Mendeley Data Showcase

These are the .dta dataset and Stata code for the paper Equity Incentives and Product Quality

This study investigated the structure–function relationship of native and enzymatically modified diluted alkali-soluble pectin (DASP) and its impact on the stability of oil-in-water emulsions during storage. The work focused on understanding how enzymatic modification of RG-I-rich pectin fractions influences molecular architecture, rheological behaviour, interfacial properties, and emulsion stability under different pH conditions. Native and modified DASP fractions were characterized using compositional analysis, spectroscopic techniques, rheological measurements, and emulsion stability assessment combining macroscopic observations and microscopy. The dataset contains raw experimental data supporting physicochemical characterization of DASP fractions and evaluation of emulsion stability, including creaming behaviour, rheology, FTIR spectroscopy, and structural analysis. The folder entitled “Creaming index (%)” contains raw data files (.xlsx) used to calculate creaming index values during storage experiments. The datasets include direct measurements performed using a caliper. The folder entitled “DM (%) DA (%)” contains raw analytical data (.xlsx) used for determination of degree of methylation (DM) and degree of acetylation (DA). The files include calibration curves for methanol and acetic acid, as well as integrated peak areas corresponding to analysed samples. The folder entitled “FTIR” contains raw Fourier-transform infrared spectroscopy datasets, including unprocessed spectra (wavelength and absorbance values) collected in experimental replicates for both native and enzymatically modified DASP samples. The folder entitled “Microscopic pictures” contains a set of original microscopy images (.JPG) acquired for emulsions on each day of the storage test. The images are organised according to pH conditions. The folder entitled “Monosaccharide composition, UA content” contains Microsoft Excel files (.xlsx) presenting raw compositional data, including quantified values of individual monosaccharides and uronic acids obtained in experimental replicates. The folder entitled “Rheology” contains raw rheological datasets exported directly from the rheometer, including original instrument export files (.txt) and Microsoft Excel files (.xlsx). The datasets include viscosity measurements of emulsions collected during a 28-day storage study, separated according to pH conditions (pH 2.5 and pH 6.0). In addition, the folder contains flow curve data for pectin solutions as well as viscosity measurements for both pectin solutions and corresponding oil-in-water emulsions. These files provide complete raw datasets used for rheological analysis.

This file provides the replication data and code for the study titled “Tail Risk Connectedness and Systemic Volatility in the Cryptocurrency Market: Evidence from a Value-at-Risk Framework.” It enables full reproduction of the empirical results reported in the paper.

As the global digital economy expands, the demand for a workforce capable of navigating cross-border e-commerce (CBEC) has surged worldwide. However, existing educational frameworks often fail to effectively translate student competencies into actual workplace performance. Integrating Social Cognitive Theory with the Knowledge, Skills, Abilities, and Other characteristics (KSAO) framework, this study examines how specific competencies influence CBEC performance among Chinese vocational college students. A cross-sectional survey of 419 participants was analysed using covariance-based structural equation modeling (CB-SEM). The results indicate that task-oriented dimensions, specifically English skills, cultural knowledge, and workplace aptitude, significantly enhance both motivation and subsequent performance. Motivation functions as a crucial mediator, transforming latent potential into active capability.

This file outlines the process for replicating the econometric analysis presented in the study: Impact of digital economy on innovation and entrepreneurship: Evidence from China. This folder contains the replication materials for the empirical analyses presented in the paper. It includes the dataset and Stata code used to reproduce the main results. The do-file contains detailed comments explaining each step of the empirical procedure, which facilitates replication and understanding. If any issues arise during replication, please ensure that all dependencies and packages are properly installed. For any questions regarding the code or data, please contact the corresponding author.

Establishing database The raw data used in this paper were only obtained through peer-reviewed laboratory research on CO₂-cured concrete because the large-scale field application of carbonation-curing technology is not very common. The database was compiled from published experimental studies reporting controlled programmes on accelerated CO₂ curing of cement-based materials (Sidhu et al., 2023; El-Hassan et al., 2013; El-Hassan & Shao, 2015; El-Hassan & Shao, 2014; Shao, 2014; Shao & Morshed, 2015; Zhang & Shao, 2016; Zhang & Shao, 2019; Rostami et al., 2011; Shao & Lin, 2011; El-Hassan, 2013; Rostami et al., 2012; Morshed & Shao, 2013; Shao et al., 2014; Ijongcan et al., 2016; Liu, 2016; Zhang et al., 2016; Zhang & Shao, 2016; Chen & Gao, 2019; Lee et al., 2018; Li et al., 2019; Chen & Gao, 2020; Zhang et al., 2020; Sharma & Goyal, 2022; Wang et al., 2022; and Xian et al., 2023 The research papers reviewed in the previous decade offer a wide range of research on concrete and mortar samples exposed to controlled CO₂ conditions. The proportion of mixtures, carbonation curing parameters, and the respective 28-day compressive strength values were reported in each investigation as a result of standardised testing procedures. An organised and clear screening procedure was adopted to provide methodological consistency and reduce selection bias. The inclusion criteria were that the studies (i) must report 28-day compressive strength calculated according to the ASTM standards; (ii) must have full documentation of the mixture constituents and carbonation curing parameters; and (iii) must be clear in the description of the preparation and testing of the specimen. Records were filtered out when they had missing variables, inconsistent unit systems, ambiguous curing conditions or duplicate experimental records. Where several curing regimes were studied in one study, each curing condition was analysed as an independent sample to maintain experimental variation and avoid aggregation bias. The screening regulations were used consistently across the sources in order to be objective. Following verification and data cleaning based on rules, 198 complete documented samples were included in the final dataset.

Contributors: Dr. Neeta Nain Research Scholars: Anand Kumar Jain Institute: Malaviya National Institute of Technology Jaipur Anadi Jain Institute: Government women engineering College Ajmer ( Bikaner Technical University, Bikaner) Domain Expert: Kota Agriculture University, Kota and Rajsthan Government Agriculture Department Crops: Cashew, Cassava, Tomato, Potato, Maize, Papaya, Sugarcane, Rice, Chilli

Raw data.

This repository contains a simulate dataset derived from the oiginal data and the R code illustrating the implementation described in the associated article: Robustillo, M.C., Senger, D., Parra, M.I., Pérez, C.J. (2026). A Multivariate Autoregressive Multilayer Perceptron Model for Predicting Internal Beehive Conditions from Sensor Data. Computers and Electronics in Agriculture, Volume 246, pages 111593. DOI: https://doi.org/10.1016/j.compag.2026.111593 The dataset (ExampleHive.txt) contains internal and external measurements simulated from a single hive (Hive 0) from the BeeObserver project (https://doi.org/10.5281/zenodo.10407693). It is based on the original hive data recorded at an hourly sampling frequency, complemented with meteorological information from the German Weather Service (DWD). A small amount of Gaussian noise was added to the measurements to prevent exact replication of the original data and to allow dataset sharing. All data were preprocessed following the procedures described in Robustillo et al. (2026) and are provided in a format ready for direct use with the R script included in this repository. Variable Description Unit _____________________________________________________________________________________ time2 Timestamp of the measurement - t_i_1…t_i_5 Internal hive temperature sensors (DS18B20, 5 positions) °C weight_kg Hive weight (Bosche H30/H40 load cell under hive) kg h Relative humidity inside hive (BME280 sensor) % t Internal temperature inside hive (BME280 sensor) °C p Internal air pressure inside hive (BME280 sensor) hPa to_imp External temperature (DS18B20 sensor) °C ho_ms External relative humidity from DWD % precip_ms Precipitation from DWD station mm wind_ms Wind speed from DWD station m/s pressure_ms Atmospheric pressure from DWD station hPa hour Hour of the measurement day Day of the measurement month Month of the measurement year Year of the measurement ext External variable capturing large hive weight variations kg ______________________________________________________________________________________ This research was funded by Ministry of Science, Innovation and Universities - Spain, and State Research Agency - Spain (Projects PID2021-122209OB-C32 and PID2024-155179NB-C21), funded by MICIU/AEI/10.13039/501100011033 and European Union (European Regional Development Fund).