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This dataset contains the open-source design and implementation files for a portable wireless optoelectronic system for liquid refractive-index sensing based on a U-shaped plastic optical-fiber (POF) sensor. The repository includes electronic design files, PCB fabrication files, editable mechanical and 3D-printing files, microcontroller firmware, Android application files, web and server software, database files, and supporting documentation required to reproduce and modify the platform. The system integrates multispectral optical detection, embedded processing, automated positioning, temperature monitoring, wireless communication, and mobile/web-based visualization. These resources accompany the related HardwareX article and are provided to support reproducibility, adaptation, and further development of low-cost optical sensing systems.

Experimental data demonstrating successful propagation of a human gut-associated picobirnavirus in bacterial cells, and thus proving the picobirnavirus is an RNA phage. Data were generated from germ-free mice and bacterial cell community cultures. A total of 68 files were provided, including qPCR/qRT-PCR, flow cytometry, fluorescence microscopy, and sequencing data.

Supplementary Fig. S1. Endothelial immunohistochemical staining. (A–C) Vascular endothelial cells are positive for ERG (A), CD34 (B), and CD31 (C) (original magnification, ×200). Supplementary Fig. S2. Additional immunohistochemical and histochemical findings. (A) CD123 staining highlights the endothelium of some small vessels. (B) CD20 staining shows focally distributed B cells. (C) Alcian blue–PAS staining shows no definite dermal mucin deposition. (D) CD3 staining shows a predominantly T-cell infiltrate (original magnification, ×100).

Child health in sub-Saharan Africa

This repository contains data associated with the X-BIO project: Cross-Architecture and Energy-Efficient Parallel Metaheuristics for Bioinformatics (PID2022-137275NA-I00), funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU. Data has been organized in the following folders: * CrossSustainability contains PHYLIP datasets used for the evaluation of cross-architecture models and sustainability aspects when running phylogenetic algorithms. Two Excel files (Profiling22cXeon6238.xlsx, Profiling32cEpyc7543.xlsx) are provided with energy results from two different platforms, reporting execution time (column "Time(s)"), power (column "Power(W)"), energy (column "Energy(J)"), and energy-delay product (column "EDP(J*s")). An additional file (ProfilingAccelerator.xlsx) reports profiling data from hardware accelerators, comparing time results from cross-architecture models. * Distances contains distance matrices in PHYLIP format, where the character '.' is employed to denote a missing distance between two organisms. Two Excel files containing profiling data on two different systems are provided. These Excel files contain measurements of execution time (in seconds) required by different imputation methods (column "Method") under different percentage of missing distances (from "5%" to "60%", with each percentage being in a separate column). The last column "Average" provides the average time across percentages. * Microbiome contains CSV data for microbiome research. OTU tables are given by the YB-GG-data.csv file, with each column denoting a separated OTU and each row a different sample. The YB-GG-labels.csv provides the labels for each OTU, using two columns (SampleID and label) to make the association. * MSA contains multiple sequence alignments in PHYLIP format, where the character '?' is employed to denote unknown site values. An Excel file containing profiling data under serial and parallel versions of different algorithms is provided. The upper half of the table in the file reports average Normalized Robinson-Foulds for the serial (column "NRF-Serial version") and parallel (column "NRF-Parallel version") implementations on each dataset. The lower half of the table reports execution times (in seconds) for each version (columns "Time-Serial version" and "Time-Parallel version"). * Phylogenetic contains reference sequences (PHYLIP format), reference phylogenies (Newick format) and query sequences (PHYLIP) for phylogenetic placement and evaluation. An Excel file containing profiling data for different search implementations is provided. The first column reports the name of the dataset and its experimental scenarios (i.e., number of query sequences). The remaining columns report the execution time (in seconds) for different independent runs (columns "Execution"), the median time (column "Median"), the variation coefficient (column "Var. Coef."), speedups over the serial version (column "Speedup") and parallel efficiency (column "Efficiency").

This dataset contains simulation-derived data used for the forward modeling and inverse design of compact lumped-element nonlinear transmission lines (NLTLs). The data were generated using a nonlinear RLC time-domain simulation framework implemented in MATLAB. The design variables include the series inductance L, zero-bias capacitance C0, capacitance reference voltage Vr, and the number of NLTL sections N. The extracted response quantities include output amplitude Aout, dominant oscillation frequency fout, transient energy Etr, and maximum current slew rate SImax. Three dataset sizes are provided, containing 1,960, 5,040, and 9,680 samples, respectively. These datasets were used to evaluate RBF, SVR, MLP, and MVPC-MTNet models for forward modeling and inverse parameter identification.

This dataset supports the quantitative analysis of sunspot group nesting, which is the tendency of solar active regions to emerge repeatedly in the same longitude-latitude zone over the course of a solar cycle. It contains processed sunspot group emergence records from two major observational catalogs, the Royal Greenwich Observatory Photoheliographic Results (RGO, 1874–1976) and the Kislovodsk Mountain Astronomical Station (KMAS, 1955–2025), together covering 14 solar cycles. Each record corresponds to the first appearance of a sunspot group on the solar disc, with its Carrington longitude, heliographic latitude, and maximum observed area in the course of its later evolution. Using these data, nesting was identified and quantified via a Kernel Density Estimation (KDE) informed DBSCAN clustering algorithm applied to the longitude-time plane in 5-degree latitude bands, separately for each solar cycle and hemisphere. The resulting nesting statistics (including nesting degree, number of significant nests, and characteristic nest separations) are provided as tabular data alongside longitude-time diagrams illustrating the identified nesting patterns. The dataset enables reproducibility of the results in the associated publication and facilitates comparison with future observational or modelling studies of solar magnetic flux emergence. See the README for details of the files provided.

This dataset contains public input data and processed benchmark outputs for deformation-induced energy-yield loss assessment in flexible photovoltaic mounting structures. It includes NASA POWER and PVGIS hourly inputs, extracted carbon-factor records, regional energy-mechanics benchmark tables, mechanics verification outputs, temporal loss diagnostics, Pareto scenario data, uncertainty/sensitivity results, and checksum metadata. The package is restricted to data files only, including CSV, JSON, MAT, and XLSX formats. It excludes manuscript files, figures, source code, executable workflows, LaTeX files, and documentation artifacts. China carbon records are provided as evidence-graded diagnostic data, while Phoenix eGRID conversion is the verified regional carbon case.

Raw data for the article "Agricultural prices and rural credit dynamics in Mato Grosso, Brazil: VECM evidence from crop and livestock". Codes are not avaliable, check material and methods of manuscript to replicate. Metadata avaliable in the file.

This data assesses the determinants influencing students’ intention to utilize AI-powered virtual tutors for digital English education. The investigation is grounded in a theoretical framework proposing that six specific features of virtual tutors, aesthetics, anthropomorphism, availability, empathy, enjoyment, and learning content, affect users’ behavioral intentions through the dual mediating roles of perceived trust and perceived value. A quantitative research methodology was adopted, utilizing a structured online questionnaire distributed to high school and university students in Vietnam. The study implemented a non-probability convenience sampling technique to gather information from the target demographic. The survey tool was designed to record demographic profiles alongside respondents’ perceptions regarding the nine established theoretical constructs. The data collection process resulted in 645 valid responses, organized into a dataset containing 40 columns, where the initial four columns detail demographic characteristics and the subsequent 36 columns represent the individual measurement items for the variables. The provided file, named AI-powered.virtual.tutors.for.digital.English.learning_dataset, delivers detailed insights into how specific technological attributes influence learners’ cognitive and emotional assessments. The analytical outcomes indicate that the majority of the hypothesized AI tutor features significantly enhance both perceived trust and perceived value. Notably, the data reveals one exception: anthropomorphism did not demonstrate a statistically significant effect on the perceived value of the tutor. Furthermore, the statistical results confirm that both perceived trust and perceived value act as robust predictors that strongly drive the students’ ultimate intention to use these digital learning systems. These findings present empirical guidance for educational technology developers and instructional designers to optimize virtual tutoring platforms. By prioritizing elements that actively build trust and educational value, such as high-quality learning content, system availability, and empathetic interactions, creators can more effectively foster student adoption. Additionally, this dataset functions as a valuable asset for exploring the technological drivers of AI acceptance in modern education. Scholars can utilize this information to test extended educational technology acceptance models, conduct comparative analyses between different educational tiers (high school versus university), and develop targeted strategies for implementing AI assistance in language acquisition programs.