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Our dataset comprises 33 studies including 136 patients with alopecia areata (AA) treated with upadacitinib across case reports, case series, and cohort studies. Patients had a mean age of 27.8 years (55.1% female), with predominantly severe disease (baseline SALT 77.1). Treatment regimens varied, with both low-dose (≤15 mg/day) and high-dose (≥30 mg/day) upadacitinib administered for an average of 24 weeks. Analyses incorporated subgroups by age, dosing, and prior JAK inhibitor exposure. The primary outcome was change in SALT score, complemented by achievement of thresholds (SALT50, SALT75, SALT90, SALT100, SALT≤20). Overall, upadacitinib led to a substantial pooled SALT reduction (−68.5), with comparable efficacy across age groups, dosing regimens, and prior JAKi exposure. In patients with severe AA, pooled outcomes showed a −79.9 mean reduction and high rates of clinically meaningful responses (SALT50: 94%; SALT≤20: 89%). In the 15 mg/day subgroup, improvements were more modest, with a −60.5 mean reduction and 24% complete regrowth (SALT100). Adverse events were reported in about one-third of patients, all mild and not requiring discontinuation, supporting an overall favorable safety profile.

Research Hypotheses: Fraction equivalence judgment serves as a valid measure of Fraction Conceptual Knowledge (FCK) Fraction Procedural Knowledge (FPK) mediates the relationship between FCK and General Mathematics Achievement (GMA) Students can be classified into distinct profiles based on FCK, FPK, and GMA Data and Methods: 282 students (grades 6-8) completed: Computer-based FCK tasks (number line estimation, fraction comparison, equivalence judgment) Paper-based FPK test (fraction arithmetic operations) Standardized mathematics exam for GMA Key Findings: Confirmatory Factor Analysis showed good model fit when including equivalence tasks in FCK measurement (CFI=1, TLI=1.013) Structural Equation Modeling revealed partial mediation: Direct effect of FCK on GMA: β=0.525 Indirect effect through FPK: β=0.108 Latent Profile Analysis identified three classes: Low-achieving (4.6%): weak in all three areas High-achieving (62.4%): strong in all areas Concept-strong (33.0%): high FCK but moderate FPK and low GMA Interpretation and Application: Understanding fraction equivalence is crucial for conceptual knowledge Procedural knowledge enables transformation of conceptual understanding into mathematical achievement Differentiated instruction needed: Low-achievers need both conceptual and procedural support Concept-strong students require procedural skill development Data Value: This multi-method study reveals the complex structure of fraction knowledge and provides empirical evidence for personalized mathematics instruction based on students' knowledge profiles.

Este é o conjunto de dados do artigo "Desigualdades na oferta de testagem rápida do HIV na atenção primária à saúde no Paraná: estudo de séries temporais e múltiplos grupos (2013-2023)", em atendimento à política editorial de acesso aberto.

This study tested the hypothesis that psychoprophylactic antenatal education, when combined with standard analgesia, would improve labour outcomes and maternal experience. The dataset comes from a randomized controlled trial of 180 women at Lagos State University Teaching Hospital, Nigeria, comparing an intervention group (structured psychoprophylactic education plus pethidine) with a control group (routine education plus pethidine). The data include demographic characteristics, pain scores, labour outcomes, and maternal childbirth experience. Findings show no difference in pain perception or obstetric/neonatal outcomes between groups, but women who received psychoprophylaxis reported significantly better childbirth experiences. The dataset can be used to explore the role of antenatal education in maternal satisfaction and to inform non-pharmacologic approaches to intrapartum care.

This dataset accompanies the paper "Can AI Detect Wash Trading? Evidence from NFTs" 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.

Adulteration Detection of Milk Powder with All Purpose Flour (Maida) Location: Mahila, Malda, West Bengal, India Time: 10 AM to 5 PM About Milk Powder: Milk powder is a dairy product made by evaporating milk to dryness, which increases its shelf life and makes it easier to store and transport. It contains essential nutrients like proteins, calcium, and vitamins, and is widely used in beverages, baking, and confectionery products. High-quality milk powder should be pure, free from contaminants, and have a characteristic milky taste. About All Purpose Flour (Maida): All-purpose (AP) flour is a milled wheat flour with a moderate protein content (9-12%) suitable for various culinary applications, such as making breads, cakes, and thickening sauces. Adulteration Concern: All Purpose Flour (Maida) is sometimes fraudulently mixed into Milk Powder to increase the product’s weight and profits. This adulteration significantly lowers the nutritional value of the milk powder and poses serious health risks such as digestive discomfort, gastrointestinal issues, and other harmful diseases. --- Mobile Details: Mobile Name: Moto G85 Camera Sensor: Sony LYT600 with OIS (Optical Image Stabilization) RAM & Storage: 4 GB RAM, 128 GB ROM Camera: Rear Camera – Dual; Front Camera – 16 MP Display: 6.5-inch Full HD+ Battery: 5000 mAh --- Process: 1. Sample Preparation: First, buy pure milk powder and all-purpose flour (maida) from the local market. 2. Precautions: Wear masks and gloves, as fine powders can cause respiratory discomfort. Conduct the experiment in a clean, dry place with proper ventilation. 3. Weighing Samples: Measure pure milk powder using a precision weighing balance. Divide the sample into 20 equal portions. 4. Adulteration Proportions: Prepare adulterated samples by mixing milk powder with All Purpose Flour at different proportions by weight: 0% (Pure milk powder), 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% (pure flour). 5. Background Setup: Use two types of backgrounds: White background Black background (For clearer identification of adulteration levels during image analysis). 6. Sample Display: On a white paper and black paper (210 mm x 297 mm, 80 gsm), draw 20 equal-sized cells. Spread each sample thinly and evenly within the cells. 7. Image Capture: Click photos of each sample using Moto G85 (Sony LYT600 sensor with OIS) for high clarity and stability. For each background, 12 sets of samples are prepared: 1 set of 100% pure milk powder 1 set of 100% All Purpose Flour 10 sets of adulterated samples (with varying proportions of flour). 8. Total Image Count: Each background sheet: 12 sets × 20 samples = 240 images. Two backgrounds → Total images = 240 × 2 = 480 images.

a total of 283 pressure injuries were identified in 149 pediatric patients hospitalised in our hospital between August 2020 and December 2024.

Data and programs for replication of the paper with the same title as above

Periphytic biomass In each of the 30 catchments, a 150-m stream reach was selected for periphytic biomass experiments and partitioned into four sections of 30 meters. To estimate periphytic biomass, referred to as the total biological material residing on submerged surfaces in streams, acetate plates (10 cm x 15 cm) were installed in four pools per stream for approximately 45 days. The periphytic biomass was obtained by the sum of chlorophyll-a concentration, organic matter and inorganic matter. The extract was measured in a spectrophotometer at 750 and at 665 nm, according to Steinman, et al. (2017). Precombusted filters were used to determine biomass through the ash-free dry mass (AFDM). The remaining material after the filter combustion was considered the periphytic inorganic matter. There were a total of 87 samples of periphytic biomass. Our aerial survey included samples of early-stage (n = 29), mid-stage (n = 16), edge-dominated forest classes (n = 30), and old-growth forest sites within a national park, which served as the control (n = 12). LiDAR data collection The aerial survey was conducted with a DJI Matrice 300 RTK Remotely Piloted Aircraft (RPA) equipped with a Zenmuse L1 LiDAR sensor. Data acquisition was performed between 9h and 16h, with the autonomous flight programmed to take place at a speed of 8 m/s at 60 meters above surface level (AGL), utilizing a "terrain follow" flight mode to maintain a consistent distance from the terrain's surface, configured in non-repetitive mode with 2-return. The collected data was exported to DJI Terra, version 3.15.24 (DJI Enterprise, Shenzhen, China), software was used to generate the 3D point cloud. The transects were executed alongside the streamline, producing a high-density point cloud (~100 ± 50 pts/m2). A D-RTK base station provided real-time kinematic (RTK) corrections for precise georeferencing, with a RMSE of 9.6 cm. The output from this procedure was a series of files containing xyzi (easting, northing, elevation, intensity) information for each laser pulse return collected from the sampled riparian ecosystem. The four reach sections and their limits were geo-located using a pair of Spectra Geospatial SP-60 GNSS receivers in RTK mode and used to assess the relationship between periphyton and forest structure. We emphasize that, although we aimed for a 30-m reach section. The fitted generalized linear model for periphytic biomass was analysed to consider the relative importance of each forest structure variable on the model output. The relative importance of each forest structure variable on the model’s output was then conducted using the eFAST analysis. The eFAST sensitivity analysis was conducted using 1,000 sampling points. First-order indices were computed based on variance decomposition, enabling the identification of each parameter’s contribution to output variability.

En español La arquitectura vernácula, como heredera de las tradiciones constructivas locales, se caracteriza por su capacidad de adaptación al entorno, ofreciendo valiosas lecciones para la arquitectura contemporánea. Sin embargo, en la República Dominicana muchas de estas tipologías se encuentran en riesgo de desaparecer, perdiéndose con ellas conocimientos esenciales sobre diseño climático y confort ambiental. En este estudio se analizaron dos modelos de arquitectura vernácula, diferenciados principalmente por su materialidad, y se compararon con una vivienda de arquitectura popular. Para ello, se realizaron mediciones de variables ambientales clave —temperatura y humedad relativa— en condiciones reales de uso. Los datos obtenidos muestran que las viviendas construidas en tejamaní alcanzan mayores niveles de confort térmico, registrando temperaturas interiores significativamente más bajas en comparación con las demás tipologías. Estos hallazgos confirman que la arquitectura vernácula, especialmente la de tejamaní, logra un desempeño ambiental sobresaliente mediante estrategias pasivas, superando en muchos casos a la arquitectura formal contemporánea. El dataset permite comprender, interpretar y reproducir la forma en que estas viviendas responden al clima, aportando evidencia para investigaciones sobre sostenibilidad, confort y adaptación arquitectónica. English Vernacular architecture, as the heir of local constructive traditions, is distinguished by its strong adaptation to the environment, offering valuable lessons for contemporary design. In the Dominican Republic, however, many of these typologies are at risk of disappearing, along with their embedded knowledge on climatic design and environmental comfort. This study analyzes two models of vernacular housing, differentiated mainly by materiality, and compares them with a popular housing unit. Environmental measurements were carried out under real use conditions, focusing on key comfort variables: indoor temperature and relative humidity. The data show that tejamaní houses achieve higher thermal comfort, consistently recording lower indoor temperatures compared to other typologies. These findings confirm that vernacular housing, particularly tejamaní, attains remarkable environmental performance through passive strategies, often surpassing contemporary formal architecture. The dataset provides a basis for understanding, interpreting, and replicating how these houses adapt to climate, contributing valuable evidence for research on sustainability, comfort, and architectural adaptation.