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This dataset on “Data for PhenoCam images Raspberry Pi models for corn growth stage classification” includes the original data, intermediate model outputs, and final results obtained (graphical and tabular forms) from the research study. The original data (images) consists of 10 different corn growing PhenoCam sites in US, which were annotated for growth stage labeling manually based on visual appearance. The intermediate model output includes learning curves (training and validation log), and predictions of test images, for respective deep learning (DL) models (4) across individual PhenoCam sites (10) and image clipping levels (5). The final results after model development and testing comprise confusion matrices and classification reports. The lightweight DL models developed for this study are ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2 using transfer learning techniques. The results are based on site-wise and five PhenoCam image clipping levels (0-40%) across individual models. The dataset provides model performance evaluation results based on intrasite (training and testing on the same PhenoCam site) and intersite (testing on a new PhenoCam site) methods. Initially, models were developed using a supercomputer (CCAST, NDSU), and they were finally deployed on Raspberry Pi (single board computer). The dataset documents the implementation and performance of the finalized model (ELiteCrop0) deployed on Raspberry Pi, including inference time, computational efficiency, and hardware utilization metrics. The contents of the dataset include: 1. Abstract, 2. PhenoCam data annotation visual class labels, 3. Training and validation accuracy and loss curves, 4. Confusion matrix plots, 5. Sample prediction plots, 6. Classification report, 7. CPU timing, 8. Sample prediction plots for Raspberry Pi, 9. Intersite evaluation with ELiteCrop0, and 10. Intersite sample prediction plots for Raspberry Pi with ELiteCrop0.

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Use of Adjuvant Immunotherapy and Early Overall Survival in Stage III Merkel Cell Carcinoma

This dataset supports a mixed-method investigation of students’ feedback on the implementation of flipped classrooms for senior secondary mathematics instruction. Quantitative data comprise responses from 242 students who completed 12-item, four-point Likert-scale questionnaire administered before and after the intervention. Qualitative data consist of semi-structured interview responses from 12 students, collected using a 12-item interview guide to explore perceived opportunities, challenges, and recommendations for improving flipped-learning experiences. These anonymized data also include emergent thematic analysis outputs from the interview transcripts.

Deciphering the Role of Core Fucosylation in IgG1 Fc–CD16a Binding Through All-Atom Simulations Core fucosylation of the IgG1 Fc N297 glycan is known to reduce binding affinity to the FcγRIIIa (CD16a) receptor and attenuate antibody-dependent cellular cytotoxicity (ADCC), yet the structural mechanisms underlying this effect remain incompletely understood. Here, we present a comprehensive molecular dynamics investigation of the Fc–CD16a complex across multiple glycoforms varying in fucosylation and galactosylation. Binding free energies calculated using MM-PBSA reveal that core fucosylation consistently reduces Fc–CD16a affinity, irrespective of single or dual fucosylation. Mechanistically, dual fucosylation increases inter-glycan contacts between the Fc N297 glycans, constraining their conformational sampling. Concurrently, the CD16a N162 glycan exhibits expanded conformational heterogeneity, indicating receptor destabilization. These glycan-mediated perturbations also impact the protein conformations as dual fucosylated systems display reduced protein–protein contact frequencies and redistribution of energetically significant residues away from the binding interface, particularly within the CD16a D1 domain. Potential of mean force analyses further demonstrate that dual afucosylated complexes adopt compact, well-defined conformational basins, whereas dual fucosylation promotes diffuse and less stable receptor orientations. Dynamic cross-correlation analysis reveals diminished inter-domain coupling in dual fucosylated systems, indicating disruption of coordinated motions across the Fc–CD16a interface. Together, our results establish that core fucosylation weakens Fc–CD16a binding, the impact of dual fucosylation on glycan confinement, receptor destabilization, altered interfacial energetics, and impaired dynamic coupling is more significant than single fucosylation. These findings provide a mechanistic framework for understanding glycoengineering strategies that enhance antibody effector function. All-atom MD simulation trajectories used in this study are shared here.

This dataset contains monthly wheat flour prices across Bangladesh alongside macroeconomic indicators and global commodity prices from January 2007 to September 2025. It includes 17,099 observations across all eight administrative divisions and supports price forecasting, structural break analysis, and studies of how domestic markets respond to international pricing and exchange rate shocks. Data comes from domestic market monitoring networks across Bangladesh, with missing values filled using spatial interpolation. Macroeconomic indices and international commodity prices are sourced from the World Bank and national statistical registries. Observations span the national, divisional, district, and market levels on a monthly frequency. The core file contains 18 columns. Spatial identifiers include adm1_name (one of eight divisions: Dhaka, Chittagong, Khulna, Barisal, Mymensingh, Rajshahi, Rangpur, Sylhet), adm2_name (district), mkt_name (specific market), lat, and lon. A unique observation ID is provided via eo_id (format: OBS_XXXXX). Temporal variables (dates, year, and month) are standardized to the first day of each calendar month. Data quality is tracked through data_coverage_recent (reporting completeness), spatially_interpolated (1 = estimated via spatial models; 0 = original field observation), and trust_wheat_flour (confidence score from reporting consistency). The target variable is wheat_flour_price in Bangladeshi Taka per kilogram. inflation_wheat_flour captures year-over-year percentage change. Economic context comes from four variables: c_food_price_index (national food basket), exchange_rate_unofficial (parallel market BDT-to-USD rate reflecting import costs), cpi_food_index (food category inflation), and international_wheat_price_usd (global wheat benchmark from the World Bank Pink Sheet). The dataset is suitable for time-series forecasting, econometric modeling, and machine learning applications targeting food security and market dynamics in import-dependent economies. Researchers can use standard cross-validation (rolling windows or forward-chaining), feed data into XGBoost, LightGBM, or LSTM models, or apply spatial econometrics using the lat and lon coordinates. Python (pandas, numpy, scikit-learn, optuna, ruptures), R, STATA, SPSS, or MATLAB can all open and analyze the files.

Este conjunto de datos comprende registros de estaciones terrestres que caracterizan el Caribe colombiano, específicamente los departamentos de La Guajira, Magdalena, Atlántico y César. Incluye datos de salida del modelo WRF (resolución horaria, un día) y reanálisis NASA POWER (2020-2025, zona: BL Lat 10, BL Lon -75.54, UR Lat 12.5, UR Lon -71.07). Los archivos contienen variables para el análisis del microclima y la cuantificación de los recursos eólicos regionales, tales como componentes vectoriales y velocidad del viento a 10 m y 50 m, presión atmosférica y temperatura en dos niveles (superficie a 10 cm y aire a 2 m). Las mediciones in situ de SISAIRE-IDEAM (red de punto KOICA-UNIMAG y estaciones locales) capturan la variabilidad específica de cada zona para un estudio particular, desde el régimen de vientos alisios en La Guajira hasta las circulaciones valle-montaña en César y las brisas costeras en Magdalena. Este conjunto de datos es una herramienta fundamental para optimizar la infraestructura de energía eólica en el norte de Colombia.

This dataset supports a landowner-typology study of the buffer zone of Cerro Castillo National Park, a mountain protected-area gateway in the Aysén region of Chilean Patagonia undergoing tourism-driven amenity transition. It contains de-identified responses from a survey of 641 landowners conducted Feb–July 2024 within 10 km of the park (637 retained for segmentation; 636 georeferenced to watershed sub-catchments), with the derived variables, segment assignments and validation results needed to reproduce the analysis. Four owner segments — Committed newcomers, Rooted landholders, Active citizens, and Park-disengaged owners — are obtained by k-means on eleven standardised variables: two territorial-bond indices (bond with own land, bond with the park), a disposition-to-change index, four conservation-citizenship sub-indices (civic identity, civic behaviour, norm compliance, participatory action), age, education, tenure length and acquisition mode. Because the bond items were administered under two survey versions (A/B), the two bond indices are harmonised across versions by mean-sigma random-groups test-equating before clustering, removing the instrument artifact; the equated inputs (idx_tierra_h, idx_parque_h) are stored alongside the raw indices. The workbook has seven sheets: (1) de-identified case-level data (641 × 191) — segment labels, all clustering inputs, the equated bond indices, an assignment-method flag, and profiling, spatial and institutional variables; (2) a codebook with variable descriptions, types and value labels (Spanish glossed in English); (3) Derived_variables — the construction of every derived index, verified to reproduce the stored values; (4) Validation — internal indices (silhouette, Davies–Bouldin) for k = 2–8 and nonparametric bootstrap stability; (5) a segment-by-sticker crosstab from a qualitative sub-sample; (6) a sub-catchment table of person- versus land-area dominance; and (7) a README documentation sheet. No direct or indirect identifiers are included; geography is retained only as coarse watershed sub-catchment (1–19). Data were collected under FONDECYT Regular project 1230020 (ANID, Chile); ethics approval IRI11_23, Universidad Austral de Chile; participants gave informed consent. This is the segmentation/typology dataset, distinct from the companion deposit (Gale & Báez-Montenegro, 2026; Mendeley Data, doi:10.17632/cf69gj7ndg.1), which holds the relational-values and place-attachment survey underlying a separate structural-equation analysis. The two share the 2024 field survey but differ in scope and structure: the present file provides the segment assignments, the equated (harmonised) clustering inputs, an education variable absent from the companion deposit, spatial and institutional variables, validation material, and the item batteries needed to reconstruct and verify every derived index; the companion deposit organises the relational-values and place-attachment items for structural-equation modelling.

The code in this replication package constructs the analysis dataset and reproduces all tables and figures in "Convenience Lost" by Zhengyang Jiang, Robert Richmond, and Tony Zhang.

This dataset presents a comprehensive real-field collection of eggplant (Solanum melongena L.) leaf images designed to support research in computer vision, deep learning, and precision agriculture. The dataset was developed through extensive field surveys conducted in agricultural regions of Bangladesh between December 2025 and January 2026 under natural environmental conditions. The dataset comprises 1,500 high-resolution RGB images categorized into three classes: Healthy leaves, Fungal-infected leaves, and Spider Mite-infected leaves, with 500 images per class. All images were captured directly from eggplant cultivation fields using smartphone cameras under diverse real-world conditions, including variations in illumination, background complexity, viewing angles, and plant growth stages. Such diversity enhances the robustness and generalization capability of machine learning models trained on this dataset. The collected images were manually inspected, annotated, and organized into class-specific directories to facilitate supervised learning tasks. The dataset can be utilized for a wide range of agricultural artificial intelligence applications, including image classification, disease detection, transfer learning, feature extraction, and model benchmarking. By providing a real-field, annotated, and diverse image collection, this dataset aims to serve as a valuable benchmark resource for developing intelligent and automated eggplant disease diagnosis systems, thereby contributing to sustainable agriculture and precision farming practices.