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Global C factor under two SSP-RCP scenarios.

The data describes clinical trials on PTSD military and civilian across 68 countries, until 2013. However, the charts capture data until 2018. Likewise, the resulting implications from the mental disorder, such as suicides, includes latest charts. These data serve a full article on the military-civilian technology duality.

In order to cooperate with the research of the paper "Stackelberg game between charging station and distribution network based on regional load forecasting and intelligent charging strategy", we have constructed a comprehensive data set, which includes the following contents: README File: This document specifies the computational environment (Python 3.8+, TensorFlow 2.5, Gurobi 9.1), outlines the step-by-step workflow (from data preprocessing and LSTM model training to Stackelberg game simulation), lists key parameter settings, and describes the result verification method. Core Scripts: The bundle includes three Python scripts: (1)data_preprocess.py (performs min-max normalization and splits the load dataset) (2)lstm_load_forecast.py (trains the LSTM model and outputs load predictions) (3)stackelberg_game.py (solves the Stackelberg game using backward induction and outputs DN pricing and CS power purchase decisions) Tiny Synthetic Sample: A 100-hour synthetic dataset, generated based on the statistical distribution of real load data from Tianjin, is provided. It includes features such as date, load demand (kW), dry-bulb temperature (℃), dew point (℃), and hour of day. This sample supports basic LSTM model training and end-to-end verification of the game-theoretic simulation. This data set supports game optimization between distribution network and charging station, load forecasting model training, and simulation and evaluation of intelligent charging strategy for electric vehicles, and is suitable for power system optimization, game theory application and related research of smart grid.

Proteins (pdb files) and ligands (sdf files) from SQM2.20 optimized complexes are provided for the modeled 67 compounds.

Supplementary Section for manuscript “Metabolic and Functional Changes in T helper cells During the Periparturient Period of Dairy Cows”.

This dataset provides information on the three macroeconomic sectoral balances, covering 195 countries over 45 years. Macroeconomic analysis often focuses on the 'twin deficits' - the government deficit and the current account deficit. This is an incomplete view which leaves out the private sector balance. The three sectoral balances must sum up to zero, by accounting identity (UN System of National Accounts 2008): (S – I) + (T – G) + (M – X) = 0 Economists using the two-dimensional view have famously missed the global financial crisis, while those using accounting models covering all three sectoral balances were able to predict it (Bezemer 2010, Galbraith 2012). However, data on private sector deficits/surpluses is not readily available. Only the public and current account balances are published regularly by the IMF's World Economic Outlook. Beyond developed countries, looking at the private sector balance is critical for analyzing and crafting policies in developing countries. The frequently recommended policy of 'fiscal consolidation', i.e. reducing public deficits, is revealed in the sectoral balances to also reduce, ceteris paribus, the private sector surplus (or increase its deficit), slowing down or even reversing development and poverty reduction (Assa and Morgan 2025). The dataset was calculated based on two publicly available series from the IMF World Economic Outlook (downloaded October 2025): General government net lending/borrowing (coded as GOV) and Current account balance (coded as CAB). From this we calculated the private sector balance as PRV = CAB - GOV. We converted CAB to ROW (ROW = -CAB), the rest of the world balance, and made sure that ROW, GOV and PRV add up to zero as required by the national accounting identity. All years containing IMF forecasts were removed. References: Assa, J., & Morgan, M. (2025). The General Relativity of Fiscal Space: Theory and Applications. Review of Political Economy, 1-35. Bezemer, D. J. (2010). Understanding financial crisis through accounting models. Accounting, organizations and society, 35(7), 676-688. Galbraith, J. K. (2012). Who are these economists, anyway?. In Contributions in Stock-flow Modeling: Essays in Honor of Wynne Godley (pp. 63-75). London: Palgrave Macmillan UK. United Nations (2008). System of National Accounts 2008. https://unstats.un.org/unsd/nationalaccount/sna2008.asp

VitralColor-12, a synthetic dataset for color classification and segmentation, utilizes LLMs in specific GPT-5 and DALL·E 3 models to generate images of stained-glass. This approach simplifies labeling by using the dark steel structure supporting the glass as a guide, which provides easy regions to label with a single color per region. After that, we obtain the images and use at least one hand-labelled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation with two iterations per operation, and a kernel size of 2 pixels. This process enables us to label a classification dataset and generate segmentation maps automatically. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color, which includes 9,509,524 labeled pixels. We include tables with pixel values in RGB, HSL, CIELAB, and YCbCr color representations, along with lightness values for neighborhoods 4 and 8, enabling detailed color analysis and training of machine learning algorithms in different color spaces. Furthermore, we also included descriptive statistics and ΔE76, ΔE94, and CIELAB a vs b Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. If you found the VitralColor-12 dataset usefull please perform citation: Rivera, M. M., Guerrero-Mendez, C., Lopez-Betancur, D., Saucedo-Anaya, T., Sánchez-Cárdenas, M., & Gómez-Jiménez, S. (2025). VitralColor-12: A Synthetic Twelve-Color Segmentation Dataset from GPT-Generated Stained-Glass Images. Data 2025, Vol. 10, Page 165, 10(10), 165. https://doi.org/10.3390/DATA10100165 @article{Rivera2025, author = {Martín Montes Rivera and Carlos Guerrero-Mendez and Daniela Lopez-Betancur and Tonatiuh Saucedo-Anaya and Manuel Sánchez-Cárdenas and Salvador Gómez-Jiménez}, doi = {10.3390/DATA10100165}, issn = {2306-5729}, issue = {10}, journal = {Data 2025, Vol. 10, Page 165}, keywords = {color benchmark,color segmentation,generative AI,synthetic dataset,synthetic images}, month = {10}, pages = {165}, publisher = {Multidisciplinary Digital Publishing Institute}, title = {VitralColor-12: A Synthetic Twelve-Color Segmentation Dataset from GPT-Generated Stained-Glass Images}, volume = {10}, url = {https://www.mdpi.com/2306-5729/10/10/165/htm https://www.mdpi.com/2306-5729/10/10/165}, year = {2025}, }

The dataset was collected from five different road projects in Pakistan. It contains genetic programming expressions for Marshall parameters, including Stability, Flow, and Air Voids for both the base and wearing courses. Additionally, the Excel sheets provide comparisons of Support Vector Regression, Genetic Programming, Multiple Linear Regression, and Non-Linear Regression models. The Python file is also available for the base and wearing course parameters using GP expressions, along with 10-fold cross-validation and SHAP analysis

The PHQ-9 Enhanced Student Depression Dataset contains comprehensive responses from 682 students to the PHQ-9 questionnaire, a well-established clinical tool for diagnosing depression. This enhanced 5th edition represents a significant advancement from previous versions, incorporating additional psychosocial factors that influence mental health outcomes among young adults aged 17-26 years. Important Note: This survey was conducted from the start under the supervision of qualified mental health professionals and clinical researchers, ensuring ethical data collection practices and participant welfare throughout the study. PHQ-9 Assessment Framework The PHQ-9 questionnaire includes 9 standardized questions assessing depression symptoms over the past two weeks, covering mood, energy levels, sleep, appetite, concentration, and suicidal ideation. Responses are scored on a 4-point scale from 0 (Not at all) to 3 (Nearly every day), with total scores ranging from 0 to 27. Depression severity is classified into five categories: Minimal (0-4): 206 participants (30.2%) Mild (5-9): 155 participants (22.7%) Moderate (10-14): 128 participants (18.8%) Moderately Severe (15-19): 125 participants (18.3%) Severe (20-27): 68 participants (10.0%) New in 5th Edition Key Improvements from Previous Editions Increased sample size from 400 to 682 participants (70% increase) Zero missing values across all 16 variables Professional supervision throughout data collection Enhanced ethical framework with IRB approval New Psychosocial Variables Three critical stress factors were added based on validated correlations with depression severity: Sleep Quality: Good (34.9%), Average (31.5%), Bad (21.0%), Worst (12.6%) Study Pressure: Good (26.7%), Average (31.1%), Bad (26.5%), Worst (15.7%) Financial Pressure: Good (26.7%), Average (32.6%), Bad (25.5%), Worst (15.2%) Demographics Age Range: 17-26 years (mean: 21.4) Gender: 418 males (61.3%), 264 females (38.7%) Applications Clinical Research: Depression prediction models, multi-factor analysis, risk stratification Machine Learning: Multi-class classification, feature engineering, predictive analytics Education: Clinical training, research methodology, statistical analysis Ethical Considerations All data collected under professional mental health supervision IRB approval obtained with informed consent protocols Crisis intervention procedures established All PII removed, maintaining strict anonymity Participant support resources provided throughout study This enhanced dataset provides a robust foundation for automated depression detection research while maintaining the highest standards of ethical data collection and clinical relevance for student populations.

Data for ULTRASVENT-1 trial