At Thomson Data, we help businesses clean up and manage messy B2B databases to ensure they are up-to-date, correct, and detailed. We believe your sales development representatives and marketing representatives should focus on building meaningful relationships with prospects, not scrubbing through bad data.

Here are the key steps involved in our B2B data cleansing process:

1. Data Auditing: We begin with a thorough audit of the database to identify errors, gaps, and inconsistencies, which majorly revolve around identifying outdated, incomplete, and duplicate information.

2. Data Standardization: Ensuring consistency in the data records is one of our prime services; it includes standardizing job titles, addresses, and company names. It ensures that they can be easily shared and used by different teams.

3. Data Deduplication: Another way we improve efficiency is by removing all duplicate records. Data deduplication is important in a large B2B dataset as multiple records from the same company may exist in the database.

4. Data Enrichment: After the first three steps, we enrich your data, fill in the missing details, and then enhance the database with up-to-date records. This is the step that ensures the database is valuable, providing insights that are actionable and complete.

What are the Key Benefits of Keeping the Data Clean with Thomson Data’s B2B Data Cleansing Service? Once you understand the benefits of our data cleansing service, it will entice you to optimize your data management practices, and it will additionally help you stay competitive in today’s data-driven market.

Here are some advantages of maintaining a clean database with Thomson Data:

1. Better ROI for your Sales and Marketing Campaigns: Our clean data will magnify your precise targeting, enabling you to strategize for effective campaigns, increased conversion rate, and ROI.

2. Compliant with Data Regulations:
   The B2B data cleansing services we provide are compliant to global data norms.

3. Streamline Operations: Your efforts are directed in the right channel when your data is clean and accurate, as your team doesn’t have to spend their valuable time fixing errors.

To summarize, we would again bring your attention to how accurate data is essential for driving sales and marketing in a B2B environment. It enhances your business prowess in the avenues of decision-making and customer relationships. Therefore, it is better to have a proactive approach toward B2B data cleansing service and outsource our offerings to stay competitive by unlocking the full potential of your data.

Send us a request and we will be happy to assist you.

Data Cleaning Tools Market Outlook

As of 2023, the global market size for data cleaning tools is estimated at $2.5 billion, with projections indicating that it will reach approximately $7.1 billion by 2032, reflecting a robust CAGR of 12.1% during the forecast period. This growth is primarily driven by the increasing importance of data quality in business intelligence and analytics workflows across various industries.

The growth of the data cleaning tools market can be attributed to several critical factors. Firstly, the exponential increase in data generation across industries necessitates efficient tools to manage data quality. Poor data quality can result in significant financial losses, inefficient business processes, and faulty decision-making. Organizations recognize the value of clean, accurate data in driving business insights and operational efficiency, thereby propelling the adoption of data cleaning tools. Additionally, regulatory requirements and compliance standards also push companies to maintain high data quality standards, further driving market growth.

Another significant growth factor is the rising adoption of AI and machine learning technologies. These advanced technologies rely heavily on high-quality data to deliver accurate results. Data cleaning tools play a crucial role in preparing datasets for AI and machine learning models, ensuring that the data is free from errors, inconsistencies, and redundancies. This surge in the use of AI and machine learning across various sectors like healthcare, finance, and retail is driving the demand for efficient data cleaning solutions.

The proliferation of big data analytics is another critical factor contributing to market growth. Big data analytics enables organizations to uncover hidden patterns, correlations, and insights from large datasets. However, the effectiveness of big data analytics is contingent upon the quality of the data being analyzed. Data cleaning tools help in sanitizing large datasets, making them suitable for analysis and thus enhancing the accuracy and reliability of analytics outcomes. This trend is expected to continue, fueling the demand for data cleaning tools.

In terms of regional growth, North America holds a dominant position in the data cleaning tools market. The region's strong technological infrastructure, coupled with the presence of major market players and a high adoption rate of advanced data management solutions, contributes to its leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitization of businesses, increasing investments in IT infrastructure, and a growing focus on data-driven decision-making are key factors driving the market in this region.

As organizations strive to maintain high data quality standards, the role of an Email List Cleaning Service becomes increasingly vital. These services ensure that email databases are free from invalid addresses, duplicates, and outdated information, thereby enhancing the effectiveness of marketing campaigns and communications. By leveraging sophisticated algorithms and validation techniques, email list cleaning services help businesses improve their email deliverability rates and reduce the risk of being flagged as spam. This not only optimizes marketing efforts but also protects the reputation of the sender. As a result, the demand for such services is expected to grow alongside the broader data cleaning tools market, as companies recognize the importance of maintaining clean and accurate contact lists.

Component Analysis

The data cleaning tools market can be segmented by component into software and services. The software segment encompasses various tools and platforms designed for data cleaning, while the services segment includes consultancy, implementation, and maintenance services provided by vendors.

The software segment holds the largest market share and is expected to continue leading during the forecast period. This dominance can be attributed to the increasing adoption of automated data cleaning solutions that offer high efficiency and accuracy. These software solutions are equipped with advanced algorithms and functionalities that can handle large volumes of data, identify errors, and correct them without manual intervention. The rising adoption of cloud-based data cleaning software further bolsters this segment, as it offers scalability and ease of

Sample data for exercises in Further Adventures in Data Cleaning.

Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

This report presents the results of a study to evaluate the performance, economics and emission characteristics of low-, medium-, and high-temperature fuel gas cleanup processes for use in coal gasification combined-cycle power plants based on high-temperature gas turbines. Processes considered were the Allied Chemical low-temperature Selexol process, METC medium-temperature iron oxide process and Conoco high-temperature half-calcined dolomite process. Process evaluations were carried out for twenty-four combinations of gasifiers and cleanup processes. Based upon the process evaluations, five combinations of gasifiers and cleanup process were selected for integration with an advanced, 2600 F gas turbine into an overall power system. Heat and mass balances and process schematics for these plants were prepared and the cost of electricity estimated. The results of the study indicate that medium- or high-temperature cleanup systems in combined-cycle power plants could meet or exceed EPA New Source Performance Standards. Performance and cost of the systems studied can be improved by high- and intermediate-temperature cleanup systems or by integration of developmental hot gas heat exchangers with suitable commercially available low-temperature cleanup systems. Unresolved problems in the use of medium- and high-temperature cleanup are efficient regeneration of iron oxide, particulate removal at high temperature and the fate of fuel bound nitrogen and trace metals that may appear in the hot fuel gas.

Computer Junk Cleanup Software Market Outlook

The global market size for computer junk cleanup software was valued at approximately USD 2.4 billion in 2023 and is projected to reach around USD 4.9 billion by 2032, growing at a CAGR of 7.8% during the forecast period. The growth of this market is fueled by increasing digitalization and the expansion of IT infrastructures across various industries, necessitating efficient management of system performance and storage solutions.

One of the primary growth factors for this market is the exponential increase in data generation, which leads to the accumulation of redundant and obsolete files that clutter computer systems. With the rise of big data and the Internet of Things (IoT), organizations are grappling with vast amounts of data, making it essential to employ computer junk cleanup software to optimize system performance and storage. Additionally, the rapid technological advancements in AI and machine learning have enabled more efficient and effective junk cleanup solutions, which further drive market growth.

Another significant factor contributing to market growth is the increasing awareness among individual users and enterprises about the importance of maintaining optimal system performance. As computers and other digital devices are integral to daily operations, both at work and home, ensuring their efficient functioning becomes crucial. Regular use of junk cleanup software helps in enhancing system speed, extending hardware lifespan, and preventing potential security vulnerabilities caused by unnecessary files and software. This awareness is pushing the adoption rate higher across various user segments.

Moreover, the growing trend of remote work and the proliferation of advanced digital devices have made it imperative for organizations to deploy junk cleanup software to maintain system efficiency and security. The shift towards a remote working model necessitates advanced software solutions for performance management and data security, further bolstering the market demand for computer junk cleanup software. Companies are increasingly investing in these solutions to ensure seamless operations, which is amplifying market growth.

In the realm of digital management, Data Cleansing Software plays a pivotal role in ensuring that systems remain efficient and free from unnecessary clutter. As organizations accumulate vast amounts of data, the need for tools that can effectively clean and organize this data becomes paramount. Data Cleansing Software helps in identifying and rectifying errors, removing duplicate entries, and ensuring that the data remains accurate and up-to-date. This not only enhances the performance of computer systems but also supports better decision-making processes by providing clean and reliable data. The integration of such software with junk cleanup solutions can significantly optimize system performance, making it an essential component for enterprises aiming to maintain high standards of data integrity.

From a regional perspective, North America is expected to dominate the computer junk cleanup software market, owing to the high digital literacy rate, robust IT infrastructure, and significant adoption of advanced technologies. However, regions such as Asia Pacific are also witnessing rapid market growth due to the increasing number of small and medium enterprises (SMEs), rising internet penetration, and growing awareness about system optimization and security. Europe follows closely with substantial investments in IT solutions and digital transformation initiatives.

Component Analysis

The computer junk cleanup software market is segmented into software and services. The software segment encompasses standalone applications and integrated system optimization tools that users can install on their devices. This segment is the largest contributor to market revenue, driven by widespread adoption among individual users and enterprises seeking to enhance system performance. These software solutions often come with features such as real-time monitoring, automated cleanup, and advanced algorithms capable of identifying and removing redundant files without compromising essential data.

The services segment, on the other hand, includes professional services, such as system audits, consultancy, installation, and maintenance offered by vendors. This segment is witnessing growth as enterprises increasingly lean on expert services for comprehen

Database Maintenance Tools Market Outlook

The global database maintenance tools market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach USD 7.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. This remarkable growth is driven by the increasing need for data management and the growing complexity of database environments across various industries.

One of the primary growth factors for this market is the exponential increase in data generation. With the rise of big data, IoT, and cloud computing, organizations are accumulating vast amounts of data that need to be effectively managed, maintained, and secured. Database maintenance tools play a crucial role in ensuring data integrity, optimizing performance, and securing data, thereby driving their demand. Additionally, the need for real-time data processing and analytics is further propelling the adoption of these tools. Companies are increasingly leveraging advanced database maintenance solutions to enhance their operational efficiency and decision-making processes.

Another significant driver is the burgeoning adoption of cloud-based solutions. As businesses migrate their databases to cloud platforms to leverage scalability, flexibility, and cost-effectiveness, the demand for cloud-based database maintenance tools is surging. These tools offer seamless integration with cloud environments and provide automated maintenance capabilities, reducing the burden on IT teams. Moreover, the growing trend of digital transformation across various sectors is necessitating the use of sophisticated database maintenance tools to ensure uninterrupted operations and optimal performance.

The evolving regulatory landscape is also playing a crucial role in market growth. Compliance with various data protection regulations, such as GDPR, HIPAA, and CCPA, mandates organizations to ensure data security and integrity. Database maintenance tools help companies adhere to these regulatory requirements by providing features like data encryption, backup, and recovery. As businesses strive to maintain compliance and avoid hefty penalties, the adoption of these tools is expected to rise significantly.

Regionally, North America holds a substantial share of the database maintenance tools market, primarily due to the presence of a large number of enterprises and the early adoption of advanced technologies. The Asia Pacific region is anticipated to witness significant growth during the forecast period, driven by the rapid digitalization and increasing investments in IT infrastructure. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, albeit at a comparatively moderate pace.

Type Analysis

The database maintenance tools market can be segmented by type into backup tools, monitoring tools, optimization tools, security tools, and others. Backup tools are essential for ensuring data availability and disaster recovery. These tools facilitate regular and automated backups, allowing organizations to restore data in case of corruption or loss. The growing instances of data breaches and cyber-attacks have underscored the importance of robust backup solutions, driving the demand for backup tools. Moreover, the increasing reliance on data-driven decision-making necessitates the availability of accurate and up-to-date data, further boosting the adoption of backup tools.

Monitoring tools are crucial for maintaining database performance and identifying potential issues before they escalate. These tools provide real-time insights into database activity, allowing IT teams to proactively address performance bottlenecks and ensure optimal operation. As databases become more complex and distributed, the need for advanced monitoring solutions is becoming increasingly apparent. Companies are investing in sophisticated monitoring tools to gain visibility into their database environments and enhance performance management.

Optimization tools play a vital role in enhancing database performance and efficiency. These tools analyze database queries, indexes, and configurations to identify optimization opportunities. By streamlining database operations, optimization tools help organizations reduce costs, improve response times, and enhance user experiences. The growing demand for high-performance databases, particularly in sectors like e-commerce, finance, and healthcare, is driving the adoption of optimization tools. As businesses strive to deliver seamless experiences to their customers, the importan

A data cleaning tool customised for cleaning and sorting the data generated during the Enviro-Champs pilot study as they are downloaded from Formshare, the platform capturing data sent from a customised ODK Collect form collection app. The dataset inclues the latest data from the pilot study as at 14 May 2024.

*The data for this dataset is updated daily. The date(s) displayed in the details section on our Open Data Portal is based on the last date the metadata was updated and not the refresh date of the data itself.*The Cleanup Sites layer provides locations and document links for sites currently in the cleanup process and sites awaiting cleanup funding. Cleanup programs include: Brownfields, Petroleum, EPA Superfund (CERCLA), Drycleaning, Responsible Party Cleanup, State Funded Cleanup, State Owned Lands Cleanup and Hazardous Waste Cleanup.Please reference the metadata for contact information.

This folder "XPS data" contains original and evaluated XPS data (.vms) on a p-GaN sample which was treated at various temperatures and underwent Ar+ irradiation.

Furthermore, the folder "REM Images" contains REM images (.tif) and EDX data (.xlsx) on the used excessively treated sample.

All images that are published in the main manuscript are collected as .tif files in the folder "images".

This dataset includes all of the data downloaded from GBIF (DOIs provided in README.md as well as below, downloaded Feb 2021) as well as data downloaded from SCAN. This dataset has 2,808,432 records and can be used as a reference to the verbatim data before it underwent the cleaning process. The only modifications made to this datset after direct download from the data portals are the following:

1) for GBIF records, I renamed the countryCode column to be "country" so that the column title is consistent across both GBIF and SCAN 2) A source column was added where I specify if the record came from GBIF or SCAN 3) Duplicate records across SCAN and GBIF were removed by identifying identical instances "catalogNumber" and "institutionCode" 4) Only the Darwin core columns (DwC) that were shared across downloaded datasets were retained. GBIF contained ~249 DwC variables, and SCAN data contained fewer, so this combined dataset only includes the ~80 columns shared between the two datasets

For GBIF, we downloaded the data in three separate chunks, therefore there are three DOIs. See below:

GBIF.org (3 February 2021) GBIF Occurrence Downloadhttps://doi.org/10.15468/dl.6cxfsw GBIF.org (3 February 2021) GBIF Occurrence Downloadhttps://doi.org/10.15468/dl.b9rfa7 GBIF.org (3 February 2021) GBIF Occurrence Downloadhttps://doi.org/10.15468/dl.w2nndm

This report satisfies the requirements for DOE contract DE-AC21-81MC16220 to list coal gasifiers and gas cleanup systems suitable for supplying fuel to molten carbonate fuel cells (MCFC) in industrial and utility power plants. The process information and data necessary for this study were extracted from sources in the public domain, including reports from DOE, EPRI, and EPA; work sponsored in whole or in part by federal agencies; and from trade journals, MCFC developers, and manufacturers. The listings included data on the state of development, operating characteristics, effluents, and effectiveness of the gasifiers and coal gas cleanup systems, to the extent that such information is available in the public domain. Information available in the public domain on the effects of contaminants on MCFC performance and on the design constraints on heat recovery equipment used to adjust coal gas temperatures to levels appropriate for available cleanup systems was also provided. Cleanup systems not chosen by DOE's MCFC contractors, General Electric and United Technologies, Inc., for their MCFC power plant work, by virtue of the resource requirements of those systems for commercial development, were extensively characterized. Such characterization is included in Appendix B, principally for the hot gas cleanup processes listed therein. One of those processes, using zinc ferrite for coal gas desulfurization, is now under active development by METC and has the potential for effective use in MCFC power plants.

The market for data center cleaning services is expected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX% during the forecast period 2025-2033. The growth of the market is attributed to the increasing number of data centers and the need to maintain these facilities in a clean environment. Data centers are critical to the functioning of the modern economy, as they house the servers that store and process vast amounts of data. Maintaining these facilities in a clean environment is essential to prevent the accumulation of dust and other contaminants, which can lead to equipment failures and downtime. The market for data center cleaning services is segmented by type, application, and region. By type, the market is segmented into equipment cleaning, ceiling cleaning, floor cleaning, and others. Equipment cleaning is the largest segment of the market, accounting for over XX% of the total market revenue in 2025. By application, the market is segmented into the internet industry, finance and insurance, manufacturing industry, government departments, and others. The internet industry is the largest segment of the market, accounting for over XX% of the total market revenue in 2025. By region, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is the largest segment of the market, accounting for over XX% of the total market revenue in 2025.

Restaurant Sales Dataset with Dirt Documentation

Overview

The Restaurant Sales Dataset with Dirt contains data for 17,534 transactions. The data introduces realistic inconsistencies ("dirt") to simulate real-world scenarios where data may have missing or incomplete information. The dataset includes sales details across multiple categories, such as starters, main dishes, desserts, drinks, and side dishes.

Dataset Use Cases

This dataset is suitable for: - Practicing data cleaning tasks, such as handling missing values and deducing missing information. - Conducting exploratory data analysis (EDA) to study restaurant sales patterns. - Feature engineering to create new variables for machine learning tasks.

Columns Description

Key Characteristics

1. Data Dirtiness:

   • Missing values in key columns (Item, Price, Quantity, Order Total, Payment Method) simulate real-world challenges.
   • At least one of the following conditions is ensured for each record to identify an item:
     • Item is present.
     • Price is present.
     • Both Quantity and Order Total are present.
   • If Price or Quantity is missing, the other is used to deduce the missing value (e.g., Order Total / Quantity).

2. Menu Categories and Items:

   • Items are divided into five categories:
     • Starters: E.g., Chicken Melt, French Fries.
     • Main Dishes: E.g., Grilled Chicken, Steak.
     • Desserts: E.g., Chocolate Cake, Ice Cream.
     • Drinks: E.g., Coca Cola, Water.
     • Side Dishes: E.g., Mashed Potatoes, Garlic Bread.

3 Time Range: - Orders span from January 1, 2022, to December 31, 2023.

Cleaning Suggestions

1. Handle Missing Values:

   • Fill missing Order Total or Quantity using the formula: Order Total = Price * Quantity.
   • Deduce missing Price from Order Total / Quantity if both are available.

2. Validate Data Consistency:

   • Ensure that calculated values (Order Total = Price * Quantity) match.

3. Analyze Missing Patterns:

   • Study the distribution of missing values across categories and payment methods.

Menu Map with Prices and Categories

The ckanext-purge extension provides a straightforward mechanism for permanently removing datasets marked as deleted within a CKAN instance. It introduces command-line tools to identify and erase these datasets, helping to maintain a clean and efficient data catalog. Designed for CKAN 2.5 and newer, this extension offers a practical solution for administrators to manage storage and improve overall system performance by eliminating unwanted data. Key Features: Dataset Deletion Management: Facilitates the management of datasets flagged as deleted, providing visibility and control over the purging process. Command-Line Interface: Offers paster commands to list and permanently delete datasets, enabling easy automation and integration with existing workflows. Targeted Purging: Allows purging all deleted datasets with a single command, providing a quick and efficient method for data cleanup. CKAN Integration: seamlessly integrate with CKAN through plugin configuration to expand on existing functionalities. Use Cases: Data Governance: Organizations following strict data retention policies can use this extension to ensure that deleted datasets are permanently removed from the system, maintaining compliance. Performance Optimization: CKAN instances with a large number of datasets can use this extension to improve performance by removing unnecessary data, freeing up storage space and reducing database size. Data Cleanup: When cleaning up a CKAN instance or preparing it for migration, this extension allows administrators to quickly and easily remove all deleted datasets. Technical Integration: The ckanext-purge extension is installed as a CKAN plugin, requiring an update to the CKAN configuration file to activate. It operates through paster commands, so developers can trigger it directly with proper access to the system's operations and configurations. Benefits & Impact: By using the ckanext-purge extension, CKAN administrators can ensure proper data management. This leads to improved storage efficiency increased performance for all users.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

This data layer provides access to Superfund National Priority List Sites as part of the CIMC web service. Superfund is a program administered by the EPA to locate, investigate, and clean up worst hazardous waste sites throughout the United States. EPA administers the Superfund program in cooperation with individual states and tribal governments. These sites include abandoned warehouses, manufacturing facilities, processing plants, and landfills - the key word here being abandoned. Only NPL sites have been included in Cleanups in My Community thus far. EPA maintains the NPL, which identifies for the States and the public those sites or other releases that appear to warrant remedial (long term) actions. These NPL sites fall into the following categories: Proposed: Sites may be proposed for the NPL and then may be placed on the NPL as final or be removed from the Proposed NPL. Final: Those sites placed on the NPL are called "final," and for these sites, a cleanup remedy is selected and implemented. However, it may be several years after construction of the remedy is completed before the hazardous substances are completely cleaned up or controlled in place. Deleted: After the clean up process is complete, and appropriate reviews confirm the area is cleaned up or the hazards are controlled, sites can be deleted from the NPL. For more information on the data provided through this web service, please see the processing steps below, and see more information here: https://www.epa.gov/cleanups/cimc-about-data#superfund. The CIMC web service was initially published in 2013, but the data are updated twice a month. The full schedule for data updates in CIMC is located here: https://ofmpub.epa.gov/frs_public2/frs_html_public_pages.frs_refresh_stats.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of South Pacific Ocean. The time period coverage is from 1000000 to -59 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Below is an explanation of the data along with some features that are available on this map (description is also provided in the "Getting Started" widget of the application).A variety of different colored circles appear throughout the map. They represent sites that are associated with the following programs:1) Department of Toxic Substances Control (DTSC) sites:a) Historical Inactive - Identifies sites from an older database that are non-active sites where, through a Preliminary Endangerment Assessment (PEA) or other evaluation, DTSC has determined that a removal or remedial action or further extensive investigation is required.b) School Cleanup - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Cleanup”, where remedial actions are or have occurred.c) School Evaluation - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Evaluation”, where further investigation is needed.d) Corrective Action - Investigation or cleanup activities at Resource Conservation and Recovery Act (RCRA) or state-only hazardous waste facilities (that were required to obtain a permit or have received a hazardous waste facility permit from DTSC or U.S. EPA).e) State Response - Identifies confirmed release sites where DTSC is involved in remediation, either in a lead or oversight capacity. These confirmed release sites are generally high-priority and high potential risk.f) Evaluation - Identifies suspected, but unconfirmed, contaminated sites that need or have gone through a limited investigation and assessment process.g) Tiered Permit - A corrective action cleanup project on a hazardous waste facility that either was eligible to treat or permitted to treat waste under the Tiered Permitting system.2) State Water Board or DTSC sites:a) Leaking Underground Storage Tank (LUST) Cleanup - Includes all Underground Storage Tank (UST) sites that have had an unauthorized release (i.e. leak or spill) of a hazardous substance, usually fuel hydrocarbons, and are being (or have been) cleaned up. These sites are regulated under the State Water Board's UST Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards or Local Oversight Programs.b) Cleanup Program - Includes all "non-federally owned" sites that are regulated under the State Water Board's Site Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards. Cleanup Program Sites are also commonly referred to as "Site Cleanup Program sites".c) Voluntary Cleanup - Identifies sites with either confirmed or unconfirmed releases, and the project proponents have requested that the State Water Board or DTSC oversee evaluation, investigation, and/or cleanup activities and have agreed to provide coverage for the lead agency’s costs.3) Othera) Permitted Tanks - The "Permitted Tanks" data set includes Facilities that are associated with permitted underground storage tanks from the California Environmental Reporting System (CERS) database. The CERS data consists of current and recently closed permitted underground storage tank (UST) facilities information provided to CERS by Certified Unified Program Agencies (CUPAs).*Note: Underground Storage Tank Cleanup and Cleanup Program project records are pulled from the State Water Board's GeoTracker database. The Permitted Tanks information was obtained from California EPA’s California Environmental Reporting System (CERS) database. All other project records were obtained from DTSC's EnviroStor database. Program descriptions come from DTSC’s EnviroStor Glossary of Terms and the State Water Board’s GeoTracker Site/Facility Type Definitions. The information associated with these records was last updated in the application on 4/24/2023.

Below is an explanation of the data along with some features that are available on this map (description is also provided in the "Getting Started" widget of the application).A variety of different colored circles appear throughout the map. They represent sites that are associated with the following programs:1) Department of Toxic Substances Control (DTSC) sites:a) Historical Inactive - Identifies sites from an older database that are non-active sites where, through a Preliminary Endangerment Assessment (PEA) or other evaluation, DTSC has determined that a removal or remedial action or further extensive investigation is required.b) School Cleanup - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Cleanup”, where remedial actions are or have occurred.c) School Evaluation - Identifies proposed and existing school sites that are being evaluated by DTSC for possible hazardous materials contamination. School sites are further defined as “Evaluation”, where further investigation is needed.d) Corrective Action - Investigation or cleanup activities at Resource Conservation and Recovery Act (RCRA) or state-only hazardous waste facilities (that were required to obtain a permit or have received a hazardous waste facility permit from DTSC or U.S. EPA).e) State Response - Identifies confirmed release sites where DTSC is involved in remediation, either in a lead or oversight capacity. These confirmed release sites are generally high-priority and high potential risk.f) Evaluation - Identifies suspected, but unconfirmed, contaminated sites that need or have gone through a limited investigation and assessment process.g) Tiered Permit - A corrective action cleanup project on a hazardous waste facility that either was eligible to treat or permitted to treat waste under the Tiered Permitting system.2) State Water Board or DTSC sites:a) Leaking Underground Storage Tank (LUST) Cleanup - Includes all Underground Storage Tank (UST) sites that have had an unauthorized release (i.e. leak or spill) of a hazardous substance, usually fuel hydrocarbons, and are being (or have been) cleaned up. These sites are regulated under the State Water Board's UST Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards or Local Oversight Programs.b) Cleanup Program - Includes all "non-federally owned" sites that are regulated under the State Water Board's Site Cleanup Program and/or similar programs conducted by each of the nine Regional Water Boards. Cleanup Program Sites are also commonly referred to as "Site Cleanup Program sites".c) Voluntary Cleanup - Identifies sites with either confirmed or unconfirmed releases, and the project proponents have requested that the State Water Board or DTSC oversee evaluation, investigation, and/or cleanup activities and have agreed to provide coverage for the lead agency’s costs.3) Othera) Permitted Tanks - The "Permitted Tanks" data set includes Facilities that are associated with permitted underground storage tanks from the California Environmental Reporting System (CERS) database. The CERS data consists of current and recently closed permitted underground storage tank (UST) facilities information provided to CERS by Certified Unified Program Agencies (CUPAs).*Note: Underground Storage Tank Cleanup and Cleanup Program project records are pulled from the State Water Board's GeoTracker database. The Permitted Tanks information was obtained from California EPA’s California Environmental Reporting System (CERS) database. All other project records were obtained from DTSC's EnviroStor database. Program descriptions come from DTSC’s EnviroStor Glossary of Terms and the State Water Board’s GeoTracker Site/Facility Type Definitions. The information associated with these records was last updated in the application on 4/24/2023.