Helmholtz Institute Freiberg for Resource Technology
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Wire-mesh sensor data for vertical upward gas-liquid flow
No full textThis data set contains the processed data of the wire-mesh sensor, obtained in a flow loop with inner diameter of 50 mm with a vertical section of 3 m length. The dimension of the sensor is 16x16 wires and a lateral wire distance of 3.125 mm. Each file contains data of 60 s measurement time with 10 kHz samling frequency.
The set up was operated with pressurized air and deionized water. The experimental matrix contains meausrements at different superficial velocities of the gas and the liquid. Thus different flow pattern are observed. For injection of the gas two different types have been used. In the first set of experiments (files 1- 61, *injection1*) the gas was injected with a small tube with inner diameter of 9 mm. In the second set of experiments (files 101 - 151, *injection2*) the gas was injected with a small pipe of 25 mm inner diameter.
An overview of the experimental conditions for the two sets of experiments are summarized in the excel file. The corresponding *.zip files contain the processed data. These are void files, which contain the gas holdup in each crossing point and for all time steps of the measurement stack. Additionally the time averaged cross sectional gas holdup distribution (*.epsxy), the time averaged radial gas holdup (*.epsrad_20) and the cross sectional average gas holdup at each time step (*.epst) is provided
supplementary material for bubble trajectories
No full textsupplementary material for bubble trajectorie
Clinical urine microscopy for urinary tract infections
No full textUrinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.
How to cite us
Liou, Natasha, Trina De, Adrian Urbanski, Catherine Chieng, Qingyang Kong, Anna L. David, Rajvinder Khasriya, Artur Yakimovich, and Harry Horsley. "A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection." Scientific Data 11, no. 1 (2024): 155.
@article{liou2024clinical,
title={A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection},
author={Liou, Natasha and De, Trina and Urbanski, Adrian and Chieng, Catherine and Kong, Qingyang and David, Anna L and Khasriya, Rajvinder and Yakimovich, Artur and Horsley, Harry},
journal={Scientific Data},
volume={11},
number={1},
pages={155},
year={2024},
publisher={Nature Publishing Group UK London}
}
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Data acquisition
300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists.
Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition.
Dataset annotation
300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image.
Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1).
Data structure
The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number.
Table1
Folder Files Objects Count Pixel Values
img 300 Raw data 0-255
bin_mask 300 Background/Foreground 0/1
mult_mask 300 Background/Class 0
Rod 1697 1
RBC/WBC 1056 2
Yeast 41 3
Miscellaneous 550 4
Single EPC 182 5
Small EPC sheet 26 6
Large EPC sheet 10 7
Total 356
Determining the axial gas dispersion coefficient in bubble columns via gas flow modulation technique and several sensing strategies
No full textCollected data refer to gas flow modulation measurements in a D=100 mm bubble column. The axial holdup wave is determined at three axial positions using different sensing stategies (gamma-ray densitometry, differential pressure sensors, transmittance optical probes and conductivity needle probes). Average gas holdup as well as amplitude damping and phase-shift have been determined at three different gas flow rates in the homogeneous regime. A description of the experimental setup is provided in the file "Experimental_setup.pdf". An overview of the performed experiments is provided in the Excel file "DataDescription.xlsx"This work was supported by the German Research Foundation (DFG), (HA 3088/18-1)
Data to Impact on various cleaning procedures on p-GaN surfaces
No full textThis folder "XPS data" contains original and evaluated XPS data (.vms) on a p-GaN sample which was treated at various temperatures and underwent Ar+ irradiation.
Furthermore, the folder "REM Images" contains REM images (.tif) and EDX data (.xlsx) on the used excessively treated sample.
All images that are published in the main manuscript are collected as .tif files in the folder "images"
HZDR Data Management Strategy — Top-Level Architecture
Get PDFTop-Level Architecture of the proposed HZDR Data Management Strategy with additional description of the various systems and services
Data publication: Switching on Cytotoxicity of Water-Soluble Diiron Organometallics by UV Irradiation
No full textdecarbonylation studies By IR, NMR, UV/vis myoglobin assay cell proliferation assay
cristallographic data available by collaboration partne
Data publication for: OCTOPOD - single bunch tomography for angular-spectral characterization of laser-driven protons
No full textThe data set comprises raw data, processed data and detector data for the OCTOPOD detector applied for characterization of proton bunches at a proton cyclotron and a laser-driven proton accelerator
Data publication: Universal radiation tolerant semiconductor
No full textEBSD data and irradiation parameter
Where2Test Website Backend
No full textSource code for the backend of the Where2Test website. The backend connects to a database and scrapes online information needed to run the following mini-applications that make up the Where2Test COVID research project website:
Retirement Home Testing Optimizer
COVID-19 Workplace Occupancy Optimizer
Saxony Wastewater Forecast Dashboard
Regional Forecast Dashboards
The backend is written in the Python programming language using the Flask web application framework