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This dataset includes raw data collected from household surveys of smallholder farmers in the dry-zone cascade farming systems of Sri Lanka. It captures information on socioeconomic and farm characteristics, agricultural challenges, resilience capacities, resilience perceptions of farmers, and risk management strategies. Data were gathered through face-to-face interviews with 300 farmers from 12 villages in the Galgamuwa Divisional Secretariat, Kurunegala District, North Western Province of Sri Lanka using a pre-tested structured questionnaire. A simple random sampling method was used, and surveys were conducted between June and September 2025. Ethical approval for the study was obtained from the Deakin University Human Research Ethics Committee. The dataset provides detailed information on the conditions and responses of smallholder farmers in this context.

Data associated with the paper.

This dataset contains the indicator data used to evaluate the coordinated development between the digital economy and tourism ecological security in Chinese cities from 2006 to 2023. The dataset covers panel data of 206 prefecture-level cities in China and includes digital economy indicators, tourism ecological security indicators, and socioeconomic driving factors. The dataset supports the analysis of the spatiotemporal evolution and nonlinear driving mechanisms of digital economy–tourism ecological security coupling coordination.

Supplementary data for the paper: Ratic, M., & Filipovic, S. - Determining the market value of residential property by incorporating intelligent energy management (The European Journal of Applied Economics). The empirical analysis relies on a synthetically generated dataset that simulates realistic distributions of the US residential market, calibrated on publicly available sources (RECS, US Census Bureau, Zillow Research, Redfin Data Center, US DOE reports) and generated in Python with a fixed random seed for full reproducibility. Contents: (1) properties.csv / dataset.xlsx - 50 residential properties across five regions (California, Florida, Texas, New York metro, Colorado) with structural attributes, HERS index, the four energy intelligence components and the composite attribute E_int, and transaction prices, used to estimate the extended hedonic model; (2) scenarios.csv - 50 operational scenarios (25 classical deterministic, 25 intelligent control: RL/MAS/MPC) with simulated monthly electricity costs under five tariff regimes and a price shock column, used to test hypotheses on operating savings and price volatility protection; (3) gen_props.py, gen_scen.py - generator scripts; (4) analysis.py - reproduction script that re-estimates the extended hedonic model (OLS with regional fixed effects; delta = 0.082, R2 = 0.847) and the scenario analysis (21% average saving, 47% variance reduction, 38% lower shock cost). See README.md for the column dictionary and step-by-step reproduction instructions.

Data includes global proteome and transcriptome matrixes described in Bateman NW et al, "Multi-omic Analysis of Uterine Leiomyomas in Self-Described Black and White Women: Molecular Insights into Health Disparities".

Molecular Dynamics SLC25A38

This dataset contains all publicly disclosable data used to train and evaluate AURA: an Adaptive Uncertainty Routing Architecture for AI-produced text detection. It contains processed versions of MAGE and PAN 2025 benchmark datasets.

Este conjunto de dados reúne informações sobre internações hospitalares por exposição às forças da natureza no Brasil, entre 2010 e 2025. Os registros foram obtidos no Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH/SUS), por meio do Departamento de Informática do SUS (Datasus), e correspondem às categorias X30 a X39 da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, 10ª revisão (CID-10). Os dados foram organizados para subsidiar o estudo “Internações hospitalares relacionadas à exposição a forças da natureza: estudo descritivo e de séries temporais, Brasil, 2010-2025”. Por se tratar de dados públicos, agregados e sem identificação individual, o conjunto não contém informações pessoais ou sigilosas.

Smartphone-scanned handwritten laboratory records from a first-year engineering Makerspace course: 65 writers x 17 lab experiments, 565 PDF records, 2,463 pages at 300 DPI. Every page was captured by the students themselves with a mobile-phone camera through consumer scanning apps (Adobe Scan, CamScanner; app watermarks appear on some pages) — no flatbed scanner anywhere in the corpus. The data is multi-session (each experiment written and scanned on a different day), diagram-rich (inline circuit and mechanical sketches, pencil shading), double-sided cursive Indian-college English — a capture condition and demographic absent from existing writer-identification benchmarks such as IAM, CVL, CERUG and Firemaker. CONTENTS. corpus/ms_<experiment>.zip — 17 zips, one per lab experiment, each containing student<NNN>.pdf records. tables/dataset_long.csv — one row per PDF (student_id, experiment, filename, page count). tables/dataset_matrix.csv — student x experiment page-count matrix. tables/dataset_summary.json — corpus totals and processing flags. tables/splits.json — the canonical writer-disjoint train/validation/test split (45/10/10) for reproducible benchmarking. checksums_sha256.txt — SHA-256 of every file. NAMING AND ANONYMISATION. Every record is student<NNN>.pdf, where NNN is a 3-digit pseudonym assigned per writer; original upload filenames are never included and no mapping to real identities is published. Valid ids: student002–student136, even numbers only. Experiment identity is defined by the folder name. Exactly one PDF per student per experiment; byte-identical re-uploads were removed (MD5-verified) and duplicate scans resolved by page count. KNOWN ARTIFACTS (documented and deliberately retained): mirrored show-through and unmirrored stack-transparency ghosts from thin double-sided paper, ruled lines, uncontrolled lighting and perspective. ETHICS AND USAGE. Handwriting is a biometric. Released for non-commercial research in document analysis and writer identification (CC BY-NC 4.0). Do not attempt to re-identify writers; do not use this data to train handwriting-forgery systems targeting real individuals.

Provided supplementary data for Graded Selenium Supplementation Restructures Yak Colostrum Composition and Metabolomic Organization under High-Altitude Conditions.