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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Awesome Industrial Datasets</title>
<!-- Bootstrap 5 CSS -->
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
<!-- DataTables CSS -->
<link href="https://cdn.datatables.net/1.13.1/css/dataTables.bootstrap5.min.css" rel="stylesheet">
<!-- Custom Styles -->
<link href="html/assets/css/styles.css" rel="stylesheet">
</head>
<body>
<div class="container mt-5">
<h1 class="mb-4 text-center">Awesome Industrial Datasets</h1>
<!-- Column Toggle Controls -->
<div class="mb-3">
<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-labeled" checked>
<label class="form-check-label" for="toggle-labeled">Labeled</label>
</div>
<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-time-series" checked>
<label class="form-check-label" for="toggle-time-series">Time Series</label>
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<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-simulation" checked>
<label class="form-check-label" for "toggle-simulation">Simulation</label>
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<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-missing-values">
<label class="form-check-label" for="toggle-missing-values">Missing Values</label>
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<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-dataset-characteristics">
<label class="form-check-label" for="toggle-dataset-characteristics">Dataset Characteristics</label>
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<input type="checkbox" class="form-check-input" id="toggle-associated-tasks">
<label class="form-check-label" for="toggle-associated-tasks">Associated Tasks</label>
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<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-number-instances">
<label class="form-check-label" for="toggle-number-instances">Number of Instances</label>
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<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-number-features">
<label class="form-check-label" for="toggle-number-features">Number of Features</label>
</div>
<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-date-donated">
<label class="form-check-label" for="toggle-date-donated">Date Donated</label>
</div>
<div class="form-check form-check-inline">
<input type="checkbox" class="form-check-input" id="toggle-summary">
<label class="form-check-label" for="toggle-summary">Summary</label>
</div>
</div>
<!-- Dataset Table -->
<table id="dataset-table" class="table table-striped table-bordered" style="width:100%">
<thead>
<tr>
<th>Dataset Name</th>
<th>Labeled</th>
<th>Time Series</th>
<th>Simulation</th>
<th>Additional Tags</th>
<th>Missing Values</th>
<th>Dataset Characteristics</th>
<th>Associated Tasks</th>
<th>Number of Instances</th>
<th>Number of Features</th>
<th>Date Donated</th>
<th>Summary</th>
</tr>
</thead>
<tbody>
<!-- JSON data will be dynamically injected here -->
</tbody>
</table>
</div>
<!-- JSON data injected by generate_documentation.py -->
<!-- Bootstrap 5 JS -->
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>
<!-- jQuery -->
<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
<!-- DataTables JS -->
<script src="https://cdn.datatables.net/1.13.1/js/jquery.dataTables.min.js"></script>
<script src="https://cdn.datatables.net/1.13.1/js/dataTables.bootstrap5.min.js"></script>
<script>
document.addEventListener("DOMContentLoaded", function () {
var jsonData = JSON.parse(document.getElementById("dataset-json").textContent);
// Initialize DataTable with correct column visibility
var table = $('#dataset-table').DataTable({
data: jsonData,
columns: [
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});
</script>
<script type="application/json" id="dataset-json">
[
{
"Dataset Name": "Green House Gas Produce by Different Industry",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Analysis",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Since 2010, Different Type of Gas Release by Different Industry in the World.",
"Additional Tags": "Environmental data; Greenhouse gases; Industry emissions; Sustainability; Emission tracking",
"Link": "html/pages/green_house_gas_produce_by_different_industry.html"
},
{
"Dataset Name": "Secure Water Treatment (SWaT) Dataset",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Classification, Anomaly Detection",
"Number of Instances": "N/A",
"Number of Features": "51",
"Date Donated": "2015-12-02",
"Summary": "SWaT is an operational testbed for water treatment, providing data under normal and attack scenarios for cybersecurity research.",
"Additional Tags": "Water treatment; Cybersecurity; Anomaly detection; Sensor data; Time-series data",
"Link": "html/pages/secure_water_treatment_swat_dataset.html"
},
{
"Dataset Name": "Air Quality",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression",
"Number of Instances": "9358",
"Number of Features": "15",
"Date Donated": "2016-03-22",
"Summary": "Contains the responses of a gas multisensor device deployed on the field in an Italian city. Hourly responses averages are recorded along with gas concentrations references from a certified analyzer.",
"Additional Tags": "Air quality monitoring; Sensor data; Pollution levels; Time-series analysis; Environmental data",
"Link": "html/pages/air_quality.html"
},
{
"Dataset Name": "Electricity Load Diagrams 2011-2014",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Regression, Clustering",
"Number of Instances": "370",
"Number of Features": "140256",
"Date Donated": "2015-03-12",
"Summary": "This data set contains electricity consumption of 370 points/clients.",
"Additional Tags": "Electricity consumption; Time-series data; Energy monitoring; Smart grid; Urban energy use",
"Link": "html/pages/electricity_load_diagrams_2011_2014.html"
},
{
"Dataset Name": "SML2010",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate, Sequential, Time-Series, Text",
"Associated Tasks": "Regression",
"Number of Instances": "4137",
"Number of Features": "24",
"Date Donated": "2014-01-08",
"Summary": "This dataset is collected from a monitor system mounted in a domotic house. It corresponds to approximately 40 days of monitoring data.",
"Additional Tags": "Domotic systems; Environmental monitoring; Home automation; Time-series data; Energy efficiency",
"Link": "html/pages/sml2010.html"
},
{
"Dataset Name": "Combined Cycle Power Plant",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Regression",
"Number of Instances": "9568",
"Number of Features": "4",
"Date Donated": "2014-03-25",
"Summary": "The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the plant was set to work with full load.",
"Additional Tags": "Power plant; Energy output; Regression tasks; Environmental data; Multivariate data",
"Link": "html/pages/combined_cycle_power_plant.html"
},
{
"Dataset Name": "Car Evaluation",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "1728",
"Number of Features": "6",
"Date Donated": "1997-05-31",
"Summary": "Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.",
"Additional Tags": "Automobile evaluation; Decision-making; Categorical data; Multivariate data; Classification task",
"Link": "html/pages/car_evaluation.html"
},
{
"Dataset Name": "Oscillation Detection Artificial Dataset",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "This dataset is developed for oscillation detection in control loops, employing machine learning techniques to enhance diagnostic capabilities in industrial processes.",
"Additional Tags": "Control loops; Oscillation detection; Machine learning; Process optimization",
"Link": "html/pages/oscillation_detection_artificial_dataset.html"
},
{
"Dataset Name": "iV2V and iV2I+ Industrial Datasets",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "N/A",
"Summary": "These datasets contain vehicle-to-vehicle and vehicle-to-infrastructure communication data captured in industrial environments. They include time-series data with features like signal strength and communication delays, aimed at enhancing connectivity and automation in industrial settings.",
"Additional Tags": "Industrial communication; V2V; V2I; Wireless networks; Signal processing; AGVs; Time-series",
"Link": "html/pages/iv2v_and_iv2i_plus_industrial_datasets.html"
},
{
"Dataset Name": "Steel Plates Faults",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "1941",
"Number of Features": "27",
"Date Donated": "2010-10-25",
"Summary": "A dataset of steel plates faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.",
"Additional Tags": "Steel plates; Fault detection; Manufacturing; Pattern recognition; Classification tasks",
"Link": "html/pages/steel_plates_faults.html"
},
{
"Dataset Name": "Predicting Manufacturing Defects Dataset",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Insights into Factors Influencing Defect Rates and Production Efficiency.",
"Additional Tags": "Manufacturing defects; Quality control; Predictive modeling; Industrial data; Process optimization",
"Link": "html/pages/predicting_manufacturing_defects_dataset.html"
},
{
"Dataset Name": "Brent Oil Prices",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Regression, Forecasting",
"Number of Instances": "INA",
"Number of Features": "2",
"Date Donated": "INA",
"Summary": "Daily historical Brent Oil Prices available on the U.S. Energy Information Admin.",
"Additional Tags": "Brent oil; Crude oil prices; Economic indicators; Market analysis; Financial markets",
"Link": "html/pages/brent_oil_prices.html"
},
{
"Dataset Name": "C-MAPSS Aircraft Engine Simulator Data",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Classification",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "N/A",
"Summary": "This dataset was generated with the C-MAPSS simulator, a tool for the simulation of realistic large commercial turbofan engine data. It includes various operational conditions to test engine performance.",
"Additional Tags": "Aircraft engine; Simulator data; Engine performance; Sensor data; Prognostics",
"Link": "html/pages/c_mapss_aircraft_engine_simulator_data.html"
},
{
"Dataset Name": "Industrial Safety and Health Analytics Database",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Analysis",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Industrial labor accident data.",
"Additional Tags": "Workplace safety; Health and safety; Accident reports; Risk management",
"Link": "html/pages/industrial_safety_and_health_analytics_database.html"
},
{
"Dataset Name": "Wine Quality",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "4898",
"Number of Features": "11",
"Date Donated": "2009-10-06",
"Summary": "Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).",
"Additional Tags": "Wine quality; Physicochemical analysis; Sensory data; Classification tasks; Regression tasks",
"Link": "html/pages/wine_quality.html"
},
{
"Dataset Name": "High Storage System Data for Energy Optimization",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "INA",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression, Clustering",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "This dataset involves energy consumption data from a high storage system, aiming at optimizing energy use through detailed monitoring and predictive analytics.",
"Additional Tags": "Energy optimization; High storage systems; Predictive analytics; Sensor data; Industrial energy efficiency",
"Link": "html/pages/high_storage_system_data_for_energy_optimization.html"
},
{
"Dataset Name": "Global Power Plant Database",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Analysis",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "The Global Power Plant Database provides a comprehensive list of power plants globally, detailing their capacity, generation type, and ownership. It includes both thermal and renewable energy sources.",
"Additional Tags": "Global energy; Power plants; Renewable energy; Energy statistics; Environmental impact",
"Link": "html/pages/global_power_plant_dataset.html"
},
{
"Dataset Name": "Turbofan Engine Degradation Simulation Data Set",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Prognostics",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "2010-09-22",
"Summary": "This dataset, generated with the C-MAPSS tool, models turbofan engine degradation under various conditions to evaluate performance and predict failures.",
"Additional Tags": "Turbofan engines; Engine degradation; Simulation data; Prognostics health management; NASA dataset",
"Link": "html/pages/turbofan_engine_degradation_simulation_data_set.html"
},
{
"Dataset Name": "Hill-Valley",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Sequential",
"Associated Tasks": "Classification",
"Number of Instances": "606",
"Number of Features": "101",
"Date Donated": "2008-03-19",
"Summary": "Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y co-ordinate, the points will create either a Hill (a \u201cbump\u201d in the terrain) or a Valley (a \u201cdip\u201d in the terrain).",
"Additional Tags": "Classification; Feature representation; Graph data; Pattern recognition",
"Link": "html/pages/hill-valley.html"
},
{
"Dataset Name": "Oil and Gas",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "The Global dataset of oil and natural gas production, prices, exports, and net exports.",
"Additional Tags": "Oil industry; Gas industry; Energy sector; Economic analysis; Market trends",
"Link": "html/pages/oil_and_gas.html"
},
{
"Dataset Name": "Steel Industry Energy Consumption",
"Labeled": "No",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Regression",
"Number of Instances": "35040",
"Number of Features": "9",
"Date Donated": "2023-08-13",
"Summary": "The data is collected from a smart small-scale steel industry in South Korea.",
"Additional Tags": "Energy consumption; Steel and iron production; Electricity usage data; CO2 emissions; Korea Electric Power Corporation",
"Link": "html/pages/steel_industry_energy_consumption.html"
},
{
"Dataset Name": "Appliances Energy Prediction",
"Labeled": "No",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression",
"Number of Instances": "19735",
"Number of Features": "28",
"Date Donated": "2017-02-14",
"Summary": "Experimental data used to create regression models of appliances energy use in a low energy building.",
"Additional Tags": "Indoor environment monitoring; ZigBee wireless network; Temperature data; Humidity data; Weather integration; Energy consumption; M-bus energy meters; Airport weather station",
"Link": "html/pages/appliances_energy_prediction.html"
},
{
"Dataset Name": "UK Domestic Appliance-Level Electricity (UK-DALE) dataset",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Clustering",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "UK-DALE dataset captures electricity usage at appliance level and whole-house mains in UK homes, recording power demand and high-frequency voltage/current.",
"Additional Tags": "Energy consumption; Smart homes; High-frequency data; Time-series analysis; Appliance monitoring",
"Link": "html/pages/uk_dale_dataset.html"
},
{
"Dataset Name": "Bosch Production Line Performance",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "2016",
"Summary": "This dataset from Bosch is aimed at reducing production line failures. It contains extensive data on manufacturing processes, focusing on minimizing testing errors and maximizing production line performance.",
"Additional Tags": "Manufacturing; Production line; Quality control; Industrial data; Machine learning",
"Link": "html/pages/bosch_production_line_performance.html"
},
{
"Dataset Name": "Gas sensor array temperature modulation",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression, Classification",
"Number of Instances": "4095000",
"Number of Features": "19",
"Date Donated": "2019-04-14",
"Summary": "A chemical detection platform composed of 14 temperature-modulated metal oxide (MOX) gas sensors was exposed during 3 weeks to mixtures of carbon monoxide and humid synthetic air in a gas chamber.",
"Additional Tags": "Gas sensing; MOX sensors; Temperature modulation; Environmental monitoring; Sensor arrays",
"Link": "html/pages/gas_sensor_array_temperature_modulation.html"
},
{
"Dataset Name": "Robot Execution Failures",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Classification",
"Number of Instances": "463",
"Number of Features": "90",
"Date Donated": "1999-04-22",
"Summary": "This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals.",
"Additional Tags": "Robotics; Failure detection; Force and torque data; Time-series analysis; Machine learning",
"Link": "html/pages/robot_execution_failures.html"
},
{
"Dataset Name": "GREEND",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Energy Consumption Analysis",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "An energy dataset of households in Austria and Italy",
"Additional Tags": "Energy consumption; Household data; Smart grids; Time-series; Open data",
"Link": "html/pages/greend.html"
},
{
"Dataset Name": "Manufacturing Defects",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Ten days of data on parts defects.",
"Additional Tags": "Manufacturing defects; Quality control; Predictive modeling; Industrial analysis; Process optimization",
"Link": "html/pages/manufacturing_defects.html"
},
{
"Dataset Name": "Parts Manufacturing",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "A dataset containing measures of parts produced by 20 operators.",
"Additional Tags": "Manufacturing; Industrial data; Quality control; Component measurements; Production optimization",
"Link": "html/pages/parts_manufacturing.html"
},
{
"Dataset Name": "The Reference Energy Disaggregation Data Set (REDD)",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Anomaly Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "2011",
"Summary": "REDD offers high and low frequency energy usage data from residential buildings, including power measurements and detailed appliance-level consumption.",
"Additional Tags": "Energy consumption; Residential data; High frequency data; Low frequency data; Circuit level monitoring",
"Link": "html/pages/reference_energy_disaggregation_data_set_redd.html"
},
{
"Dataset Name": "Chemical Composition of Ceramic Samples",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Clustering",
"Number of Instances": "88",
"Number of Features": "19",
"Date Donated": "2019-01-28",
"Summary": "Classify ceramic samples based on their chemical composition from energy dispersive X-ray fluorescence.",
"Additional Tags": "Ceramic analysis; Chemical composition; X-ray fluorescence; Material science; Historical ceramics",
"Link": "html/pages/chemical_composition_of_ceramic_samples.html"
},
{
"Dataset Name": "Individual household electric power consumption",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression, Clustering",
"Number of Instances": "2075259",
"Number of Features": "9",
"Date Donated": "2012-08-29",
"Summary": "Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.",
"Additional Tags": "Electric power consumption; Time-series data; Energy monitoring; Smart grid; Household energy use",
"Link": "html/pages/individual_household_electric_power_consumption.html"
},
{
"Dataset Name": "OECD Data - Crude Oil Production",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Analysis",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Crude Oil Production data by OECD.org.",
"Additional Tags": "Crude oil; OECD countries; Energy production; Economic analysis; Market trends",
"Link": "html/pages/oecd_data_crude_oil_production.html"
},
{
"Dataset Name": "SECOM",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Causal-Discovery",
"Number of Instances": "1567",
"Number of Features": "591",
"Date Donated": "2008-11-18",
"Summary": "Data from a semi-conductor manufacturing process.",
"Additional Tags": "Manufacturing; Semi-conductor; Process optimization; Feature selection; Industrial data",
"Link": "html/pages/secom.html"
},
{
"Dataset Name": "Gas sensor arrays in open sampling settings",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Classification",
"Number of Instances": "18000",
"Number of Features": "1950000",
"Date Donated": "2013-06-04",
"Summary": "The dataset contains 18000 time-series recordings from a chemical detection platform at six different locations in a wind tunnel facility in response to ten high-priority chemical gaseous substances",
"Additional Tags": "Gas sensing; Sensor arrays; Environmental monitoring; Chemical detection; Open sampling settings",
"Link": "html/pages/gas_sensor_arrays_in_open_sampling_settings.html"
},
{
"Dataset Name": "C-MAPSS Aircraft Engine Simulator Data",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "NIA",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Classification",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "NIA",
"Summary": "This dataset was generated with the C-MAPSS simulator, a tool for the simulation of realistic large commercial turbofan engine data. The data consists of a series of flights with a reasonable linear transition period to allow the engine to change from one flight condition to the next. The fault was injected at a given time in one of the flights and persists throughout the remaining flights, effectively increasing the age of the engine. The intent is to identify which flight and when in the flight the fault occurred.",
"Additional Tags": "Aircraft engine; Simulator data; Engine performance; Sensor data; Prognostics",
"Link": "html/pages/c-mapss_aircraft_engine_simulator_data.html"
},
{
"Dataset Name": "CMAPSS Jet Engine Simulated Data",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Anomaly Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "The dataset comprises simulated jet engine data from NASA's CMAPSS. It includes operational settings and sensor measurements for engines under various fault conditions, aimed at prognostics and health management research.",
"Additional Tags": "Jet engines; Simulation data; Prognostics; Health management; Aerospace engineering",
"Link": "html/pages/cmapss_jet_engine_simulated_data.html"
},
{
"Dataset Name": "Severstal Steel Defect Detection",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Image, Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "2019",
"Summary": "The dataset consists of steel plate images, and the goal is to detect and classify four different types of defects on the surface of the steel. The images are labeled with the type of defect present.",
"Additional Tags": "Steel defects; Surface defects; Industrial quality control; Image classification; Machine learning",
"Link": "html/pages/severstal_steel_defect_detection.html"
},
{
"Dataset Name": "ECO Dataset",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Non-Intrusive Load Monitoring, Occupancy Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "The ECO dataset includes electricity consumption and occupancy data from six Swiss households, collected over eight months for non-intrusive load monitoring and occupancy detection research.",
"Additional Tags": "Electricity consumption; Occupancy detection; Smart meters; Energy efficiency; Household data",
"Link": "html/pages/eco_dataset.html"
},
{
"Dataset Name": "Mechanical Analysis",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "209",
"Number of Features": "8",
"Date Donated": "1990-05-31",
"Summary": "Fault diagnosis problem of electromechanical devices; also PUMPS DATA SET is newer version with domain theory and results",
"Additional Tags": "Fault diagnosis; Electromechanical devices; Component analysis; Classification tasks; Pump analysis",
"Link": "html/pages/mechanical_analysis.html"
},
{
"Dataset Name": "Data-driven prediction of battery cycle life before capacity degradation",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "INA",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "This dataset facilitates the prediction of lithium-ion batteries' cycle life before they reach capacity degradation. It includes charge-discharge cycles and various operational parameters.",
"Additional Tags": "Battery life prediction; Lithium-ion batteries; Charge-discharge cycles; Predictive maintenance; Energy storage",
"Link": "html/pages/data_driven_prediction_of_battery_cycle_life_before_capacity_degradation.html"
},
{
"Dataset Name": "Oil Storage Tanks",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Image",
"Associated Tasks": "Object Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Image data of industrial tanks with bounding box annotations.",
"Additional Tags": "Satellite imagery; Oil storage tanks; Object detection; Remote sensing; Geospatial analysis",
"Link": "html/pages/oil_storage_tanks.html"
},
{
"Dataset Name": "SISO-RAW",
"Labeled": "No",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "2020-12-07",
"Summary": "SISO-RAW contains raw data from 52 control loops in the oil and gas sector, recorded over two and a half days. It includes variables like OP, PV, SP, and MV, stored in an HDF5 file.",
"Additional Tags": "Control loops; Process monitoring; Oil and gas industry; Data visualization; Process control",
"Link": "html/pages/siso_raw.html"
},
{
"Dataset Name": "Concrete Compressive Strength",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Regression",
"Number of Instances": "1030",
"Number of Features": "8",
"Date Donated": "2007-08-02",
"Summary": "Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.",
"Additional Tags": "Civil engineering; Material properties; Concrete strength; Regression data; Multivariate data",
"Link": "html/pages/concrete_compressive_strength.html"
},
{
"Dataset Name": "Milling Wear",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Regression, Anomaly Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "2022-11-22",
"Summary": "The dataset comprises experimental data from milling operations, focusing on the wear of milling inserts at different speeds, feeds, and depths of cut. It's aimed at studying the degradation patterns to predict and understand wear mechanisms.",
"Additional Tags": "Milling operations; Tool wear analysis; Degradation study; Predictive maintenance; Operational efficiency",
"Link": "html/pages/milling_wear.html"
},
{
"Dataset Name": "Li-ion Battery Aging Datasets",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Prognostics",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "2010-09-13",
"Summary": "The dataset includes data from Li-ion batteries tested under varying conditions to model degradation and predict failure, focusing on discharge cycles and environmental impacts.",
"Additional Tags": "Battery health; Prognostics; Electrochemical impedance spectroscopy; Deep discharge; Aging effects",
"Link": "html/pages/li_ion_battery_aging_datasets.html"
},
{
"Dataset Name": "NASA Bearing Dataset",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Anomaly Detection, Prognostics",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Prognostic Dataset for Predictive/Preventive Maintenance",
"Additional Tags": "Bearing failure; Vibration analysis; Predictive maintenance; Mechanical diagnostics; Operational monitoring",
"Link": "html/pages/nasa_bearing_dataset.html"
},
{
"Dataset Name": "Steel Dataset",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "This dataset contains information on steel defects, characterized through features related to production variables. It is intended for use in developing models that can predict and classify different types of defects in steel manufacturing.",
"Additional Tags": "Steel manufacturing; Defect detection; Quality control; Industrial inspection; Manufacturing processes",
"Link": "html/pages/steel_dataset.html"
},
{
"Dataset Name": "Detecting Anomalies in Wafer Manufacturing",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Anomaly Detection",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Detecting Anomalies using Machine Learning",
"Additional Tags": "Wafer manufacturing; Sensor data; Defect detection; Anomaly detection; Manufacturing quality",
"Link": "html/pages/detecting_anomalies_in_wafer_manufacturing.html"
},
{
"Dataset Name": "ISDB - International Stiction Data Base",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Time-Series, Multivariate",
"Associated Tasks": "Classification",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "An international database comprising industrial control loops predominantly suffering from stiction. It encompasses data from various sectors contributed by multiple international experts.",
"Additional Tags": "Valve stiction; Control loop analysis; Industrial process optimization; Fault diagnosis",
"Link": "html/pages/international_stiction_data_base.html"
},
{
"Dataset Name": "Manufacturing Cost",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Regression",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Relationship between manufacturing cost and number of units.",
"Additional Tags": "Cost analysis; Production efficiency; Cost reduction; Economic analysis; Operational costs",
"Link": "html/pages/manufacturing_cost.html"
},
{
"Dataset Name": "Quality Prediction in a Mining Process",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate, Time-Series",
"Associated Tasks": "Regression",
"Number of Instances": "INA",
"Number of Features": "INA",
"Date Donated": "INA",
"Summary": "Explore real industrial data and help manufacturing plants to be more efficient.",
"Additional Tags": "Mining industry; Process optimization; Quality control; Predictive analytics; Operational efficiency",
"Link": "html/pages/quality_prediction_in_a_mining_process.html"
},
{
"Dataset Name": "PHM Data Challenge",
"Labeled": "Yes",
"Time Series": "Yes",
"Simulation": "No",
"Missing Values": "Yes",
"Dataset Characteristics": "Time-Series",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "N/A",
"Number of Features": "N/A",
"Date Donated": "2015",
"Summary": "The PHM Data Challenge focuses on fault detection and prognostics in industrial plant monitoring, requiring participants to predict and precisely localize plant faults.",
"Additional Tags": "PHM; Fault detection; Prognostics; Industrial monitoring; Time-series analysis; Plant monitoring",
"Link": "html/pages/phm_data_challenge.html"
},
{
"Dataset Name": "Energy efficiency",
"Labeled": "Yes",
"Time Series": "No",
"Simulation": "Yes",
"Missing Values": "No",
"Dataset Characteristics": "Multivariate",
"Associated Tasks": "Classification, Regression",
"Number of Instances": "768",
"Number of Features": "8",
"Date Donated": "2012-11-29",
"Summary": "This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.",
"Additional Tags": "Energy efficiency; Building simulation; Heating load; Cooling load; Environmental data",
"Link": "html/pages/energy_efficiency.html"
},
{