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Moisture Absorption Analysis Table shows the moisture absorption behavior of five different material configurations (A–E) subjected to two environmental conditions—elevated temperature (70°C) and room temperature (23°C)—over a 7-day exposure period. The results indicate significant differences in moisture uptake patterns across configurations and temperatures. Configuration A at 70°C exhibited the highest moisture absorption, reaching 10.00 units by Day 7, while Configuration B showed the lowest at 0.59 units, suggesting a relatively hydrophobic nature or protective structure. In general, all configurations absorbed more moisture at 70°C compared to 23°C, highlighting the temperature's accelerating effect on diffusion and hygroscopic activity. Notably, the absorption trends for each configuration appear linear or quasi-linear over the duration, particularly for configurations with higher uptake.

The ALERT dataset includes 4,003 Bengali texts classified into four categories, addressing the shortage of resources for identifying religiously offensive content in regional languages and facilitating the advancement of NLP models.

The SDNCampus dataset provides a collection of flow statistics data across thirty applications available in a campus and other enterprise networks. It serves as a valuable resource for developing and evaluating deep learning models intended for precisely classifying applications generating traffic in a network. Key Features: The dataset precisely documents and labels thirty applications organized in single file for each application. To ensure inclusivity, a range of applications which are found on conventional computers, smart phones and Internet of Things (IoT) gadgets is represented. Efforts have been made to maintain a balanced distribution of samples across applications classes to enhance model training efficacy and generalization.

Supplemental Material for: Personalized UVA1 Phototherapy Strategies for Steroid-Refractory Hand-Foot Eczema: A Prospective Study This supplemental file provides additional figures and a table supporting the main findings of the study. Contents: Supplemental Figure 1: Representative cases of hand eczema patients undergoing UVA1 phototherapy in both low and high-frequency treatment cohorts. The figure illustrates the progression of skin lesions in patients with fissured and hyperkeratotic conditions, as well as those with dyshidrotic or recurrent vesicular eczema, at various treatment timepoints. Supplemental Figure 2: UVA1 phototherapy outcomes in hand-foot eczema patients with different lesion types across low and high-frequency regimens after 15 sessions. The figure shows the proportions of patients achieving HECSI-75 and HECSI-90 response rates. Supplemental Figure 3: Additional UVA1 phototherapy outcomes, including changes in HF-Peak Pruritus NRS and HF-Peak Pain NRS scores, recurrence times, and the correlation between initial palmar epidermal thickness and the number of treatments needed to reach IGA 0/1. Supplemental Table 1: Baseline demographics and clinical characteristics of the study participants, comparing the low-frequency and high-frequency treatment groups. These supplemental materials provide detailed clinical data and visual documentation to enhance the interpretation of the study results.

This dataset contains refined data from TALIS 2018 for measuring School climate, work stress, and teacher retention,.

This replication folder contains the necessary codes to replicate the quantitative results of the paper.

Acetabualr-vision hip developmental dysplasia’s (AV-DDH) Description: AV-DDH dataset is a comprehensive collection of 2417 raw X-ray images and their corresponding annotated labels for diagnosing developmental dysplasia of the hip (DDH). The dataset has been meticulously curated to aid in developing and validating machine learning models for DDH classification and diagnosis using hip X-rays. Each image in this dataset is paired with relevant demographic data, including age, gender, and the Acetabular Index in degrees for both hips. The annotations for the images were performed by a team of eight expert doctors and further reviewed by four orthopedic surgeons with extensive experience. Additionally, the dataset includes: • Unlabeled Data: A file containing raw, unannotated X-ray images. • Labeled Data: A file containing augmented, annotated X-ray images. The labeled data includes essential diagnostic information, including the Acetabular Index angle measurements for both the right and left hips, which specialized medical professionals have verified. The labeled dataset is organized as follows: • Training Set: 16,205 images • Validation Set: 463 images • Test Set: 926 images Content: • Images: 4630 raw X-ray images in PNG format depicting hip X-rays from infants under three years of age. • Annotations: Each image is annotated with the locations of critical anatomical features relevant to DDH, stored in both JSON and YAML formats. • Demographics: Each record includes demographic data such as age, gender, and the Acetabular Index in degrees for both the left and right hips. • Angle Measurements: The dataset includes angle measurements for the Acetabular Index for both hips, verified and labeled by a team of medical professionals. The dataset was collected from the radiology department at the Jordan University Hospital and is enriched with diagnostic information confirmed by orthopedic specialists. The images were gathered following strict inclusion criteria and are free from duplicates, ensuring the integrity of the dataset. Use Case: The AV-DDH dataset is specifically designed to aid in the development of machine learning models, especially deep learning-based approaches, to automate the diagnosis and analysis of DDH using hip X-ray images. It provides a high-quality, reliable source of labeled data that can be used for training, testing, and validating AI models aimed at improving the early detection and diagnosis of DDH, which is crucial for timely treatment and preventing long-term complications such as osteoarthritis and hip replacement surgery. Data Format: • Images: Raw X-ray images in JPG format. • Annotations: Annotations in JSON and YAML formats. • Additional Information: Demographic data in an Excel sheet. Keywords: Developmental Dysplasia of the Hip (DDH), X-ray, Machine Learning, Medical Imaging, Deep Learning, Data Annotation, Acetabular Index, AI, Dataset, Hip Imaging, Radiology, Diagnosis.

This dataset includes concentrations of eight nitro-aromatic compounds in fine particulate matters and trace gases, aerosol surface area density, meteorological data collected in different seasons at nine urban, rural and mountain sites in eastern China. They were inputted into the ensemble machine learning model and the receptor source apportionment model to obtain comprehensive understanding on the contributions of primary emission sources, secondary formation pathways, and meteorological conditions.

Raw data for all figures in the manuscript by Gao and Liu to be published by Geochimica et Cosmochimica Acta as "Path-integral molecular dynamics predictions of H isotope fractionation between brucite and water at elevated temperatures and pressures".

Supplemental Table 1. Model metrics for turnaround time and message quantity analyses. This table summarizes key metrics from the linear regression (LR) and negative binomial regression (NBR) models evaluating message turnaround times and message quantity, respectively. The linear regression model reports the Root Mean Squared Error (RMSE) and R2 as measures of fit. The negative binomial regression model includes pseudo-R2, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Deviance to assess model performance and fit. Supplemental Table 2. Characteristics of faculty dermatologists included in this study, including gender, specialization, rank, years in practice, message quantity, message turnaround time, and weekly patient volume.