Data Repositry

SHM BRIDGE DATA 2025

Rudransh S, Kartik M.

SHM BRIDGE DATA 2025

Structural Health Monitoring (SHM) plays a crucial role in modern infrastructure management by enabling continuous tracking of structural behaviour under real-world operating and environmental conditions. The Extended Bridge SHM Dataset contains synchronized time-series measurements collected from an instrumented bridge, offering 200 timestamped records across 35 variables. These include multi-channel acceleration data from nine sensors, GPS coordinates, sensor status indicators, sampling rate, battery level, traffic metrics, event types, and assigned damage labels. The dataset supports vibration analysis, anomaly detection, and predictive maintenance by capturing subtle dynamic responses essential for modal and damage detection studies. Additional contextual fields—such as traffic counts, vehicle loop triggers, maintenance flags, and operator notes—enhance supervised learning and event classification tasks. By integrating physical sensor signals with operational metadata, this dataset provides a robust foundation for developing and validating SHM algorithms, intelligent diagnostics, and infrastructure prediction models. This data is basically a synthetic data to understand and use in sample implementation for the purpose of illustration only. The real purpose use is useless as it has not been taken from any survey or real collection data set from the live or any real place or object. These types of data is only prepared for the hint/assistance/sample or illustrative purpose.

The Extended Bridge Structural Health Monitoring (SHM) Dataset offers a high-quality collection of synchronized time-series measurements obtained from an instrumented bridge. Developed to support advanced research in infrastructure monitoring and data-driven engineering, the dataset provides valuable insights into structural behavior under operational and environmental conditions. It includes 200 timestamped records across 35 attributes, featuring multi-channel acceleration signals from nine accelerometer nodes, GPS coordinates, sensor performance metrics, sampling rate, battery voltage, traffic flow indicators, event classifications, and assigned damage labels. These variables enable comprehensive analysis for dynamic response evaluation, vibration-based diagnostics, anomaly detection, and predictive maintenance modeling. By integrating physical sensor data with contextual operational information, this dataset serves as a robust resource for researchers, engineers, and machine learning practitioners working on structural assessment, event classification, and intelligent monitoring systems. Its organized structure and diverse feature set make it ideal for SHM model development, real-time analytics, and benchmarking across a wide range of experimental and computational studies. This data is basically a synthetic data to understand and use in sample implementation for the purpose of illustration only. The real purpose use is useless as it has not been taken from any survey or real collection data set from the live or any real place or object. These types of data is only prepared for the hint/assistance/sample or illustrative purpose

27:12:2024

COVID-19 Country-Wise Daily Statistics Dataset 2020

Rudransh S, Kartik M.

COVID-19 Country-Wise Daily Statistics Dataset 2020

The COVID-19 Country-Wise Daily Statistics Dataset is a structured dataset developed to support public health analysis, epidemiological research, and data-driven learning. The dataset provides country-level daily records of COVID-19 cases, including confirmed infections, reported deaths, and recoveries over a defined time period. It is designed to reflect the temporal progression of the pandemic across multiple countries, enabling comparative analysis, trend evaluation, and visualization of disease spread patterns. The dataset is suitable for academic research, student projects, dashboard development, and analytical demonstrations in public health, data science, and policy studies. All records are organized in a clean, standardized format that allows immediate use in analytical tools such as Excel, Power BI, Tableau, Python, and R. The dataset is intended for educational and research purposes and facilitates an understanding of global pandemic trends without requiring access to sensitive or restricted data sources.

The COVID-19 Country-Wise Daily Statistics Dataset represents a comprehensive collection of daily pandemic statistics aggregated at the country level. Each record corresponds to a single country on a specific date and captures the cumulative number of confirmed COVID-19 cases, deaths, and recoveries reported up to that day. The dataset is structured to resemble commonly used public health reporting formats employed by global health organizations and research institutions. The dataset includes clearly defined columns for country name, reporting date, confirmed cases, deaths, and recovered cases. Dates are consistently formatted and populated across all entries, making the dataset suitable for time-series analysis, trend identification, and comparative studies between countries. By organizing the data on a daily basis, the dataset supports longitudinal analysis of pandemic waves, growth rates, and recovery patterns. This dataset is particularly useful for public health analytics, epidemiological modeling, academic coursework, and data visualization projects. It enables users to explore country-wise variations in COVID-19 impact, compare outbreak trajectories, and demonstrate analytical techniques using real-world-style data. As the dataset is prepared for open use, it does not contain any personal, individual-level, or sensitive information, ensuring compliance with ethical and data protection standards. The dataset is provided in CSV format to ensure broad compatibility and ease of use across platforms and analytical environments. It is suitable for public hosting, research demonstrations, and educational resources aimed at understanding pandemic dynamics and data analysis methodologies.This dataset is provided for educational and research purposes only. The data is structured for analytical demonstration and may not exactly match official real-time government statistics

15:03:2020

Golden Gate Bridge – Structural Inspection & Condition

Vishal S, KunalM.

Golden Gate Bridge

The Golden Gate Bridge – Structural Inspection & Condition Dataset is a structured, inspection-level dataset designed to support research, education, and analytical applications in the fields of civil engineering, infrastructure management, transportation studies, and data analytics. The dataset focuses exclusively on the Golden Gate Bridge, one of the most significant suspension bridges in the United States, and presents a detailed representation of its structural attributes and inspection records across multiple observations. The dataset captures essential information such as construction details, bridge length, structural design, inspection year, inspection type, condition status, and numerical structural ratings. By modeling the dataset on public-domain National Bridge Inventory (NBI) standards maintained by the Federal Highway Administration (FHWA), it reflects real-world infrastructure assessment practices while remaining fully synthetic and free from sensitive or proprietary information. The clean CSV format and standardized schema enable immediate use in analytical tools such as Excel, Power BI, Tableau, Python, and R, making the dataset suitable for demonstrations, academic projects, and infrastructure analytics without legal or privacy constraints

The Golden Gate Bridge – Structural Inspection & Condition Dataset represents a comprehensive collection of inspection-level records for a single major bridge asset located in San Francisco, California, USA. The dataset consists of 500–800 structured records, with each record corresponding to an inspection or assessment instance associated with the Golden Gate Bridge. This approach allows users to explore variations in inspection outcomes, condition ratings, and assessment types over time while focusing on a single, well-known infrastructure system. Each entry in the dataset includes a unique record identifier, bridge identification details, geographic location, year of construction, bridge length, and structural design type. In addition, inspection-specific attributes such as inspection year, inspection type, numerical structural rating, and descriptive condition status are provided. These variables enable detailed analysis of infrastructure condition trends, inspection frequency, and performance assessment practices commonly used in bridge management systems. The dataset is inspired by the data structure and terminology of the National Bridge Inventory (NBI) published by the Federal Highway Administration (FHWA), a public-domain U.S. government resource. However, all records in this dataset are synthetically generated for demonstration and analytical purposes and do not represent official inspection results or authoritative condition assessments. This dataset is synthetically generated and inspired by public-domain National Bridge Inventory (NBI) standards. It is intended solely for educational, research, and analytical demonstration purposes and does not represent official inspection data or authoritative assessments of the Golden Gate Bridge.

24:07:2024

Retail Sales Dataset

Rudransh S

Sales

The Retail Sales Dataset is a carefully designed synthetic dataset created to simulate real-world retail transaction data without using any actual customer or business information. It has been developed specifically to support learning, research, and practical analysis in domains such as business analytics, retail operations, marketing research, and data science. The dataset consists of structured records that represent individual retail transactions occurring across different regions and cities. These records are designed to reflect realistic shopping patterns commonly observed in retail environments, including variations in product categories, pricing, quantities purchased, customer types, and payment methods. This realism allows users to perform meaningful analyses while ensuring complete data privacy and ethical compliance. Each transaction entry includes time-based information, such as the transaction date, enabling users to conduct trend analysis, seasonal studies, and time-series evaluations. In addition, geographical attributes like region and city help in analyzing location-based sales performance, regional demand patterns, and comparative market behavior. Customer segmentation fields distinguish between different customer types, supporting studies related to consumer behavior and loyalty analysis.

The Retail Sales Dataset is a comprehensive and carefully structured collection of simulated retail transaction records developed specifically for public access and unrestricted academic or analytical use. The dataset contains 600 individual records, with each record representing a single retail invoice, closely mirroring the structure and characteristics of real-world point-of-sale (POS) data used in retail analytics, business intelligence, and decision-support systems. The dataset has been designed to replicate typical retail operations across multiple geographic regions and cities. Each transaction is uniquely identified through an invoice ID, allowing users to track, filter, and aggregate individual sales entries efficiently. The inclusion of a clearly defined transaction date for every record enables robust time-based analysis, such as daily, weekly, or monthly sales trends, seasonal pattern detection, and revenue growth studies. Geographical attributes such as region and city provide an additional analytical dimension, making the dataset suitable for regional performance comparisons, location-based demand analysis, and market segmentation studies. Customer-related information categorizes transactions by customer type, supporting investigations into purchasing behavior, customer loyalty patterns, and segmentation-based sales strategies. This dataset is synthetically generated for educational, research, and demonstration purposes only and does not represent real commercial transactions or actual customer information.

11:02:2022