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In this study, we found that infection with two unrelated RNA viruses reduced amyloid-beta plaque burden in a mouse model of amyloidosis. There is 1 Excel file containing all raw data generated from mice.

This dataset contains multimodal neuroscience data collected from 25 adult Thy1-GCaMP transgenic mice (~8–12 months of age) undergoing photothrombotic (PT) stroke, generated to assess functional and behavioral changes at acute and chronic timepoints following ischemic injury. Data were acquired from a single biological source — live mice — with tissue samples subsequently processed for post-mortem histological analysis. Three data modalities are included: widefield calcium imaging (WFCI) capturing cortex-wide neural activity via the genetically encoded calcium indicator GCaMP; behavioral data from the cylinder rearing test, which quantifies forelimb asymmetry as a measure of sensorimotor deficit; and histological data comprising both Cresyl Violet (Nissl) staining for lesion visualization and immunohistochemistry (IHC) for protein-specific labeling. Each mouse was assessed at three experimental timepoints: baseline (pre-stroke), acute (24 hours post-PT stroke), and chronic (one week post-PT stroke), with histology collected only at the post-chronic endpoint. The dataset is hierarchically organized under a top-level /data directory containing three primary modality folders: /behavior, /WFCI, and /histology. The /behavior folder is organized by mouse ID (e.g., /M01) and then by timepoint (/bsl, /acute, /weekone), containing raw video recordings, Excel-format scoring sheets, and aggregated GraphPad Prism files summarizing results across all animals. The /WFCI folder is subdivided into three processing tiers — /Raw (unprocessed imaging files), /Tier1_Derived (intermediate processed data), and /Tier2_SourceData (analysis-ready outputs organized by mouse and timepoint) — supporting reproducibility from raw acquisition through final analysis. The /histology folder contains two subfolders, /cresyl_violet and /IHC, holding stained tissue section images. File formats include TIFF images for histology, CSV and XLSX for behavioral scoring, and standard video formats for raw behavior recordings. The organizational rationale — mouse ID → modality → timepoint — enables straightforward longitudinal comparisons within subjects and cross-sectional comparisons across the cohort. Data were generated by Jake Lee and Arnav Ajay Jadav in the Landsness Lab, Department of Neurology, Washington University in St. Louis (contact: jake.l@wustl.edu), and are suitable for analyses of stroke-induced changes in cortical dynamics, motor behavior, and structural pathology.

This dataset provides numerical simulation inputs and archived results for behavior-enhanced network clustering and data-driven robust consensus in large-scale group decision making. It supports the numerical example, sensitivity analyses, comparative analyses, component ablations, and supplementary validation reported in the accompanying manuscript. The data include initial expert evaluations, incomplete directed trust information, behavioral parameters, attribute weights, adjustment-cost samples, clustering outputs, consensus trajectories, and optimization results. Extended validation covers attribute-weight perturbations, out-of-sample cost evaluation, scale experiments with different group sizes, and a pure-similarity network configuration. Run records retain parameter settings, random seeds, solver information, and termination outcomes. Files are provided as NumPy arrays (.npy) and JSON records, accompanied by an English README and a SHA-256 checksum manifest. The documentation explains variable definitions, indexing conventions, cost scaling, measurement conventions, and correspondence with the manuscript and supplementary material. All data originate from numerical simulations. This deposit retains the original 17 data files available at https://doi.org/10.17632/fw7d7v8gwz.1 and adds the extended validation data and documentation. It is a separate dataset record rather than a new version under the original DOI. The archive contains data and documentation; analysis and solver source code are not included.

This dataset contains gene-expression data from oocyte-associated cumulus cells collected from MSTN (myostatin)-mutant and wild-type animals. It is intended to characterize transcriptomic differences associated with MSTN mutation and investigate the molecular mechanisms underlying cumulus-cell function, oocyte maturation, and oocyte–cumulus cell communication. The dataset can be used to identify differentially expressed genes (DEGs), investigate biological pathways and molecular functions, and explore changes in processes such as follicular development, cell signaling, metabolism, extracellular matrix remodeling, and reproductive function. Comparative analysis between the two genotypes may reveal candidate genes and pathways associated with altered cumulus-cell biology and oocyte developmental competence.

This dataset contains X-ray diffraction data used to characterize phase evolution in kaolin and kaolin-graphite ceramics before and after sintering. The deposited records cover unfired K0, sintered K0, and sintered KG3, KG5, and KG10 compositions.

Data and reproducible code for “Target Scale Degree and Accuracy in Cued Two-Note Sight-Singing” (RECIEM). The main dataset contains 6,656 observed binary scores from 104 music students completing 64 staff-notated two-note items in C major (C4–C5), without notated rhythm or a time limit. Each item followed a C4–E4–G4 tonic chord and a supplied starting pitch; item order was fixed. Correct = 1 only when both starting and target pitches were correct on their final renditions; spontaneous corrections were allowed. Scoring was auditory. A separate preliminary sample (n = 57) is not included. Forty randomly selected main-study recordings were independently rescored: 99.29% agreement over 2,240 paired decisions (Cohen’s κ = .986, 95% participant-bootstrap CI [.977, .993]). Original main-study scores were retained. Primary logistic mixed-effects models use 56 non-unison items (5,824 responses) to assess categorical target degree, interval category, and melodic direction. A post hoc 49-item comparison (5,096 responses) examines tonic-chord targets versus other diatonic targets. In this single-key, one-octave design, degree also identifies exact pitch/register. Sensitivity checks cover C4 exclusion, item order, interval quality, expanded random effects, and starting degree. A further post hoc comparison uses 11 cells containing both directions (22 items; 2,288 responses); starting pitches are not matched. Analyses are exploratory where indicated and were not preregistered. The ZIP contains 17 files: three unchanged observed-data inputs, twelve R/Python scripts, a README, and checksums. Six analysis workflows, agreement statistics, and figures can be regenerated; outputs are produced under rerun/. No responses were imputed or altered. Findings are task-specific associations, not evidence of a causal direction effect or an isolated tonal-hierarchy mechanism.

This is the original research dataset associated with the manuscript entitled "Impacts of plasticizers in fin whales (Balaenoptera physalus): Exploring endocrine disruptive potential of phthalates and their metabolites using in vitro methods." Please see the READ ME.txt file for descriptions of all files and associated metadata. Methodology is described in the manuscript and associated supplementary materials.

Dataset underlying the analysis in the above mentioned paper.

Microscopy images and source data related to "Structural and functional insights into the Rad51 paralog complexes Shu and Rad55-Rad57 in association with Rad51 in homologous recombination"

Data and code for the article "Devitrification-aware multi-objective inverse design of oxide optical glasses using explainable machine learning" (A. Gahmousse, A. Gahmousse, H. Taallah). Data: two CSV files of oxide glass compositions (atomic fractions) extracted from the SciGlass database. TgTliqND300oxides.csv gives the refractive index, glass transition temperature and liquidus temperature (K); ICMC_oxides_Abbe.csv gives the Abbe number. Code: the Jupyter notebook MLGLASS.ipynb (Python 3.14.7) trains Random Forest models for the four properties, interprets them with feature importance, SHAP and accumulated local effects, and performs a four-objective NSGA-II inverse design over a charge-neutral composition space restricted to an applicability domain. It reproduces all results, Tables 1 and 2 and Figures 1 to 10 of the article. MLGLASS_py3139.ipynb is an earlier run of the same notebook under Python 3.13.9. The Python scripts compare the Random Forest with XGBoost, LightGBM and ElasticNet on identical cross-validation folds (Table 1) and run the nested hyperparameter searches (Supplementary Table S1). The outputs folder contains the result tables. README.txt gives the file descriptions, run order and software versions. The data are derived from SciGlass (https://github.com/epam/SciGlass) and are distributed under the same Open Database License (ODbL).