txt and general_files/indices_train.txt respectively.
Structure of the data after extraction of the seed_dataset.zip and raw_3d_station_images_*.zip into corresponding directories:
seed_dataset
├── raw_3d_station_images
│ ├── train
│ │ └── 0000
│ │ ├── rotation_000.tif
│ │ ├── rotation_010.tif
│ │ ├── ...
│ │ └── rotation_350.tif
│ └── test
│ └── 0003
│ ├── rotation_000.tif
│ ├── rotation_120.tif
│ └── rotation_240.tif
├── raw_point_clouds
│ └── train
│ └── 0000_Surface.ply
├── projection_matrices
│ ├── train
│ │ └── 0000_ProjectionMatrices.txt
│ └── test
│ └── 0003_ProjectionMatrices.txt
├── 2d_station_images
│ ├── train
│ │ └── 0000_2D.png
│ └── test
│ └── 0003_2D.png
├── preprocessed_3d_station_images
│ ├── train
│ │ └── 0000
│ │ ├── rotation_000.tif
│ │ ├── rotation_010.tif
│ │ ├── ...
│ │ └── rotation_350.tif
│ └── test
│ └── 0003
│ ├── rotation_000.tif
│ ├── rotation_120.tif
│ └── rotation_240.tif
├── preprocessed_gt_point_clouds
│ └── train
│ ├── 0000
│ ├── rotation_000.ply
│ ├── rotation_010.ply
│ ├── ...
│ └── rotation_350.ply
└── general_files
├── directions.csv
├── fibo_msh.ply
├── indices_test.txt
├── indices_train.txt
├── train_gt_volumes.csv
└── triangles.txt
It is mandatory to cite our paper when using this dataset:
[1] V.Cherepashkin, E.Yildiz, A.Fischbach, L.Kobbelt, and H. Scharr. Deep learning based 3d reconstruction for phenotyping of wheat seeds: a dataset, challenge, and baseline method. CVPPA at ICCV 2023, Paris, 2 October 202