Mendeley Data Showcase

This dataset contains survey responses collected from 2,614 Bangladeshi university students to investigate cognitive dependence on generative Artificial Intelligence (AI) tools in academic environments. The dataset includes demographic information, AI usage behavior, trust in AI systems, critical thinking tendencies, and AI-assisted academic decision-making patterns. Responses were collected using a structured Likert-scale questionnaire from students across public and private universities in Bangladesh. The dataset may support research in Human-AI Interaction, Educational Technology, Behavioral Analytics, AI Ethics, and Social Computing.

Mendeley supplemental files for "comparative meta-analysis of timing of patient, diagnostic, and treatment intervals in rare versus common skin cancers"

The dataset comprises composite images generated from three predefined photographic views (facial, occlusal, and lingual) for each tooth preparation, along with corresponding expert annotation records. The data repository contains three separate files. The first file, “Annotation.xlsx”, includes the evaluation criteria and corresponding results for each composite image (referenced by filename), in addition to summary statistics. The second file, “Stitched Images.zip,” is a compressed archive containing four subfolders organized by tooth type (Canines, Incisors, Molars, and Premolars); each folder includes the composite multi-view images following the specified naming convention. The third file, “Separate View Images.zip”, is similarly organized into four tooth-type subfolders, each containing three individual view images (facial, occlusal, and lingual) per preparation, labeled according to the same naming convention. The images were stitched (i.e., combined) using the python code stitching.py

This dataset contains the derived geospatial products, trained machine-learning model and source code accompanying the article "Geomorphological and Radiative Controls on a Hypersaline Karst Doline Lake: Insights from Polat Lake (Eastern Anatolia)" submitted to CATENA (Elsevier, 2026). The study characterises Polat Lake — a small (~1.1 ha) hypersaline doline lake in the Erzincan-Divrigi Basin (Eastern Anatolia, Turkiye) — through UAV-based photogrammetry, deep-learning point-cloud classification, geomorphometric analysis and solar-radiation modelling. All raster products are georeferenced to WGS 84 / UTM zone 37N (EPSG:32637); the original DSM was produced at 5.68 cm/pixel ground sampling distance. Folder structure: 01_DSM_and_Orthomosaic -- UAV DSM (5.68 cm + 1 m) and multidirectional hillshade 02_Classification_outputs -- MLP-classified 341-million-point cloud + figure source PNGs 03_MLP_model -- PyTorch weights, scaler params, feature names, architecture 04_TWI_and_SolarRadiation -- TWI, annual solar radiation, aspect classes 05_Vector_layers -- Faults, geology (MTA-derived), water, peaks, contours (shapefiles) 06_Scripts_and_Code -- Training, inference, plotting Python scripts 07_Training_data -- Per-class training-sample CSVs (Bedrock, Evaporite, Soil, Wetland) 08_Geomorphometric_outputs -- Cross-section profile CSV and 2.5D bathymetric raster 09_UAV_metadata -- Agisoft Metashape processing report (camera positions, residuals) 10_Field_photos -- 18 representative UAV / handheld field photographs See README.md and LICENSE.txt at the root of the dataset for full documentation and citation guidance.

The efficacy of the case was evaluated using two IRB approved sources of evidence: 1) a post implementation survey administered to all students after completing the case and 2) questions embedded in the case itself that students completed while interacting with GenAI. Both instruments contained Likert-scale questions, the data of which is openly stored here.

This Stata do-file reproduces the empirical analysis for the study on rice specialization and female youth NEET in African rice economies. It imports the harmonized country-year dataset, prepares the panel structure, constructs the main variables and interaction terms, defines the estimation samples, and runs the dynamic panel models using two-step System GMM. The code includes the preferred specification, alternative institutional specifications, sensitivity checks with male NEET, deeper lag structures, reduced instruments, year-dummy controls, price-channel checks, and selected robustness diagnostics. It also computes marginal effects for institutional and political moderators and generates the figures used to interpret the heterogeneity of the relationship between rice intensity and female NEET. The do-file is intended to allow full replication of the main tables, diagnostic outputs, and marginal-effect figures reported in the manuscript.

This data set contains ascii files of resolved fluorescence spectra from gas-phase iron monohydride, FeH, following excitation with a single mode continuous-wave laser operating between 507 and 520 nm. Most of the transitions correspond to the I 4Φ -X 4∆ (0,0) transition, which has not yet been reported in the literature. The spectra show some evidence of perturbation in the upper (I 4Φ) state. Data are supplied as starting wavenumber (cm-1) and interval between points, followed by a single column of fluorescence intensities, uncorrected for instrumental response.

Replicate file for: On the Right Track? Railroads, Industrial Development, and Distributional Effects in Historical China

The dataset and STATA codes that allow replication of the results published in the article Substitution or market creation? Causal evidence from Poland’s domestic mobile payment scheme.

Research hypothesis: We hypothesized that a cable‑free, STM32‑based inclinometer with integrated hoisting and 180° mechanical rotation can achieve high‑precision, repeatable inclination measurements for foundation pit pipelines, while eliminating sensor zero‑drift without extra electronic circuits. What the data show: - Figure 10 Data.xlsx: Eleven consecutive inclination measurements (0–10 cycles) at the same depth point inside a fixed vertical inclinometer casing. Measured angles (°) demonstrate repeatability. - Figure 11 Data.xlsx: Three test groups at true angle 2.5°. Forward and reverse measurements are provided; compensated angle = (Forward – Reverse)/2, showing zero‑drift removal. - STM32‑CODE (Master Control Board & Probe): Complete firmware for STM32WLE5JC (master) and STM32F103C8T6 (probe). Implements FreeRTOS tasks, Bluetooth communication, finite state machine, moving average filter, and motor control. Notable findings: - Maximum deviation among 11 repeats is –0.300° (Figure 10), confirming excellent repeatability. - Compensated angles (e.g., 2.482°, 2.472°, 2.4305°) deviate <0.03° from true 2.5°, reducing zero‑drift by ≈10× (Figure 11). - The firmware enables fully automated “descend – measure – ascend – rotate – remeasure” cycles with wireless data transmission. How data were gathered: - Inclinometer tube was fixed vertically (Fig.10) or at 2.5° (Fig.11). Probe moved by hoisting mechanism; depth position monitored via microswitches. - For Fig.10: 10 automatic cycles; angle recorded at the same depth each cycle. - For Fig.11: Probe lowered to same depth → forward measurement → 180° mechanical rotation → reverse measurement. True angle verified by reference inclinometer. - All raw readings transmitted via Bluetooth to ground unit; no post‑processing filtering applied to these values. - Code developed in Keil MDK, using STM32 HAL libraries. Master board controls motors, reads sensors, runs state machine; probe board reads SCA103T sensor and responds to Bluetooth commands. How to interpret and use the data: - Figure 10: Calculate standard deviation to assess repeatability. Small variation validates mechanical and wireless stability. - Figure 11: Apply (Forward – Reverse)/2 to any opposite‑direction pair to obtain drift‑free angle. This mechanical compensation works without temperature calibration. - Firmware: Can be reused or modified for other automated inclinometer or motor‑controlled sensor platforms. The state machine and FreeRTOS task architecture are extensible. Corresponding author: Shuai Zhang (zhangshuai@nynu.edu.cn)