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Zircon U-Pb, Lu-Hf and O isotopic data, major and trace element content and whole-rock Sr-Nd isotopic composition, EMPA Feldspar and biotite mineral chemical composition, Zircon LA-ICPMS trace elements, Plagioclase LA-MC-ICPMS in situ Sr isotopic composition, Geochemical data of Crustal melting events, Geophysical and modelling data

This dataset contains raw and processed characterization data obtained from Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA) of cellulose-based films prepared from mangosteen rind. The dataset is intended to support the structural, chemical and thermal characterization of the prepared material and to facilitate reproducibility and further analysis. Further data sets for calculating protein diffusion across the membrane are also included.

This dataset contains the research data for the study entitled "Evaluating Enterprise Resource Planning Success in an Automotive Manufacturer: A Modified DeLone & McLean IS Success Model Approach." The dataset includes: 1. Raw questionnaire responses from 250 active ERP users at a major automotive manufacturer in Indonesia. 2. Measurement items for six latent constructs: System Quality (X1), Information Quality (X2), Service Quality (X3), User Satisfaction (Z1), Operational Support (Z2), and Net Benefit (Y). 3. Demographic information of respondents (age, gender, department, job position, length of ERP usage). 4. SmartPLS 4 output files, including outer loadings, reliability tests (Cronbach's alpha, composite reliability, AVE), discriminant validity (Fornell-Larcker), path coefficients, and bootstrapping results (5,000 resamples). 5. The final structural model and hypothesis testing results for H1–H14. The data were collected via structured questionnaires using a purposive sampling strategy, with a minimum of 6 months of ERP operational experience as the inclusion criterion. The dataset supports the analysis of direct effects (H1–H8) and indirect/mediation effects (H9–H14) using Partial Least Squares-Structural Equation Modeling (PLS-SEM). This dataset is shared to support transparency, reproducibility, and further research in ERP implementation success and information systems evaluation.

The data was collected from January - March 2020 from 19 and 11 Local Government Areas of Imo and Anambra States of Nigeria respectively. It contains information on youths and migration.

Abstract—Oral Squamous Cell Carcinoma (OSCC) is a malignant disease with high mortality that is often diagnosed at an advanced stage. This study systematically evaluates local-global feature representation for OSCC histopathology classification using a CNN-ViT hybrid architecture that integrates EfficientNetB3 with a Transformer Encoder. Rather than focusing solely on architecture comparison, a phased ablation study was conducted to examine the contributions of the Transformer Encoder, data augmentation, and class weighting to classification performance. Grad-CAM++ was further employed as an Explainable AI (XAI) method to examine model attention patterns in histopathological images. Experiments were performed on the Histopathologic Oral Cancer Detection dataset from Kaggle, with multi-seed evaluation using five random seeds. The complete configuration combining the Transformer Encoder, data augmentation, and class weighting achieved the highest observed performance, with 92.86% accuracy, 90.04% F1-score, and 96.84% sensitivity. Across five seeds, the model achieved an average accuracy of 91.27% ± 1.51% and sensitivity of 94.53% ± 1.23%. Grad-CAM++ visualizations showed activation patterns concentrated on histopathological regions relevant to OSCC classification. However, expert pathologist validation is required to establish the clinical relevance of these interpretations.

This dataset provides the code, frozen configurations, simulation-derived data, and supporting materials associated with the study "Transition-Aware Dynamic Capability Assessment for Aircraft under Pitch-Effectiveness Degradation." The study investigates the ambiguity between normal closed-loop task-transition lag and degradation-induced response deficits in fixed-wing aircraft. It presents a complementary assessment framework that preserves static task-criterion compliance while introducing a dynamic capability margin based on causal command-history features and nominal response prediction. The dataset includes development and prospective evaluation records, frozen assessment parameters, nominal-response model configurations, matched-comparison data, and scripts supporting the reported analyses and numerical figures and tables. The materials also support verification of the registered prospective criteria and the causal-truncation diagnostic. The packaged analysis and evaluation results have been independently reproduced from the included frozen data, with all 35 manuscript consistency checks passing. However, the package does not provide all external aircraft-model and controller assets required to regenerate the complete JSBSim simulation campaign. The study's limitations, including deterministic simulation conditions, restricted operating domains, and the unsuccessful registered numerical-integrity criterion, are retained in the documentation. The dataset is intended to support transparent assessment, verification, and reuse of the reported analytical workflow.

This IRF‑A Classroom Dialogue Dataset contains lesson‑level cognitive and structural dialogue indicators for 437 de‑identified Chinese secondary‑school lessons (Grades 10‑12), coded via the extended IRF‑A discourse framework and Bloom’s revised taxonomy using AI‑assisted annotation with manual reliability checks; the main CSV file records metrics for teacher initiation, student response, teacher feedback and student‑initiated questioning alongside composite sequence indicators, with supplementary files offering sample stratification and non‑joinable teacher contextual metadata, and the dataset is released under CC BY 4.0 following institutional ethical approval, without identifiable personal or school information.

Full research manuscript and complete reproducibility package for Threshold Calibration under Environmental Shift: A Reproducible Audit with Synthetic Wildlife Monitoring Scenarios. manuscript.docx is the research draft. code-and-data.zip contains both the original Gaussian/digit/misspecification study and synthetic wildlife extension, including code, inputs, output tables and automated checks. FILE_DESCRIPTIONS.json describes members; THIRD_PARTY_ATTRIBUTION.txt credits the UCI digit dataset (10.24432/C50P49, CC BY 4.0). The digit snapshot is reused material, not newly collected data. AI assisted coding, execution, checking and manuscript preparation. Numerical outputs come from actual execution. Automated checks do not constitute independent human validation. The submitting representative has affirmed reliability. Synthetic wildlife scenarios are not field observations. The manuscript is a preprint/research draft, not a claim of peer-reviewed acceptance.

Research hypothesis This study tested whether soluble Hemin (Ferriprotoporphyrin IX chloride) and particulate TiO₂ act through distinct catalytic mechanisms during anaerobic digestion of corn ethanol stillage under fed-batch conditions. It was hypothesized that: Hemin, as a soluble homogeneous electron mediator, would enhance extracellular electron transfer and methanogenic activity, especially when combined with laboratory-enriched photosynthetic bacteria (LE-PSB). TiO₂, as a heterogeneous conductive particle, would promote electron exchange at particle interfaces, favor hydrolytic and fermentative populations, and accelerate the conversion of metabolic intermediates. The synergistic combination of Hemin and LE-PSB would improve methane yield, methane-to-TOC conversion coefficient (MCC), coenzyme F₄₂₀, cytochrome c (Cyt-c), electrochemical responses, and microbial community structure compared with the control and single treatments. Data content The dataset was generated from a five-cycle fed-batch anaerobic digestion system treating corn ethanol stillage. The treatments include control, Hemin, TiO₂, and Hemin + LE-PSB. The data contain: Cumulative methane production per cycle and across five cycles, Methane-to-TOC conversion coefficient (MCC), Coenzyme F₄₂₀ content, Cytochrome c (Cyt-c) content, Cyclic voltammetry (CV) responses, Microbial community composition. Data collection Laboratory-scale anaerobic digesters were operated in fed-batch mode for five consecutive cycles using corn ethanol stillage as substrate. Hemin and TiO₂ were added at defined concentrations, while LE-PSB was enriched in the laboratory and co-inoculated in the synergistic treatment. Methane production was monitored by gas chromatography or a gas flow meter. TOC was measured with a TOC analyzer. Coenzyme F₄₂₀ and Cyt-c were quantified spectrophotometrically or by HPLC. Cyclic voltammetry was performed using a potentiostat with a three-electrode system. Microbial community composition was analyzed by high-throughput sequencing of 16S rRNA gene amplicons. Each treatment was generally run in replicate, and data are reported as mean ± standard deviation where applicable. Significant findings The synergistic combination of Hemin and LE-PSB enhanced methanogenic performance. Data interpretation and use These data can be used to: Compare the catalytic effects of soluble Hemin and particulate TiO₂ on anaerobic digestion of corn ethanol stillage. Evaluate the synergistic effect of Hemin and LE-PSB on methane production, MCC, F₄₂₀, Cyt-c, and electrochemical activity and so on.

This dataset accompanies the study “Using ChatGPT to Evaluate the Integration Status of Wetland Conservation in Local Plans” and contains archived materials for evaluating how wetland conservation is addressed in local comprehensive plans in Nebraska, USA. The planning documents were obtained from publicly accessible government websites. The study corpus comprises 25 plan units, with five used for prompt development and 20 for validation. Human reference scores cover 56 indicators per plan, giving 1,400 plan–indicator scores on an ordinal scale: 0 = not addressed, 1 = partially addressed, and 2 = fully addressed. The validation prediction table contains 4,480 scheduled evaluations across GPT-4o and GPT-4o mini under basic and optimized prompts. Fifteen model scores are unavailable and remain explicitly missing. Binary scores are post-hoc recodings of the ternary scores, with 0 indicating absence and 1–2 indicating presence. The package includes archived planning PDFs and a checksum manifest; a plan inventory and development/validation assignments; human reference labels; model predictions, requests, responses, and explanations; prompt and scoring-framework materials; optimization-history records; and statistical CSV tables with an offline analysis script. The resolved model-output archives cover 40 historical plan units, whereas the study validation analyses use only the 20 designated validation plans. The README documents file contents, variables, missing values, and provenance limitations, including three scores retained in the prediction table but absent from the supplied resolved JSON files. The model scores, explanations, and optimization text are AI/LLM-generated experimental outputs. The study code is shared on GitHub at https://github.com/HaishanLinHZNU/LLM_Plan_Evaluation. A local historical code-and-results snapshot is also included in this package. The offline analysis uses the supplied archived prediction table and requires no model API calls.