Best Automotive Data Software

Guide to Automotive Data Software

Automotive data software refers to platforms and tools that collect, process, and analyze data generated by vehicles, drivers, and surrounding infrastructure. Modern vehicles produce vast amounts of data from sensors, control units, cameras, and connectivity systems, covering everything from engine performance and battery health to driver behavior and environmental conditions. Automotive data software helps automakers, suppliers, and fleet operators turn this raw data into usable insights that support design decisions, operational efficiency, and real-time vehicle functions.

As vehicles become more connected, electric, and autonomous, automotive data software plays a central role in enabling advanced capabilities. It supports use cases such as predictive maintenance, over-the-air updates, advanced driver assistance systems, and autonomous driving algorithms. These systems often rely on cloud computing, edge processing, and artificial intelligence to manage high data volumes while meeting strict requirements for latency, safety, and reliability. Interoperability and the use of open source components are also common, helping companies integrate data across different vehicle models and technology stacks.

Security, privacy, and compliance are critical considerations in automotive data software. Vehicle data can include sensitive information about drivers, locations, and usage patterns, making robust cybersecurity and data governance essential. Software platforms must comply with industry standards and regulations while ensuring data accuracy and integrity across the vehicle lifecycle. As the automotive industry continues to evolve toward software-defined vehicles, automotive data software is becoming a foundational layer that shapes innovation, customer experience, and long-term business models.

Features Provided by Automotive Data Software

• Vehicle Data Acquisition: Collects raw data directly from vehicles through onboard diagnostics ports, electronic control units (ECUs), sensors, and embedded systems, enabling continuous access to parameters such as speed, engine temperature, RPM, fuel usage, battery status, and fault codes without manual intervention.
• Real-Time Data Streaming: Provides live data transmission from vehicles to centralized platforms, allowing stakeholders to monitor vehicle performance, driver behavior, and system health as events occur, which is especially valuable for fleet operations, safety monitoring, and rapid incident response.
• Historical Data Storage and Management: Stores large volumes of time-series automotive data in structured databases, making it possible to analyze trends over days, months, or years, support regulatory audits, and perform long-term performance and reliability studies.
• Predictive Maintenance and Diagnostics: Uses historical and real-time data combined with analytics or machine learning models to identify early signs of component wear or failure, helping organizations schedule maintenance before breakdowns occur and reduce downtime and repair costs.
• Fault Detection and Error Code Analysis: Interprets diagnostic trouble codes and abnormal sensor readings to pinpoint specific system or component issues, simplifying troubleshooting for technicians and enabling faster, more accurate repairs.
• Fleet Management and Optimization: Aggregates data from multiple vehicles to provide insights into utilization, routing efficiency, idle time, fuel consumption, and overall fleet health, supporting cost reduction and operational efficiency across commercial vehicle fleets.
• Driver Behavior Monitoring: Analyzes driving patterns such as harsh braking, rapid acceleration, speeding, and cornering to assess driver safety and performance, which can be used for training programs, insurance assessments, and risk reduction initiatives.
• Fuel and Energy Consumption Analytics: Tracks fuel usage for internal combustion vehicles and energy consumption for electric vehicles, helping organizations understand efficiency, reduce emissions, and optimize driving strategies or charging behaviors.
• Electric Vehicle and Battery Management: Monitors battery state of charge, state of health, temperature, charging cycles, and degradation patterns, supporting range prediction, battery longevity optimization, and safe operation of electric and hybrid vehicles.
• Telematics and GPS Integration: Combines vehicle data with location information to enable navigation analysis, route tracking, geofencing, and trip reconstruction, which is essential for logistics, theft prevention, and usage-based services.
• Data Visualization and Dashboards: Presents complex automotive data through charts, graphs, heat maps, and dashboards that make trends and anomalies easy to understand for engineers, managers, and non-technical users alike.
• Advanced Analytics and Reporting: Applies statistical analysis, machine learning, or rules-based logic to generate actionable insights, automated reports, and performance benchmarks that support strategic decision-making.
• Over-the-Air Update Support: Enables remote software and firmware updates for vehicle systems, reducing the need for physical service visits while ensuring vehicles run the latest features, bug fixes, and security patches.
• Integration with Enterprise Systems: Connects automotive data platforms with enterprise tools such as ERP, CRM, insurance, and supply chain systems, allowing vehicle data to inform broader business processes and analytics.
• Security and Access Control: Protects sensitive vehicle and driver data through encryption, authentication, and role-based access controls, helping ensure compliance with data protection standards and reduce cybersecurity risks.
• Compliance and Regulatory Reporting: Supports the generation of reports required for emissions regulations, safety standards, and transportation authorities, reducing manual reporting effort and improving accuracy.
• Scalability and Cloud Support: Designed to handle data from a small number of vehicles up to millions of connected cars, often leveraging cloud infrastructure to scale storage, processing, and analytics as demand grows.
• Customization and API Access: Provides configurable data models, rules, and application programming interfaces that allow developers and organizations to tailor the software to specific vehicle types, business needs, or industry use cases.

What Are the Different Types of Automotive Data Software?

• Vehicle telemetry and sensor data software: This type of software focuses on collecting raw and processed data from onboard sensors and vehicle networks. It captures information such as speed, temperature, pressure, and system status, then organizes it for monitoring and analysis. The software is designed to handle continuous data flows and ensure accuracy and reliability under real-world driving conditions.
• Embedded vehicle data management software: Embedded data software operates directly within the vehicle’s computing environment. It manages how data is stored, filtered, and prioritized before being shared with other systems. By preprocessing data locally, it reduces bandwidth usage and ensures critical information is handled with appropriate timing and reliability.
• Connected vehicle data platforms: These platforms serve as the bridge between vehicles and external systems. They receive transmitted vehicle data, standardize it across different models and configurations, and make it usable for analytics and services. They also support secure communication back to the vehicle for updates and remote interactions.
• Fleet and asset data software: Fleet-focused software aggregates data from many vehicles into a single operational view. It enables tracking of location, utilization, performance, and maintenance needs across an entire fleet. This type of software is commonly used to improve efficiency, reduce operating costs, and support data-driven decision-making at scale.
• Diagnostics and fault analysis software: Diagnostics software interprets vehicle-generated diagnostic data to identify faults and abnormal behavior. It translates technical signals and fault codes into actionable insights that help technicians and engineers understand what went wrong. This software supports both real-time alerts and deeper post-event investigations.
• Predictive maintenance and reliability analytics software: Predictive maintenance software analyzes historical and real-time vehicle data to anticipate component failures. Instead of relying on fixed service intervals, it enables maintenance based on actual vehicle condition. This approach helps reduce unexpected breakdowns and extends the usable life of vehicle components.
• Driver behavior and usage analytics software: This software examines how vehicles are driven by analyzing patterns such as acceleration, braking, and speed variability. It identifies trends related to safety, efficiency, and compliance. The insights generated can be used for feedback, training, and performance improvement initiatives.
• Energy and efficiency data software: Energy-focused software tracks how vehicles consume fuel or electrical energy over time. It evaluates efficiency under different conditions and monitors energy-related components such as batteries. The goal is to optimize energy usage, reduce operating costs, and support long-term performance planning.
• Autonomous and advanced driver assistance data software: This category handles extremely large and complex datasets generated by advanced sensing systems. It manages perception, sensor fusion, and event data used to evaluate automated driving features. The software supports development, testing, and validation by enabling detailed analysis of system behavior.
• Mapping and localization data software: Mapping and localization software processes positioning and environmental data to support navigation and situational awareness. It combines vehicle-generated data with geographic context to improve accuracy and reliability. This type of software is critical for precise vehicle positioning and route-related functions.
• Manufacturing and production data software: This software manages data generated during vehicle assembly and testing. It links production information with in-field performance to support quality control and traceability. By connecting manufacturing data to operational outcomes, it helps identify root causes of defects and process improvements.
• Compliance, reporting, and regulatory data software: Compliance software organizes and validates vehicle data required for regulatory and reporting purposes. It ensures that data is accurate, auditable, and aligned with applicable standards. This software reduces manual effort and supports consistent reporting across regions and requirements.
• Data integration and interoperability software: Integration software enables automotive data to flow between technical, operational, and enterprise systems. It transforms and standardizes data so it can be shared and reused effectively. This helps eliminate data silos and supports broader analytics and decision-making efforts.
• Automotive data security and governance software: This type of software focuses on protecting automotive data and controlling how it is accessed and used. It enforces security policies, monitors data flows, and supports long-term governance requirements. The goal is to maintain trust, integrity, and compliance across the entire data ecosystem.

Benefits of Using Automotive Data Software

• Improved vehicle performance optimization: Automotive data software collects and analyzes real-time and historical vehicle data such as engine load, throttle response, braking behavior, and drivetrain efficiency. By processing this information, manufacturers and operators can fine-tune powertrain calibration, suspension settings, and energy usage to improve acceleration, fuel efficiency, and overall driving dynamics. Over time, performance improvements can be rolled out through software updates rather than mechanical changes.
• Predictive maintenance and reduced downtime: By continuously monitoring components like batteries, brakes, transmissions, and sensors, automotive data software can detect early warning signs of wear or failure. Machine learning models identify abnormal patterns before breakdowns occur, allowing maintenance to be scheduled proactively. This reduces unexpected vehicle downtime, lowers repair costs, and extends the lifespan of critical components, especially for fleets and commercial vehicles.
• Enhanced vehicle safety and risk reduction: Automotive data software supports advanced safety systems by analyzing data from cameras, radar, lidar, and vehicle control units. These insights help improve driver assistance features such as collision avoidance, lane keeping, and adaptive cruise control. Over time, aggregated safety data enables manufacturers to identify common accident scenarios, refine algorithms, and design safer vehicles while also supporting compliance with safety regulations.
• Data-driven product development and faster innovation: Engineers use vehicle data to understand how cars are actually driven in real-world conditions rather than relying only on lab testing. This feedback loop shortens development cycles, validates design assumptions, and highlights opportunities for improvement. Automotive data software allows new features to be tested, refined, and deployed faster, supporting continuous innovation and more competitive product roadmaps.
• Over-the-air updates and feature flexibility: Automotive data software enables over-the-air software updates that improve vehicle functionality without requiring a dealership visit. Manufacturers can fix bugs, enhance performance, or add new features remotely. This flexibility reduces recall costs, improves customer satisfaction, and allows vehicles to evolve after purchase, extending their value over time.
• Improved fleet management and operational efficiency: Fleet operators benefit from centralized dashboards that track vehicle location, usage patterns, fuel consumption, and driver behavior. Automotive data software enables route optimization, idle time reduction, and more efficient asset utilization. These insights help lower operating costs, improve delivery reliability, and support data-backed decisions about fleet size and replacement cycles.
• Better energy management for electric and hybrid vehicles: For electric and hybrid vehicles, automotive data software plays a critical role in battery monitoring, charging optimization, and range prediction. By analyzing charging habits, temperature effects, and driving patterns, the software improves energy efficiency and battery health. This leads to more accurate range estimates, reduced degradation, and better integration with charging infrastructure.
• Personalized driver and user experiences: Automotive data software supports personalization by learning driver preferences such as seat position, climate control, infotainment settings, and driving style. Vehicles can automatically adapt to individual users, creating a more comfortable and intuitive experience. Personalization also extends to navigation, recommendations, and usage-based features that evolve over time.
• Support for autonomous and assisted driving systems: Autonomous and semi-autonomous driving depends heavily on large volumes of high-quality data. Automotive data software manages sensor fusion, data labeling, simulation, and continuous learning. This enables systems to improve perception, decision-making, and control accuracy while handling edge cases more effectively through real-world feedback.
• Regulatory compliance and reporting accuracy: Automotive data software helps manufacturers and operators comply with emissions standards, safety regulations, and reporting requirements. Automated data collection and analysis reduce manual errors and simplify audits. Accurate records support transparency with regulators and help organizations adapt quickly to changing compliance frameworks.
• Cost reduction through operational insights: By identifying inefficiencies in fuel usage, maintenance practices, and driver behavior, automotive data software uncovers opportunities to reduce costs. Organizations can make informed decisions about procurement, maintenance schedules, and operational policies, leading to measurable financial savings over time.
• Scalability and ecosystem integration: Modern automotive data platforms are designed to scale across millions of vehicles and integrate with cloud services, analytics tools, and open source frameworks. This scalability supports global deployments and partnerships while enabling data sharing across suppliers, developers, and service providers in a controlled and secure manner.
• New revenue streams and business models: Automotive data software enables usage-based insurance, subscription features, mobility services, and data-driven partnerships. By monetizing insights responsibly and securely, companies can create new revenue opportunities beyond traditional vehicle sales while delivering added value to customers.
• Improved decision-making through advanced analytics: Executives and engineers gain access to dashboards, forecasts, and scenario models powered by automotive data software. These tools transform raw data into actionable insights, supporting strategic planning, market analysis, and long-term investment decisions with greater confidence and precision.

Who Uses Automotive Data Software?

• Automotive OEM Executives and Strategy Teams: Senior leaders and corporate planners who use automotive data software to understand market trends, competitive positioning, regional demand shifts, pricing dynamics, and long-term technology adoption such as electrification, autonomy, and software-defined vehicles, helping guide investment decisions and corporate strategy
• Product Planning and Portfolio Managers: Teams responsible for deciding what vehicles to build and when, using data to evaluate customer preferences, feature adoption rates, powertrain mix, trim strategies, and lifecycle timing to optimize product roadmaps and reduce risk
• Engineering and R&D Teams: Mechanical, electrical, and software engineers who rely on detailed vehicle, component, and performance data to benchmark competitors, validate design choices, monitor real-world usage, and support development of new platforms, materials, and vehicle architectures
• Manufacturing and Operations Managers: Plant leaders and operations analysts who use production, supply chain, and quality data to improve throughput, reduce defects, manage supplier performance, forecast capacity needs, and respond quickly to disruptions or demand changes
• Supply Chain and Procurement Professionals: Buyers and sourcing teams who analyze part availability, supplier risk, cost trends, and geopolitical factors to negotiate contracts, diversify suppliers, and ensure continuity for critical components such as semiconductors and batteries
• Dealership Owners and General Managers: Retail leaders who use sales velocity, inventory aging, regional demand, and pricing data to optimize stock levels, improve margins, plan promotions, and align dealership operations with manufacturer incentives
• Sales and Marketing Teams: Professionals who depend on customer segmentation, purchase behavior, lead conversion, and campaign performance data to design targeted marketing, personalize messaging, forecast sales, and measure return on investment across channels
• Aftermarket and Service Providers: Independent repair shops, service networks, and parts distributors who use vehicle parc data, failure rates, maintenance intervals, and diagnostic information to plan inventory, train technicians, and identify new service opportunities
• Fleet Managers and Mobility Operators: Organizations managing commercial fleets, rental cars, ride-hailing, or delivery vehicles who use telematics and utilization data to reduce downtime, control costs, optimize routing, manage maintenance, and plan vehicle replacement cycles
• Insurance and Risk Analysts: Actuaries and underwriting teams who analyze vehicle safety data, repair costs, driver behavior, and crash statistics to price policies accurately, detect fraud, and develop new insurance products for emerging vehicle technologies
• Financial Analysts and Investors: Equity analysts, private equity firms, and institutional investors who use automotive data to evaluate company performance, forecast revenue, assess technology leadership, and model risks related to regulation, supply chains, and consumer demand
• Regulatory and Compliance Professionals: Government agencies and compliance teams who rely on emissions data, safety records, recall history, and testing results to enforce regulations, monitor industry compliance, and shape future automotive policy
• Data Scientists and Analytics Teams: Specialists who work directly with large automotive datasets, building models, dashboards, and predictive tools that turn raw data into actionable insights for stakeholders across engineering, sales, operations, and strategy
• Technology Vendors and Software Developers: Companies building automotive platforms, infotainment systems, ADAS, and open source or proprietary tools who use vehicle and usage data to validate features, improve user experience, and ensure compatibility across models and regions
• Researchers, Journalists, and Industry Analysts: Academics, media professionals, and consultants who use automotive data to study industry trends, publish reports, benchmark performance, and explain complex developments to broader audiences

How Much Does Automotive Data Software Cost?

The cost of automotive data software can vary widely depending on the scope of features, the volume of data processed, and the level of customization required. Basic packages that offer essential data collection and reporting tools might start at a few thousand dollars per year, making them accessible for smaller shops or pilot projects. As the software scales up to include advanced analytics, real-time data streaming, and integration with multiple data sources, the price typically increases. Larger fleets or enterprises that need robust data handling, predictive insights, and high uptime can expect to pay significantly more, often stretching into the tens of thousands or even six figures annually.

In addition to subscription fees, implementation and support costs also play a role in the total investment. Initial setup, data migration, and staff training can add to upfront expenses, especially when tailoring the software to a specific business environment. Ongoing support and updates are usually included in higher-tier plans or as part of a managed service agreement, which can further affect the overall cost. Ultimately, organizations should weigh their specific data needs, expected benefits, and long-term goals to determine how much they are willing to invest in automotive data software.

What Software Does Automotive Data Software Integrate With?

Automotive data software can integrate with a wide range of software types because vehicle data is valuable across engineering, operations, and business functions. One major category is embedded and vehicle systems software, including electronic control units, advanced driver assistance systems, infotainment platforms, and over-the-air update systems. These integrations allow real-time or near-real-time data exchange for diagnostics, calibration, performance optimization, and feature updates directly within the vehicle.

Cloud platforms and data infrastructure software are another core integration area. Automotive data software commonly connects to cloud storage, data lakes, streaming platforms, and analytics engines to ingest high-volume telemetry, sensor data, and event logs. This enables large-scale processing for fleet monitoring, predictive maintenance, usage-based insurance models, and long-term trend analysis across vehicles and regions.

Enterprise and business software also integrates heavily with automotive data systems. This includes customer relationship management, enterprise resource planning, billing, warranty management, and supply chain systems. Vehicle data can trigger service workflows, support recalls, optimize inventory, and personalize customer experiences by linking driving behavior or vehicle health data with business operations.

Engineering, simulation, and development tools form another important integration category. Automotive data software often feeds digital twins, simulation platforms, testing frameworks, and machine learning environments. Engineers use real-world vehicle data to validate models, train algorithms for perception or autonomy, improve energy efficiency, and shorten development cycles.

Finally, third-party and consumer-facing applications integrate with automotive data software through secure APIs and data-sharing frameworks. These include navigation apps, fleet management dashboards, insurance platforms, smart city systems, and energy management software for electric vehicles. Such integrations allow automotive data to support broader ecosystems while maintaining controls around privacy, security, and regulatory compliance.

Recent Trends Related to Automotive Data Software

• Software-defined vehicles and data-first design: Automotive platforms are increasingly built around software rather than fixed hardware, which makes data collection, telemetry, and software lifecycle visibility core requirements. Centralized compute and zonal architectures simplify how data is gathered and analyzed across different vehicle models and software versions.
• Cloud-native automotive data platforms: Vehicle data is now expected to flow into cloud platforms that act as the system of record for connected services, diagnostics, and analytics. These platforms borrow heavily from enterprise data stacks, combining streaming ingestion, scalable storage, analytics, and machine learning tailored to automotive workloads.
• Smarter, value-driven data ingestion: Instead of transmitting all available signals at full fidelity, teams are prioritizing selective and event-driven data capture. Techniques like edge filtering, adaptive sampling, and triggered uploads help control costs while preserving the data that actually supports quality, safety, and product decisions.
• Data contracts, schemas, and semantic consistency: As fleets grow and software updates accelerate, consistent signal definitions and versioned schemas become essential. Automotive data software is increasingly responsible for maintaining semantic consistency so data can be reliably compared across vehicle programs, regions, and generations.
• OTA updates paired with observability and analytics: Over-the-air updates are no longer just about delivering software but about measuring outcomes in the field. Data platforms track which versions are deployed, monitor behavior after updates, and support rapid detection of regressions or unintended side effects.
• Regulatory-driven requirements for traceability and governance: Cybersecurity and software update regulations are pushing automakers to maintain auditable records of vehicle software behavior. This has elevated logging, retention policies, and traceable data pipelines from operational details to core platform capabilities.
• Security and privacy embedded in the data pipeline: Automotive data software increasingly integrates encryption, access control, and privacy safeguards directly into ingestion and storage layers. This shift reflects the sensitivity of vehicle data, especially around location and driver behavior, and the need to manage access at a granular level.
• Expansion of real-time and scenario-aware analytics: Streaming analytics is being used for quality monitoring, safety detection, and fleet health rather than only user-facing features. Scenario-aware analysis, which combines telemetry with environmental and contextual data, is becoming critical for understanding real-world vehicle behavior.
• AI-driven early warning and quality systems: Machine learning is widely applied to detect anomalies and emerging issues across large fleets before they escalate. This increases demand for clean training data, labeled events, and feedback loops that connect in-field data with engineering and service outcomes.
• Generative AI as an interface to complex vehicle data: Early GenAI adoption focuses on improving productivity by helping teams query data, summarize incidents, and accelerate investigations. These tools tend to amplify the importance of high-quality metadata, schemas, and governance rather than replacing foundational data infrastructure.
• Vehicle-level observability as a core platform goal: Automotive data tooling is increasingly modeled after observability practices in software engineering. This includes version-aware monitoring, fleet-level health indicators, and tools that help teams understand what changed and why after updates or configuration shifts.
• Data platforms supporting recurring revenue models: Automakers and suppliers are packaging analytics, diagnostics, and fleet insights as ongoing services. This pushes automotive data software toward reusable, multi-tenant platforms that can support multiple products, customers, and business models.
• Rising importance of partnerships and ecosystem integration: The complexity of automotive software is driving partnerships and consolidation across data, cloud, and embedded software providers. Integrated stacks increase the need for unified data models and cross-layer visibility from hardware through applications.
• Higher data demands from ADAS and autonomy tools: Advanced driver assistance systems require large volumes of high-quality, well-synchronized data for validation and improvement. Even as autonomy timelines evolve, the supporting data infrastructure continues to benefit safety, quality, and development workflows more broadly.
• Growing reliance on open source with stronger controls: Many automotive data platforms rely on open source technologies common in cloud-native environments. At the same time, organizations are investing more in governance, reproducibility, and supply-chain security to meet safety and compliance expectations.
• Strategic focus on data retention and cost management: As telemetry volume grows, retention strategies such as tiered storage and selective preservation are becoming core design considerations. Automotive data software increasingly balances long-term compliance needs with cost efficiency and analytical value.
• Overall direction of automotive data software: The market is moving toward end-to-end platforms that connect signal definition, ingestion, analytics, and operational action. Success increasingly depends on combining scalability, compliance, real-time insight, and cost discipline into a coherent data strategy.

How To Pick the Right Automotive Data Software

Selecting the right automotive data software starts with clearly understanding what problems you are trying to solve and who will use the system. Automotive data can support many functions, such as vehicle diagnostics, predictive maintenance, fleet management, manufacturing optimization, regulatory reporting, or customer analytics. A solution that works well for engineering teams may not be suitable for business analysts or operations staff, so it is important to match the software’s capabilities, interface, and outputs to real user needs and decision-making workflows.

Data sources and compatibility are critical considerations. Automotive data often comes from a mix of vehicle sensors, telematics systems, ECUs, manufacturing equipment, and third-party providers. The software should be able to ingest, normalize, and process data from all relevant sources without excessive custom development. It should also support industry standards and protocols commonly used in the automotive sector to reduce integration risk and future-proof the investment as vehicles and infrastructure evolve.

Scalability and performance matter because automotive data volumes grow quickly. As fleets expand, sensor resolution increases, or real-time analytics become more important, the software must handle higher data throughput without degradation. Evaluating how the system performs under peak loads, how it manages historical data, and whether it can scale horizontally or vertically will help avoid costly migrations later. Cloud compatibility and deployment flexibility can be especially important if data growth is unpredictable.

Security, compliance, and data governance should be evaluated early rather than treated as add-ons. Automotive data can include sensitive information related to vehicle behavior, location, and user identity. The software should provide strong access controls, encryption, audit trails, and compliance support for relevant regulations. Clear data ownership, retention policies, and role-based permissions help ensure that data is used responsibly and legally across teams and partners.

Usability and analytics capabilities often determine whether the software delivers long-term value. A powerful platform that is difficult to configure or interpret may go underused. Look for intuitive dashboards, customizable reports, and analytics tools that align with your organization’s technical maturity. Support for advanced analytics, such as machine learning or anomaly detection, can be valuable, but only if the organization has the skills and processes to act on those insights.

Finally, vendor stability and ecosystem fit should influence the decision. Automotive data software is rarely a short-term purchase, so it is important to assess the vendor’s track record, support model, and product roadmap. Strong documentation, responsive support, and an active user or partner ecosystem can significantly reduce risk. Choosing software that aligns with your long-term automotive strategy, rather than just immediate requirements, increases the likelihood of sustained return on investment.

Compare automotive data software according to cost, capabilities, integrations, user feedback, and more using the resources available on this page.