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This dataset provides the aggregate research inputs, Python code, reference results and verification documentation supporting the revised manuscript “Territorial solar-inclusion gaps: Registered electricity assistance and distributed photovoltaic deployment in Brazil, 2018–2023”. The municipality-month panel covers 5,570 Brazilian municipalities from January 2018 to December 2023, comprising 401,040 observations before analytical restrictions. The principal estimation sample contains 387,066 observations across 5,567 municipalities. Variables describe distributed photovoltaic deployment, published Social Electricity Tariff (TSEE) record intensity, electricity tariffs, supply reliability, solar resources and municipal socioeconomic conditions. TSEE intensity counts distinct complete published administrative records per 1,000 inhabitants; it does not identify independently verified unique households. The package includes the current master panel in Parquet and compressed CSV formats, a data dictionary, source inventories, analysis scripts, reference tables, verification reports and English instructions for execution locally or in Google Colab. The computational workflow completed 26 stages and passed 566 numerical comparisons. The standard workflow reconstructs the analytical panel from packaged inputs, estimates the statistical models and reproduces the documented sensitivity analyses and territorial classifications. A separate optional workflow audits the raw TSEE source editions. Individual TSEE records are not redistributed. Some inputs are preserved municipal or system-level aggregates. The package therefore does not reconstruct every historical primary-source collection. Source versions, transformations and verification boundaries are documented within the release. Licensing: The CC BY 4.0 designation applies only to the authors’ original documentation. It does not apply to the databases, source materials or software included in this deposit. The TSEE-derived database and its adapted versions remain licensed under ODbL 1.0. Author-developed code is licensed under the MIT License. Other third-party materials retain their applicable source terms. See LICENSES.md within the replication package for license and attribution notices.

This dataset provides the data, code, numerical results, raw simulation outputs, and verification materials supporting the manuscript “Mission-reliability-constrained maintenance under correlated demand: Decision consequences of model simplification.” It supports reproduction of the CTMC, uniformization, DES, decision-map, robustness, and input-error analyses. No field or personal data are included.

The Food Commodity Price (OHLC) Dataset of Bangladesh is a preprocessed and integrated multivariate time-series dataset designed for agricultural commodity price forecasting, market volatility analysis, and agro-climatic machine learning research. The dataset integrates historical market price records with station-mapped meteorological indicators across 76 local markets spanning all 8 administrative divisions of Bangladesh (Dhaka, Chittagong, Rajshahi, Khulna, Barisal, Sylhet, Rangpur, and Mymensingh). Dataset Structure and Attributes (12 Columns): 1. Date: Standardized temporal timestamp of the monthly record. 2. Region: Administrative division in Bangladesh (8 divisions). 3. Market: Specific local market identifier (76 markets). 4. Product: Essential staple food commodity category (rice, wheat_flour, lentils, and oil). 5. WindSpeed: Average wind speed (m/s). 6. Temperature: Representative mean air temperature (degrees Celsius). 7. Rainfall: Total monthly rainfall (mm). 8. Humidity: Relative humidity (%). 9. Open: Monthly opening market price. 10. High: Monthly highest market price. 11. Low: Monthly lowest market price. 12. Close: Monthly closing market price (primary target variable for forecasting). All records have been systematically cleaned, spatially aligned between markets and nearest weather stations, and processed through a hierarchical missing-value imputation pipeline to ensure zero null values for direct use in statistical, machine learning, and deep learning pipelines. Citation and Associated Publication: If you use this dataset in your research, please cite this Mendeley Data repository (mandatory under CC BY 4.0). Additionally, to reference the spatial station-mapping methodology, hierarchical imputation pipeline, and baseline deep learning benchmarks, please also cite our associated IEEE conference paper: 1. Dataset Citation (Mandatory under CC BY 4.0): Hossain, Arman; Palas, Md. Rajibul; Quader, Montaser Abdul; Alam, Md. Shoab; Quader, Mozdaher Abdul (2026), "Food Commodity Price (OHLC) Dataset of Bangladesh", Mendeley Data, V1, doi: 10.17632/dd58ks2p5y.1. 2. Associated IEEE Paper Citation (Methodology & Benchmark Reference): A. Hossain, M. R. Palas, M. A. Quader, M. S. Alam, and M. A. Quader, "GRU-Based Time-Series Forecasting of Agricultural Commodity Prices Using Market and Climate Data," in 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), Chittagong, Bangladesh, 2026, pp. 1-7, doi: 10.1109/QPAIN69676.2026.11546003.

## Steps to Reproduce 1. Load the 8,260 radiographs using the documented FDGI-based training, tuning, and held-out internal test partitions. FDGI is a filename-derived laterality-pair grouping proxy, not a verified clinical patient identifier. The partitions are disjoint at the FDGI level; patient-level independence cannot be established from the available source metadata. 2. Resize all radiographs to 300 × 300 pixels using the documented Keras image-generator pipeline. 3. Apply the EfficientNetB3 preprocessing configuration documented in the training code. The Keras EfficientNet implementation includes input rescaling within the model; its `preprocess_input` function does not perform additional ImageNet mean–standard deviation normalization. 4. Apply training-only data augmentation using the documented rotation, width and height shifts, zoom, horizontal flipping, and nearest-neighbor filling. Do not augment the tuning or held-out internal test images. 5. Initialize the ImageNet-pretrained EfficientNetB3 backbone and construct the corresponding classification head. For the hybrid formulation, train with the original five KL grades (KL 0–4). For the direct comparator, train using the predefined three-category severity mapping. 6. Follow the documented staged transfer-learning protocol: an initial warm-up with the backbone frozen, followed by fine-tuning of the designated upper backbone layers. Use the archived training configuration and checkpoint-selection procedure rather than substituting a new training schedule. 7. For the historical five-class optimization, use the documented Optuna Tree-structured Parzen Estimator search and its recorded hyperparameter configuration. For the principal matched comparison, use the fixed, shared experimental configuration and the three documented random seeds (42, 123, and 2026). Select checkpoints using tuning-subset performance without using the held-out internal test subset for model selection. 8. Evaluate the original five-class KL formulation and the common three-class severity output space. For the hybrid model, map the five-class categorical prediction into Non-OA (KL 0–1), Mild–Moderate OA (KL 2–3), or Severe OA (KL 4). Aggregate the corresponding five-class probabilities for probability-based three-class analyses. Compare the hybrid formulation with the matched directly trained three-class model. 9. Perform the documented internal-validation analyses, including five-fold stratified group cross-validation within the development cohort, the FDGI-disjoint held-out internal test, matched three-seed comparison, and FDGI-level grouped bootstrap analysis. Report the documented classification, discrimination, calibration, and uncertainty metrics, keeping historical fixed-artifact analyses separate from the principal matched experiment.experiment.

Dataset containing results of 3H analysis of biobased (PLA, PE) and fossil carbon-based polymers.

This dataset provides the reproducibility data, numerical results, and verification code accompanying the manuscript “Cross-Timescale Stability and Stochastic Performance of Reputation-Aware Pricing with Hysteretic O-Cloud Scaling.” The archive contains the exact finite-CTMC and tagged-delay analyses, independent uncensored discrete-event simulation (DES), seven-environment sensitivity experiments, three trace-informed traffic regimes, fixed-buffer and service-distribution robustness studies, computational-scaling experiments, symbolic verification, and an independent stationary-solver cross-check. The primary stationary analysis permits transient enumerated states when the finite CTMC has exactly one closed communicating class, assigning zero stationary mass to transient states. The archive also includes diagnostic analyses of communicating-class assumptions and expanded price domains used to assess the sensitivity of the policy comparisons to design-domain restrictions. Reproduction scripts, frozen numerical ledgers, figure-generation files, integrity manifests, execution logs, and provenance records are included. The Madrid LTE traces are used only to construct traffic-side contrast and persistence scenarios. The original raw trace files are not redistributed; upstream source identifiers, file hashes, preprocessing rules, and processed records are provided for reproducibility. The accompanying code reproduces the exact baseline analysis, sensitivity and robustness experiments, trace-informed scenarios, independent DES validations, and computational-scaling results reported in the manuscript.

This dataset comprises original 1602 images & augmentation 7885 images of jujube, star fruit, and guava leaves, categorized into healthy and diseased labels. It is specifically designed for the development and training of machine learning models aimed at early disease detection in tropical fruit crops. The dataset includes various types of leaf diseases, making it a valuable resource for researchers in plant pathology and agricultural technology. It offers a solid foundation for advancing automated systems to aid in crop management and improve sustainable farming practices. Jujube Leaf: Jujube Original data: Defect Jujube leaf:124 Healthy Jujube Leaf:227 Insect Feeding:177 Leaf Curl:33 Jujube Augmentation data: Defect Jujube leaf:620 Healthy Jujube Leaf:1010 Insect Feeding:885 Leaf Curl:165 Star Fruit Leaf: Star Fruit Original data: Defect Star Fruit Leaf:183 Healthy Star Fruit Leaf:298 Insect Feeding:95 Star Fruit old yellow leaf:68 Star Fruit Augmentation data: Defect Star Fruit Leaf:915 Healthy Star Fruit Leaf:1490 Insect Feeding:475 Star Fruit old yellow leaf:340 Guava Leaf: Guava Original data: Defect Guava leaf:158 Fungal leaf: 39 Healthy Guava leaf:200 Guava Augmentation data: Defect Guava leaf:790 Fungal leaf: 195 Healthy Guava leaf:1000 Purpose: The purpose of this research is to develop an efficient and accurate system for detecting diseases in jujube, star fruit, and guava leaves using advanced image processing and machine learning techniques. By identifying diseases early, this study aims to enhance crop management, minimize agricultural losses, and promote sustainable farming practices.

The development of remote blood pressure (BP) measurement algorithms using remote photoplethysmography (rPPG) has significant limitations, including the small size of publicly available datasets, privacy concerns regarding facial videos, and a lack of diverse, realistic datasets associated with actual BP measurements. To address these challenges, this study aimed to provide comprehensive, simultaneous recordings of participants' faces, along with reference physiological measurements, for 400 adult participants aged 18–79 years. For each imaging session, systolic and diastolic blood pressure and reference heart rate (HR) were recorded using clinical electronic BP monitors in addition to recording illuminance (lux) values for indoor and outdoor environments. The collected data, called CLBP-300, is a crucial resource for developing and evaluating remote vital signs from facial rPPG signals. A sample of videos is publicly available to demonstrate data quality, while academic researchers can access the complete dataset under a strict data use agreement. ⚠️ IMPORTANT Notice: Controlled Access Only ⚠️ The full CLBP-300 Dataset (87 GB) is NOT available for public download due to its sensitive medical nature and privacy concerns. Access is EXCLUSIVELY managed under a formal Data Use Agreement (DUA) and requires a non-profit administrative support fee. For all inquiries and access requests, you MUST visit our official website: https://www.clbp300.org/ Requirements for Access: Official Institutional Email. Signed Data Use Agreement (DUA). Research Sustainability Contribution.

We present PATHMC (Particle-based Approach To simulate cHemical dynamics using Monte Carlo), an open-source, cross-platform C++ software package for simulating the spatiotemporal track-structure evolution following radiolysis. PATHMC is designed to solve coupled chemical dynamics while accounting for explicit Coulomb interactions and dielectric saturation effects. Electrostatic forces enter the stochastic equations of motion, while a Coulomb-corrected missed-reaction probability accounts for reactive encounters at each step. Diffusion-, partially diffusion-, and spin-controlled reactions are treated within the local force field.

This repository contains the computational scripts and numerical models supporting the findings of the research article: "Soil arching in granular slopes: a comprehensive three-dimensional DEM study"