Helmholtz Institute Freiberg for Resource Technology
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C++ & Python API for Scientific I/O with openPMD
No full textopenPMD is an open metadata format for open data workflows in open science. This library provides a common high-level API for openPMD writing and reading. It provides a common interface to I/O libraries and file formats such as HDF5 and ADIOS. Where supported, openPMD-api implements both serial and MPI parallel I/O capabilities.Supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration). Supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. Previously supported by the Consortium for Advanced Modeling of Particles Accelerators (CAMPA), funded by the U.S. DOE Office of Science under Contract No. DE-AC02-05CH11231. This work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament
Data publication: Validation experiments for gamma-ray densitometry uncertainty prediction algorithm in gas flow modulation
No full textThe gas flow modulation technique is a recently proposed approach for measuring the axial gas dispersion coefficient in bubble columns. The approach uses a marginal sinusoidal distrubance on the gas inlet flow and relates the propagation of such disturbance in the column's axial direction to the axial gas dispersion coefficient. The approach requires to measure sinusoidally changing gas holdup in time with high accuracy. The presented data set was used to experimentally validate a newly developed simualtion algorithm for predicting the effect of various uncertainty sources on the measured holdup wave. The algorithm is able to predict the uncertianty related to the applycation of gamma-ray densitometry and of the data ensable-averaging approach. Experiments were preformed using an elliptical rotating disc able to mimik a controlled "material holdup wave", similar to the gas holdup wave measured in gas flow modulation.This work was supported by the German Research Foundation (DFG), (HA 3088/18-1)
Dataset on European Project ENTENTE Deliverable D4.5 "Hybrid hardening models from SANS and nanoindentation experiments
No full textThis dataset covers experimental data obtained for neutron-irradiated reactor pressure vessel (RPV) steels, in detail type and composition of steels, initial microstructure, initial properties, irradiation conditions, irradiation-induced microstructure changes and irradiation-induced property changes. The metadata sections of the data compilation include references to published journal articles and reports, where the data were originally published. A special feature of the data compilation is the availability of the characteristics of irradiation-induced nm-sized solute atom clusters derived from small-angle neutron scattering (SANS) experiments for each of the RPV materials and irradiation conditions considered. These characteristics, including cluster volume fraction and size, are statistically reliable and macroscopically representative. Moreover, results of Vickers hardness tests obtained using the same samples and also probing macroscopic volumes are provided. The data compilation is organized in the format of an Excel workbook called SANS-RPV. There are numerous potential applications of the data compilation such as the comparison of cluster characteristics derived from SANS and APT experiments, the correlation between cluster volume fraction and irradiation-induced hardness increase or brittle-ductile transition temperature shift, microstructure-informed predictions of the initial yield stress or irradiation-induced yield stress increase or the assessment of embrittlement trend curce (ETC) models, to mention a few. As an illustration, selected applications are saved in a separate Excel file called SANS_RPV_Analyses, which is included in the dataset. The background and usage of the data compilation as well as applications and implications are part of a study performed within the European Project ENTENTE. A copy of the project report draft (Deliverable D4.5) is included in the dataset.This project has received funding from the Euratom research and training programme 2019/2020 under grant agreement No 900018
Data publication: Comparative saturation binding analysis of ⁶⁴Cu-labeled somatostatin analogs using cell homogenates and intact cells
No full textSaturation binding dat
Data publication: Gallium bioionflotation using rhamnolipid: Influence of frother addition and foam properties
No full textThe current study investigated the application of rhamnolipid biosurfactant as an ion collector in bioionflotation. In a top-down approach, the influence of rhamnolipid on gallium (Ga) ion flotation and recovery were investigated followed by detailed studies on the influencing parameters and foam characterization. Rhamnolipid exhibits extensive foaming properties and foam produced by rhamnolipid alone has higher stability making it difficult to collect the flotation concentrates. An addition of 1,2-decanediol introduces instability to this foam and also aids in concentrate collection. Observations during the flotation studies resulted in a series of investigations on rhamnolipid properties such as aggregate size, surface tension, and foam characterization. These results indicated that the addition of Ga and/or 1,2-decanediol affected the molecular aggregation of rhamnolipid. Moreover, the surface activity, foamability, foam drainage, and foam coarsening/coalescence of the rhamnolipid changed in the presence of Ga and/or 1,2-decanediol. In a batch bioionflotation process, rhamnolipid was able to remove nearly 80% Ga at pH 7 in presence of 1,2-decanediol. However, upgrading was higher at pH 6 without 1,2-decanediol, which is important when considering the flotation recovery in presence of other metals. Such biosurfactants have a high potential for wide applications in ion flotation and further optimization of flotation parameters is essential
Data publication: EE+GENTOP approach with swirling flow/2 FLuid 3 Phase simulation
No full textDATA: EE/GENTOP Simulation in twisted/rifle pipe considering swirling flow & 2 Fluid 3 Phase flow simulatio
Yamdb - Yet Another Materials DataBase
No full textYamdb (Yet Another Materials Database/YAMl materials DataBase) is a
Python library providing thermophysical properties of liquid metals
and molten salts in an easily accessible manner. Mathematical
relations describing material properties - usually determined by
experiment - are taken from the literature and implemented in
Python. The coefficients of these equations are stored separately in
YAML files
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.
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
StarDist Models for "Hydrodynamics in a bubble column – Part 1: Two-phase flow"
No full textThis package contains the software and the trained models described in the publication "Hydrodynamics in a bubble column – Part 1: Two-phase flow". Please refer to the readme.md for installation instructions and to the Prediction_demo.ipynb for usage demonstration.This project has received funding from the European Union's Horizon 2020 Marie Skłodowska-Curie Actions (MSCA), Innovative Training Networks (ITN), H2020-MSCA-ITN-2020 under grant agreement No. 955805, and the European Institute of Innovation and Technology (EIT). This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme
Dataset: Basic verification of an industrial type of wire-mesh sensor
No full textThe experimental data presented here was recorded with an industrial type of wire-mesh sensor and additional equipment. The experiments aim at verifying the main functionalities of the developed sensor and include tests of
Temperature compensation
Flow pattern identification in vertical gas-liquid flow
Flow pattern identification in horizontal gas-liquid flow
The experimental procedure and the results are described in detail in Wiedemann et al.: Towards Real-Time Analysis of Gas-Liquid Pipe Flow: A Wire-Mesh Sensor for Industrial Applications, Sensors 23 (2023) 4067, https://doi.org/10.3390/s2308406