European School of Materials

Abstract This article examines whether Austrians hold different attitudes towards Ukrainian refugees compared to Arab/Afghan refugees and explores key mechanisms driving these attitudinal differences. Using data from the third wave of the Austrian Values in Crisis Panel ( N = 1357), the study confirms that Austrians exhibit more favorable views towards Ukrainian refugees. Negative attitudes towards Arab/Afghan refugees are associated with feelings of realistic threat (particularly crime), while both Arabs/Afghans and Ukrainians are associated with symbolic threat. Basic value orientations further impact attitudes towards refugees. Self-Transcendence values correlate with favorable attitudes towards refugees overall, Self-Enhancement values only correlate with more favorable attitudes towards Ukrainians. Conservation values indirectly impact attitudes towards Arab/Afghan refugees by intensifying the influence of perceived threat. These results suggest that differences in attitudes stem from concerns about realistic threats and the differing impact of value orientations.

This record provides an open-access scanning electron microscopy (SEM) dataset of high-chromium cast iron (HCCI, 26 wt.% Cr) microstructures, designed to isolate acquisition-induced image variance from material-induced variance. The dataset comprises 777 micrographs systematically acquired across eight controlled variation axes: four SEM instruments (Helios G4 PFIB CXe, Helios NanoLab, VEGA3 XMH, Zeiss Gemini; FEG and W sources), three detector types (SE, BSE, InLens), three accelerating voltages (5, 10, 20 kV), two etching reagents (Vilella's reagent, Nital), three sample states (as-cast; 980 °C + water quenched; 980 °C for 9 h + air cooled), two image scales (overview and detail), and two dwell-time categories. The defining feature of the dataset is its correlative structure. For each of 12 unique regions of interest (3 specimens × 2 image scales × 2 etching conditions), the identical microstructural area was imaged under as many acquisition conditions as physically permitted, and all micrographs of a stack were registered onto a common reference frame using SIFT-based and manual feature registration in Fiji. Consequently, the underlying microstructure remains fixed while the imaging modality varies systematically, and 12 manually annotated ground-truth masks propagate across the full set of 777 images. Annotation was performed class-wise using trainable WEKA segmentation with subsequent expert correction, exploiting complementary SE and BSE contrast; the label scheme distinguishes up to seven phase classes (austenite, fresh martensite, retained austenite, eutectic carbides, tempered martensite, secondary carbides, and a combined tempered martensite + secondary carbide class where resolution is insufficient), supporting binary, four-class, and seven-class segmentation tasks. Every micrograph is accompanied by a machine-readable metadata record (CSV) containing unique ID and filename, microscope, detector, accelerating voltage, magnification, etching agent, sample, imaging kind, pixel size, beam current, dwell time (numeric and categorical), chamber pressure, and working distance, with all numeric values in SI units. The dataset follows FAIR principles. The correlative design makes the data suitable for uses beyond standard supervised segmentation, including domain-adaptation and domain-generalization benchmarking, multi-detector fusion, metadata-conditioned segmentation, genuinely paired image-to-image translation between detectors and instruments, and paired denoising and super-resolution studies based on the low-/high-dwell-time and overview/detail acquisitions. A segmentation showcase using this dataset, together with a discussion of acquisition-related robustness and suggested directions for further work, is presented in the accompanying publication of the same title [Journal / DOI — to be added].

This record provides an open-access scanning electron microscopy (SEM) dataset of high-chromium cast iron (HCCI, 26 wt.% Cr) microstructures, designed to isolate acquisition-induced image variance from material-induced variance. The dataset comprises 777 micrographs systematically acquired across eight controlled variation axes: four SEM instruments (Helios G4 PFIB CXe, Helios NanoLab, VEGA3 XMH, Zeiss Gemini; FEG and W sources), three detector types (SE, BSE, InLens), three accelerating voltages (5, 10, 20 kV), two etching reagents (Vilella's reagent, Nital), three sample states (as-cast; 980 °C + water quenched; 980 °C for 9 h + air cooled), two image scales (overview and detail), and two dwell-time categories. The defining feature of the dataset is its correlative structure. For each of 12 unique regions of interest (3 specimens × 2 image scales × 2 etching conditions), the identical microstructural area was imaged under as many acquisition conditions as physically permitted, and all micrographs of a stack were registered onto a common reference frame using SIFT-based and manual feature registration in Fiji. Consequently, the underlying microstructure remains fixed while the imaging modality varies systematically, and 12 manually annotated ground-truth masks propagate across the full set of 777 images. Annotation was performed class-wise using trainable WEKA segmentation with subsequent expert correction, exploiting complementary SE and BSE contrast; the label scheme distinguishes up to seven phase classes (austenite, fresh martensite, retained austenite, eutectic carbides, tempered martensite, secondary carbides, and a combined tempered martensite + secondary carbide class where resolution is insufficient), supporting binary, four-class, and seven-class segmentation tasks. Every micrograph is accompanied by a machine-readable metadata record (CSV) containing unique ID and filename, microscope, detector, accelerating voltage, magnification, etching agent, sample, imaging kind, pixel size, beam current, dwell time (numeric and categorical), chamber pressure, and working distance, with all numeric values in SI units. The dataset follows FAIR principles. The correlative design makes the data suitable for uses beyond standard supervised segmentation, including domain-adaptation and domain-generalization benchmarking, multi-detector fusion, metadata-conditioned segmentation, genuinely paired image-to-image translation between detectors and instruments, and paired denoising and super-resolution studies based on the low-/high-dwell-time and overview/detail acquisitions. A segmentation showcase using this dataset, together with a discussion of acquisition-related robustness and suggested directions for further work, is presented in the accompanying publication of the same title [Journal / DOI — to be added].

Abstract Purpose Triple-negative breast cancer (TNBC) is a high-risk molecular subtype defined by absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2) overexpression. Immune checkpoint blockade (ICB) benefits only 20% to 40% of patients, with T cell exhaustion driven by chronic programmed death receptor 1 (PD-1, PDCD1) inhibitory signalling being the dominant barrier. This work presents a computational pipeline that converts single-cell immune phenotypes into per-patient synthetic immune-cell engineering recommendations, filling the gap for quantitative patient-level ligand design parameters. Methods The pipeline was applied to the GSE176078 single-cell RNA sequencing (scRNA-seq) atlas (100,064 cells, 26 treatment-naive TNBC patients). After quality control, normalisation, and Leiden clustering, T cells were extracted and scored for exhaustion and cytotoxicity using defined gene-set modules. The PDCD1/CD2 ratio was computed per cell and aggregated per patient. Cross-modal validation used TCGA-BRCA bulk RNA-seq through univariate Cox regression and Kaplan–Meier analysis. A targeted ligand-receptor proxy screen was performed across five receptor-ligand pairs and a rule-based DesignPriorityScore was assessed by bootstrap resampling ( n = 200). Results Leiden clustering was validated at adjusted rand index (ARI) 0.288 to 0.311 and normalised mutual information (NMI) 0.616 to 0.671. Three patient phenotype groups were identified based on exhaustion burden and PDCD1/CD2 imbalance. The bulk PDCD1/CD2 ratio showed an exploratory association with overall survival in TCGA-BRCA (HR=0.47, 95%CI 0.28–0.79; P < 0.005), reflecting immune infiltration rather than per-cell exhaustion state. Patient rankings were stable across bootstrap resamples (mean Spearman ρ > 0.85; top-quartile retention > 90%). Conclusion This pipeline shows that per-patient PDCD1/CD2 ratios derived from scRNA-seq can be translated into ranked synthetic ligand engineering priorities, offering a prototype framework for single-cell-informed synthetic immunology design in TNBC immunotherapy.

Machine learning (ML) for microstructure analysis has seen rapid progress, yet a fundamental challenge remains largely unaddressed: models trained on one imaging configuration frequently fail to generalize to others, a problem known as domain shift. This issue is particularly acute for complex steel microstructures — specifically bainite and martensite — whose visual appearance is highly sensitive to microscopy modality, magnification, and acquisition settings, and for which no standardized nomenclature or ground truth assignment procedure exists. This dataset was designed to systematically capture and enable the study of these imaging-induced variances. Its defining characteristic is the correlative, pixel-wise registration of identical regions of interest (ROI) imaged across three distinct light optical microscopes — an Olympus LEXT confocal laser scanning microscope, a Leica DM6000, and a Zeiss Axio Imager M2 — at multiple magnifications (20x, 50x, and 100x for the LSM) and under deliberately varied acquisition conditions, including aperture, exposure, and optical filter settings. Since registration to a common reference is performed prior to annotation, ground truth labels apply consistently across all imaging variants of each ROI, ensuring label coherence and eliminating the annotation bottleneck typically associated with multi-condition datasets. The dataset consists of 5,327 annotated image patches extracted from 20 steel samples representing a broad range of quenched and quenched-and-tempered microstructures, covering typical bainitic and martensititic constituents - simplified to two classes. Patches are provided at standardized sizes (256 px at 50x, 96 px at 20x), ready for direct use in standard convolutional neural network (CNN) architectures without further resizing. All micrographs are accompanied by harmonized metadata encoding microscope identity, magnification, pixel size, aperture, exposure level, and filter configuration, assigned via unique identifiers that allow full traceability of each patch to its imaging origin. In the accompanying publication, the dataset was used to systematically evaluate the influence of microscopy modality and magnification on CNN-based bainite/martensite classification, revealing a resolution-dependent asymmetry in cross-domain transfer, systematic class bias inversions under cross-scale validation, and a hierarchy of imaging variance types in their contribution to model robustness. Beyond this initial analysis, the dataset is well suited for cross-microscope and cross-magnification domain adaptation studies, generative image-to-image translation and super-resolution (enabled by the spatially aligned multi-condition image pairs), unsupervised representation and embedding analyses, the development and validation of domain coverage metrics, the study of metadata-informed or physics-aware ML approaches and much more. The dataset complies with FAIR data principles and is intended as a community benchmark for reproducible and transferable ML workflows in microstructure-based materials science.

The raw dataset comprises three distinct types of measurements: · Folder (1) ‘Files SEM’ contains scanning electron micrographs acquired with a Thermo Fisher Helios G4 PFIB CXe dual beam system. The metadata is contained within the files. · Folder (2) ‘Files CLSM’ contains .lext files corresponding to the characterization of laser structured Cu surfaces. These files are in a proprietary format used by the Olympus LEXT OLS4100 measurement software. All associated images and geometrical data are extracted directly from these measurement files. The metadata are included in the files. · Folder (3) ‘Files Tribometry’ includes the raw data from the tribometry measurements, provided in .ixf format from CSM Instruments, compatible with the InstrumX software. The information on the load, acquisition frequency, speed, number of cycles and counterbody is included in each measurement file. All files across the folders follow a standardized naming convention: For the SEM Files: date_material_structural period_surface inclination-track number For the CLSM Files: date_material_structural period_surface inclination-magnification For the Tribometry Files: material_structural period_surface inclination-number of sliding cycles-track number

The raw dataset comprises three distinct types of measurements: · Folder (1) ‘Files SEM’ contains scanning electron micrographs acquired with a Thermo Fisher Helios G4 PFIB CXe dual beam system. The metadata is contained within the files. · Folder (2) ‘Files CLSM’ contains .lext files corresponding to the characterization of laser structured Cu surfaces. These files are in a proprietary format used by the Olympus LEXT OLS4100 measurement software. All associated images and geometrical data are extracted directly from these measurement files. The metadata are included in the files. · Folder (3) ‘Files Tribometry’ includes the raw data from the tribometry measurements, provided in .ixf format from CSM Instruments, compatible with the InstrumX software. The information on the load, acquisition frequency, speed, number of cycles and counterbody is included in each measurement file. All files across the folders follow a standardized naming convention: For the SEM Files: date_material_structural period_surface inclination-track number For the CLSM Files: date_material_structural period_surface inclination-magnification For the Tribometry Files: material_structural period_surface inclination-number of sliding cycles-track number

This interim report of NFDI-MatWerk outlines the consortium's progress towards a National Research Data Infrastructure for Materials Science & Engineering in Germany, covering the period from October 2021 to August 2024. Part B-1 of the report is published on the DFG website. Part B-2 is published here.

This interim report of NFDI-MatWerk outlines the consortium's progress towards a National Research Data Infrastructure for Materials Science & Engineering in Germany, covering the period from October 2021 to August 2024. Part B-1 of the report is published on the DFG website. Part B-2 is published here.