Helmholtz Institute Freiberg for Resource Technology
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Data publication: Hot-Electron Dynamics in a Semiconductor Nanowire under Intense THz Excitation
No full textPython code for data processing and raw dat
Variability of fracture surface roughness in crystalline host rocks: implications for transport model simplifications
No full textThis data provides the original inputs and COMOSL scripts for the paper 'Variability of fracture surface roughness in crystalline host rocks: implications for transport model simplifications'
Source Data: Laser-driven high-energy proton beams from cascaded acceleration regimes
No full textThis dataset contains all source data used to generate figures and all other findings of the publication: "Laser-driven high-energy proton beams from cascaded acceleration regimes"
Stonepark Hyperspectral Drillcore Data
No full textThis hyperspectral drillcore dataset (shed) contains 14 drill holes, totalling 389 boxes that cumulatively contain 2513 meters of scanned cores. Hyperspectral data is stored in the widely used ENVI format (.dat and associated .hdr files), which can be opened using e.g., napari-hippo (GUI) and hylite (python). The whole directory structure is compatible with hycore, for easier out-of-core processing and visualisation.
These hyperspectral data and associated visualisations can also be viewed interactively here.
The scanned drillcores were selected to cover the main mineralised intervals and key stratigraphic horizons, sedimentary facies, igneous intrusions, brecciation and alteration zones present at Stonepark. These include the Knockroe Fm and Knockseefin Fm, the Lough Gur Formation, the Waulsortian Limestone Formation, and the Ballysteen Formation (Argillaceous Bioclastic Limestone). Drillcore selection was further based on the availability and accessibility of drill core and sought to capture the variations both laterally and vertically, in consultation with geologists from Group Eleven Resources.
These data were acquired as part of the Horizons Europe project Vector. We would also like to acknowledge Group Eleven, for providing access to core material and their support during data acquisition
Source data: Revealing the 3D structure of microbunched plasma-wakefield-accelerated electron beams
No full textSource data for the publication titled "Revealing the 3D structure of microbunched plasma-wakefield-accelerated electron beams.
Data publication: Nonreciprocal spin-wave dispersion in magnetic bilayers
No full textThe repository contains the experimental BLS data and script files to generate the simulated data with TetraX. The data is distributed into directories with the file name referring to the figures, where the script files were used to simulate the presented data. The script files were used with Tetrax 1.3.3 or when the figure directory contains an additional text file with a reference to the newer TetraX version 2.0.0
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
Data Publication: Boron isotopic and mineral chemical composition in greisen-related Li-Fe micas at the Sadisdorf Li-Sn-(W-Cu) prospect, Erzgebirge, Germany: Pathways and mechanisms for hydrothermal lithium enrichment
No full textDataset containing research data associated to the publication: Boron isotopic and mineral chemical composition in greisen-related Li-Fe micas: Pathways and mechanisms for hydrothermal lithium enrichment submitted to Geochimica et Cosmochimica Acta.
The dataset is composed of sample locations descriptions (Table S1), major (Table S2) and trace element (Table S3) compositions of micas, whole-rock (Table S4) and in-situ (Table S5) B isotopic analyses of micas and major lithologies from the Sadisdorf prospect. In addition it contains calculated relative activities of elements in hydrothermal micas from the Sadisdorf prospect as well as a compitlation of compositionas of magmatic and hydrothermal micas associated to Li-Sn-W-Mo-Nb-Ta deposits worlwide (Table S7).This project is funded as part of the DFG (Project no. 441189074) priority program Dynamics of Ore Enrichment (DOME; SPP 2238). FIERCE is financially supported by the Wilhelm and Else Heraeus Foundation and by the DFG
Data Publication: Boron isotopic and mineral chemical composition in greisen-related Li-Fe micas at the Sadisdorf Li-Sn-(W-Cu) prospect, Erzgebirge, Germany: Pathways and mechanisms for hydrothermal lithium enrichment
No full textDataset containing research data associated to the publication: Boron isotopic and mineral chemical composition in greisen-related Li-Fe micas: Pathways and mechanisms for hydrothermal lithium enrichment submitted to Geochimica et Cosmochimica Acta.
The dataset is composed of sample locations descriptions (Table S1), major (Table S2) and trace element (Table S3) compositions of micas, whole-rock (Table S4) and in-situ (Table S5) B isotopic analyses of micas and major lithologies from the Sadisdorf prospect. In addition it contains calculated relative activities of elements in hydrothermal micas from the Sadisdorf prospect as well as a compitlation of compositionas of magmatic and hydrothermal micas associated to Li-Sn-W-Mo-Nb-Ta deposits worlwide (Table S7).This project is funded as part of the DFG (Project no. 441189074) priority program Dynamics of Ore Enrichment (DOME; SPP 2238). FIERCE is financially supported by the Wilhelm and Else Heraeus Foundation and by the DFG
The Chemical Journey of Europium(III) through Winter Rye (Secale cereale L.) – Understanding through Mass Spectrometry and Chemical Microscopy
No full textArchiv of research data for the manuscrip