Helmholtz Institute Freiberg for Resource Technology
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Robust Computation and Analysis of Vibrational Spectra of Layered Framework Materials Including Host-Guest Interactions
No full textThe dataset contains supplementary material for the journal article "Robust Computation and Analysis of Vibrational Spectra of Layered Framework Materials Including Host-Guest Interactions"
Data publication: The origin of native metal-arsenide mineralization in the world-class Schlema-Alberoda uranium deposit (Germany): Insights from arsenide compositions and fluid inclusion systematics
No full textThis dataset contains the mineral descriptions, mineral chemical compositions and fluid inclusion data from the samples investigated in the study as well as the analytical parameters for the analytical methods used
A Dataset for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy
No full textHow to cite us
Wyrzykowska, Maria, Gabriel Della Maggiora, Nikita Deshpande, Ashkan Mokarian, and Artur Yakimovich. "A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy." Scientific Data 12, no. 1 (2025): 1-11.
@article{wyrzykowska2025benchmark,
title={A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy},
author={Wyrzykowska, Maria and Della Maggiora, Gabriel and Deshpande, Nikita and Mokarian, Ashkan and Yakimovich, Artur},
journal={Scientific Data},
volume={12},
number={1},
pages={1--11},
year={2025},
publisher={Nature Publishing Group}
}
Data sources
Raw data used during the study can be found in corresponding references.
VACV: Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF. Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion. PLoS One. 2015 Sep 28;10(9):e0138760. doi: 10.1371/journal.pone.0138760. PMID: 26413745; PMCID: PMC4587671.
HADV: Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Witte R, Puntener D, Greber UF. Microscopy deep learning predicts virus infections and reveals the mechanics of lytic-infected cells. iScience. 2021 May 15;24(6):102543. doi: 10.1016/j.isci.2021.102543. PMID: 34151222; PMCID: PMC8192562.
HSV, IAV, RV: Olszewski, D., Georgi, F., Murer, L. et al. High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections. Sci Data 9, 610 (2022). https://doi.org/10.1038/s41597-022-01733-4
Data organisation
For each virus (HADV, VACV, IAV, RV and HSV) we provide the processed data in a separate directory, divided into three subdirectories: `train`, `val` and `test`, containing the proposed data split. Each of the subfolders contains two npy files: `x.npy` and `y.npy`, where `x.npy` contains the fluorescence or brightfield signal (both for HADV, as separate channels) of the cells or nuclei and `y.npy` contains the viral signal. The data is already processed as described in the Data preparation section.
Additionally, Cellpose masks are made available for the test data in separate masks directory. For each virus except for VACV, there is a subdirectory `test` containing nuclei masks (`nuc.npy`). For HADV cell masks are also available (`cell.npy`).
Data preparation
Each of VACV plaques was imaged to produce 9 files per channel, that need to be stitched to recreate the whole plaque. To achieve this, multiview-stitcher toolbox has been used. The stitching was first performed on the third channel, representing the brightfield microscopy image of the samples. Then, the parameters found for this channel were used to stitch the rest of the channels. VACV dataset represents a timelapse, from which timesteps 100, 108 and 115 have been selected to produce the data then used in the experiments. Images have been center-cropped to 5948x6048 to match the size of the smallest image in the dataset (rounded down to the closest multiple of 2). The data was additionally manually filtered to remove the samples that constituted only uninfected cells (C02, C07, D02, D07, E02, E07, F02, F07). The HAdV dataset is also a timelapse, from which only the last timestep (49th) has been selected.
For the rest of the datasets (HSV, IAV, RV) only the negative control data was used, which was selected in the following way: from the data collected at the University of Zürich, from the Screen samples only the first 2 columns were selected and from the ZPlates and prePlates samples only the first 12 columns. All of the datasets were divided into training, validation and test holdouts in 0.7:0.2:0.1 ratios, using random seed 42 to ensure reproducibility. For the time-lapse data, it was ensured that the same sample from different timesteps only exists in one of the holdouts, to prevent information leakage and ensure fair evaluation. All of the samples were normalised to [-1, 1] range, by subtracting the 3rd percentile and dividing by the difference between percentile 99.8 and 3, clipping to [0, 1] and scaling to [-1, 1] range. For the brightfield channel of HAdV, percentiles 0.1 and 99.9 were used. These cutoff points were selected based on the analysis of the histograms of the values attained by the data, to make the best use of the available data range. Specific values used for the normalization are summarized in Figure 3 of the manuscript in Related/alternate identifiers.
To prepare the cell nuclei masks, Cellpose model with pre-trained weights cyto3 has been used on the fluorescence channel. The diameter was set to 7 for all the datasets except for HAdV, for which the automatic estimation of the diameter was employed. Cell masks were prepared using Cellpose with pre-trained weights cyto3 with a diameter set to 70 on brightfield images stacked with fluorescence nuclei signal. The data preparation can be reproduced by first downloading the datasets and then running scripts that are located in `scripts/data_processing` directory of the [VIRVS repository](https://github.com/casus/virvs), first modifying the paths in them:
for HAdV data: `preprocess_hadv.py`
for VACV data: `stitch_vacv.py` + `preprocess_vacv.py`
for the rest of the viruses: `preprocess_other.py`
to prepare Cellpose predictions: `prepare_cellpose_preds.py` (for cells) and `prepare_cellpose_preds_nuc.py` (for nuclei)
Additional Dataset in v1.2: GFP-transgenic human coronavirus OC43 (CoV-GFP)
This dataset comprises raw fluorescence microscopy images acquired from a 384-well control plate, half of which was infected with GFP-transgenic human coronavirus OC43 (CoV-GFP). The plate was imaged using two fluorescence channels: CoV-GFP to visualize viral infection, and Hoechst 33342 to stain cell nuclei. The raw images of two plates are provided in the cov_raw.zip. Each plate has half a plate infected with CoV-GFP and another is a mock-infected (no virus). Images were captured using a 4× objective on an ImageXpress Micro imaging system (Molecular Devices). The dataset was derived from a published high-throughput screening study by Murer et al. [1], aimed at identifying broad-spectrum antiviral compounds.
Murer, L. et al. Identification of broad anti-coronavirus chemical agents for repurposing against SARS-CoV-2 and variants of concern. Current Research in Virological Science, 3, 100019 (2022)
Data publication: In search of phytoremediation candidates: Eu(III) bioassociation and root exudation in hydroponically grown plants
No full textPublication of bioassociation, spectroscopic, chromatographic and thermodynamically modelled data obtained in hydroponic plant experiments with Eu(III).europium; speciation; phytoremediation; bioassociation; laser spectroscopy; lanthanides; hydroponics; plant uptake; root exudates; thermodynamic modellin
Multiphase Code Repository by HZDR for OpenFOAM Foundation Software
No full textThe Multiphase Code Repository by HZDR for OpenFOAM Foundation Software is a software publication released by Helmholtz-Zentrum Dresden-Rossendorf according to the FAIR principles (Findability, Accessibility, Interoperability, and Reuseability). It contains experimental research work for the open-source software released by The OpenFOAM Foundation. The developments are dedicated to the numerical simulation of multiphase flows, in particular to the multi-field two-fluid model (Euler-Euler method).Acknowledgement: OpenFOAM(R) is a registered trade mark of OpenCFD Limited, producer and distributor of the OpenFOAM(R) software via www.openfoam.com. The Multiphase Code Repository by HZDR for OpenFOAM Foundation Software is not compatible with the software released by OpenCFD Limited, but is based on the software released by the OpenFOAM Foundation via www.openfoam.orgHighlights of the Multiphase Code Repository by HZDRHZDR Baseline Model: addonMultiphaseEuler solver with full support of the HZDR baseline model set for polydisperse bubbly flows, including configuration files and tutorials for simplified setup of Baseline cases (Hänsch et al., 2021).Population Balance Modelling: A GPU-accelerated population balance method according to Petelin et al. (2021).Morphology-adaptive Multifield Two-fluid Model (MultiMorph): cipsaMultiphaseEuler solver featuring a morphology-adaptive modelling approach (dispersed and resolved interfaces, Meller et al., 2021) with an interface to the multiphaseEuler framework to utilise all available interfacial models, and configuration files and tutorials for easy setup of cases with the MultiMorph Model.more ...This work was supported by the Helmholtz European Partnering Program in the project "Crossing borders and scales (Crossing)"
Contribution to the recyclability assessment of multi-material structures with a focus on shredding (Data)
No full textThe repository contains selected data of the dissertation:
Title: Contribution to the recyclability assessment of multi-material structures with a focus on shredding
Author: M.Eng. Magdalena Heibeck
Faculty: Faculty of Mechanical Science and Engineering of the TUD Dresden University of Technology
Year: 2024
The repository contains zipped folders with selected data from the investigations discussed in thesis chapters (ch) 3, 4, and 5. It contains the following datasets, metadata, and scripts related to the research. More information is provided through README.txt files within the folders.
Experimental data for the shredding of profile and plate specimens (folders: ch3_profile_exp, ch5_plates_exp):
Feed characterization: photographs, mass, main dimensions of specimens
Shredding process evaluation:
Videos and screenshots of the shredding process, along with derived process descriptors (e.g., specimen orientation, number of rotor disks engaged)
Rotor moments of the shredder, including calculated specific mechanical energy consumption
Fragment characterization: photographs, 2D image analysis to determine fragment sizes, fragment properties (mass, material composition, liberation degree, size, final joint state, fracture phenomena, form-locks)
Simulation data for the shredding of profile and plate specimens (folders: ch4_profile_sim, ch5_plates_sim):
Ansys LS-DYNA input files: .k-files including geometry meshes (rotary shredder, specimen geometries, constraint boxes), boundary conditions (initial position and orientation of specimen, rotor angles), material models (steel, organosheet, rib structure), and tiebreak contact parameters for adhesion joints
Shredding process evaluation (refer to the experimental section)
Fragment characterization: .stl-files of fragments, fragment properties (refer to the experimental section)
Scripts:
to characterize simulated fragments from .stl-files
to read temporal simulation data from .binout-file
Experimental data for investigating proton bunch monitors for clinical translation of prompt gamma-ray timing
No full textThe dataset contains the data reported on https://www.hzdr.de/publications/Publ-39073 where 2 proton bunch monitors (PBMs), namely the diamond detector and the cyclotron monitoring signal Uphi, are established, characterized, and applied for correcting the prompt gamma-ray timing (PGT) data. Experimental setup, irradiation modalities, data acquisition, and data pre- and postprocessing are described there.
The process is summarized in the following:
Experimental setup: A homogeneous cylindrical PMMA phantom was irradiated with a proton beam. Two sets of measurements were considered:
S1) measurements at the horizontal fixed beamline with the control of the beam time structure and current. These data establish the relation between the investigated PBMs and calibrate them to the scattering setup that provides the proton bunch arrival time in the experimental room. The phantom was irradiated with 7 different proton energies Ep = {70, 90, 110, 130, 160, 190, 224} MeV. For each Ep, 3 irradiation modalities were applied:
CW-mode represented the continuous beam lasting for 30 s, the beam current Ibeam = 2 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA);
Plan I represented a clinically realistic plan with a spot duration of 4 ms and a spot repetition time of 7 ms. The beam current Ibeam = 1 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA);
Plan II aimed to reproduce the measurements of Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). For that, the spot duration was set to 69 ms, and the repetition time was 72 ms. The beam current Ibeam = 1 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA).
S2) measurements at the pencil beam scanning (PBS) beamline were similar to those at the clinical beam delivery nozzle. The PBS beamline delivers the beam as spots of given intensity (expressed in MU), (x,y)-coordinates, and energy (corresponds to the penetration depth or z-coordinate). These data comprise data from the PGT detector and PBMs and are used to correct the PGT data employing the investigated PBMs. The phantom was irradiated with 8 different proton energies Ep = {70, 90, 110, 130, 162, 180, 200, 220} MeV. For every energy, 2 spot intensities were considered: 0.1 MU per 1 spot (~1e7 protons) and 1 MU per 1 spot (~1e8 protons). For Ep = 162 MeV, an additional spot intensity of 10 MU per 1 spot (~1e9 protons) was applied to reproduce the measurements of Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d).
Data preprocessing:
The raw data of each measurement were converted from the binary list-mode format to ROOT TTrees. The data were corrected for the photomultiplier gain drift, and digitalization time non-linearities, and the integral signal was converted into deposited energy. For the measurements at the fixed beamline, the coincidence analysis was applied additionally for non-PBM detectors. The data were assigned to individual corresponding spots for the PBS beamline measurements.
Data structure:
The ROOT files are named u100-p00XX-yyyy-mm-dd_HH.MM.SS+TZ.root where p00XX is the detector’s number, yyyy-mm-dd_HH.MM.SS is the time of the measurement, and TZ is the time zone. Here, p0012 and p0019 mean scintillating detectors that were used both at the fixed beamline, and only detector p0012 was used for PGT measurements at the PBS beamline. P0015 is the diamond detector, and p0017 contains data of the Uphi signal.
In general, the data structure inside the ROOT files is different depending on the purpose of the detector. However, there are some general includes:
data (TTree) contains list-mode data which comprises
uncorrected data: before corrections and calibrations steps;
corrected data: after correcations and calibrations steps;
meta (TTree) is a measurement metadata (applied detector voltage, the start time of the measurements, etc.);
histograms is a directory with selected example histograms (uncorrected);
analysis is a directory with histograms with corrected data used for the analysis.
For further questions, please refer to the contact persons stated above
Data publication: Microstructure-informed prediction of hardening in ion-irradiated reactor pressure vessel steels
No full textMainly the original data for model establishment
Data publication: MRF timing system characterization and 1-wire sensor calibration using a climate chamber
No full textThis data was taken at DSEY (04-08.12.2023) using a climate chamber.
Multiple temperature and humidity sensors were put into the climate chamber.
Due to problems with the ChimeraTK server not all data was collected by a single ChimeraTK server,
but the sensors were grouped and read by different 1-wire servers (`1-wire_1`, `1-wire_2`, `1-wire_3`, `1-wire_4`, `1-wire_5`). Each sensor identification is listed in the owfs.xlmap file. First sensor in owfs.xlmap corresponds e.g. to DS18B20/0. Data is available as HDF5 and ROOT file.
In addition the MRF timing system was running. Two EVRs (EVR2, EVR3) were connected via long fibers (100m) to the EVM. The fibers routed through the climate chamber, such that most of the fiber was inside the chamber. A Rhode&Schwartz oscilloscope was used to measure the delay of the timing output signals with respect to a third EVR (EVR1), that was connected via a short cable outside the climate chamber. That data is included in timing-data.root, which includes:
Delay of EVR2 with respect to EVR1 -> Delay_C1C2
Delay of EVR3 with respect to EVR1 -> Delay_C1C3
Delay compensation (actual, correction) for each EVR
The intended measurement, was to use active delay compensation for EVR2 and deactivated delay compensation for EVR3. However, the measurement was spoiled by periodic delay shifts in case of EVR2. On 07.12. 10:20 the delay compensation was also activated for EVR3.
For technical reasons not all timing related data is included in rs-data.root. The delay compensation data (actual, correction) should be taken from the aggregated raw data. It includes basically all data (temperature, humidity, oscilloscope data), but in the beginning the actual delay measurement was missing (which should be taken from timing-data.root).
Selected data periods are listed in the file data.ods.
Some analysis results are already included here for convenience:
Plots includes:
Temperature calibration
Humidity calibration
Delay measurements
Calibration.root includes calibration constants for humidity/temperature calibration and graphs/plot
Data publication: Real-time 3D Particle Tracking using Ultrafast Electron Beam X-ray Computed Tomography
No full textThis dataset includes all raw data used in the linked publication "Real-time 3D Particle Tracking using Ultrafast Electron Beam X-ray Computed Tomography"