Background: A chronic disease such as asthma is the result of a complex sequence of biological interactions involving multiple genes and pathways in response to a multitude of environmental exposures. However, methods to model jointly all factors are still evolving. Some of the current challenges include how to integrate knowledge from different data types and different disciplines, as well as how to utilize relevant external information such as gene annotation to identify novel disease genes and gene-environment inter-actions. Methods: Using a Bayesian hierarchical modeling framework, we developed two alternative methods for joint analysis of an epidemiologic study of a disease endpoint and an experimental study of intermediate phenotypes, while incorporating external information. Results: Our simulation studies demonstrated superior performance of the proposed hierarchical models compared to separate analysis with the standard single-level regression modeling approach. The combined analyses of the Southern California Children's Health Study and challenge study data suggest that these joint analytical methods detected more significant genetic main and gene-environment interaction effects than the conventional analysis. Conclusion: The proposed prior framework is very flexible and can be generalized for an integrative analysis of diverse sources of relevant biological data.

Data Clean Rooms for Joint Analytics Market Outlook

According to our latest research, the global Data Clean Rooms for Joint Analytics market size reached USD 1.67 billion in 2024, reflecting the rapid adoption of privacy-centric data collaboration solutions across industries. The market is projected to grow at a robust CAGR of 22.4% from 2025 to 2033, reaching a forecasted value of USD 12.17 billion by 2033. This impressive growth is driven by the increasing demand for secure data sharing, regulatory compliance, and the need for advanced analytics in a data-driven business environment.

One of the primary growth factors for the Data Clean Rooms for Joint Analytics market is the escalating concern over data privacy and security. As regulatory frameworks such as GDPR, CCPA, and other privacy legislations become more stringent, organizations are compelled to adopt solutions that enable data collaboration without compromising individual privacy. Data clean rooms offer a controlled environment where multiple entities can analyze joint datasets while ensuring that sensitive information remains confidential and compliant with legal requirements. This capability is particularly crucial in industries like healthcare, finance, and advertising, where the use of personal data is both valuable and highly regulated.

Another significant driver is the proliferation of digital advertising and marketing initiatives that rely on third-party data. The phasing out of third-party cookies and heightened scrutiny over cross-platform data sharing have forced advertisers and publishers to seek innovative alternatives for audience insights and campaign measurement. Data clean rooms for joint analytics facilitate secure data matching and attribution across partners, enabling brands to measure performance and optimize strategies without exposing raw user data. This not only enhances marketing effectiveness but also builds consumer trust, which is increasingly vital in today’s digital ecosystem.

The market is also witnessing robust growth due to advancements in cloud computing and analytics technologies. The integration of data clean rooms with AI-powered analytics and scalable cloud infrastructure has made it easier for organizations of all sizes to leverage these solutions. Enterprises are now able to perform complex joint analytics on large datasets in real time, unlocking deeper insights while maintaining data governance. The emergence of managed service providers and specialized software platforms is further lowering the barriers to adoption, making data clean rooms accessible to a broader range of sectors including retail, media, and life sciences.

From a regional perspective, North America currently dominates the Data Clean Rooms for Joint Analytics market, accounting for the largest share in 2024. This leadership is attributed to the region’s early adoption of privacy technologies, a mature digital advertising ecosystem, and proactive regulatory compliance. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, expanding e-commerce markets, and increasing awareness of data privacy. Europe follows closely, with strong regulatory mandates driving adoption across financial services and healthcare. Latin America and the Middle East & Africa are also showing steady growth, supported by investments in digital infrastructure and rising demand for secure data collaboration tools.

Component Analysis

The Data Clean Rooms for Joint Analytics market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. Software solutions form the backbone of data clean rooms, providing the technological framework necessary for secure multi-party computation, data encryption, and privacy-preserving analytics. These platforms are increasingly incorporating advanced features such as automated data onboarding, customizable access controls, and built-in compliance checks to address the evolving needs of enter

This data release contains data in support of "The Joint Effect of Changes in Impervious Cover and Climate on Trends in Floods: A Comparison of Panel and Single-Station Quantile Regression Approaches" (Over and others, 2025). It contains input and output data used to analyze the effect of impervious cover and climate changes on trends in floods using three regression approaches. The input consists of two files: "finalStationList.csv," which contains streamgage information for the 127 streamgages used in this study, and "regressionInput.csv," which contains data used as input into regressions for each streamgage. The output consists of "lm_trends.csv," "byStation-log10_ann_max_MWBM_Q.csv," and "FixedEffects-log10_ann_max_MWBM_Q.csv." "lm_trends.csv" contains trend analysis results by streamgage. "byStation-log10_ann_max_MWBM_Q.csv" contains the regression results for annual maximum streamflow from MWBM and impervious fraction by streamgage. "FixedEffects-log10_ann_max_MWBM_Q.csv" contains fixed effects for annual maximum streamflow from MWBM and impervious fraction by streamgage. Also included is "modelArchive.zip", which contains the scripts used to create the data provided in this data release and in Over and others, 2025. It contains the input data necessary to run the scripts and readMe files with directions for running the scripts locally.

Data Clean Rooms for Joint Analytics Market Outlook

According to our latest research, the Global Data Clean Rooms for Joint Analytics market size was valued at $1.3 billion in 2024 and is projected to reach $7.6 billion by 2033, expanding at a robust CAGR of 21.7% during the forecast period of 2025–2033. The primary driver fueling this exponential growth is the escalating demand for privacy-compliant data collaboration across industries, as organizations seek to extract actionable insights from combined datasets without compromising sensitive information. The proliferation of stringent data privacy regulations, coupled with the increasing complexity of digital ecosystems, has made secure, privacy-preserving analytics a business imperative. As enterprises across sectors such as BFSI, healthcare, and retail accelerate their digital transformation journeys, the adoption of data clean rooms for joint analytics is becoming a cornerstone for secure data partnerships and advanced analytics initiatives worldwide.

Regional Outlook

North America currently dominates the global Data Clean Rooms for Joint Analytics market, accounting for the largest share in terms of both revenue and deployment. This leadership is primarily attributed to the region's mature digital infrastructure, high adoption of advanced analytics, and the presence of major technology providers. The United States, in particular, has been at the forefront, driven by aggressive investments in data governance frameworks and a culture of innovation in the tech and BFSI sectors. Furthermore, the region benefits from a robust regulatory environment, including compliance with CCPA and HIPAA, which has accelerated the adoption of data clean rooms as a privacy-centric solution. As a result, North America’s market value is expected to remain significant, with established enterprises and large-scale joint analytics projects leading the way.

In terms of growth trajectory, Asia Pacific is emerging as the fastest-growing region, expected to register a CAGR exceeding 25% during the forecast period. This rapid expansion is underpinned by the surging digitalization of businesses, rising investments in cloud infrastructure, and increasing awareness about data privacy. Countries like China, India, and Singapore are witnessing a surge in cross-industry collaborations, particularly in the financial services and retail sectors, which is propelling demand for secure data sharing and analytics platforms. Additionally, government initiatives aimed at fostering digital economies and the proliferation of regional data privacy laws are further catalyzing market growth. The influx of venture capital and strategic partnerships between local and international players is also contributing to the dynamic evolution of the data clean rooms ecosystem in Asia Pacific.

Meanwhile, emerging economies in Latin America and Middle East & Africa are gradually embracing data clean rooms for joint analytics, albeit at a slower pace due to infrastructural challenges and varied regulatory landscapes. While organizations in these regions recognize the potential of joint analytics for unlocking new business opportunities, adoption is often hampered by limited access to advanced technologies and a shortage of skilled professionals. Nevertheless, localized demand is growing in sectors such as healthcare and retail, where secure data collaboration is becoming increasingly vital. Policymakers are beginning to introduce frameworks to support privacy-preserving analytics, which could pave the way for accelerated adoption in the coming years, especially as global technology providers expand their footprint in these markets.

Report Scope

We describe a regularized regression model for the selection of gene-environment (G × E) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (G × E) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package.

The Southeast Michigan Operational Data Environment (SEMI-ODE) is a real-time data acquisition and distribution software system that processes vehicle and infrastructure data collected from sources such as the Southeast Michigan testbed Situational Data Clearinghouse (SDC) and the Situational Data Warehouse (SDW), along with other non-connected vehicle sources of data. The ODE offers four core functions to supply tailored and custom-requested data from the SEMI Testbed to subscribing client software applications. The core functions are: 1) Valuation (V), 2) Integration (I), 3) Sanitization (S) (also called de-identification), and 4) Aggregation (A). These four VISA functions are critical to the field test as they enable the subscribing emulated applications to receive data tailored to support their operation. These functions also serve to increase the general usability of the data being generated in the SEMI Test Bed.

This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Data Clean Rooms Market Outlook

According to our latest research, the global Data Clean Rooms market size reached USD 1.62 billion in 2024, reflecting robust adoption across multiple sectors. The market is poised for significant expansion, forecasted to reach USD 8.73 billion by 2033, growing at a remarkable CAGR of 20.6% from 2025 to 2033. This impressive growth trajectory is primarily driven by the increasing demand for privacy-compliant data collaboration, stringent regulatory requirements, and the proliferation of data-driven marketing strategies worldwide.

The surge in demand for Data Clean Rooms is closely linked to the evolving landscape of data privacy regulations such as GDPR, CCPA, and other global standards. As organizations strive to harness the power of consumer data while remaining compliant, data clean rooms have emerged as a vital solution. These platforms enable secure, privacy-centric data collaboration between multiple parties without exposing raw, personally identifiable information. The rising adoption of advanced analytics and AI-driven marketing, especially in sectors like advertising, healthcare, and financial services, is further catalyzing the need for sophisticated data clean room solutions. The rapid shift to digital platforms and the phasing out of third-party cookies are also compelling enterprises to seek new ways to unlock value from their data assets without compromising consumer trust or regulatory compliance.

Another crucial growth driver for the Data Clean Rooms market is the increasing complexity and volume of data being generated across industries. As organizations collect vast amounts of first-party and second-party data, the need for secure environments to analyze and share this data with partners is becoming paramount. Data clean rooms offer a secure infrastructure that supports advanced analytics, machine learning, and audience segmentation while maintaining strict data governance. The proliferation of cloud-based data ecosystems and the integration of data clean rooms with existing martech and adtech stacks are also accelerating market adoption, enabling organizations to derive actionable insights without breaching privacy norms.

The growing emphasis on collaborative data partnerships is further fueling the expansion of the Data Clean Rooms market. Enterprises are increasingly recognizing the value of combining their data with that of trusted partners to enhance customer intelligence, optimize campaigns, and drive innovation. Data clean rooms facilitate these partnerships by providing a neutral, privacy-preserving environment for joint data analysis. This trend is particularly pronounced in sectors like retail, BFSI, and media, where the ability to share insights without exposing sensitive information is critical to maintaining competitive advantage and customer trust. The ongoing advancements in encryption, federated learning, and privacy-enhancing technologies are also making data clean rooms more scalable, secure, and accessible to organizations of all sizes.

Regionally, North America continues to dominate the Data Clean Rooms market due to its mature digital ecosystem, stringent privacy regulations, and early adoption of advanced data management solutions. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding e-commerce, and increasing regulatory focus on data privacy. Europe also holds a significant share, propelled by the strict enforcement of GDPR and the widespread adoption of privacy-centric technologies. Latin America and the Middle East & Africa are gradually catching up, with growing investments in digital infrastructure and rising awareness of data privacy risks. The global outlook remains highly positive, with all regions expected to witness substantial growth through 2033.

Component Analysis

The Data Clean Rooms market is segmented by component into Software and Services, each playing a pivotal role in the overall ecosystem. The s

This data publication contains the collection of data assembled to address the ‘Fuels treatment effectiveness across landscapes’ question in Task 1 of the Joint Fire Science Program Project Announcement FA-FON 14-5. The data specifically address the 'Programmatic scale' portion of the question (https://www.firescience.gov/AFPs/14-1-01/14-1-01_FON_Announcement.pdf). The data consist of a collection of 240 meter rasters with an associated raster attribute table. Four different National Parks were used as study sites: Big Cypress National Preserve (BICY) in Florida, Glacier National Park (GLAC) in Montana, Shenandoah National Park (SHEN) in Virginia, and Sequoia and Kings Canyon National Parks (SEKI) in California. For each study site seven test scenarios were generated: a baseline analysis and the post-treatment results after six different fuel treatment budget alternatives have been applied to the landscape. These six fuel treatment budget alternatives included the current fuel treatment budget, a maximum value of 170% of the preparedness budget and increments of 20%, 40%, 60% and 80% of the maximum value. The current 2014 Preparedness budget and Fuel Treatment budget was provided by the National Park Service for each study site. The data were modeled using a spatial wildfire budget system known as STARFire. The inputs into the system and the associated outputs are contained within fields in the attribute tables. Included for each study site are individual rasters representing the fire affected resources for that study site. In addition, supplemental files such as the ArcPy python script for calculating the time since last fire and a file summarizing the wind and fuel moisture parameters used in the FlamMap runs for generating fire behavior inputs are also included.These data were collected to address the requirements of the 'Programmatic scale' question of Task 1 of the Joint Fire Science Program Project Announcement FA-FON 14-5.

The Deepwater Horizon Joint Analysis Group (JAG) for Surface and Sub-Surface Oceanography, Oil and Dispersant Data was a working group with membership from federal agencies, BP, and academia that was formed to analyze sub-surface oceanographic data being derived from the on-going coordinated sampling efforts by private, federal and academic scientists as part of the spill response. The goal of the JAG was to provide comprehensive characterization of the Gulf of Mexico sub-surface conditions as well as the fate and transport of dispersed petroleum as a result of the Deepwater Horizon oil spill. JAG findings were published in a series of reports for the Unified Area Command as well as the public. This accession contains Total Petroleum Hydrocarbon and Volatile Organic Analysis data from laboratory analysis, as well as in situ Chromophoric Dissolved Organic Matter and dissolved oxygen data. This dataset was compiled as part of the final JAG summary report, and referred to in Appendix 3 of that report, NOAA Technical Report NOS OR&R 27 (2012).

EO Data Clean Room Collaboration Tools Market Outlook

According to our latest research, the EO Data Clean Room Collaboration Tools market size reached USD 1.42 billion globally in 2024, and it is anticipated to grow at a robust CAGR of 17.8% during the forecast period. By 2033, the market is forecasted to attain a value of USD 6.18 billion. The primary growth driver for this market is the increasing demand for privacy-compliant data collaboration solutions across various industries, propelled by stricter data privacy regulations and the need for secure, scalable data sharing environments.

The EO Data Clean Room Collaboration Tools market is experiencing significant momentum due to the rising emphasis on data privacy and regulatory compliance. Organizations are increasingly seeking solutions that allow them to collaborate on sensitive data without compromising privacy or breaching regulations such as GDPR, CCPA, and HIPAA. The proliferation of digital data and the growing use of third-party data for analytics, marketing, and operational improvements have made data clean rooms indispensable. These tools enable organizations to extract insights from combined datasets while maintaining strict access controls, encryption, and anonymization, helping them mitigate the risks associated with data breaches and non-compliance. As a result, enterprises across industries are ramping up investments in EO data clean room collaboration technologies to future-proof their data strategies.

Another key growth factor is the rapid digital transformation across sectors such as healthcare, financial services, retail, and government. The integration of advanced analytics, artificial intelligence, and machine learning into business operations has increased the need for collaborative data environments that are both secure and scalable. EO Data Clean Room Collaboration Tools are uniquely positioned to address these needs by offering robust capabilities for data integration, analytics, and privacy management. The surge in cloud adoption, remote work, and cross-border data collaborations has further amplified the demand for these tools, as organizations strive to enable seamless data sharing while adhering to local and international privacy laws. This trend is expected to accelerate as more organizations recognize the strategic value of secure data collaboration in driving innovation and competitive advantage.

Furthermore, the market is benefiting from technological advancements and the emergence of new business models that rely heavily on data-driven decision-making. The ability to securely collaborate on data with external partners, suppliers, and customers is becoming a critical differentiator for organizations aiming to enhance customer experiences, optimize supply chains, and drive targeted marketing campaigns. EO Data Clean Room Collaboration Tools facilitate these collaborations by providing a secure environment for joint data analysis, reducing the risk of data leakage and ensuring that sensitive information remains protected. The growing awareness of the potential financial and reputational damage caused by data breaches is prompting organizations to adopt these tools proactively, fueling market growth.

Regionally, North America continues to dominate the EO Data Clean Room Collaboration Tools market, driven by the presence of leading technology providers, early adoption of privacy regulations, and a strong focus on data-driven innovation. However, Asia Pacific is emerging as a high-growth region, supported by rapid digitalization, increasing regulatory scrutiny, and the expansion of cloud infrastructure. Europe also holds a significant market share, owing to stringent data privacy laws and a mature technology ecosystem. Latin America and the Middle East & Africa are witnessing steady growth, albeit from a smaller base, as organizations in these regions begin to prioritize data privacy and secure collaboration in their digital transformation journeys.

Component Analysis

The EO Data Clean Room Collaboration Tools market is segmented by component into software and services, each playing a pivotal role in facilitating secure, privacy-compliant data collaboration. The software segment comprises platforms and solutions that enable organizations to manage, analyze, and share data securely within a controlled environment. These platforms typically offer features such as data encryption, a

Privacy-Enhancing Computation Clean Rooms Market Outlook

According to our latest research, the global Privacy-Enhancing Computation Clean Rooms market size reached USD 1.72 billion in 2024, reflecting the rapid adoption of privacy-centric data collaboration solutions across industries. The market is expanding at a robust CAGR of 24.1% and is projected to achieve a value of USD 11.18 billion by 2033. The primary growth driver is the escalating demand for secure, compliant data sharing and analytics, particularly in light of evolving data privacy regulations and the increasing need for collaborative data environments that do not compromise user confidentiality.

The growth trajectory of the Privacy-Enhancing Computation Clean Rooms market is propelled by the tightening regulatory landscape surrounding data privacy and security. With the global enforcement of frameworks such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other similar regulations, organizations are compelled to adopt advanced technologies that ensure secure data processing and sharing. Privacy-enhancing computation clean rooms provide a controlled environment where multiple parties can analyze and collaborate on sensitive data without exposing raw information. This capability is particularly vital for sectors handling sensitive personal or financial data, such as healthcare, finance, and advertising, where compliance and trust are paramount. The market is further buoyed by the increasing frequency of data breaches and cyber threats, which have elevated the importance of privacy-preserving technologies in corporate strategies.

Another significant factor driving the market is the surge in data-driven business models and the corresponding need for collaborative analytics. As organizations increasingly rely on data partnerships to fuel innovation, improve targeting, and enhance customer experiences, the challenge of balancing data utility with privacy becomes more acute. Privacy-enhancing computation clean rooms address this challenge by enabling joint analysis and machine learning on encrypted or anonymized datasets, ensuring that proprietary or personal information remains secure throughout the process. This has led to widespread adoption in sectors such as advertising and marketing, where clean rooms allow brands and publishers to measure campaign effectiveness and optimize targeting without direct access to each other's raw data. The scalability and flexibility offered by these solutions, especially with cloud-based deployment, further contribute to their growing popularity.

Technological advancements are also playing a pivotal role in shaping the growth of the Privacy-Enhancing Computation Clean Rooms market. Innovations in cryptographic techniques, such as secure multi-party computation (SMPC), homomorphic encryption, and federated learning, have significantly enhanced the security and efficiency of clean room environments. These advancements make it feasible to perform complex computations on encrypted data, broadening the range of use cases and reducing the risk of data leakage. Additionally, the integration of artificial intelligence and machine learning into privacy-enhancing computation frameworks is enabling more sophisticated analytics while maintaining compliance with privacy standards. The growing ecosystem of vendors and solution providers, coupled with increasing investment in research and development, is accelerating the evolution of this market, making privacy-enhancing computation clean rooms an indispensable tool for modern enterprises.

From a regional perspective, North America currently dominates the Privacy-Enhancing Computation Clean Rooms market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technology giants, stringent regulatory requirements, and a mature digital infrastructure contribute to North America's leadership. Europe is also a significant market, driven by strict privacy laws and a proactive approach to data protection. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, increasing awareness of data privacy, and expanding adoption of cloud-based solutions. Latin America and the Middle East & Africa are gradually catching up, with growing investments in digital infrastructure and regulatory frameworks promoting privacy-enhancing technologies.

Secure Multi-Party Data-Clean Room Market Outlook

According to our latest research, the global Secure Multi-Party Data-Clean Room market size reached USD 1.92 billion in 2024, reflecting robust momentum driven by increasing data privacy concerns and regulatory demands. The industry is experiencing a strong growth trajectory, with a projected compound annual growth rate (CAGR) of 23.4% from 2025 to 2033. By the end of this period, the market is forecasted to achieve a valuation of USD 14.07 billion. The rapid expansion is primarily fueled by the surge in collaborative data analytics, the necessity for secure data sharing among enterprises, and the rise in privacy-centric digital ecosystems.

A key growth factor for the Secure Multi-Party Data-Clean Room market is the intensifying regulatory landscape surrounding data privacy and protection. With regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks emerging in other regions, organizations are compelled to adopt technologies that ensure compliant data collaboration. Secure Multi-Party Computation (SMPC) and data-clean room environments allow multiple parties to analyze and process data collectively without exposing raw datasets, thus enabling organizations to extract value from data while maintaining compliance. This regulatory push is particularly significant for industries such as healthcare, financial services, and advertising, where sensitive customer data is prevalent and the risks of data breaches are high.

Another significant driver is the growing need for advanced analytics and data monetization strategies. As enterprises increasingly leverage big data and machine learning, the ability to collaborate securely with partners, suppliers, and stakeholders becomes a competitive differentiator. Secure multi-party data-clean rooms facilitate joint analysis and insights generation without compromising proprietary or personal information. This capability is especially valuable for sectors like advertising and marketing, where brands and publishers can combine datasets for richer audience insights, and for financial services, where institutions can detect fraud or assess creditworthiness without sharing confidential client information. The result is a surge in demand for robust, scalable, and interoperable clean room solutions that support complex analytics and business intelligence use cases.

Technological advancements and the proliferation of cloud-based infrastructure are further accelerating market growth. The integration of privacy-enhancing technologies such as homomorphic encryption, federated learning, and blockchain with clean room platforms is enhancing security, scalability, and ease of use. Cloud deployment models, in particular, are democratizing access to secure data collaboration tools for small and medium enterprises (SMEs) as well as large organizations, reducing the need for heavy upfront investments in hardware and on-premises infrastructure. This technological evolution is fostering innovation and expanding the addressable market for secure multi-party data-clean room solutions across a diverse range of verticals.

From a regional outlook, North America continues to dominate the Secure Multi-Party Data-Clean Room market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by a mature digital ecosystem, high adoption of privacy-preserving technologies, and the presence of leading vendors and early adopters in industries such as BFSI, healthcare, and digital advertising. Europe follows closely, buoyed by stringent privacy regulations and a rapidly evolving data economy. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by digital transformation initiatives, expanding cloud adoption, and the increasing importance of data-driven decision-making in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as awareness and investment in privacy-preserving data analytics gain traction.

The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include:

Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector.

Methods The dataset used in this report was created using the following public data sources:

2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping

American Community Survey: https://www.census.gov/programs-surveys/acs

This data publication contains the location data for the plots in which pre- and/or post-fire fuels, and fire behavior data were collected by the Fire Behavior Assessment Team (FBAT) on a subset of wildland fires in the United States from 2003-2017. Logistics (proximity to instrumentation/crew members), funding, fire activity, and monitoring questions influenced the geographic regions and fires where sampling was attempted, for instance, fuel treatments recorded in the Forest Service Activity Tracking System (FACTS), as well as tree mortality areas, were each targets for FBAT sampling in the past. FBAT is an interagency group of primarily Forest Service employees with both monitoring and fireline qualifications which collects pre- and post-fire fuels and tree data along with fire behavior measurements on wildland fires. This package contains a shapefile for each fire that includes the point locations of each plot. The fuels and fire behavior data are archived separately.The purpose of these data is to provide the location data for spatial applications of the FBAT fuels/fire behavior/fire severity datasets.For more information about FBAT data see: https://www.fs.fed.us/adaptivemanagement/projects_main_fbat.php.

Data were originally published on 10/03/2018. Minor metadata updates were made on 06/13/2019. On 04/16/2020 minor updates were made to the metadata and these data were updated to include fires through 2019, which included: Alder (2019) and Walker (2019). On 12/03/2020 this data publication was updated to include a supplemental file that provides a diagram of the FBAT plot layout and a brief list of key method changes throughout the years FBAT has taken data to present (2020).

The data elements contained in this data environment were collected during the Prototype Operational Data Environment (P-ODE) formal data collection period. The Prototype Operational Data Environment (P-ODE) is a system that receives data from multiple sources in real-time, is capable of performing validation, integration, and sanitization checks, transforms the data into a consistent format, and makes the data available to applications as well as stores the data in ITS JPO data system. This data environment contains speed, volume, occupancy, travel time, and incident data collected along I-66 in Northern Virginia between May 2016 and August 2016. The ASN.1 data set contains data records in their original binary form, while Detector and Incident data sets each contain records that have been converted to text format.

This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

The Robot Joint Reducer market is experiencing a significant transformation, driven by the growing adoption of automation across various industries. These essential components play a crucial role in robotic systems, facilitating precise motion control and enhancing performance in applications such as assembly, logis

The Strip Seal Joint market plays a pivotal role in the construction and infrastructure sectors, functioning as a critical component in ensuring the integrity and durability of joints in various structures, such as bridges, highways, and industrial buildings. Strip seal joints are designed to accommodate movement wh

Charging Data Clean Rooms for Partnerships Market Outlook

According to our latest research, the global Charging Data Clean Rooms for Partnerships market size reached USD 1.87 billion in 2024, demonstrating robust adoption across industries. The market is forecasted to grow at a CAGR of 23.4% from 2025 to 2033, reaching a projected value of USD 14.06 billion by 2033. This remarkable growth is primarily driven by increasing regulatory pressures on data privacy, the proliferation of collaborative data-driven partnerships, and the rising need for secure environments to enable compliant data sharing between organizations.

One of the primary growth factors for the Charging Data Clean Rooms for Partnerships market is the intensifying global focus on data privacy and compliance. With regulations such as GDPR, CCPA, and emerging data protection laws worldwide, enterprises are under mounting pressure to ensure that data collaboration does not compromise user privacy. Data clean rooms provide a secure, privacy-focused environment where multiple parties can analyze shared datasets without exposing raw data, thus facilitating compliant partnerships. This capability is crucial for industries like advertising, financial services, and healthcare, where sensitive customer data is routinely handled. As organizations increasingly seek to extract value from data partnerships while minimizing privacy risks, the adoption of charging data clean rooms is expected to accelerate significantly.

Another significant driver for market expansion is the surge in demand for advanced analytics and personalized customer experiences. Enterprises are leveraging data clean rooms to combine first-party and third-party data, enabling richer insights and more targeted strategies without breaching privacy boundaries. This is particularly prevalent in sectors such as retail, e-commerce, and media, where understanding customer behavior across platforms is key to competitive advantage. The integration of artificial intelligence and machine learning within clean rooms is further enhancing the analytical capabilities, allowing organizations to derive actionable intelligence while maintaining strict data governance. The scalability and flexibility offered by cloud-based deployment models are also contributing to the widespread adoption of data clean rooms, as they allow seamless integration with existing data infrastructures.

The evolving partnership ecosystem is another critical factor propelling the charging data clean rooms market forward. As businesses increasingly collaborate with external partners, advertisers, and technology vendors, the need for a neutral and secure environment to facilitate data exchange becomes paramount. Data clean rooms act as trusted intermediaries, enabling joint data analysis without compromising proprietary information. This is fostering innovation in areas such as collaborative marketing campaigns, cross-industry research, and co-developed financial products. The ability to monetize data assets securely and compliantly is also emerging as a compelling use case, further fueling market growth. As the partnership landscape becomes more complex and data-driven, the strategic importance of data clean rooms is set to rise.

From a regional perspective, North America currently dominates the Charging Data Clean Rooms for Partnerships market, accounting for the largest share in 2024 due to the presence of leading technology providers, stringent data regulations, and a mature digital ecosystem. Europe follows closely, driven by robust regulatory frameworks and growing demand for privacy-centric solutions. The Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, fueled by rapid digitalization, expanding e-commerce sectors, and increasing awareness of data privacy. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as enterprises in these regions begin to recognize the value of secure data collaboration in driving business innovation and compliance.

Component Analysis

The Component segment of the Charging Data Clean Rooms for Partnerships market is bifurcated into Software and Services. The software segment encompasses the core platforms and tools that enable the creation, management, and operation of data clean rooms. These solutions are designed to facilitate secure data collaboration, enfo

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat condition change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.

VH/VV files have been generated for Sentinel-1A ascending orbit granules covering England and Scotland for the period from 2015 to 2025.

Contains modified Copernicus Sentinel data 2015-2025

Joint Targeting Integrated Framework Market Outlook

According to our latest research, the global Joint Targeting Integrated Framework market size reached $2.37 billion in 2024, with robust adoption across defense and security sectors. The market is expected to expand at a CAGR of 9.4% during the forecast period, reaching a projected value of $5.44 billion by 2033. This growth is primarily fueled by the increasing complexity of modern warfare, the pressing need for real-time data integration, and the heightened focus on collaborative operations among allied forces. As per our latest analysis, the demand for advanced targeting solutions is surging as agencies strive to enhance operational efficiency and decision-making accuracy in rapidly evolving threat environments.

One of the primary growth drivers for the Joint Targeting Integrated Framework market is the rising frequency and sophistication of asymmetric threats, which necessitate seamless coordination and data sharing among military, intelligence, and security agencies. The proliferation of advanced technologies such as artificial intelligence, machine learning, and big data analytics has transformed the landscape of joint targeting operations, enabling faster and more accurate identification, tracking, and neutralization of adversarial targets. Additionally, the integration of multi-domain operations—spanning land, air, sea, space, and cyber—requires interoperable frameworks that can synthesize information from diverse sources, thereby driving the demand for comprehensive joint targeting solutions.

Another significant factor propelling market growth is the increasing emphasis on coalition and allied operations, particularly among NATO member countries and their strategic partners. The need for interoperability and standardized protocols has led to the development and deployment of integrated frameworks that enable secure, real-time exchange of targeting data across different platforms and command structures. This trend is further supported by substantial government investments in defense modernization programs, which prioritize the adoption of cutting-edge targeting technologies to maintain tactical superiority and operational readiness in contested environments. The growing collaboration between defense contractors, technology providers, and government agencies is also fostering innovation and accelerating the deployment of next-generation joint targeting systems.

The market is also benefiting from the expanding application of joint targeting frameworks in non-traditional security domains such as homeland security, law enforcement, and private security operations. As urbanization and geopolitical instability continue to rise, there is an increasing need for integrated solutions that can support rapid response, situational awareness, and coordinated action across various agencies. This broadening of end-user segments is creating new avenues for market growth, particularly in regions facing persistent security challenges and evolving threat landscapes. Furthermore, advancements in cloud computing and secure communication networks are enabling more flexible and scalable deployment models, further enhancing the accessibility and effectiveness of joint targeting solutions.

Regionally, North America remains the dominant market, accounting for the largest share due to its substantial defense budget, advanced technological infrastructure, and ongoing modernization initiatives. Europe is also witnessing significant growth, driven by increased defense spending and collaborative security efforts among EU member states. The Asia Pacific region is emerging as a key growth area, fueled by rising geopolitical tensions, military modernization programs, and the adoption of advanced targeting systems by major regional powers. Meanwhile, Latin America and the Middle East & Africa are gradually embracing joint targeting frameworks to address evolving security challenges, although market penetration remains comparatively lower due to budgetary constraints and infrastructural limitations.