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Study question: Does fetal sepsis alter the regional proteomic profile of the canine placenta, and are these changes associated with distinct functional alterations across placental regions? The data presented reflect the regional placental proteomic profile of canine fetuses distributed into two groups: healthy (HEA, n = 4) and septic (SEP, n = 4). Three placental regions were sampled from each placenta: marginal hematoma (MARGINAL), central placental labyrinth (MIDDLE), and umbilical cord insertion (CORD). For proteomic analysis, placental samples were prepared and subjected to in-gel tryptic digestion, followed by individual analysis using LC–MS/MS through a shotgun proteomics approach. The dataset comprises proteomic data from the different placental regions of healthy and septic canine fetuses, allowing comparisons of protein abundance between experimental groups and anatomical regions. A total of 3,461 proteins were identified across the placental regions. Proteomic data were subjected to multivariate and univariate statistical analyses, including volcano plots and ANOVA followed by Fisher's LSD test, as well as gene ontology categorization, protein–protein interaction analysis, and clustering. Overall, 265 upregulated and 74 downregulated proteins were identified in the SEP versus HEA comparison across all placental regions. These data provide a resource for investigating region-specific proteomic alterations associated with fetal sepsis and the functional pathways involved in canine placental dysfunction.

We provide an SPSS dataset used to prepare a research article entitled "Stress and Sleep Quality in University Students: The Role of Cognitive Pre-Sleep Arousal and Coping." The following abstract provides an overview of our aims, findings, and limitations: High amounts of stress as well as sleep disturbances are common among students, with higher perceived stress being associated with poorer sleep quality. It is also known that higher stress increases cognitive pre-sleep arousal (PSA) and that PSA mediates the stress-sleep relationship. Furthermore, it has been proposed that coping styles (in particular, problem-oriented coping) moderate the relationship between stress and PSA. To further investigate this empirical evidence, we conducted an online study analyzing the influence of cognitive PSA and problem-oriented coping on the stress-sleep relationship suggesting a moderated mediation model. A total of N = 220 university students (174 female, 43 male, 3 diverse; age: M = 25.15 years) with diverse subjects of study completed self-report measures of stress (Perceived Stress Questionnaire), subjective sleep quality (Pittsburgh Sleep Quality Index), cognitive pre-sleep arousal (Pre-Sleep Arousal Scale), and problem-oriented coping (Stress and Coping Inventory). As expected, higher perceived stress predicted poorer subjective sleep quality. Furthermore, cognitive PSA partially mediated the link between stress and sleep quality. However, contrary to expectations, problem-oriented coping did not moderate the association between stress and cognitive PSA. Interestingly, an exploratory serial mediation model indicated that problem-oriented coping decreases stress (first mediator) and that stress predicts cognitive PSA (second mediator), which in turn predicts poorer subjective sleep quality. These findings suggest that the effect of problem-oriented coping on subjective sleep-quality is indirect, being completely mediated by stress and cognitive PSA. According to the present data, the role of problem-oriented coping for the stress-sleep relationship may lie in reducing stress initially, rather than directly influencing the relationship between stress and cognitive PSA. Limitations, recommendations for future research, and practical implications are discussed, including the role of stress and sleep for physical and mental health and the importance of stress management as well as sleep focused interventions.

MALDI TOF MS spectra

Natural radioactivity and radiological hazard assessment of mine tailings, soils and sediments in the historic silver mining district of Zacatecas, Mexico

This workbook contains eight worksheets with measurements from two champion CZTSe solar cells from the same absorber batch, one with a CdS electron transport layer and the other with a ZTO electron transport layer. For each device, the workbook includes illuminated and dark current density–voltage (J–V) curves, an external quantum efficiency (EQE) spectrum, and a reflectance spectrum. The first two rows of each worksheet identify the measured quantities and their units. J–V measurements were performed at 25 °C, scanning from reverse to forward bias. Illuminated curves were recorded under simulated AM1.5G illumination (100 mW cm⁻²). Current density was normalized to the total cell area of 0.087 cm². Photocurrent is reported as positive at short circuit. In all four J–V worksheets, the measured current density reaches a lower limit of approximately −120.7 mA cm⁻² at high forward bias.

Heat-treated microalgae may be candidate sorptive materials for contaminated waters, but a decrease in the aqueous analytical signal does not establish a sorption mechanism. We compared chromatographic responses for clomazone (nominally 20 µg L⁻¹) and imazethapyr (100 µg L⁻¹) after contact with heat-treated biomass of Chaetoceros muelleri, Desmodesmus sp., and Scenedesmus obliquus. Each herbicide–biomass combination was evaluated across 12 central-composite-design runs (pH 5.6–8.4; NaCl preparation concentrations 28.02–165.20 mg L⁻¹). The endpoint was the calculated relative change between initial and final aqueous LC–ESI–MS/MS peak areas, rather than a recovery-corrected mass-removal efficiency. Positive calculated values occurred in 30 of 36 clomazone runs and 5 of 36 imazethapyr runs. For clomazone, all 12 responses were positive with S. obliquus (10.55 ± 4.08% across design conditions) and Desmodesmus sp. (16.47 ± 19.06%), although the latter showed greater dispersion. Responses for imazethapyr were predominantly negative or inconsistent. Five of six fitted quadratic models showed significant lack of fit and did not support process optimization. Because herbicide-only controls, biomass blanks, matrix-matched recovery measurements and a solid–liquid mass balance were unavailable, the observed changes must be interpreted as apparent aqueous-phase signals; biosorption was not demonstrated. For treatment-development decisions, the results prioritize confirmatory tests of clomazone with S. obliquus and Desmodesmus sp., without demonstrating remediation performance.

This dataset contains the network topologies, topological metrics, and simulation results supporting the study "Efficient Identification of Low-Blocking Topologies in Optical Networks through Topological Parameters". It covers 22 reference optical network topologies and their structural variations generated by adding or modifying links. The repository is organized into three main folders: Topologies/: Contains one folder per base network (<network>_topologies), subdivided into draw/ (the original reference topology in .graphml format) and topologies/ (text files describing the adjacency/link structure of each modified topology variant). Blocking/: CSV files (<network>_blocking.csv) with the simulated request blocking ratio across different traffic loads for each topology variant. Topology_analysis/: CSV files (<network>_analysis.csv) summarizing the extracted topological parameters (such as shortest-path counts, node and edge betweenness centrality standard deviations, and centralization metrics) alongside the resulting blocking performance and ranking position for each variant.

This dataset and code archive supports the revised manuscript “Direct maize–wheat comparison of yield associations with co-located precipitation-deficit–heat concurrence across African cropping systems.” The study compares national maize and wheat yield associations with hot–dry concurrence across 11 African countries during 2001–2018. Precipitation deficit, maximum-temperature anomaly, and their continuous product were constructed within the same native NASA POWER cell and active MIRCA crop month before crop-season and national aggregation. An outcome-blind, pair-preserving overlap rule retained 192 of 198 matched country-years (384 crop observations). The locked pooled model includes country × crop effects and linear trends, crop × year effects, and irrigation-composition interactions; uncertainty is evaluated with country-clustered CR1 inference and exhaustive restricted-null wild-cluster inference. The archive contains the frozen analytical specification, implementation addenda, source manifests, SHA-256 checksums, MIRCA-to-POWER mappings, derived native cell-month and crop-season exposures, common-support scores, national yield and irrigation inputs, the locked analytical panel, model and numerical-audit outputs, rainfed-exposure and FAO-56 SPEI-3 measurement sensitivities, an independent numerical validation, and code that regenerates the manuscript tables and figures. The primary result is a conditional national cropping-system contrast and should not be interpreted as an intrinsic physiological crop parameter. The two exposure sensitivities share observations with the primary analysis and are not independent replications. Public source data are attributed to FAOSTAT/Our World in Data, NASA POWER, and MIRCA-OS. Third-party terms remain controlling for source-derived material. The README provides offline primary reproduction, archive verification, figure/table generation, and an optional full source-to-results rebuild.

This dataset (ML-CFD-Wavy-Channel-Surrogate_version3.zip) supports the published article “Machine learning surrogate for CFD-based prediction of heat transfer and pressure drop in partially porous wavy channels” in AI Thermal Fluids. It contains the cleaned long-form CFD-derived dataset, fixed train-test split information, trained-model outputs, prediction files, validation tables, plot data, and the final reproducibility script used to train Random Forest surrogate models for predicting average Nusselt number and pressure drop. The dataset includes 4,608 CFD-derived samples generated from 18 geometric configurations and 256 operating-condition combinations. The input features are Reynolds number, Prandtl number, Darcy number, porosity, porous slab thickness, wave amplitude, and wavelength. The target outputs are average Nusselt number and pressure drop. The associated published article is available at: https://doi.org/10.1016/j.aitf.2026.100051

Indic scripts have played a significant role in preserving the linguistic, literary, cultural, and historical heritage of the Indian subcontinent. Several Indian languages have evolved through distinct writing systems, each possessing unique character structures, writing styles, and graphical characteristics. These scripts are widely used in historical manuscripts, literary works, inscriptions, and other forms of written documentation. The development of automated recognition systems for Indic scripts is an important research area, particularly for handwritten character recognition, digital document preservation, and multilingual optical character recognition (OCR). This dataset presents a collection of handwritten character images from seven Indic scripts: **Devanagari, Kannada, Bengali, Nandinagari, Sharada, Telugu, and Malayalam**. It is designed to support research and experimentation in multiscript character recognition, handwritten character classification, and image-based script processing. The dataset is organized into separate categories for vowels, consonants, and numerals. Each character class contains 100 handwritten image samples. All images are converted into grayscale-derived binary (black-and-white) representations and normalized to a uniform resolution of **28 × 28 pixels**. This standardized image format facilitates consistent preprocessing and provides a suitable input representation for machine learning and deep learning-based character recognition models. For the seven scripts with the currently documented class counts, the dataset comprises 426 distinct character classes and 42,600 handwritten character images. The class distribution includes consonants, vowels, and numerals, providing a structured and balanced collection for character-level recognition experiments. The dataset can be utilized for developing and evaluating machine learning and deep learning approaches for handwritten Indic character recognition, multiscript classification, comparative analysis of script recognition models, and preprocessing techniques for low-resolution handwritten character images. It may also serve as a foundation for further research in multilingual OCR systems and digital preservation of Indic language resources.