Mendeley Data Showcase

A Four-Factor Model for the Indonesia Stock Market. The factors include market, size, value, and profitability factors. Asset spanning tests identify size (market capitalization), value (operating cash flow-to-price), and profitability (return on net operating assets) as the most robust characteristics for forming factors. Please visit http://idnfactors.online/ for latest factors.

source data

Dataset shared for the article "Modeling and Optimization Scheduling of Whole-Process Material-Energy-Economic Flow in Iron and Steel Industry for Grid Supply-Demand Interaction"

This dataset contains standardized core data records, including key statistical indicators, time-series data and related supporting information. It is mainly used for data analysis, business decision-making, report compilation and internal management reference, with complete and reliable data quality.

This dataset presents a structured qualitative review of restriction typologies in the social science literature on electric vehicles (EVs). The corpus consists of 62 journal articles and scholarly sources indexed through Scopus and Web of Science and selected for their relevance to electric vehicles within the field of social sciences. Each article was manually reviewed and classified according to the extent to which it engages with the theme of “restriction” in EV transitions. The dataset groups the literature into three levels of relevance: highly relevant (21 articles), moderately relevant (24 articles), and least relevant (17 articles).

The widespread adoption of social networking sites (SNS) has fundamentally reconfigured social communication, identity expression, and leisure Behaviour in the twenty-first century. Facebook, launched in 2004, has grown to encompass over 2.9 billion monthly active users globally, making it the world’s most utilised social networking platform (Statista, 2023). In Bangladesh, 13.7 million active Facebook users engage primarily via mobile devices, predominantly within the 18–34 age bracket — a demographic profile that overlaps precisely with the university student population (Dhaka Tribune, 2016). This convergence of platform ubiquity, mobile accessibility, and a young, academically engaged user base establishes a fertile context for the emergence of Facebook Addiction Disorder. Social Networking Addiction (SNA) is conceptualized as the excessive, compulsive use of SNS to the extent that it undermines occupational, academic, social, and psychological functioning. Theoretically, SNA shares mechanistic overlap with substance addictions, engaging dopaminergic reward circuits through variable-ratio reinforcement schedules delivered via social feedback mechanisms: likes, comments, shares, and notifications (Khang, Kim, & Kim, 2013). Bergen’s Facebook Addiction Scale (BFAS), developed and validated by Andreassen, Torsheim, Brunborg, and Pallesen (2012) in a Norwegian sample of 423 university students, operationalizes FAD across six established addiction criteria: salience, mood modification, tolerance, withdrawal, conflict, and relapse. Under the original polythetic scoring convention, individuals scoring ≥4 on at least 4 of 6 items are classified as exhibiting addictive patterns. The present study employs both the original polythetic threshold and an extended rubric (total score 11–30 classified as problematic or addicted) to facilitate comparison with studies that use aggregate scoring approaches.

Supporting Information for Magnetic Properties of Serpentinized Peridotites from Yinshanzhai, Hong'an Orogen Contents of this file Figures S1 to S3 Tables S1 Additional Supporting Information (Files uploaded separately) Captions for Tables S1 to S38 (if larger than 1 page, upload as separate file) Introduction This document provides supplementary data to facilitate interpretation of the rock magnetic results from the Yinzhaishan section, Hong'an (Figures 1 and 3). Figure S1 presents temperature-dependent magnetic susceptibility (χ-T) curves. Figure S2 shows First-Order Reversal Curve (FORC) diagrams. Figure S3 illustrates hysteresis loops for representative Yinzhaishan specimens. Table S1 summarizes the magnetic parameters obtained from Yinzhaishan samples. Data Processing and Software Tables S1–S3: Temperature-dependent magnetic susceptibility data were processed using Cureval 8 software. Raw data files (.cur format) were opened and imported into Microsoft Excel for visualization. These data support the production of Figure 1. Tables S4–S6: FORC data were analyzed and plotted using FORCinel software. These data support the production of Figure 2. Tables S7–S38: Hysteresis parameters were extracted and converted to appropriate units. Hysteresis loops were generated in Origin software using mass-normalized magnetic moment [Moment (Am²/kg) or emu/g] versus field [Field (mT) ÷ 10] as coordinate axes. Slope correction, saturation magnetization (Ms), saturation remanence (Mrs), and coercivity (Bc) are reported in the .hys files; remanent coercivity (Bcr) is reported in the .bcr files. These data support the production of Figure 3.

The replication dataset (data.dta) contains an unbalanced panel of Chinese listed firms from 2009 to 2021 and includes all variables required to construct the climate risk perception index, the three perception-bias measures, firm characteristics, carbon-risk indicators, and climate-sensitivity classifications. The dataset allows full replication of all tables and figures in “Risk-Shaped Cognition: How Climate-Risk Bias Influences Corporate M&A Decisions”, including baseline results, robustness checks, and heterogeneity analyses. All variables are derived from publicly available sources or standard databases, and detailed construction steps are provided in the Supplementary Material.

Includes Table S1 and Table S2

This dataset compiles reported concentrations of per- and polyfluoroalkyl substances (PFAS) measured in marine surface waters from multiple regions worldwide, including the Pacific Ocean, Atlantic Ocean, Arctic Ocean, Indian Ocean, Southern Ocean, and Antarctic waters. Data were extracted from peer-reviewed publications and included sampling location (region and specific site), sampling year, PFAS compound name, measured concentration (ng L⁻¹), compound classification (legacy or emerging), and calculated summary values such as average concentrations and summed concentrations by compound type. The dataset spans studies published between 2002 and 2025 and integrates measurements from coastal environments, open-ocean regions, and polar waters. All concentration values are reported as presented in the original studies and harmonized into a consistent format to facilitate comparative analysis of PFAS occurrence and distribution across global marine environments.