Stations and a table of download links for time-series data, from DWR's continuous environmental monitoring database. For more information, see DWR's Water Data Library, continuous data section: https://wdl.water.ca.gov/ContinuousData.aspx, where this data is also available.

Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen–Shannon divergence of two or more data sets.

Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

This is an early version of the continuous endpoint. It is feature-complete and provides access to the full continuous data record, and is being made available as we continue to work on performance improvements. Continuous data are collected via automated sensors installed at a monitoring location. They are collected at a high frequency and often at a fixed 15-minute interval. Depending on the specific monitoring location, the data may be transmitted automatically via telemetry and be available on WDFN within minutes of collection, while other times the delivery of data may be delayed if the monitoring location does not have the capacity to automatically transmit data. Continuous data are described by parameter name and parameter code (pcode). These data might also be referred to as "instantaneous values" or "IV".

Continuous Data Protection Market Outlook

As per our latest research, the global Continuous Data Protection (CDP) market size reached USD 4.8 billion in 2024, driven by the increasing need for robust data security and real-time backup solutions across various industries. The market is exhibiting a strong compound annual growth rate (CAGR) of 12.1% from 2025 to 2033. By the end of 2033, the Continuous Data Protection market is forecasted to attain a value of approximately USD 13.5 billion. The primary growth factor is the rising frequency of ransomware attacks and data breaches, compelling organizations to invest in advanced data protection and disaster recovery solutions.

One of the major growth drivers for the Continuous Data Protection market is the exponential increase in data generation and digital transformation initiatives worldwide. Enterprises are generating massive volumes of data from a variety of sources, including IoT devices, cloud applications, and mobile endpoints. This surge in data, coupled with the critical need to ensure business continuity, has heightened the demand for CDP solutions. Unlike traditional backup systems, CDP offers real-time or near-real-time backup, minimizing data loss and enabling rapid recovery in the event of system failures or cyber incidents. As organizations become more data-centric, the adoption of continuous data protection technologies is expected to accelerate, particularly among sectors that handle sensitive or mission-critical information.

Another significant factor fueling the growth of the Continuous Data Protection market is the evolving regulatory landscape. Governments and regulatory bodies across the globe are implementing stringent data protection and privacy regulations, such as GDPR in Europe and CCPA in California. These regulations require organizations to maintain robust data protection strategies, including real-time backup and rapid recovery capabilities. As non-compliance can lead to severe financial penalties and reputational damage, enterprises are increasingly turning to CDP solutions to ensure adherence to these mandates. The ability of CDP to provide point-in-time recovery and granular restoration of data aligns perfectly with regulatory requirements, further boosting market adoption.

Technological advancements and integration with cloud platforms are also shaping the trajectory of the Continuous Data Protection market. Modern CDP solutions are leveraging artificial intelligence, machine learning, and automation to enhance data backup, anomaly detection, and threat response. The proliferation of hybrid and multi-cloud environments has necessitated the development of CDP solutions that can seamlessly protect data across on-premises and cloud infrastructures. This trend is particularly prominent among large enterprises and organizations with distributed IT environments. Furthermore, the growing awareness of the financial and operational impacts of data loss is prompting even small and medium-sized enterprises to invest in continuous data protection, thus expanding the market’s addressable base.

From a regional perspective, North America continues to dominate the Continuous Data Protection market due to its advanced IT infrastructure, high adoption of cloud computing, and heightened focus on cybersecurity. However, the Asia Pacific region is witnessing the fastest growth, attributed to rapid digitalization, increasing investments in IT security, and rising awareness about data protection among enterprises. Europe also holds a significant market share, driven by strict data privacy regulations and a mature enterprise landscape. The Middle East & Africa and Latin America are emerging markets, where growing digital transformation and regulatory developments are expected to create new opportunities for CDP vendors in the coming years.

Component Analysis

The Continuous Data Protection market is segmented by component into software, hardware, and services. The software segment holds the largest share, accounting for more than 55% of the global m

The size of the Continuous Data Protection and Recovery Software market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

Provides an aggregate of data for the Office of the Actuary and the Office of Research, Evaluation and Statistics.

Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

DWR continuous groundwater level measurements contains continuous time-series data from automated recorders at sites operated by the Department of Water Resources. Readings are taken at 15-minute to one-hour intervals. Some of the readings are relayed to the California Data Exchange Center. However, most of the monitoring sites are visited once every month or two, when readings are off-loaded from data recorders, then finalized and published. Wells monitored for this dataset are located within Butte, Colusa, Glenn, Mendocino, Modoc, Sacramento, San Joaquin, Shasta, Siskiyou, Solano, Sutter, Tehama, Yolo, and Yuba Counties.

Water-level measurements are the principal source of information about changes in groundwater storage and movement in a basin, and how these are affected by various forms of recharge (e.g., precipitation, seepage from streams, irrigation return) and discharge (e.g., seepage to streams, groundwater pumping).

Water-level monitoring involves "continuous" or periodic measurements. Continuous monitoring makes use of automatic water-level sensing and recording instruments that are programmed to make scheduled measurements in wells. This provides a high-resolution record of water-level fluctuations. Resulting hydrographs can accurately identify the effects of various stresses on the aquifer system and provide measurements of maximum and minimum water levels in aquifers. Continuous monitoring may be the best technique to use for monitoring fluctuations in groundwater levels during droughts and other critical periods when hydraulic stresses may change at relatively rapid rates, or when real-time data are needed for making water management decisions see usgs reference.

These data represent a set of capture histories of rainbow trout (Oncorhynchus mykiss or RBT) captured in the Colorado River (CR) and(or) detected on the multiplexer array in the Little Colorado River (LCR). Capture trips to the Colorado River occurred in April 2012, July 2012, September 2012, January 2013, April 2013, July 2013, September 2013, January 2014, April 2014, July 2014, and September 2014. Rainbow trout were detected on the PIT array system (MUX) from October 2013 - April 2014.

Near Real Time CMAQ Simulations compared to AQS Observations. This dataset is associated with the following publication: Eder, B., R. Gilliam, G. Pouliot, R. Mathur, and J. Pleim. Continuous, Near Real-Time Evaluation of Air Quality Models: An Approach for the Rapid Scientific Evolution of Modeling Systems. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2017).

The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating the spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific child page is to document the temporal high-resolution (15 minute) in-situ sensor data collected at the four primary CMS locations. The four primary CMS locations chosen for this study included: a) a Sacramento R. dominated site in the northern portion of the Delta (Freeport, FPT, USGS Station_no. 11447650); b) a site in western portion of the cen ...

This paper builds on the identification results and estimation tools for continuous DiD designs in Callaway, Goodman-Bacon, and Sant'Anna (2023) to discuss aggregation strategies for event studies with continuous treatments. Estimates from continuous designs are functions of the treatment dosage/intensity variable. Nonparametric plots of these functions show heterogeneity across doses, but not heterogeneity over time. Event-study-type plots of aggregated parameters achieve the opposite. We describe how partially aggregating across treatment doses and event time can lead to readable yet nuanced figures that reflect how causal effects evolve over time, potentially in different parts of the treatment dose distribution.

The global Continuous Data Protection and Recovery Software market is poised for robust growth, projected to reach an estimated market size of $25,000 million by 2025, with a projected Compound Annual Growth Rate (CAGR) of 15% through 2033. This expansion is primarily fueled by the escalating volume of digital data generated across industries and the paramount importance of business continuity in the face of cyber threats and system failures. Organizations are increasingly recognizing CDP and recovery solutions not as mere IT utilities but as critical components of their overall risk management strategies, safeguarding against potential financial losses, reputational damage, and regulatory non-compliance. The demand for instant data recovery and minimal downtime is driving innovation and adoption, particularly within sectors such as banking, securities, and enterprise resource planning (ERP) systems, where data integrity and availability are non-negotiable. Key drivers propelling this market forward include the rising sophistication of cyberattacks, the growing adoption of cloud computing and hybrid environments, and the increasing regulatory pressure for data resilience. CDP and recovery software plays a vital role in enabling organizations to meet these challenges by providing granular recovery options and near real-time data protection. The market is segmented across various applications, including Bank, Securities Company, Enterprise ERP System, Campus Card System, and Hospital HIS System, each with unique data protection needs. While cloud deployments are gaining significant traction due to their scalability and cost-effectiveness, on-premise solutions continue to cater to organizations with specific security or regulatory requirements. Leading players such as DELL EMC, Commvault, IBM, Veritas, and Veeam are actively investing in research and development to offer advanced features and cater to evolving customer demands, further shaping the competitive landscape and driving market expansion. This comprehensive report delves into the dynamic market for Continuous Data Protection and Recovery (CDP) software, offering a detailed analysis of its current landscape, historical trajectory, and future projections. With a study period spanning from 2019 to 2033, and a base year of 2025, this research provides invaluable insights into market concentration, evolving trends, dominant regions and segments, product innovations, and the strategic moves of key industry players. The estimated market size is projected to reach $25 million by the end of the forecast period in 2033, indicating substantial growth.

Continuous Data Protection Platform Market Outlook

According to our latest research, the Global Continuous Data Protection Platform market size was valued at $2.1 billion in 2024 and is projected to reach $7.3 billion by 2033, expanding at a CAGR of 14.7% during 2024–2033. One of the major factors driving the growth of the Continuous Data Protection Platform market is the mounting need for robust data security and real-time backup solutions across industries. With the exponential growth of digital data, organizations are increasingly prioritizing business continuity, compliance, and cyber-resilience, all of which are fueling investments in continuous data protection platforms that offer seamless backup, instant recovery, and advanced data integrity features.

Regional Outlook

North America currently holds the largest share of the Continuous Data Protection Platform market, accounting for over 38% of global revenue in 2024. The region's dominance is primarily attributed to its mature IT infrastructure, high adoption of advanced cybersecurity protocols, and a strong regulatory framework mandating stringent data protection. Major economies such as the United States and Canada have witnessed rapid deployment of continuous data protection solutions, particularly in sectors like BFSI, healthcare, and government, where data integrity and uptime are mission-critical. The presence of leading technology vendors, coupled with a robust ecosystem of managed service providers, has further accelerated market penetration. Additionally, North American enterprises are early adopters of cloud-based data protection solutions, leveraging hybrid and multi-cloud environments to ensure seamless business operations and regulatory compliance.

The Asia Pacific region is projected to be the fastest-growing market for Continuous Data Protection Platforms, with a remarkable CAGR of 18.2% from 2024 to 2033. This rapid growth is driven by increasing digital transformation initiatives, surging investments in IT infrastructure, and a rising incidence of cyberattacks and ransomware threats. Countries such as China, India, Japan, and South Korea are witnessing a surge in demand for advanced data protection solutions as businesses expand their digital footprints and adopt cloud technologies. The proliferation of small and medium enterprises (SMEs) in the region, coupled with government-led digitalization programs, is further propelling market growth. Strategic collaborations between global technology providers and regional players are also fostering innovation and accelerating the adoption of continuous data protection platforms in Asia Pacific.

In emerging economies across Latin America, the Middle East, and Africa, the Continuous Data Protection Platform market is experiencing gradual adoption, albeit at a slower pace compared to developed regions. Factors such as limited IT budgets, lack of skilled cybersecurity professionals, and fragmented regulatory frameworks pose significant challenges to widespread implementation. However, as digitalization accelerates and awareness around data security grows, organizations in these regions are increasingly exploring continuous data protection solutions to safeguard critical business information and ensure compliance with evolving data privacy laws. Localized demand is also being shaped by sector-specific requirements, such as in government, banking, and healthcare, where data loss can have severe operational and reputational repercussions. Tailored solutions and capacity-building initiatives are expected to gradually bridge the adoption gap in these markets.

Report Scope

The ALTUS Cloud Electrification Study (ACES) was Based at the Naval Air Facility Key West in Florida. ACES researchers in August 2002 conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, it allowed scientists to collect cloud electrification data for the first time from above the storm from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely, and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil magnetometer, Accelerometers, Gerdien Conductivity Prove, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels.

Access to up-to-date socio-economic data is a widespread challenge in Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

In Fiji, monthly HFPS data collection commenced in February 2024 on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in rounds roughly one month in length in a panel method, where each household was only recontacted at least thirty days after the previous interview. Each month has approximately 700 households in the sample and is representative of urban and rural areas and divisions. This dataset contains combined monthly survey data between February and October 2024. There is one date file for household level data with a unique household ID, and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households

Cleaned, labelled and anonymized version of the master file.

• Collection start: 2024
• Collection end: 2024

Description. Real-world data used to test our implementation of the average continuous Straightness, and associated results.

Source code. The source code is available on GitHub: https://github.com/CompNet/SpatialMeasures

Citation. If you use these data, please cite the following article:

V. Labatut, “Continuous Average Straightness in Spatial Graphs,” Journal of Complex Networks, 6(2):269–296, 2018. ⟨hal-01571212⟩ DOI: 10.1093/comnet/cnx033

@Article{Labatut2018, author = {Labatut, Vincent}, title = {Continuous Average Straightness in Spatial Graphs}, journal = {Journal of Complex Networks}, year = {2018}, volume = {6}, number = {2}, pages = {269-296}, doi = {10.1093/comnet/cnx033},}

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an L1 regularization in mDAG to ensure it can recover a large sparse DAG with limited sample size. We demonstrate through extensive simulations that mDAG outperforms two well-known methods in recovering the true underlying DAG. We apply mDAG to a cross-sectional immunological study of Chlamydia trachomatis infection and successfully infer the regularity network of cytokines. We also apply mDAG to a large cohort study, generating sensible mechanistic hypotheses underlying plasma adiponectin level. The R package mDAG is publicly available from CRAN at https://CRAN.R-project.org/package=mDAG.

The global Continuous Data Protection (CDP) software market is poised for robust expansion, projected to reach a substantial market size of approximately $6,800 million by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of around 12.5% through 2033. This significant growth is fueled by the escalating need for near-zero downtime and rapid data recovery across various industries, driven by an increasing volume of critical business data and the ever-present threat of cyberattacks and operational failures. The financial industry and healthcare sectors are leading the adoption of CDP solutions due to stringent regulatory compliance requirements and the mission-critical nature of their data, where even a few minutes of downtime can result in substantial financial losses and reputational damage. The burgeoning e-commerce landscape also contributes significantly, demanding constant availability to serve a global customer base. The adoption of cloud-based CDP solutions is a dominant trend, offering scalability, cost-effectiveness, and enhanced flexibility compared to traditional on-premises deployments. This shift is further propelled by the increasing reliance on cloud infrastructure and the growing sophistication of cloud-native applications. However, the market also faces certain restraints, including the initial implementation costs and the complexity of integrating CDP solutions with existing IT infrastructures, particularly for legacy systems. Geographically, North America is expected to maintain its leading position, driven by early adoption of advanced technologies and a strong presence of major CDP vendors. Asia Pacific, with its rapidly digitizing economies and increasing awareness of data protection, is anticipated to exhibit the highest growth rate in the forecast period. Key players like Veeam, Acronis, Zerto, and Dell EMC are actively innovating, offering advanced features such as granular recovery, ransomware protection, and seamless integration with hybrid cloud environments, thereby shaping the competitive landscape and driving market evolution. This in-depth report provides a comprehensive analysis of the Continuous Data Protection (CDP) software market, forecasting its trajectory from 2019 to 2033. With a base year of 2025 and an estimated market size exceeding $15,000 million in the same year, the market is poised for significant expansion throughout the forecast period (2025-2033). The historical period (2019-2024) has laid the groundwork for understanding the foundational growth drivers and adoption patterns of CDP solutions across diverse industries. This report delves into the intricacies of market concentration, key trends, regional dominance, product insights, driving forces, challenges, emerging trends, growth catalysts, leading players, and significant developments shaping the CDP landscape.