The Rapid Refresh (RAP) numerical weather model took the place of the Rapid Update Cycle (RUC) on May 1, 2012. Run by the National Centers for Environmental Prediction (NCEP), RAP runs with two versions. The first generates weather data on a 13-km (8-mile) resolution horizontal grid and the second, the High-Resolution Rapid Refresh (HRRR), generates data down to a 3-km (2-mile) resolution grid for smaller regions of interest. RAP forecasts are generated every hour with forecast lengths going out 18 hours with a 1 hour temporal resolution. Multiple data sources go into the generation of RAP forecasts: commercial aircraft weather data, balloon data, radar data, surface observations, and satellite data. This dataset contains a 13 km horizontal resolution Lambert Conformal grid covering the Continental United States (CONUS) domain.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Plant pathogen database for RAPiD pipline at https://github.com/SteveKnobloch/RAPiD_pipeline

Version 230309

Pathogen index for machines with < 8GB RAM

This data set contains raw GC-MS data files of neat ignitable liquids, substrates, and simulated fire debris samples. All samples were analyzed by conventional GC-MS and rapid GC-MS, a fast chromatographic screening technique. The raw data files contain chromatographic data (counts vs. retention time) for all samples analyzed. Gasoline and diesel fuel were chosen as ignitable liquids. Substrates (both unburned and burned without ignitable liquids) included carpet, wood, and primed wood. The simulated fire debris samples were generated by pouring aliquots of each ignitable liquid onto each substrate and subsequently igniting. The debris samples were prepared by passive-headspace extraction and analyzed by rapid GC-MS and traditional GC-MS. Major compounds in both gasoline and diesel fuel were identified following analysis by both techniques.Certain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Comprehensive dataset containing 18 verified Rapid locations in United States with complete contact information, ratings, reviews, and location data.

This dataset contains raw datafiles that support the development of rapid gas chromatography mass spectrometry (GC-MS) methods for seized drug analysis. Files are provided in the native ".D" format collected from an Agilent GC-MS system. Files can be opened using Agilent proprietary software or freely available software such as AMDIS (which can be downloaded at chemdata.nist.gov). Included here is data of seized drug mixtures and adjudicated case samples that were analyzed as part of the method development process for rapid GC-MS. Information about the naming of datafiles and the contents of each mixture and case sample can be found in the associated Excel sheet ("File Names and Comments.xlsx").

Hurricane Maria is an example of a natural disaster that caused disruptions to infrastructure resulting in concerns with water treatment failures and potential contamination of drinking water supplies. This dataset is focused on the water quality data collected in Puerto Rico after Hurricane Maria and is part of the larger collaborative RAPID Hurricane Maria project.

This resource consists of Excel workbooks and a SQLite database. Both were populated with data and metadata corresponding to discrete water quality analysis of drinking water systems in Puerto Rico impacted by Hurricane Maria collected as part of the RAPID Maria project. Sampling and analysis was performed by a team from Virginia Tech in February-April 2018. Discrete samples were collected and returned to the lab for ICPMS analysis. Sampling was also conducted in the field for temperature, pH, free and total chlorine, turbidity, and dissolved oxygen. Complete method and variable descriptions are contained in the workbooks and database. There are two separate workbooks: one for ICPMS data and one for field data. All results are contained in the single database. Sites were sampled corresponding to several water distribution systems and source streams in southwestern Puerto Rico. Coordinates are included for the stream sites, but to preserve the security of the water distribution sites, the locations are only identified as within Puerto Rico.

The workbooks follow the specifications for YAML Observations Data Archive (YODA) exchange format (https://github.com/ODM2/YODA-File). The workbooks are templates with sheets containing tables that are mapped to entities in the Observations Data Model 2 (ODM2 - https://github.com/ODM2). Each sheet in the workbook contains directions for its completion and brief descriptions of the attributes. The data in the sheets was converted to an SQLite database following the ODM2 schema that is also contained in this resource. Conversion was performed using a prototype Python translation software (https://github.com/ODM2/YODA-Tools).

In-Memory Database Market Outlook

According to our latest research, the global In-Memory Database market size reached USD 7.2 billion in 2024, reflecting a robust surge in adoption across industries. The market is projected to expand at a CAGR of 17.1% from 2025 to 2033, culminating in a forecasted market size of USD 27.8 billion by 2033. This remarkable growth is primarily driven by the increasing need for real-time analytics, rapid digital transformation, and the exponential rise of big data workloads across enterprises worldwide.

One of the primary growth factors propelling the in-memory database market is the urgent demand for high-speed data processing and real-time analytics. As organizations strive to derive actionable insights from rapidly growing datasets, traditional disk-based databases are proving inadequate in terms of speed and scalability. In-memory databases, leveraging RAM for data storage, deliver lightning-fast data access, drastically reducing latency and enabling instant decision-making. This capability is especially crucial in sectors such as finance, telecommunications, and e-commerce, where milliseconds can make a significant difference in customer experience and business outcomes. Additionally, the proliferation of IoT devices and the corresponding data deluge further necessitate efficient, real-time data management solutions, thereby fueling the adoption of in-memory databases.

Another significant driver is the ongoing digital transformation sweeping across various industries. Enterprises are increasingly migrating their core operations to digital platforms, which requires robust, high-performance data management solutions. In-memory databases are being integrated into enterprise IT architectures to support mission-critical applications, such as fraud detection in banking, personalized recommendations in retail, and predictive maintenance in manufacturing. These databases not only enhance the agility and responsiveness of business processes but also support advanced analytics and artificial intelligence workloads. Furthermore, the shift toward cloud-based solutions and hybrid IT environments is making it easier for organizations to deploy and scale in-memory databases without incurring prohibitive infrastructure costs.

The in-memory database market is also benefiting from advancements in hardware technologies, such as the widespread availability of affordable RAM and the development of non-volatile memory (NVM). These innovations have significantly lowered the total cost of ownership for in-memory solutions, making them accessible to a broader range of organizations, including small and medium enterprises. Furthermore, software vendors are continuously enhancing the capabilities of in-memory databases by introducing features such as horizontal scaling, robust security, and support for multi-cloud environments. These improvements are expanding the use cases for in-memory databases, driving market growth across both established and emerging economies.

Regionally, North America continues to dominate the in-memory database market, owing to its advanced IT infrastructure, high adoption of cloud technologies, and presence of major technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding internet penetration, and increasing investments in data-driven technologies by enterprises and governments. Europe also represents a significant share of the market, supported by stringent data regulations and the growing demand for real-time analytics in sectors such as finance and healthcare. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, fueled by the modernization of IT infrastructures and the rising importance of data-driven decision-making in business operations.

Component Analysis

The in-memory database market is segmented by component into Software and Services, each playing a pivotal role in the overall ecosystem. The soft

40241 Global import shipment records of Rapid Test with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

In-Database Analytics Market Outlook

According to our latest research, the global In-Database Analytics market size reached USD 5.8 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 24.5 billion by 2033. This remarkable growth is primarily driven by the increasing need for real-time data-driven decision-making, the proliferation of big data, and the integration of advanced analytics within existing database systems.

The surge in demand for in-database analytics is largely attributed to the exponential growth in data generation across industries such as BFSI, healthcare, retail, and manufacturing. Enterprises are increasingly seeking solutions that allow them to perform advanced analytics directly within their database environments, eliminating the latency and complexity associated with data movement. As organizations strive to extract actionable insights in real time, the adoption of in-database analytics is becoming a strategic imperative. The ability to run complex analytical queries without transferring data to separate analytics platforms significantly reduces processing time and enhances operational efficiency, making in-database analytics an essential tool for organizations aiming to remain competitive in today's data-driven landscape.

Another key growth factor is the rapid evolution and adoption of cloud technologies, which are enabling more scalable and flexible deployment of in-database analytics solutions. Cloud-based deployment models offer organizations the agility to scale resources dynamically, support distributed data environments, and reduce the total cost of ownership. As more enterprises move their workloads to the cloud, the integration of advanced analytics capabilities within cloud databases is becoming increasingly seamless. The rise of cloud-native databases and managed analytics services is further accelerating this trend, as organizations look for solutions that can deliver high performance, security, and compliance without the overhead of managing on-premises infrastructure.

The proliferation of artificial intelligence (AI) and machine learning (ML) models is also fueling the growth of the in-database analytics market. Modern in-database analytics platforms are integrating AI and ML capabilities to offer predictive and prescriptive analytics, allowing organizations to anticipate trends, detect anomalies, and optimize processes. This convergence of AI/ML with in-database analytics is enabling enterprises to unlock deeper insights and drive innovation across various functions such as risk management, fraud detection, and customer analytics. As organizations continue to invest in digital transformation initiatives, the demand for advanced analytics capabilities embedded within database environments is expected to surge, further propelling market growth.

Regionally, North America continues to dominate the in-database analytics market, driven by the presence of major technology providers, high digital adoption rates, and a strong focus on innovation. However, Asia Pacific is emerging as a high-growth region, fueled by rapid digitalization, increasing investments in big data and analytics infrastructure, and the growing adoption of cloud technologies. Europe is also witnessing steady growth, supported by stringent data regulations and the emphasis on data-driven decision-making across industries. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, as organizations in these regions recognize the value of in-database analytics for enhancing operational efficiency and gaining a competitive edge.

Component Analysis

The component segment of the in-database analytics market is bifurcated into software and services, both of which play critical roles in enabling organizations to leverage advanced analytics within their database environments. The software sub-segment comprises analytics engines, data integration tools, and visualization platforms that are embedded or integrated with databases. These tools facilitate the execution of complex analytical queries, support real-time data processing, and provide intuitive interfaces for business users and data scientists. The rapid advancement of analytics software, including the integration of AI and ML capabilities, is enhancing the functionality and performance of in-database analytics solutions, making

The Rapid Preliminary Inpatient Data (RAPID) dataset contains the underlying admissions data which is used by System Watch. It enables access to the hospital admissions data submitted in its unprocessed state e.g. it does not contain predictions.

Document Database Platform Market Outlook

According to our latest research, the Global Document Database Platform market size was valued at $7.8 billion in 2024 and is projected to reach $26.4 billion by 2033, expanding at a CAGR of 14.5% during the forecast period of 2025–2033. The primary driver behind this robust growth is the exponential surge in unstructured data generation across various industries, which has significantly increased the need for scalable, flexible, and high-performance document database platforms. As enterprises transition to digital-first operations and cloud-native architectures, document database platforms are becoming critical for efficient data management, real-time analytics, and seamless integration with next-generation applications. This market is further propelled by the increasing adoption of artificial intelligence and machine learning technologies, which demand sophisticated data storage and retrieval solutions capable of handling diverse and complex data types.

Regional Outlook

North America holds the largest share of the global Document Database Platform market, accounting for nearly 39% of the total market value in 2024. This dominance stems from the region’s mature IT infrastructure, high cloud adoption rates, and a strong presence of leading technology vendors such as MongoDB, Amazon Web Services, and Microsoft. The United States, in particular, has seen a rapid uptake of document database platforms in sectors like BFSI, healthcare, and retail, driven by stringent regulatory compliance requirements and the need for robust data security. Furthermore, North America’s innovation ecosystem, characterized by substantial investments in R&D and a vibrant startup culture, continues to foster advancements in database technologies, ensuring sustained market leadership throughout the forecast period.

In contrast, the Asia Pacific region is projected to be the fastest-growing market for document database platforms, with a forecasted CAGR of 17.2% from 2025 to 2033. The surge in digital transformation initiatives across countries such as China, India, and Japan is fueling unprecedented demand for scalable data management solutions. Rapid urbanization, the proliferation of e-commerce, and the expansion of fintech and healthcare sectors are key contributors to this growth. Governments in the region are actively promoting digital infrastructure development, which, coupled with increasing investments from global cloud service providers, is accelerating the adoption of document database platforms. Notably, the region’s large population base and growing internet penetration present significant opportunities for market expansion, particularly among small and medium enterprises seeking cost-effective and agile database solutions.

Emerging economies in Latin America and the Middle East & Africa are also witnessing gradual adoption of document database platforms, albeit at a slower pace compared to mature markets. Localized challenges such as limited access to advanced IT infrastructure, budget constraints, and data sovereignty concerns hinder widespread implementation. However, increasing awareness about the benefits of cloud-based database solutions and supportive government policies aimed at digitalization are gradually mitigating these barriers. In Latin America, countries like Brazil and Mexico are experiencing a rise in demand from the retail and government sectors, while in the Middle East & Africa, the focus is on leveraging document databases for smart city initiatives and enhancing public sector efficiency.

Report Scope

Data from the numerous biodiversity surveys that Conservation International's Rapid Assessment Program (RAP) has conducted since 1990.

This database can be used as the input runoff files in the RAPID model [David et al., 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [Rodell et al., 2004] LSM outputs, available at;

http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi

The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;

  GLDAS.2.0_NOAHres_3H_yyyy.tar.gz      (Note: res = 10 or 025; yyyy = 2000 to 2009)

Note: These runoff data were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.

Other necessary links associated with this database:

RAPID model: https://github.com/c-h-david/rapid

RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr

GLDAS outputs: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS

References:

David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1

Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381

Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171

Comprehensive dataset containing 35 verified Rapid Cash locations in United States with complete contact information, ratings, reviews, and location data.

Database Observability Platform Market Outlook

According to our latest research, the global database observability platform market size reached USD 2.14 billion in 2024, reflecting a robust expansion driven by the increasing adoption of cloud-native technologies and the growing complexity of modern data environments. The market is set to grow at a CAGR of 13.2% during the forecast period, with projections indicating a value of USD 6.13 billion by 2033. This remarkable growth is largely attributed to the escalating need for real-time monitoring, advanced diagnostics, and proactive analytics to ensure optimal database performance and security in enterprises worldwide.

A primary growth factor for the database observability platform market is the exponential rise in data volume and complexity across industries. Enterprises are increasingly migrating to hybrid and multi-cloud environments, which has introduced new layers of intricacy and heightened the demand for comprehensive observability solutions. Organizations now require advanced tools that can provide end-to-end visibility into their database operations, facilitate rapid troubleshooting, and enable predictive analytics for performance optimization. The ability of database observability platforms to deliver actionable insights, automate anomaly detection, and support compliance requirements has made them indispensable in today’s data-driven landscape. This surge in data complexity, coupled with the critical need for uptime and reliability, is fueling the rapid adoption of these platforms across sectors such as BFSI, healthcare, and IT & telecommunications.

Another significant driver is the increasing emphasis on security and regulatory compliance in managing sensitive data. With the proliferation of sophisticated cyber threats and stringent data protection regulations such as GDPR and HIPAA, enterprises are prioritizing database security and compliance as core business imperatives. Database observability platforms are now equipped with advanced security monitoring, real-time alerting, and forensic analysis capabilities, enabling organizations to detect and respond to potential breaches swiftly. Furthermore, these platforms facilitate audit trails and compliance reporting, helping enterprises meet regulatory obligations efficiently. The convergence of performance monitoring and security within a unified observability framework is a key factor propelling the market’s growth trajectory.

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies is also catalyzing innovation in the database observability platform market. Vendors are increasingly integrating AI-driven analytics, automated root cause analysis, and intelligent alerting into their solutions. These advancements empower organizations to transition from reactive to proactive database management, reducing downtime and optimizing resource allocation. As databases become mission-critical components of digital transformation initiatives, the demand for intelligent observability tools that can scale with business needs is accelerating. This technological evolution is expected to further amplify the market’s expansion over the forecast period.

Regionally, North America remains the dominant force in the global database observability platform market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by the presence of major technology vendors, early adoption of advanced IT infrastructure, and a strong focus on innovation. Europe and Asia Pacific are also witnessing significant growth, with Asia Pacific forecasted to register the highest CAGR through 2033. The proliferation of cloud adoption, digital transformation initiatives, and increasing investments in IT modernization across emerging economies are key factors contributing to the market’s regional expansion. Latin America and the Middle East & Africa are gradually embracing database observability platforms, albeit at a slower pace, as organizations in these regions prioritize modernization and security in their database environments.

Component Analysis

The database observability platform market is segmented by component into software and services, each playing a pivotal role in shaping the industry’s landscape. The software segment encompasses a wide array of solutions designed to provide real-time monitoring, analytics, and diagnostics for complex database environments. As organiza

This dataset provides information about the number of properties, residents, and average property values for 225th Street cross streets in Rapid City, SD.

The Rapid Refresh (RAP) numerical weather model took the place of the Rapid Update Cycle (RUC) on May 1, 2012. Run by the National Centers for Environmental Prediction (NCEP), RAP runs with two versions. The first generates weather data on a 13-km (8-mile) resolution horizontal grid and the second, the High-Resolution Rapid Refresh (HRRR), generates data down to a 3-km (2-mile) resolution grid for smaller regions of interest. RAP forecasts are generated every hour with forecast lengths going out 18 hours with a 1 hour temporal resolution. Multiple data sources go into the generation of RAP forecasts: commercial aircraft weather data, balloon data, radar data, surface observations, and satellite data. This dataset contains a 20 km horizontal resolution Lambert Conformal grid covering the Continental United States (CONUS) domain.

Demographic analysis from our first iteration of our development of admin-based migration estimates (ABMEs) using the Registration and Population Interaction Database (RAPID) and numerical examples of the adjustments applied to the estimates.

1977 Global import shipment records of Dengue Rapid with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.