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This dataset contains reproducible statistical analysis templates developed in JASP for evaluating a brief AI-assisted emotional regulation intervention delivered through a task-sharing model in university students. The repository includes parametric and non-parametric analyses, as well as session-level evaluation structures.

This database is a firm-level panel dataset for China’s NEV industry, combining policy, firm, patent, and regional data. It includes measures of policy mix diversity and balance, green innovation, absorptive capacity, and related control variables, and is intended for empirical analysis of the relationship between policy mix characteristics and firm green innovation.

Comparison of Risk Factors, Habits, and Vitiligo Characteristics According to the Presence of Neurological Disease.

Comparison of Demographic and Clinical Characteristics According to the Presence of Neurological Disease.

This dataset contains anonymized, cross-sectional raw data from a study conducted among LGBTQ+ people living with HIV in Mexico who receive support from community-based organizations. The primary aim of the study was to evaluate adherence to antiretroviral therapy (ART) and its association with lifestyle factors. The dataset includes sociodemographic variables, clinical information (such as time on ART and self-reported CD4 counts), and responses to standardized instruments, including the Simplified Medication Adherence Questionnaire (SMAQ) and the FANTASTIC lifestyle questionnaire. All questionnaire items are presented in their original (raw) format. Data processing, variable recoding, and construction of derived variables (e.g., binary adherence classification and categorized predictors) were performed separately during statistical analysis using IBM SPSS Statistics and are not included in this dataset. All data have been de-identified to protect participant confidentiality, and no personal identifiers are included. This dataset is intended to support transparency and reproducibility of the study findings.

This dataset accompanies the paper "Detecting Crypto Wash Trades via Machine Learning" and the associated Github repository (see Related Links). It contains labeled machine learning samples constructed from on-chain transactions of major NFT exchanges and Mt. Gox. Each row corresponds to a trade with engineered features and a binary label indicating legitimate or wash transaction. These files are the inputs used to train and test the machine learning models described in the paper. The raw data sources and preprocessing steps are documented in the paper and GitHub repository.

Data associated with the article submitted to Geoenergy Science and Engineering. The dataset includes synthetic seismic cubes, dRMS seismic maps, and property maps for the three zones defined in the article. Seismic cubes are provided in SEG-Y format, and dRMS maps are provided in EarthVision grid format (ASCII). Title: Evaluating reservoir simulation models against 4D seismic data for open access data from the Sleipner field Autores: Felipe L. Cavalcante (1), Daiane Rossi Rosa Lessa (1), Leonildes Soares de Melo Filho (2), Denis José Schiozer (1), Alessandra Davolio Gomes (1) (1) Center for Energy and Petroleum Studies, University of Campinas, Cora Coralina Street, 350, Campinas, SP, Brazil (2) Repsol Sinopec Brasil - Praia de Botafogo, 300 – ZIP Code 22250-040, Rio de Janeiro, RJ – Brazil Correspondence: clfelipe@unicamp.br Abstract The Sleipner CO₂ storage project in the North Sea has become a global reference for carbon capture, utilization, and storage (CCUS), offering a dataset for testing geophysical methodologies. In this study, we assess the capacity of ten publicly available three-dimensional flow models from the British Geological Survey (BGS) to replicate observed four-dimensional seismic responses at the Sleipner East site. Real 4D seismic data provided by Equinor were compared against synthetic responses generated via petroelastic modeling and one-dimensional convolutional forward modeling of the simulation outputs. To address gaps in model documentation, we implemented a robust workflow that integrates literature-derived rock physics parameters with targeted calibration of petroelastic models. Model performance was quantified using the Structural Normalized Cross Correlation (NCC) and Root Mean Square Error (RMSE) metrics applied to attribute maps across three discrete reservoir intervals. Although none of the evaluated models capture every feature of the field data—reflecting their simplified geological frameworks—several closely reproduce the CO₂ plume morphology and signal polarity in specific zones. Our workflow enables reproducible seismic modeling for CCUS benchmarking, with Model 10 achieving the highest similarity (NCC = 0.754) in Zone 1. Moreover, this work provides a reproducible protocol for constructing petroelastic models and conducting seismic forward modeling using publicly accessible datasets for Sleipner.

Source data for Remodeling Activity of ChAHP Restricts Transcription Factor Access to Chromatin

Monthly counts of violent crimes at the municipality level for the state of Minas Gerais (MG), Brazil, covering January 2019 through early 2026. Source: SEJUSP-MG Dados Abertos (https://dados.mg.gov.br/dataset/dados-abertos-ocorrencias-policiais). 860 municipalities × 8 years × 15 violent-crime categories = 69,504 non-zero aggregate rows. Columns: year, month, municipality_code (IBGE), municipality, natureza (crime type), risp (integrated safety region), rmbh_belo_horizonte, registros. Related: Crime Brasil full interactive map + free REST API https://crimebrasil.com.br (CC BY 4.0, press kit at https://crimebrasil.com.br/imprensa). DOI-backed mirrors: Zenodo 10.5281/zenodo.19711971, Figshare 10.6084/m9.figshare.32086188, Harvard Dataverse doi:10.7910/DVN/5TZFSX, Kaggle valdovaldo/minas-gerais-violent-crime-2019-2026, data.world crimebrasil/minas-gerais-violent-crime-statistics-2019-2026.

In this study we assessed vegetation responses to mastication treatments across three dominant soil types in two-needle piñon (Pinus edulis Engelm. [Pinaceae]) - one-seed juniper (Juniperus monosperma [Engelm.] Sarg.) woodlands in southeast Colorado, USA – a region characterized by monsoonal precipitation, limited presence of introduced plant species, and relatively high grazing intensity by cattle and wildlife. We surveyed understory species composition in the year prior to mastication (2018) as well as 1 (2019) and 3 years (2021) post-mastication. Fifteen sites were established, with paired treatment and control plots that measured 10 by 50 meters. The line point intercept method was used to survey ground cover and plant community composition across all years. Targeted sampling of understory plants also occurred in small quadrats in 2019 and 2021 under where canopy trees existed prior to mastication, on the edge of the tree canopy prior to mastication, and in the interspace.