Helmholtz Institute Freiberg for Resource Technology
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Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and set-ups for the open-source software released by the OpenFOAM Foundation. This includes among others utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and continuous development environment in a self-hosted Gitlab instance
Data publication: Revealing the 3D structure of microbunched plasma-wakefield-accelerated electron beams
No full textThis repository contains data on coherent optical transition radiation (COTR) from laser wakefield accelerated electron beams. This includes raw (COTR) images and electron spectra, as well as analysis code for evaluating the COTR data and using it as an input for a differential-evolution-based reconstruction of the electron bunch
Retrained Models and Scripts for Aluminum at 298K and 933K
No full textRetrained Models and Scripts for Aluminum at 298K and 933K
Authors
- Fiedler, Lenz (HZDR/CASUS)
- Cangi, Attila (HZDR/CASUS)
Affiliations:
HZDR - Helmholtz-Zentrum Dresden-Rossendorf
CASUS - Center for Advanced Systems Understanding
Data set description
This data sets contains models, scripts and inference results for aluminum at room temperature and the melting point. Training data, hyperparameters and general methodology follow Ref. [1]. The models here are retrained versions of the ones discussed in this publication, and therefore retrained versions of the models contained in Ref. [2]. As such, data from Ref. [2] has been used. Only a subset of models contained in Ref. [1] have been retrained, namely the room temperature model, one liquid and one solid melting point model with four training snapshot each, and the final melting point hybrid model (six training snapshots per phase). Furthermore, for both the hybrid melting temperature model and the room temperature model, multiple models with different initializations were trained.
All models were trained with the MALA code [3] version 1.2.1. They show better accuracy than their original counterparts, as they were trained using the inter-snapshot shuffling algorithm first discussed for the MALA code in Ref. [4].
[1] - "Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks", Physical Review B, doi.org/10.1103/PhysRevB.104.035120
[2] - "RODARE", doi.org/10.14278/rodare.2485 (v1.0.0)
[3] - "MALA", Zenodo, doi.org/10.5281/zenodo.5557254
[4] - "Machine learning the electronic structure of matter across temperatures", Physical Review B, doi.org/10.1103/PhysRevB.108.125146
Contents
- The models themselves, labeled as either Al298K or Al933K, given as one .zip file per model
- For 933K, additionally "liquid", "solid" and "hybrid" denotes the training data set
- For ensembles, a running index denotes the number in the ensemble
- Inference results, given as a single .zip file
- For all models, band energy and total free energy results are given in the .csv format
- The columns in these files correspond to "Calculated via DFT LDOS", "Calculated via ML-DFT LDOS", "Calculated via Kohn-Sham system", respectively
- For some models, additionally the predicted electronic density and density of states on select snapshots is given
- Shuffling, training and testing scripts, given as a single .zip file
- Scripts are ready-to-use with suitable MALA installation, however, correct data paths have to be filled i
Data publication: Metal Deportment in Complex Secondary Raw Materials: The Case of Vanadium in Basic Oxygen Furnace Slags
No full textCompilation of all available raw data for the publication "Metal Deportment in Complex Secondary Raw Materials: The Case of Vanadium in Basic Oxygen Furnace Slags".
List of Supplementary Information
Table A 1: Compilation of elements analyzed by XRF including lower and upper limits of determination (LoD).
Table A 2: Compilation of the subsample designations of the three BOS samples analyzed for the XRF and XRD as well as the MLA and EPMA analyses. All subsamples correspond to representative subsets of the bulk sample.
Table A 3: Compilation of the EPMA measurement parameters with spectrometer position (Spec), lower background (LB), upper background (UB), dwelltime on peak (DTP) and background (DTB) and the complete list of reference materials.
Table App 4: Compilation of all major and minor element contents determined by XRF recalculated as “water-free” and normalized to 100 wt.-%.
Table A 5: Comparison of LOI values for one sample each of the delivered material "as delivered" and fully hydrated.
Table A 6: Compilation of significant differences between the samples with regard to the chemical composition.
Table A 7: Detailed compilation of the results of the MLA measurements of all investigated samples.
Table A 8: Compilation of the phases predefined for EPMA analyses and the number of measurements performed and usable for further MLA and deportation analyses.
Table A 9: Complete summary of all EPMA data used for the deportment analysis (xls File)
Data publication: An LSC approach for tritium determination in gaseous mixtures optimized with respect to handling, reaction parameters and miniaturization towards microfluidic analysis
No full textThe data consists of LSC measurements of HTO for different sample volumes as well as HTO and scinitllation cocktail concentrations prepared using a microfluidic chip
Data publication: Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation
No full textThis dataset includes the primary research data for the publication "Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation"
Data publication: An LSC approach for tritium determination in gaseous mixtures optimized with respect to handling, reaction parameters and miniaturization towards microfluidic analysis
No full textThe data consists of LSC measurements of HTO for different sample volumes as well as HTO and scinitllation cocktail concentrations prepared using a microfluidic chip
Data publication: Advanced setup for in situ positron annihilation lifetime measurements under variable gas atmospheres and humidity: from cryogenic to high temperatures
No full textPositronium data of Maltodextrin at variable humidity levels
Evaluation data set for the GravelSensor
No full textIn this study, we used gamma-ray computed tomography (GammaCT) as reference measurement system to evaluate a novel, non-destructive, smart gravel sensor that is based on the well-known wire-mesh sensor. Various sediment fillings with different infiltrating particle sizes are applied to the gravel sensor and the generated particle holdup is locally determined with both measurement systems simultaneously.Gefördert durch die DF
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." bioRxiv (2024): 2024-08.
@article{wyrzykowska2024benchmark,
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={bioRxiv},
pages={2024--08},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}
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