Share your research data

De-identified qualitative dataset of Black and Latino young men in Tampa Bay, Florida. Interviews were conducted across three phases of data collection between 2020 and 2023. While semi-structured interview guides were used, questions were iteratively refined to follow emerging themes. The analytic dataset includes participant-level experiential theme tables documenting coded excerpts and representative quotations linked to transcript locations.

• The dataset provides combined optical, modal, spectral, and acoustic measurements of plasma-assisted swirl flames, enabling a comprehensive assessment of how sinusoidal plasma discharges affect flame structure, stability, and combustion dynamics. • High-speed CH* chemiluminescence images document the evolution of flame lift-off, attachment, reaction-zone position, and heat-release fluctuations across equivalence ratios from Φ = 1.0 to Φ = 0.45, under plasma-off and plasma-on conditions. • The data demonstrate that plasma actuation extends the stable operating range of the combustor: at Φ = 0.45, a self-sustained flame is observed only when plasma is applied, providing direct experimental evidence of plasma-enhanced lean flame stabilization. • POD and SPOD results quantify the redistribution of fluctuation energy among coherent flame structures, showing that plasma reduces the dominance of low-order modes and suppresses energetic high-frequency oscillations. • The simultaneous analysis of microphone signals and POD mode coefficients links acoustic pressure fluctuations to flame-structure dynamics, supporting the interpretation of plasma-induced stabilization through reduced unsteady heat release and improved flame anchoring. • The dataset can be used to validate reduced-order and high-fidelity models of plasma–flame interaction, including models of plasma-enhanced ignition, radical production, mixing, heat release, and thermoacoustic response. • The paired plasma-off and plasma-on measurements, acquired under identical imaging settings, provide a consistent benchmark for future experimental, numerical, and machine-learning-based studies of plasma-assisted combustion.

HAMOS is a multimodal, multilingual dataset for fine-grained aspect-based sentiment analysis of hotel reviews. Each review is paired with its images and original captions, and every opinion is annotated as a sentiment quadruple — (aspect term, aspect category, opinion term, sentiment polarity) — under a taxonomy of six aspect categories, 31 sub-aspects, and three polarities, with emojis preserved and explicit/implicit expressions distinguished. It contains 3,399 hotels, 8,796 reviews, 12,601 text segments, 9,219 images, and 23,995 quadruples across English, Vietnamese, and other languages, collected from Booking.com and split into hotel-disjoint train/validation/test sets. Data are provided in JSON, JSONL, and CSV formats.

This study aimed to evaluate the effects of pecan (Carya illinoinensis) exocarp mulching on soil microbial community structure after 12-month treatment, including Cover group (F1,F2,FH3,FS4,FS5) and Uncover group (B1,B2,B3,BS4,BX5). The experiment was conducted in a newly established pecan (Carya illinoinensis) orchard located in Shiling Village, Hangtou Town, Jiande City, Hangzhou, Zhejiang Province (29°13'56.9512"N, 119°12'12.9016"E). This new orchard block was established for a cultivar configuration trial; before planting, the site was uniformly cleared, deep-tilled, leveled, and planted. The experimental trees were one-year-old transplants after transplanting, with similar age, vigor, and site conditions. Five independent trees were used per treatment, and each tree was considered an experimental unit. Mulched and unmulched trees were arranged in an alternating and randomized spatial pattern within the same orchard block to balance potential environmental gradients. All trees received identical management, including irrigation, fertilization, weed control, and pest management. In the treatment group, crushed pecan exocarp (pieces <3 cm) was evenly applied within a radius of 1.5 m around the tree trunk, avoiding a 20 cm area around the trunk base, at a thickness of approximately 5–10 cm. The control group was managed conventionally without any exocarp covering. The experiment lasted for 12 months (from Year 1 to Year 2). At the end of the experiment, soil samples were collected from the 20–30 cm depth below the surface within a 1 m radius around the trunk base of each tree. For each replicate, soil from three sampling points was pooled to form one composite sample. After removing stones and debris, the samples were brought to the laboratory. A portion of fresh soil was used for microbial community analysis, and the remaining soil was air-dried for determination of soil physicochemical properties.

This repository provides a comprehensive mid-infrared (FTIR) spectral dataset of defect-free and defective coffee beans at both green and roasted processing stages, together with spectral preprocessing workflows and machine learning tools for defect classification. The dataset includes spectra acquired in the wavenumber range of 4000–650 cm⁻¹ using ATR-FTIR spectroscopy, representing Control samples (defect-free) and five industry-relevant defect categories: bitten, discolored, insect-damaged (drill bit), sour (vinegar), and black defects. To support robust spectral analysis and facilitate reproducible modeling, the repository also includes spectra preprocessed using commonly applied chemometric techniques, including baseline correction, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay first and second derivative transformations. These preprocessing methods allow users to evaluate the influence of spectral correction strategies on classification performance and feature extraction. In addition to the spectral datasets, this repository provides R-based machine learning workflows for the simultaneous classification of defective and non-defective coffee samples in both green and roasted states. The computational tools include Support Vector Machine (SVM) and Random Forest (RF) algorithms, together with scripts for data preprocessing, model calibration, validation, and performance evaluation. These tools enable reproducible development and benchmarking of classification models for spectroscopy-based food quality assessment. This dataset may be valuable for researchers in food science, spectroscopy, chemometrics, and machine learning, as well as for coffee industry stakeholders interested in developing rapid, non-destructive quality control systems. Furthermore, the availability of both spectral data and computational tools facilitates reuse in applications such as food authentication, defect detection, and the development of data-driven quality monitoring strategies in agri-food systems.

This dataset presents the experimental and numerical simulation data for the manuscript "Mechanical behavior and reinforcement mechanism of polymer-stabilized sand pile groups under horizontal loading". The experimental data were generated through indoor model tests conducted in a custom 1 m × 1 m × 1 m box filled with Longyan sandy soil, using a pulley system for horizontal loading and a geotechnical data acquisition instrument for monitoring. The dataset includes load-displacement curves, pile strain records, and soil stress data for single, double, and triple pile configurations, as well as shear strength parameters of polymer-sand mixtures with varying polymer contents (0%, 15%, 20%, 25%). Numerical simulation data were produced using ABAQUS, containing model input files and output results for different pile numbers and arrangement patterns (parallel, perpendicular, and equilateral triangular). All data are provided in standard formats (Excel, CSV, and ABAQUS input/output files) with a README file describing the file structure, units, and experimental conditions. No missing data were encountered, and measurement errors were within the standard accuracy of the calibrated instruments. These data support the analysis of horizontal bearing capacity and reinforcement mechanisms of polymer-stabilized sand pile groups.

This dataset contains the processed coefficient-of-friction (COF) data used in the study “Prediction of friction service life for high-entropy alloy/carbon multilayer coatings.” The data were obtained from reciprocating friction and wear tests of three groups of FeCoNiCrMn high-entropy alloy/carbon (HEA/C) multilayer coatings deposited on H13 steel substrates. The friction tests were conducted at a sliding speed of 12 mm/s, a reciprocating amplitude of 6 mm, an ambient temperature of 20 °C, and a relative humidity of 30%. The sampling frequency was 20 Hz. Groups I and II were tested under a Hertzian contact pressure of 290 MPa, whereas Group III was tested under 365 MPa. Groups I and II had different multilayer structural parameters, while Groups II and III had the same coating structure but different loading conditions. The continuously recorded friction data were processed to obtain coefficient-of-friction sequences for subsequent degradation analysis. These data were used to construct RMS-based friction degradation indicators, identify the onset of significant degradation, determine the functional failure position, and develop and validate the remaining friction service life (RFSL) prediction model based on a nonlinear Wiener process. The dataset provides the friction-state evolution data supporting the analyses and results reported in the associated article and can be used for further studies on coating friction degradation, degradation-feature extraction, and remaining service life prediction.

This repository contains anonymized research materials supporting the article: “Design and Evaluation of a Human-in-the-Loop Expert System for Sustainability Gap Analysis and Academic Expertise Matching in SMEs”. The repository provides the datasets, prompt templates, and evaluation artefacts used in the development and assessment of LAB2FAB, a human-in-the-loop expert system that combines sustainability assessment, knowledge-based inference, generative AI, expertise retrieval, and human supervision. Repository contents: • README.md - Description of the dataset package, structure, and anonymization procedures. • 01_sdg_criteria.md - Seven SDG-aligned sustainability assessment criteria in Portuguese, together with their associated Sustainable Development Goals and targets. • 02_prompt_templates.md - Prompt templates used during the experimental evaluation, including keyword extraction, keyword refinement, gap generation, and recommendation generation prompts across the evaluated prompting strategies. • 03_sus_item_distribution.csv - Item-level response distribution for the System Usability Scale (SUS) evaluation (N=10). • 03_sus_summary.md - Summary statistics of the SUS evaluation, including overall usability measures reported in the study. • 04_insitu_ratings_by_organisation.csv - Aggregated organisational ratings for generated artefacts collected during the in-situ evaluation. • 04_insitu_ratings_by_dimension.csv - Aggregated ratings by evaluation dimension (keywords, gap statements, and recommendations) covering 113 assessed gaps. The repository intentionally excludes identifiable SME assessment responses, organisational records, specialist identities, and other commercially sensitive data. All published files have been anonymized and aggregated to comply with confidentiality obligations and data protection requirements. The objective of this dataset is to support transparency, reproducibility, and future research on human-in-the-loop expert systems, sustainability assessment, generative AI, and academic expertise matching.

This dateset contains three stage of liver condition along with clinical data and demographic data on the classification of Non-Alcoholic Fatty Liver Disease (NAFLD). This dataset meant to help in medical image analysis, machine learning, and deep learning for early liver disease detection and severity assessment. The ultrasound images are acquired with standard diagnostic ultrasound equipment in B-mode. Ultrasound imaging is a widely used, and cost-effective modality for liver assessment, making it particularly suitable for large-scale screening and early detection of NAFLD. Each sample is labelled according to NAFLD status, and disease severity grades (Normal, Benign, Malignant). These labels enable both binary and multi-classification tasks. Along with images of liver, we include clinical and demographic data relevant to NAFLD diagnosis. The clinical data included with patient age, gender, body mass index (BMI), liver enzyme measurements such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), waist size, glucose, cholesterol such as (LDL,HDL, Triglycerides). This combination of data (image and Tabular) makes possible research on multi-modal learning, which improve diagnostic accuracy and model robustness. All data on our dataset are anonymized to de-identified patient's personal information and to ensure compliance with ethical research standards and data protection guidelines. The dataset is strictly provided for research and educational purposes and does not contain any information that can be used to identify individual patients. This dataset can be used for image preprocessing, classical machine learning classification, convolutional neural network (CNN)-based deep learning, disease severity grading, and comparative studies between image-only and multi-modal diagnostic approaches. the dataset Publicly accessible under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

UAV and InSAR data in a typical Retrogressive thaw slump