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This dataset supports the study “Physics-Guided Machine Learning for Transient Reservoir Characterization: Accuracy, Out-of-Distribution Generalization, and Noise Robustness in Niger Delta Sandstones.” It contains synthetic pressure-transient reservoir data generated across representative sandstone reservoir and fluid-property ranges, together with variables used for physics-guided machine-learning model development, training, validation, testing, out-of-distribution generalization assessment, and noise-robustness evaluation. The dataset includes reservoir and fluid parameters, transient pressure-response features, and corresponding target reservoir properties. It is provided to support reproducibility, independent verification, and further research on machine-learning-assisted transient reservoir characterization.

This dataset provides the technical documentation and data files for a Multi-Regional Social Accounting Matrix (MRSAM) for the United Kingdom, featuring a granular resolution of 12 regions and 42 economic sectors. Developed to support Spatial Computable General Equilibrium (SCGE) modeling specifically the UK UCL Subnational CGE model framework. This MRSAM integrates national input-output tables, national income and product accounts, and regional accounts via a rigorous top-down regionalization approach. The repository serves as a validation and methodological reference for researchers, economists, and policymakers studying regional economic dynamics, industrial decarbonization, and spatial spillovers.

The project was conceived and developed based on an automated computational flowchart structured through a decision tree using the CART (Classification and Regression Tree) method, and it was approved by the Ethics Committee of Santa Casa de São Paulo. The resulting algorithm, named DOACT, was fully documented and made available in a private GitHub repository, ensuring scientific transparency and reproducibility. The code repository associated with this study can be accessed at: https://github.com/italoeugenioabreu/doact . The flowchart was structured according to the most recent recommendations from leading international clinical guidelines, including those from the American College of Chest Physicians (CHEST) and the European Society for Vascular Surgery (ESVS). Study Design This was an observational, comparative, and exploratory single-blind study conducted across three analytical groups: Vascular surgery specialists Non-vascular medical specialists Artificial intelligence (AI) models based on Large Language Models (LLMs) - (ChatGPT-4o, Gemini 2.5, Grok 3.0, and Claude Sonnet 4.0) The total sample consisted of 53 participants, and the main objective was to evaluate the correct responses and diagnostic performance of the DOACT tool across 15 clinical case vignettes involving superficial venous thrombosis, deep vein thrombosis, and pulmonary thromboembolism.Case

This dataset includes drug sensitivity analyses and functional enrichment analyses of UNC93B1. In addition, the in vitro functional experiments in this dataset validate the role of UNC93B1 in LIHC cells.

Anexos de mi TFM

Supplementary material for JDS publication titled, "A novel life cycle assessment demonstrates that targeted genetic improvement can reduce the environmental impact of beef from beef x dairy production systems in the US and the UK"

Пациенты с острым нарушением мозгового кровообращения (ОНМК) нередко нуждаются в искусственной вентиляции лёгких (ИВЛ). Увеличивая внутригрудное давление, ИВЛ с положительным давлением модифицирует кардиореспираторные взаимодействия вследствие изменений пред- и постнагрузки на правый (ПЖ) и левый (ЛЖ) желудочки сердца. Данные о влиянии положительного давления в конце выдоха (ПДКВ) и движущего давления (PDRIVE) на систолическую функцию и релаксацию миокарда у нейрореанимационных пациентов крайне ограничены.

This dataset contains georeferenced locations of individual Amaranthus retroflexus L. plants mapped across two vegetable field trials at the North Willamette Research and Extension Center, Aurora, Oregon, in 2025, together with the plot boundaries and the trained instance-segmentation models used to generate them. The data were collected to address whether conventional quadrat sampling can adequately characterize weed density and spatial distribution for evaluating site-specific weed management. Because the benefit of site-specific control depends on how weeds are distributed within a field, mapping every detectable plant provides a reference against which sampling-based estimates can be assessed. Plant locations were derived from low-altitude unmanned aerial vehicle imagery (approximately 1.5 mm ground sampling distance) using a YOLO26 instance-segmentation model. Each point represents the centroid of a detected plant. The dataset comprises 6,279 detections across eight plots in a beet trial and 6,182 across eight plots in a turnip trial, with each plot covering 707 m². Plot-level densities range from 0.16 to 3.11 plants m⁻² in the beet trial and 0.21 to 2.45 plants m⁻² in the turnip trial. Both fields show strong spatial aggregation, with clustering detectable from below 0.1 m out to several meters after accounting for plot-level differences in density. The point patterns can be used to examine weed spatial structure, to simulate alternative sampling designs, or as training or validation reference for weed detection work. Files include point shapefiles of detected plant locations for both trials, polygon shapefiles of the plot layouts, and the trained model weights: a base model trained on beet-trial imagery and a fine-tuned model derived from it for the turnip trial. All spatial layers use WGS 1984 UTM Zone 10N (EPSG:32610), with coordinates in meters. The dataset of annotated image tiles is included to document the annotation format and allow assessment of label quality. Tiles are 640 × 640 px extracts from the orthomosaics with 20% overlap, and labels are polygon annotations in YOLO segmentation format. Tile counts, including background tiles containing no A. retroflexus instances, are given in the associated publication. These locations are model-derived rather than field-verified. Detection performance on independent test sets is reported in the associated publication and should be considered when reusing the data.

The attached csv files contain the results to the synthetic testbed for disaster variants: PO-B, PA-L, and PA-B. These are in reference to the paper: Optimizing Resilient Facility Locations under Natural Disaster Disruptions.

This dataset contains 1,831,254 records specifically curated to support research on detecting Cross-Site Scripting (XSS) attacks using machine learning techniques. Each record consists of two fields: Query: A text input representing potential web payloads, user inputs, or script content extracted from diverse sources of benign and malicious web traffic. Label: A binary classification label indicating whether the entry is malicious (1) or benign (0). The dataset is heavily diversified and deduplicated to ensure minimal bias and high generalization capacity for model training. It features a malicious-to-benign ratio of approximately 60:40, making it particularly suitable for evaluating both detection and false positive rates of machine learning and deep learning models. This dataset underpins the research paper “Bi-LSTM Approach for Cross-Site Scripting (XSS) Attack Detection” submitted to the International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar. The data can be used to benchmark traditional models (logistic regression, random forest, naive Bayes, decision tree, XGBoost) alongside advanced sequence models such as Bi-LSTM to exploit contextual dependencies in payload patterns. If u find this dataset useful dont forget to cite https://ieeexplore.ieee.org/document/11491109 Key Features: Total Records: 1,831,254 Benign Records (Label = 0): 666,484 Malicious Records (Label = 1): 1,164,770 Structure: Two columns – “Query” (textual payload) and “Label” (binary indicator: 1 = malicious, 0 = benign). Diversity: Collected from a broad range of benign and malicious payload sources to ensure high variability. Preprocessing: Cleaned, normalized, and deduplicated to eliminate redundant patterns. Intended Use: Training and evaluation of machine learning models for XSS detection, adversarial pattern recognition, and other web security research tasks. This dataset is valuable for cybersecurity practitioners, researchers, and academic institutions focusing on web application security, adversarial input detection, and real-time intrusion prevention systems.