Data replication files. Visit https://dataone.org/datasets/sha256%3A52413e59f285612203efcd9771ff07bc4dddab268f185bd8689a58e49ff5a1bc for complete metadata about this dataset.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

*Due to AIHW suppression rules in the data provided for this study, the figures presented omit attempt and death counts for motor vehicle exhaust and firearms in Western Australia and South Australia in 2006 and 2007.#Lethality  =  deaths/(deaths + attempts) * 100†Categories based on coding of deaths and hospital separations according to the International Statistical Classification of Diseases and Related Health Problems (versions 9 and 10): poisoning (E950, X60–X66, X68, X69); motor vehicle exhaust (E951, E952, X67); hanging (E953, X70); firearms (E955, X72–X75); all other methods (E954, E956–E959, X71, X76–X84)

This is the replication data for ``Comment on “Voter Identification Laws and the Suppression of Minority Votes". Please contact us with any questions.

Data Center Fire Suppression System Market Outlook

According to our latest research, the global Data Center Fire Suppression System market size reached USD 2.48 billion in 2024. The market is expected to demonstrate robust growth at a CAGR of 6.7% from 2025 to 2033, culminating in a projected market value of USD 4.48 billion by 2033. This expansion is primarily driven by the escalating demand for advanced fire safety solutions in data centers, propelled by the exponential growth in data generation and storage needs, as well as stringent regulatory requirements for data center safety and operational continuity.

The primary growth factor fueling the Data Center Fire Suppression System market is the surging proliferation of hyperscale and colocation data centers worldwide. As digital transformation initiatives intensify across industries, organizations are investing heavily in robust IT infrastructure, leading to the construction of larger and more complex data centers. These facilities house mission-critical equipment and vast volumes of sensitive data, making them highly susceptible to fire hazards. Consequently, there is an increased emphasis on deploying state-of-the-art fire suppression systems that can mitigate risks without causing collateral damage to expensive hardware. The adoption of advanced technologies such as gas-based and aerosol-based suppression systems further enhances the market’s growth trajectory, as these solutions offer rapid response times and minimal disruption to ongoing operations.

Another significant growth driver is the tightening of regulatory frameworks and industry standards concerning fire safety in data centers. Governments and industry bodies across the globe have introduced stringent codes and guidelines mandating the installation of effective fire detection and suppression systems in data storage facilities. Compliance with standards such as NFPA 75, NFPA 76, and ISO/IEC 27001 has become imperative, compelling data center operators to upgrade or retrofit their existing fire safety infrastructure. This regulatory push not only ensures the safety of personnel and assets but also minimizes potential downtime and business losses due to fire incidents, thereby reinforcing the demand for innovative fire suppression technologies.

Technological advancements are also playing a pivotal role in shaping the Data Center Fire Suppression System market. The integration of intelligent sensors, real-time monitoring, and IoT-enabled control panels has revolutionized fire detection and response mechanisms. Modern systems are equipped with predictive analytics and remote management capabilities, allowing facility managers to proactively identify fire risks and initiate suppression protocols with precision. Furthermore, the increasing adoption of environmentally friendly suppression agents and sustainable system designs aligns with the growing focus on green data centers. These innovations not only enhance operational efficiency but also address environmental concerns, making them highly attractive to forward-thinking enterprises.

From a regional perspective, North America continues to dominate the Data Center Fire Suppression System market, accounting for the largest revenue share in 2024. This leadership can be attributed to the high concentration of data centers, rapid technological adoption, and stringent regulatory environment in the region. However, Asia Pacific is emerging as the fastest-growing market, driven by the rapid expansion of IT infrastructure, increasing digitalization, and rising investments in data center construction across countries such as China, India, and Singapore. Europe also holds a significant market share, underpinned by strong regulatory compliance and a mature data center ecosystem. The Middle East & Africa and Latin America are witnessing steady growth, fueled by increasing digital initiatives and infrastructure modernization efforts.

Product Type Analysis

The Data Center Fire Suppression System market is segmented by product

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

A total of 133 adults between the age range of 20-60 years were selected using purposive/snowball sampling. Data collection occurred over a month, and participants were provided useful resources for their participation. The questionnaire was circulated to 175 adults, out of which 62 were excluded due to incomplete responses. The participants were from 16 countries, which were: The United States, The United Kingdom, Australia, Canada, Czech Republic, Denmark, France, Hungary, Netherlands, Norway, Pakistan, Romania, Saudi Arabia, Sweden, Switzerland, and India. The participants in India belonged to different states and union territories which were Karnataka, Bihar, Delhi, Gujarat, Haryana, Telangana, Jharkhand, Kashmir, Uttar Pradesh, Tamil Nadu, Pondicherry, and West Bengal. Inclusion Criteria. The participants in the study were included based on the following criteria. Age. The participants had to fall in the age range of 20-60 years. Language. Due to the nature of the survey, the language compatibility was expected to be English.

This survey was conducted by the National Youth Gang Intervention and Suppression Program. The primary goals of the program were to assess the national scope of the gang crime problem, to identify promising programs and approaches for dealing with the problem, to develop prototypes from the information gained about the most promising programs, and to provide technical assistance for the development of gang intervention and suppression programs nationwide. The survey was designed to encompass every agency in the country that was engaged in or had recently engaged in organized responses specifically intended to deal with gang crime problems. Cities were screened with selection criteria including the presence and recognition of a youth gang problem and the presence of a youth gang program as an organized response to the problem. Respondents were classified into several major categories and subcategories: law enforcement (mainly police, prosecutors, judges, probation, corrections, and parole), schools (subdivided into security and academic personnel), community, county, or state planners, other, and community/service (subdivided into youth service, youth and family service/treatment, comprehensive crisis intervention, and grassroots groups). These data include variables coded from respondents' definitions of the gang, gang member, and gang incident. Also included are respondents' historical accounts of the gang problems in their areas. Information on the size and scope of the gang problem and response was also solicited.

The Local Tobacco Control Profiles data update for November 2017 has been published by Public Health England (PHE).

The data are presented in an interactive tool that allows users to view them in a user-friendly format. The profiles provide a snapshot of the extent of tobacco use, tobacco related harm, and measures being taken to reduce this harm at a local level. These profiles have been designed to help local government and health services to assess the effect of tobacco use on their local populations.

This update contains:

• more recent data for 17 indicators, including smoking attributable mortality and quitters
• inequalities data for 20 indicators
• a new indicator based on an updated calculation method for smoking status at time of delivery
• recalculation of smoking attributable deaths due to updated suppression rules and to reflect geographical changes

See the attached data to be included document for full details of what’s in this update.

View previous Local Tobacco Control Profiles updates.

Data Center Fire Suppression Market Outlook

As per our latest research, the global Data Center Fire Suppression market size reached USD 2.13 billion in 2024, reflecting robust investments in critical infrastructure protection. The market is projected to grow at a CAGR of 6.8% during the forecast period, reaching a value of USD 3.98 billion by 2033. This growth is primarily driven by the increasing demand for advanced fire protection solutions in data centers, the proliferation of hyperscale facilities, and stringent regulatory mandates for safety and business continuity.

One of the key growth factors for the Data Center Fire Suppression market is the rapid expansion of cloud computing and digital transformation initiatives across industries. As organizations increasingly migrate their workloads to cloud environments, the number and size of data centers have surged worldwide. This expansion has heightened the need for robust fire suppression systems, as even a minor fire incident can result in catastrophic data loss, service outages, and significant financial repercussions. Modern data centers are adopting sophisticated fire detection and suppression technologies that not only ensure quick response but also minimize damage to sensitive electronic equipment. The integration of IoT-based monitoring and AI-driven predictive maintenance further enhances the reliability and effectiveness of these systems, making them indispensable in the current digital era.

Another significant growth driver is the evolving regulatory landscape, which mandates strict compliance with fire safety standards in data centers. Regulatory bodies such as the National Fire Protection Association (NFPA), European Union directives, and local authorities have introduced stringent guidelines for fire detection, suppression, and alarm systems. These regulations are compelling data center operators to upgrade legacy fire protection infrastructure and invest in advanced, environmentally friendly suppression agents. The adoption of clean agents and inert gases, which are both effective and safe for electronic equipment, is witnessing a marked rise. Additionally, insurance providers are increasingly factoring in the presence and quality of fire suppression systems when determining premiums for data center facilities, further incentivizing investments in this market.

Technological advancements are also fueling market growth by enabling the development of more efficient and less invasive fire suppression solutions. Traditional water-based systems, while effective, often pose risks to sensitive IT equipment. In response, the market has seen a shift towards clean agent and inert gas systems that quickly extinguish fires without damaging hardware or leaving residues. The integration of fire detection with building management systems (BMS) and real-time analytics is providing data center operators with actionable insights, enabling proactive risk management. These innovations are not only enhancing the safety and resilience of data centers but also reducing operational downtime and maintenance costs, making advanced fire suppression solutions a strategic investment for facility operators.

In the realm of automation, the integration of Clean Agent Fire Suppression for Automation Cells is becoming increasingly crucial. Automation cells, which are often equipped with sensitive electronic components and robotics, require fire suppression systems that can effectively extinguish fires without causing damage to the intricate machinery. Clean agents, known for their non-conductive and residue-free properties, are ideal for such environments. These agents rapidly suppress fires, ensuring minimal downtime and protecting valuable assets. As automation continues to advance across industries, the demand for specialized fire suppression solutions tailored to automation cells is expected to rise, driving innovation and investment in this niche market.

Regionally, North America continues to dominate the Data Center Fire Suppression market, accounting for the largest share in 2024, driven by the presence of major technology firms, hyperscale data centers, and rigorous regulatory compliance. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid digitalization, substantial investments in new data center construction, and increasing awareness of fire safety standards

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Duplicate Records Issue A bug was found on 12/28/2021 that caused many records in the dataset to be duplicated. This issue was resolved on 01/06/2022.

Data Center Fire Suppression System Market Outlook

As per our latest research, the global Data Center Fire Suppression System market size reached USD 2.85 billion in 2024, reflecting robust investment in digital infrastructure safety. The market is expected to grow at a CAGR of 7.2% during the forecast period, reaching a projected value of USD 5.33 billion by 2033. The primary growth factor driving this expansion is the escalating demand for resilient, uninterrupted data center operations amid rising digitalization and stringent regulatory mandates for fire safety.

The rapid proliferation of hyperscale and edge data centers globally is a significant catalyst for the Data Center Fire Suppression System market. With businesses increasingly dependent on real-time data processing, the tolerance for downtime or data loss has plummeted. Consequently, organizations are prioritizing advanced fire suppression technologies that can swiftly detect and extinguish fires without damaging sensitive electronic equipment. The advent of high-density server environments and the integration of AI and IoT in data center management further amplify the need for sophisticated fire protection solutions capable of rapid response and minimal collateral impact. This market’s growth is also fueled by the increasing adoption of cloud computing, big data analytics, and the Internet of Things (IoT), all of which demand robust data infrastructure protected by state-of-the-art fire suppression systems.

Another key growth driver is the tightening of regulatory frameworks and industry standards regarding fire safety in mission-critical facilities. Governments and regulatory bodies worldwide are enforcing stricter compliance requirements, particularly for sectors such as BFSI, healthcare, and government, where data center downtime can have catastrophic consequences. These regulations necessitate the deployment of advanced fire suppression systems that not only ensure rapid response but also align with environmental and safety standards. For example, the shift towards eco-friendly, gas-based suppression agents is a direct result of environmental regulations banning ozone-depleting substances. This regulatory landscape compels data center operators to invest in compliant and future-proof fire suppression technologies, further propelling market growth.

The emergence of new construction and retrofit projects in developing economies is also shaping the market trajectory. Countries in Asia Pacific and the Middle East, in particular, are witnessing a surge in data center investments, driven by rapid digital transformation and favorable government initiatives. The expansion of colocation and cloud service providers in these regions is creating fresh demand for fire suppression systems tailored to diverse data center architectures. Additionally, technological advancements such as early warning smoke detection, smart control panels, and integrated fire management systems are enhancing the effectiveness and reliability of fire suppression solutions, making them indispensable for modern data centers.

Regionally, North America continues to dominate the Data Center Fire Suppression System market due to its concentration of hyperscale data centers and a mature regulatory environment. However, Asia Pacific is emerging as the fastest-growing region, fueled by aggressive digital infrastructure investments in countries like China, India, and Singapore. Europe follows closely, driven by stringent safety standards and the proliferation of green data centers. Latin America and the Middle East & Africa are also gaining traction, albeit at a slower pace, as enterprises in these regions increasingly recognize the importance of robust fire safety in supporting digital transformation. This regional diversification is expected to create a more balanced global market landscape over the forecast period.

Product Type Analysis

The Product Type segment of the Data Center Fire Suppression System market is categorized into gas-based, water-based, aerosol-based, and other suppression systems. Gas-based fire suppression systems, such as FM-200, Novec 1230, and inert gas solutions, are widely preferred in data centers due to their non-conductive, residue-free properties that protect sensitive electronic equipment from both fire and water damage. These systems are especially effective in high-value environments where downtime is costly, and equipment integrity is paramount. The adoption of gas-based systems is further pro

Discover the booming Rack Fire Suppression System market! This in-depth analysis reveals a $2.5B market (2025 est.) with an 8% CAGR, driven by cloud computing and stringent regulations. Explore key players, regional trends, and future growth projections for data center and server room fire protection.

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

The Data Center Gas Fire Extinguishing System market is booming, projected to reach $6.3 billion by 2033 with a 12% CAGR. Learn about market drivers, trends, key players (Nippon Dry-Chemical, Johnson Controls, etc.), and regional insights in this comprehensive analysis. Discover the growth potential in various segments like Carbon Dioxide and Inert Gas systems.

Isoprene is emitted by some plants and is the most abundant biogenic hydrocarbon entering the atmosphere. Multiple studies have elucidated protective roles of isoprene against several environmental stresses, including high temperature, excessive ozone, and herbivory attack. However, isoprene emission adversely affects atmospheric chemistry by contributing to ozone production and aerosol formation. Thus, understanding the regulation of isoprene emission in response to varying environmental conditions, for example elevated CO2, is critical to comprehend how plants will respond to climate change. Isoprene emission decreases with increasing CO2 concentration; however, the underlying mechanism of this response is currently unknown. We demonstrated that high-CO2-mediated suppression of isoprene emission is independent of photosynthesis and light intensity, but it is reduced with increasing temperature. Furthermore, we measured methylerythritol 4-phosphate pathway metabolites in poplar leaves harvested at ambient and high CO2 to identify why isoprene emission is reduced under high CO2. We found that hydroxymethylbutenyl diphosphate (HMBDP) was increased and dimethylallyl diphosphate (DMADP) decreased at high CO2. This implies that high CO2 impeded the conversion of HMBDP to DMADP, possibly through the inhibition of HMBDP reductase activity, resulting in reduced isoprene emission. We further demonstrated that although this phenomenon appears similar to ABA-dependent stomatal regulation, it is unrelated as abscisic acid treatment did not alter the effect of elevated CO2 on the suppression of isoprene emission. Thus, this study provides a comprehensive understanding of the regulation of the MEP pathway and isoprene emission in the face of increasing CO2.

This lookup table relates to the web service 2018 Census dwelling data by SA1. The web service contains data from the 2018 Census only, no data from previous censuses has been included.

The dwelling dataset is displayed by statistical area 1 geography and contains information on: • Occupied private dwelling type • Dwelling record type, for occupied dwellings • Occupied non-private dwelling type • Number of rooms • Number of bedrooms • Main types of heating used to heat dwellings (total responses) • Fuel types used to heat dwelling (total responses) • Dwelling occupancy status • Access to basic amenities • Dwelling dampness indicator • Dwelling mould indicator

The data uses fixed random rounding to protect confidentiality. Some counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of ‘-999’ indicate suppressed data, and values of ‘Null’ indicate data not collected.

For further information on this dataset please refer to the Statistical area 1 dataset for 2018 Census webpage - footnotes for dwelling, Excel workbooks, and CSV files are available to download. Data quality ratings for 2018 Census variables, summarising the quality rating and priority levels for 2018 Census variables, are available.

For information on the statistical area 1 geography please refer to the Statistical standard for geographic areas 2018.

The booming data center fire detection and suppression market is projected to reach $4 billion by 2033, driven by cloud adoption and stringent regulations. Learn about market trends, key players (Fike, ORR, Marioff), and growth opportunities in this comprehensive analysis.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students in pre-K and 15,480 in 3K for the school year 2020-2021. • Charter: Charter School • FCC: Family Child Care Center, Network Level (FCC enrollment data is reported at the Network level) • Missing – DBN: Missing Site ID, enrollment reported at DBN level • NYCEEC: NYC Early Education Centers (NYCEECs) are independent, community-based organizations that partner with the NYC Department of Education to provide free full-day high-quality pre-K • PKC: Pre-K Center • PS: Public School • SE: Special Education" In order to comply with regulations of the Family Educational Rights and Privacy Act (FERPA) on public reporting of education data, groups with 5 or students are suppressed with an “s”. In addition, groups with the next lowest number of students are suppressed when they could reveal, through addition or subtraction, the underlying numbers that have been redacted.

PLEASE NOTE: The complete data file can be downloaded from the "ATTACHMENT" section

Data Center Fire Suppression Market Outlook

As per our latest research, the global Data Center Fire Suppression market size reached USD 2.38 billion in 2024, reflecting robust adoption across mission-critical digital infrastructure. The market is anticipated to expand at a CAGR of 6.9% from 2025 to 2033, with the forecasted market size projected to reach USD 4.47 billion by 2033. This growth trajectory is primarily driven by the increasing construction of hyperscale and colocation data centers, rising regulatory compliance requirements, and the growing frequency of high-value data center fire incidents globally.

The primary growth factor for the Data Center Fire Suppression market is the exponential surge in digital transformation initiatives, cloud computing adoption, and the proliferation of data-intensive applications across all sectors. As organizations migrate workloads to cloud platforms and expand enterprise data centers, the criticality of uninterrupted operations and data integrity has never been higher. Any downtime or data loss due to fire incidents can result in catastrophic financial and reputational losses. This reality is compelling data center operators to invest in advanced fire detection and suppression systems that offer rapid response, minimal collateral damage, and compliance with evolving global safety standards. The integration of AI-powered fire detection, IoT-enabled monitoring, and eco-friendly suppression agents is further enhancing the effectiveness and appeal of modern fire suppression solutions.

Another significant driver is the tightening of regulatory frameworks and insurance mandates, especially in regions with high data center densities such as North America, Europe, and parts of Asia Pacific. Authorities are mandating rigorous fire safety protocols and certifications, including the use of non-toxic, residue-free suppression agents and advanced detection technologies. Insurance companies are also demanding robust fire protection measures as a prerequisite for insuring high-value data center assets. These regulatory and insurance-driven requirements are pushing both new and existing data center facilities to upgrade their fire suppression infrastructure, thereby fueling market growth. Furthermore, the trend towards green data centers and sustainability is shaping the adoption of water mist and clean agent-based systems, which offer both efficacy and environmental safety.

The market is also experiencing a surge in demand due to the increasing complexity and scale of modern data centers, particularly hyperscale and colocation facilities. As these facilities house thousands of servers and critical IT equipment, the potential impact of fire incidents is magnified. Operators are therefore prioritizing integrated fire safety architectures that combine detection, suppression, and alarm/control systems for comprehensive protection. The growing adoption of modular and edge data centers in emerging economies is opening new avenues for fire suppression vendors, as these installations require compact, scalable, and cost-effective solutions. The convergence of fire suppression with building management and security systems is further driving innovation and market expansion.

Regionally, North America leads the Data Center Fire Suppression market owing to its dense concentration of hyperscale data centers, stringent regulatory environment, and early adoption of advanced fire safety technologies. Europe follows closely, driven by GDPR compliance, green data center initiatives, and increasing investments in digital infrastructure. Asia Pacific is emerging as the fastest-growing region, supported by rapid data center construction in China, India, Singapore, and Australia, as well as rising awareness of fire safety standards. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as digital transformation accelerates across these regions.

Product Type Analysis

The Product Type segment of the Data Center Fire Suppression market encompasses fire detection systems, fire suppression systems, fire alarm and control panels, and other related technologies. Fire detection systems represent a foundational element, leveraging advanced sensors and AI-powered analytics to provide early warning of fire incidents. These systems are increasingly integrating with IoT platforms, allowing real-time monitoring and predictive maintenance,