Mendeley Data Showcase

climate vulnerability- financial inclusion and poverty - data

Dataset used to assess the association between smartphone use profile and postural control variability.

ILC-BD contains 10,000 short everyday English sentences with their translations into Chakma and Marma, two indigenous languages of Bangladesh (10,000 Chakma-English and 10,000 Marma-English sentence pairs). The English sentences come from the Tatoeba Project. Five native speakers of each language translated them by hand, writing Chakma and Marma in English (Latin) letters. Five typists digitized the translations with cross-checking, and two independent language experts per language reviewed every pair. The data are provided as an Excel file and a CSV file with the columns ID, English, Chakma and Marma. The dataset can be used for machine translation, transfer learning, and linguistic studies of Chakma and Marma. English sentences: Tatoeba Project (https://tatoeba.org), licensed CC BY 2.0 FR. An earlier version of this corpus is listed on IEEE DataPort (https://doi.org/10.21227/e6jn-fz88).

This dataset contains processed behavioral data and statistical summary tables supporting Figures 1–4 of the manuscript “Cocaine disrupts stress resilience through state-dependent dopaminergic mechanisms in the nucleus accumbens.” The data include social interaction tests, cocaine-conditioned place preference, conspecific interaction, elevated plus maze, forced swim test, and chemogenetic manipulation of TH-positive NAc-projecting neurons. Raw video files are not included. Statistical analyses were performed using SigmaPlot version 16.

This dataset contains the input data and detailed computational results associated with the three case studies presented in the article “An integrated optimization model for diversified dry bulk export terminals with conveyor constraints and multiple work calendars”. The first two case studies concern the integrated port management model. Case Study 1 illustrates the proposed extensions, including multiple work calendars, conveyor operations, flexible maintenance scheduling and detailed loading constraints, using an efficiency-based objective. Case Study 2 examines the impact of replacing this economic efficiency criterion with an effectiveness-based objective that minimizes vessel dwell time. The third case study concerns the complementary batch location and availability model, which determines the storage locations and availability dates of make-to-stock batches before these data are transferred to the port management model. The case studies draw on benchmark and real industrial data related to dry bulk operations at the Jorf Lasfar industrial port in Morocco. The Excel workbook is organized into six worksheets. DATA_CASE 1 & 2 contains the input data shared by the first two case studies. Result_CASE1_Efficiency reports the detailed results of Case Study 1. Result_CASE 2_Efficiency and Result_CASE 2_Effectiveness provide the solutions used to compare the two optimization criteria in Case Study 2. Data_CASE 3 contains the input data for the batch location and availability model, while Result_CASE 3 reports its detailed optimization results. The result worksheets also provide the information required to examine the resulting schedules and verify compliance with the main temporal and operational constraints.

L*, a*, and b* (CIELab) color coordinates of the shell and mantle edge of Crassostrea gigas (Pacific oyster) strains with black, gold, and white coloration, plus a control group representing the initial stock that served as a phenotypic reference for the selected strains.

This dataset contains 200 stocks that got shariah screened for each islamic legal school (Hanafi, Maliki, Shafi'i, Hanbali) to check if the stock to invest is compliant or not-compliant for each school. The data got extracted from yfinance using Python web-scraping scripts.

This repository contains the data used in the study “Integrated Modeling of Stored-Grain Insect Dynamics and Infestation Risk in Wheat Silos”. The repository includes the original source files Three-Silos.xls, Temperature.xls, and Moisture.xls, containing the stored-grain silo observations, grain temperature measurements, and grain moisture measurements, respectively. It also includes an integrated analysis-ready Excel dataset, named after the study, which combines the relevant information from the source files and was used for the statistical analyses reported in the manuscript. The study comprises observations from three wheat silos, five stored-product insect species, three sampling categories, and 15 sampling occasions. These files are provided to support transparency and reproducibility of the analyses of insect population dynamics and infestation risk.

This is the replication package for for 'Centralized Governance, Household Preferences and Publicly Provided Education: Evidence from Vietnam', published in the Journal of Economic Behavior & Organization.

This dataset is a specialized annotated image dataset developed for microcontroller board damage detection using computer vision and deep learning. The dataset contains 7,169 images across 14 classes, covering both good-condition microcontroller boards and defective components. The classes include ATmega2560_Damage, ATmega328_Damage, Board_Damage, Boot_Button_Damage, Capacitor_Damage, Good_Arduino_Mega, Good_Arduino_Nano, Good_Arduino_Uno, Good_ESP32, Pin_Damage, Power_Port_Damage, Reset_Button_Damage, USB-B_Port_Damage, and USB_Input_Damage. The images represent real-world microcontroller board conditions with variations in illumination, board orientation, background, and image quality. All images are standardized to a resolution of 640 × 640 pixels and are provided with YOLO-compatible object-detection annotations. The dataset is organized into training, validation, and testing subsets. The dataset was prepared using Roboflow, including image preprocessing and augmentation applied to the training subset. The final dataset contains 6,273 training images, 597 validation images, and 299 test images. The training-set augmentation was performed after the original dataset split, with the original dataset containing 2,987 images before training-set augmentation. The dataset is provided to support reproducible research in microcontroller defect detection, object detection, automated visual inspection, and computer vision-based quality assessment. A metadata file, metadata_MicrocontrollerBoard.csv, is also provided to facilitate image-level analysis, dataset exploration, and further reuse of the dataset.