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This dataset provides the reproducibility package for the Monte Carlo mitigation-pathway analysis reported in the study “Spatiotemporal Dynamics and Production–Emission Decoupling of the Carbon Footprints of China’s Staple Crops.” The package includes executable Python scripts for the M0–M4 Monte Carlo analyses, fixed random seeds, analysis-ready emission-source shares for rice, wheat, and maize during 2006–2021, mitigation-parameter ranges derived from the literature, model and data dictionaries, machine-readable supporting tables, software-environment information, validation scripts, and generated outputs. The original Monte Carlo results reported in the manuscript were obtained from the reference specification using independent uniform distributions. M0 provides a fixed-seed implementation of the same reference specification for reproducibility, whereas M1–M4 evaluate robustness to alternative marginal distributions, Gaussian-copula dependence structures, and year-specific emission shares. The reported dominance probabilities are conditional on the selected literature-derived parameter ranges and assumed probability distributions. They represent the frequency with which a mitigation pathway ranks first within the specified Monte Carlo model and should not be interpreted as probabilities of intervention adoption or realized farm-level outcomes. The deposited files are intended to support transparent verification and reproduction of the Monte Carlo robustness, sensitivity, and pathway-ranking analyses presented in the manuscript and Supporting Information.

Total of more than 10,000 LDIR spectra were identified in the samples from the Mersing River, filtered by Aluminum 25mm disc, 0.8 microM pore filter.

Analysis code accompanying the manuscript "Machine Learning Prediction of Significant Liver Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease Using Routine Clinical Indicators: A Cross-Sectional Study Based on NHANES 2017–2020". The archive contains 14 scripts (12 Python, 2 R) covering data download, cohort construction, model development (logistic regression, random forest, XGBoost, LightGBM, SVM, MLP), hyperparameter tuning, probability calibration, decision curve analysis, SHAP interpretability, internal validation, subgroup fairness analysis, and figure/table generation. Source data are publicly available from NHANES ( https://www.cdc.gov/nchs/nhanes/) and are not redistributed; script 01_download.py retrieves them automatically. Environment: Python 3.12, R 4.6.1. Run scripts in numerical order. See README.md in the archive for details.

Raw data for "Surviving the Storm: The Impact of Extreme Weather on Small Retail Business Survival in China"

This dataset contains processed UAV LiDAR-derived dune morphology data and vegetation data derived from field surveys and UAV imagery, supporting the study entitled “Seasonal dynamics and coupling of coastal dune morphology and vegetation on Pingtan Island, China”. The dataset includes dune elevation, volume, slope, vegetation cover, plant height, and species composition data from the Changjiang’ao coastal dune field on Pingtan Island, China.

This dataset contains survey responses from N = 296 adult Generation Z consumers (aged 18–29, born 1997–2012) evaluating personalized social media advertising, cognitive advertising evaluations, privacy concerns, and recent self-reported purchase behavior.The survey incorporates 35 variables across multiple theoretical and behavioral domains:Demographics & Digital Habits: Gender, birth year/age, education level, daily social media usage, online shopping frequency, and ad click-through frequency.Outcome Measure: Self-reported post-ad purchase within the last 30 days.Perceived Personalization (PPA): 5 items adapted from Tran (2017).Advertising Value (AV): 5 items based on Ducoffe’s (1995) Advertising Value Model.Perceived Relevance (PR): 5 items adapted from Alalwan (2018).Perceived Novelty (PN): 4 items adapted from Dodoo & Wu (2019) and Frasquet et al. (2024).Privacy Concern (PC): 4 items based on Dinev & Hart (2006) and Dodoo & Wu (2019).General Social Commerce Tendencies: 5 complementary behavioral items.All scale items were recorded on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The dataset is suitable for hierarchical econometric modeling, structural equation modeling (SEM), and predictive machine learning benchmarking (e.g., nested cross-validation, XAI interpretability via SHAP/PDP).

This dataset contains the compiled provincial panel data used in the empirical analysis of the manuscript "Digital transformation empowering energy industrial chain resilience: Evidence from China's provincial panel data." The sample covers 30 provinces, autonomous regions, and municipalities in China from 2010 to 2023 (420 observations in total). The dataset includes the raw and processed variables for the core explanatory variable (Digital Economy Development Index), the dependent variable (Energy Industrial Chain Resilience Index, constructed via the "4R" framework: resistance, recovery, restructuring, and renewal), the mediating variable (Financial Depth), the moderating variable (Carbon Emissions Trading Policy dummy), and a set of control variables (e.g., GDP per capita, industrial structure, openness, human capital, government intervention, environmental regulation). The primary data are compiled from publicly available statistical yearbooks (China Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, and provincial statistical yearbooks), as well as databases from the Peking University Digital Finance Research Center and Wind Information. Missing data points were interpolated using linear interpolation and weighted growth rate methods. This dataset is provided to ensure the reproducibility of the empirical results.

Reanalysis materials for “Confidence at transitions between personal and group responsibility”. The study compares prior Group versus Self context within each fixed current context at boundaries between equal-exposure pairs in the public data of Bao, Edelson, and Hare (2026), Science Advances, doi:10.1126/sciadv.ady0441; source dataset doi:10.5281/zenodo.16994312, version 1.0.0, CC BY 4.0. This archive contains analysis and visualization code, anonymized derivatives of the protocol and numerical-exposure records, all 21 saved model outputs, four derived trial tables, and six figures. Main estimates are inconclusive; all prespecified results are retained. Derived tables retain pseudonymous source participant identifiers and replace date-by-group cluster labels with neutral IDs while preserving group membership. Raw source ZIP, article XML, and supplementary PDF are obtained separately using documented URLs and checksums. Newly authored code is MIT licensed; source and derived data retain the original CC BY 4.0 attribution. The package README separates these licenses and documents transformations. This is code and research data, not a manuscript or preprint.

This dataset provides TruckSim simulation data, trained model weights, code, and evaluation results for RM-THF multi-state prediction of tractor–semitrailer combinations with chassis flexibility. It contains 648 training, 144 validation, and 288 test cases covering Fishhook, J-turn, and double lane-change steering replay. The model predicts semitrailer roll, chassis twist, and fourteen tire normal loads over 3 s using 1 s of response history and prescribed future steering. Data definitions and reproduction instructions are provided in the README.

This dataset provides the analytical data, out-of-sample predictions, statistical results, figure-source data and code accompanying “What Matters in MD&A Text: A Modular Decision Support System for Attributing Predictive Content Across Sentiment, Structure, and Fundamentals”. It corresponds to the frozen Revision V2 experiment used in the October 2026 revised manuscript. The study examines annual and semi-annual reports of Chinese A-share firms over input report years 2016–2024 and predicts next-fiscal-year return on equity (ROE) and return on assets (ROA). The accepted union panel contains 20,450 firm-report observations. Target-specific samples contain 19,983 ROE observations and 20,432 ROA observations. Historical rolling out-of-sample evaluation covers report years 2022–2024, with 9,366 ROE and 9,616 ROA observations. The package contains 1,908 formal prediction runs comprising 6,036,276 prediction rows, sample and variable records, model and information-layer attribution results, statistical comparisons, robustness analyses, computational-cost tables, and the data used for the manuscript figures. Six model families compare financial fundamentals, disclosure quantity and sentiment in matched F, F+Q, F+S and F+Q+S configurations. RandomForest, XGBoost, LightGBM and MLP use five fixed seeds; OLS and ElasticNet are deterministic. English documentation describes the variables, original sources, external resources, environment and reproduction steps. Code is provided for data processing, feature extraction, model training, performance evaluation, statistical analysis and figure generation. The three archives form one folder structure and should be extracted into the same directory. Keywords Management discussion and analysis; Corporate profitability; Financial text analysis; Disclosure quantity; Sentiment analysis; Information-layer attribution; Out-of-sample prediction; Machine learning; Reproducible research Version update note Updated to the frozen Revision V2 materials used in the revised manuscript: target-specific analytical samples, 2022–2024 historical rolling evaluation, five stochastic seeds, matched F/Q/S comparisons, formal prediction files, attribution and statistical results, robustness analyses, manuscript figure data, reproduction code and English documentation. This version replaces the earlier single-2024-holdout package.