Mendeley Data Showcase

The Summer Jute Environmental Sensor Dataset from Bangladesh is a field-level agricultural dataset collected using a low-cost Internet of Things (IoT) sensing platform during Kharif-1 (summer) jute cultivation at Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, Bangladesh. The dataset contains 5,235 cleaned observations in CSV format. The dataset comprises 11 attributes: DateTime, Location, Season, Temperature, Humidity, Rainfall, Soil_moisture, Type, Sowing, Growth, and Harvest. Environmental measurements were acquired using an ESP32 S3-WROOM-1, DHT22 AM2302, a capacitive soil moisture sensor, and a rain detection sensor, while the remaining attributes describe the cultivation period. The data were collected under natural field conditions through automated sensor monitoring and cleaned by removing missing values, duplicate records, formatting inconsistencies, and inconsistent categorical labels. The dataset is compatible with Python, R, MATLAB, TensorFlow, PyTorch, and scikit-learn. This dataset supports research in smart agriculture, precision farming, environmental monitoring, agricultural IoT, irrigation management, and agricultural data analytics. It can be reused for exploratory data analysis, statistical modelling, environmental pattern analysis, feature engineering, predictive modelling, and benchmarking machine learning methods. Value of the Data: 1. Provides a real-world field-level environmental dataset collected during summer jute cultivation in Bangladesh using a low-cost IoT sensing platform. 2. Supports research in smart agriculture, precision farming, environmental monitoring, irrigation management, agricultural IoT, and machine learning. 3. Enables exploratory data analysis, statistical modelling, feature engineering, environmental pattern analysis, and benchmarking of agricultural methods. 4. Serves as an educational resource for agricultural data analytics, sensor data processing, visualization, and predictive modelling. 5. Facilitates the development and evaluation of IoT-based agricultural monitoring systems using structured environmental sensor data.

Replication package for "Distributive Politics in Opposition: Electoral Targeting of Social Assistance in Istanbul" (European Journal of Political Economy). The package contains the consolidated neighborhood-level dataset for Istanbul (954 neighborhoods: municipal cash-assistance recipients, election results 2018-2024, and socioeconomic indicators, with polygon geometry), the codebook and provenance log, and Python scripts that reproduce every table, figure, and robustness exercise in the paper, including the Appendix C.9 band-width consistency exercise added in this version. See README.md for structure and run instructions, and CHANGELOG.md for version notes.

Supplemental data, R analysis script, figures and tables plus captions associated with the article "A fair lottery mechanism underlies self-determined caste development in Melipona bees"

We examine how stablecoin flows affect U.S. T-bill yields and money market conditions.

This is survey data of fish farmers from the the West, Centre, and Littoral Regions of Cameroon, which represent the country’s main aquaculture production zones. Data was collected using a structured questionnaire covering farmer characteristics, farm attributes, and production and marketing practices was administered to fish farmers between June and July 2021 by trained personnel from the Centre for Independent Development Research (CIDR), Buea, Cameroon.

This repository contains a curated time-series dataset of daily peak electricity demand for the Bangladesh national power grid, compiled from official monthly operational reports published by Power Grid Bangladesh PLC (PGCB). The dataset is intended to support electricity demand forecasting, time-series analysis, and energy systems research, particularly at the national grid level. It has been used to evaluate statistical, deep learning, and hybrid forecasting models in peer-reviewed research. Temporal Coverage Start date: 2016-01-01 End date: 2024-09-30 Frequency: Daily Timezone: Bangladesh Standard Time (BST, UTC+6) Data Source and Compilation Process Original data were obtained from monthly operational spreadsheets published on the official website of Power Grid Bangladesh PLC (PGCB). Each monthly file contains daily records of day peak and evening peak demand. The published dataset was created by: Downloading monthly operational files Extracting daily day and evening peak demand values Merging all months into two continuous time series Standardizing date format to ISO-8601 Data Quality Notes The dataset contains 3196 daily observations. All values in Evening_Peak_Demand_MW are complete (no missing entries). Extreme values reflect real operational conditions (e.g., seasonal peaks, rapid demand growth). Users are encouraged to apply their own preprocessing (e.g., deseasonalization, normalization) depending on modeling requirements. Intended Use This dataset is suitable for: Short-term and medium-term load forecasting Peak demand prediction Benchmarking forecasting algorithms Non-stationary time-series modeling Energy system planning and reliability studies imitations The dataset does not include exogenous variables such as temperature, humidity, holidays, or economic indicators. It represents aggregate national demand only; regional or feeder-level analysis is not possible. Users should account for structural changes in demand due to policy, industrial growth, and electrification.

This dataset contains 622 valid observations from users with prior experience of AI-enabled features in mobile banking applications in Vietnam. It includes five demographic variables and 22 measurement items covering six constructs: perceived intelligence (INT), perceived anthropomorphism (ANT), confirmation (CFI), perceived usefulness (USE), trust (TRU), and continuance intention (CON).

This dataset provides the reproducibility package for the study "A Two-Branch TCN with Underforecast-Aware Correction for Short-Term Load Forecasting During Critical Periods". The package contains the implementation scripts, experimental configurations, processed data, and generated results required to reproduce the reported short-term load forecasting experiments. The study investigates critical-period underforecast risk in power-system load forecasting and proposes a two-branch temporal convolutional network framework combining a standard TCN-Huber branch and an underforecast-aware TCN-H3-CRW branch. The package includes: (1) Python scripts for frozen final validation and baseline audit on the Panama dataset; (2) the processed feature dataset used in the experiments; (3) generated experimental results and manuscript-related tables. The provided files correspond to the final experimental configuration used in the manuscript.

This dataset contains survey responses collected to investigate the effectiveness of Agile Software Development (ASD) practices in Global Software Development (GSD) environments. The dataset comprises responses from software professionals regarding their demographic and professional characteristics, experience with software development projects, use of traditional and Agile process models, Agile adoption in global environments, hybrid development models, and perceived project and software quality outcomes. The dataset includes 32 valid survey responses and 74 variables. The questionnaire uses categorical, ordinal, Likert-scale, and open-ended questions. The survey examines factors including communication, coordination, time-zone differences, cultural differences, contextual issues, trust, knowledge sharing, team size, resources, hierarchical structures, and employee turnover. The dataset also evaluates the perceived effectiveness of Agile practices in improving functional requirements, reliability, maintainability, performance, scalability, usability, customer satisfaction, defect reduction, collaboration, and software quality. In addition, respondents were asked about Agile project-management tools, collaboration tools, Agile ceremonies, DevOps-related practices, and contextual adaptation of Agile practices. The dataset is intended to support empirical research on Agile Software Development, Global Software Development, distributed software teams, software quality, Agile effectiveness, and organizational challenges. It may be useful for researchers conducting statistical analysis, comparative studies, exploratory analysis, or replication studies concerning Agile adoption and effectiveness in globally distributed software-development environments.

About the dataset : This is the version 4 of a comprehensive phishing dataset which includes labelled phishing as well as legitimate URLs along with their respective HTML codes. Each URL and its HTML code file is associated with the same serial number. The dataset size is 15,358 samples, where 7,204 samples are phishing, and 8,154 are legitimate. Data Collection: Phishing URLs are collected from PhishTank and OpenPhish repositories and legitimate URLs from the DataForSEO Top-1000 websites list. The HTML codes of the URLs are downloaded by using the Python Programming Language after visiting the URL while it is active. Data collection period is from September 2024 to August 2025. Label Information: Labels 0 for legitimate and 1 for phishing are used. Information about Features: 70 features are extracted from the raw URLs and their HTML codes. These features cover various types of phishing attacks: URL-based phishing attacks, brand-jacking, phishing sites hosted on compromised domains (PSHCD), and auto-downloadable malicious files links. Usage: The processed dataset can be used by researchers for further analysis by applying various ML algorithms or feature selection techniques to achieve considerable results. The raw URLs and their HTML source code can also be used for extracting novel features and proposing novel detection methodologies. This version presents additional files to improve the reproducibility for the users (requirements.txt for python library versions, updated Data Dictionary for computational rules/ Thresholds/criteria and example values, additional auxiliary files that are used for computation of some features). README file gives a description of each file shared on this repository.