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This dataset supports a study on the role of fluid residence time in silicate weathering profiles. The main hypothesis is that fluid residence time links hydrological transport, mineral transformation, and Ca–Mg weathering fluxes. The dataset includes four natural weathering profiles: the CM and ZK01 basalt profiles and the TT and DY granite profiles. Tables S1–S4 provide sample depth, mineral abundances, CaO and MgO concentrations, and Ca and Mg mass-transfer coefficients. Tables S5–S8 provide Nb and Ti concentrations, calculated porosity, unit and cumulative fluid residence times, flow velocity, permeability coefficient k, and Ca and Mg weathering fluxes. Mineral abundances were measured by X-ray diffraction. Major oxides were measured from bulk saprolite samples. The hydrological parameters and weathering fluxes were calculated using a one-dimensional model that combines Darcy flow, geochemically constrained porosity, and depth-dependent permeability. The data show that basalt profiles have shorter residence times and stronger flow-dependent Ca–Mg fluxes. Granite profiles have longer residence times and more complex flux patterns. These data can be used to reproduce the results in the manuscript and to compare residence time controls on weathering across different lithologies.

The In Vitro Fertilisation (IVF) dataset comprised 5,000 patient records with 20 input variables, including clinical, laboratory, and embryological characteristics. The output field indicates implantation success, classified into two groups (0 and 1): 0 for failed implantation and 1 for successful implantation. Preprocessing and normalisation were applied to the dataset to enhance the quality of the data and balance the contributions of all features in predictive modelling. To avoid the dominance of particular features and numerical instability in the optimisation process, key variables were normalised to 0-1, such as patient age, AMH levels, number of oocytes retrieved, sperm quality, embryo grading and implantation outcomes. The missing values were reviewed. Coding categorical data into numerical data for machine learning was performed.

Paper title: "Worldwide well-being efficiency and its determinants: New insights from the Ordered Probit Stochastic Frontier Model". Authors: * Oleg Badunenko Brunel University of London, UK, oleg.badunenko@brunel.ac.uk, ORCID: 0000-0001-7216-0861 * José M. Cordero Universidad de Extremadura, Badajoz, Spain, jmcordero@unex.es, ORCID: 0000-0001-8783-6748 * Subal C. Kumbhakar State University of New York, Binghamton, USA, kkar@binghamton.edu, ORCID: 0000-0002-1366-379X This replication package provides instructions required to reproduce the main empirical results of the research paper.

This dataset evaluates a Cu-Fe oxide catalyst for amoxicillin (AMX) removal and degradation in water under batch and continuous-flow conditions via adsorption (ADS) and advanced oxidation processes (AOP). The catalyst showed combined adsorption and photocatalytic functionality, high degradation efficiency under LED irradiation, better performance in one-step ADS/AOP than in sequential operation, and good stability over reuse cycles. The dataset includes pH and catalyst dose optimization in batch ADS, adsorption kinetics and isotherms, catalyst screening, preliminary treatment tests, comparison between sequential and one-step ADS/AOP, optimization of catalyst and H2O2 doses in AOP, AOP kinetics, ROS scavenger tests, catalyst reuse, continuous-flow adsorption at different flow rates, sequential ADS/AOP in flow at different bed saturation levels, one-step AOP in flow, and residual H2O2 monitoring in influent and effluent streams. Method: batch experiments were performed in aqueous AMX solutions by varying pH, catalyst dose, oxidant dose, irradiation source, contact time, and initial concentration. Continuous-flow tests were conducted in a packed-bed column using CuFeOx2 under defined flow rates and bed saturation conditions. AMX concentration was determined by HPLC, while residual H2O2 in continuous-flow AOP was monitored electrochemically.

Replication package for paper "Reducing trade with Russia: Sanctions vs. firms’ voluntary suspension of activities"

The datasets uploaded with this article contain the raw and processed data used in the study, including XRD patterns, SEM micrographs, impedance spectroscopy measurements, and calculated dielectric parameters of Nb₂O₅ thin films deposited at substrate temperatures of 200, 450, 600, and 700 °C. The data support the analysis of phase composition, surface morphology, frequency-dependent dielectric constant, dielectric loss, and resistance behavior. These datasets are made available to ensure transparency and reproducibility of the reported results.

This dataset contains 800 annotated apple leaf and fruit images for apple disease detection using deep learning and computer vision techniques. The dataset is organized into train, validation, and test sets in YOLOv8 format. It was prepared for object detection and classification tasks related to identifying diseased and healthy apples. The images were preprocessed using auto-orientation and resized to 640×640 resolution. This dataset can be used for training, validating, and evaluating machine learning models for agricultural disease detection systems and smart farming applications. Dataset Split: Train Images: 560 Validation Images: 120 Test Images: 120 Total Images: 800

This dataset contains the raw data collected for the research study titled" A correlation study on balance and agility performance among middle -aged adults" . the study was conducted to examine the relationship betweeen dynamic balance and agility performance in healthy middle aged adults.

Genome structural equation model GWAS file and AlphaFold3 predicted protein structure

Supplementary data for the Original Article entitled “A distinct psoriasis–atopic dermatitis overlapping phenotype in adults with dual type 2 and type 3 immune features and favorable response to JAK1 inhibition”