Mendeley Data Showcase

This study examined how AI, peer, expert, and instructor-guideline feedback relate to students’ scientific writing and reasoning in undergraduate biology. Data from 104 students included rubric-scored essays, scientific reasoning assessments, and written reflections. Final essay performance was similar across conditions, with feedback type explaining little variance. However, scientific reasoning showed domain-specific differences with small to moderate effects and some ceiling effects. Qualitative results revealed distinct engagement patterns: guidelines supported task understanding, peer feedback fostered evaluative judgment, AI supported organization, and expert feedback supported discipline-aligned revision. Overall, feedback influenced interpretation and revision processes more than final performance outcomes.

This dataset contains a de-identified, incident-level collection of U.S. consumer product-related events compiled from publicly available reporting records. Each record represents a single incident involving a consumer product and includes standardized categorical classifications for product type (PRD), defect type (DFT), failure mode (FLW), usage context (USC), and injury outcome (DAM). The dataset is designed to support structured analysis of product-related hazards, failure mechanisms, and resulting injuries. All records have been processed using predefined classification rules to ensure consistency and reproducibility. Supporting lookup tables defining all classification systems used in the dataset are provided. Free-text narratives used during the classification process are not included in this public dataset.

This dataset contains the structural and pedagogical design of a 45-minute community health intervention titled "Vacúnate" as part of the Niño Sano community project. The Medical School of the Universidad Nacional Pedro Henríquez Ureña developed the project to be implemented in the rural community of Yaguate, San Cristobal, targeting families and young adults. The material includes a detailed logistics plan for the 45-minute intervention, which is divided into five stages: a diagnostic pre-test, a session on vaccine fundamentals, a session on debunking myths, a session on lifelong immunization schedules, and a post-test to measure impact. The data highlights a dual-plan strategy (Plan A utilizes digital resources, while Plan B is designed for low-resource environments), ensuring the intervention's adaptability to various community settings, such as school classrooms or open-air courts. This dataset aims to provide medical and public health students with a replicable model to address vaccine reluctance and improve health literacy in underserved populations.

This dataset includes historical population in the Loess Plateau of China, and annual water and sediment discharges at the Huayuankou hydrological station on the Yellow River of China.

Postpartum depression (PPD) is a common mood disorder that may occur after childbirth and can harm maternal well-being, mother– Although general awareness of PPD exists among HCPs in Shendi, practical screening and standardized management are limited. Strengthening training, implementing culturally adapted screening, and integrating psychosocial support into postnatal care are recommended to improve maternal mental health outcomes. infant bonding, and family functioning. In low-resource settings such as Sudan, PPD is often underdiagnosed because of limited training, cultural stigma, and few mental health services. Healthcare providers (HCPs) are essential for detection and referral, but their readiness (knowledge, attitudes, practices — KAP) is not well known in Shendi.

Reproducibility package for YOLOv12-DynaFocus: a lightweight dynamic-attention detector for PCB inspection

We provide the replication package for the paper *Penalizing Brown Industries or Stimulating Green Innovation? A Model Incorporating Endogenous Green Innovation* (Youbo Xu, Honglei Zhang and Ruochi Hu).

This dataset is the result of a large scoping review on child well-being indicators in OECD countries and accompanies a manuscript with the same title. Background: Governments in OECD countries increasingly rely on population-level indicators and dashboards to monitor child wellbeing, guide budgeting, and demonstrate accountability, yet the scope, equity, developmental coverage, and cultural relevance of these monitoring systems have not been systematically assessed. Methods: We conducted a scoping review of peer-reviewed and grey literature on population-level child and youth wellbeing indicators in OECD countries (Jan 1, 2013–Dec 31, 2024), charted against the OECD Child Wellbeing Framework. Two reviewers independently screened records; indicators were charted by domain, influence level, population group, age, framing (positive/negative), and country. Descriptive analyses summarised distributions and trends; an iterative qualitative process grouped concepts into domains. Findings: From 605 publications (512 peer-reviewed; 93 grey) we extracted 5,873 indicators encompassing 1,976 unique concepts across 35 recurring domains. Peer-reviewed literature emphasized emotional and psychosocial constructs, while grey literature increasingly prioritized system-performance and material/economic indicators, particularly after 2020. Adolescents aged 10–14 were most frequently measured; early childhood (1–4) and late youth (20–24) were substantially under-measured. Underserved populations were rarely the focus. Most publications originated from North American and European contexts, with non-Western OECD members under-represented. Rights-based, identity-related, and culturally grounded domains remained sparsely measured relative to emotional and physical constructs. Interpretation: Population-level monitoring of child wellbeing has expanded substantially but remains concentrated in Western contexts, developmentally skewed toward adolescence, and poorly representative of Indigenous, racialized, and sexual minority children. Conceptual breadth has outpaced measurement depth, with rights-based and structural dimensions of wellbeing under-specified. To close these gaps, future monitoring systems should embed equity stratifiers as default components of core indicator sets, extend measurement into early childhood and late youth, and co-develop culturally grounded indicators with children, families, and communities through a core-plus-modular architecture. Funding: This study was funded by the Alberta Children’s Hospital Foundation, the Alberta Children’s Hospital Research Institute, and the BMO Financial Group Endowed Research Fund in collaboration with the University of Calgary’s One Child Every Child Initiative, funded by the Canada First Research Excellence Fund. The funders had no role in the study design, analysis, interpretation, or preparation of the manuscript.

This study examines the concept of place through the lens of ageism by employing neuroarchitectural approaches to investigate visual complexity. Therefore, it develops a detailed understanding of how older adults perceive visual complexity in urban environments using an instrument called CLARITY. The study utilizes a mixed-methods strategy, analysing the visual complexity of the selected urban environments using CLARITY and conducting semi-structured interviews together with a rating scale. For this purpose, older adults evaluated the visual complexity of selected urban environments and provided information on their overall well-being.

This dataset supports the study 'What Really Drives Urban Traffic Congestion? A Causal Feature Importance Analysis Across Four Major U.S. Metropolitan Areas' submitted to Journal of Transport Geography. The dataset contains the processed analytical feature matrix for 134,530 census blocks across Chicago, Houston, Los Angeles, and New York City. Each block record includes 46 causally upstream predictor features spanning six thematic categories (Built Environment, Accessibility/Network, Safety & Environment, Demographics, Mode Choice, and Land Use) along with the outcome variables (Congestion Index and three-class Level of Service classification). All features were derived from five primary sources: TomTom GPS probe traffic data (2024 AM peak), US Census Bureau Decennial 2020 and ACS 5-Year 2019-2023 estimates, city Open Data Portal building footprints and parcel land use records, OpenStreetMap and city street network centerlines, and EPA AirNow PM2.5 monitoring and state DOT crash records. Twenty-three circular and endogenous variables were excluded prior to model training as described in the accompanying manuscript. The Python analysis code (scikit-learn, XGBoost, LightGBM) for model training, evaluation, and feature importance extraction is included.