Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries. Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.

This dataset includes polygons that describe areas of rock outcrop in the area of the Stillwater Complex, Montana. The Stillwater Complex is an Archean, ultramafic to mafic layered intrusion exposed in the Beartooth Mountains in south-central Montana. This igneous intrusion contains magmatic mineralization that is variably enriched in strategic and critical commodities such as chromium, nickel, and the platinum-group elements (PGE). Polygons representing rock outcrops were digitized in a Geographic Information System (GIS) using georeferenced maps and orthophoto imagery from published reports and field mapping sheets. This is a compilation of both legacy data and outcrops from recent field mapping. This dataset contains overlapping polygons, as some areas had mapping from different sources that overlapped the same locations.

Database of non-redundant sets of protein - small-molecule complexes that are especially suitable for structure-based drug design and protein - small-molecule interaction research. PSMB supports: * Support frequent updates - The number of new structures in the PDB is growing rapidly. In order to utilize these structures, frequent updates are required. In contrast to manual procedures which require significant time and effort per update, generation of the PSMDB database is fully automatic thereby facilitating frequent database updates. * Consider both protein and ligand structural redundancy - In the database, two complexes are considered redundant if they share a similar protein and ligand (the protein - small-molecule non-redundant set). This allows the database to contain structural information for the same protein bound to several different ligands (and vice-versa). Additionally, for completeness, the database contains a set of non-redundant complexes when only protein structural redundancy is considered (our protein non-redundant set). The following images demonstrate the structural redundancy of the protein complexes in the PDB compared to the PSMDB. * Efficient handling of covalent bonds -Many protein complexes contain covalently bound ligands. Typically, protein-ligand databases discard these complexes; however, the PSMDB simply removes the covalently bound ligand from the complex, retaining any non-covalently bound ligands. This increases the number of usable complexes in the database. * Separate complexes into protein and ligand files -The PSMDB contains individual structure files for both the protein and all non-covalently bound ligands. The unbound proteins are in PDB format while the individual ligands are in SDF format (in their native coordinate frame).

The paper shows, on the example of manuscripts from Ethiopia containing the Kitāb al-farāʾiḍ which were surveyed by the IslHornAfr project, how a relational database can manage data on complex (composite and multiple-text) manuscripts.

The Amish Research Group of the University of Maryland School of Medicine has been studying the Old Order Amish population in Lancaster County, PA, since 1993. This database currently consists of health-related data on over 7,000 adults resulting from studies ranging from population and basic science to clinical and translational research. Areas of investigation include: Cardiovascular Risk, Diabetes, Bone Health, Blood Pressure, Vascular Imaging, Aging, Breast Tissue Density, Platelet Aggregation, Microbiome, Wellness, and Brain Imaging. Extensive genetic data (genotyping and sequencing) is also available.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 23, 2019 Database was open, publicly accessible platform for DNA and clinical data related to human Major Histocompatibility Complex (MHC). Data from IHWG workshops were provided as well.

Database Automation Software Market Outlook

The global database automation software market size in 2023 is projected at approximately USD 1.8 billion, and it is anticipated to reach around USD 3.9 billion by 2032, growing at a CAGR of 9.2% during the forecast period. The robust growth can be attributed to various factors, including the increasing need for businesses to manage large volumes of data efficiently, the rise of cloud computing, and the rapid adoption of automation technologies in a variety of industries.

The growing emphasis on reducing operational costs is one of the primary factors propelling the market. Organizations are continuously looking for ways to enhance productivity while minimizing costs. Database automation software helps in achieving this by automating routine database management tasks such as backup, recovery, and performance tuning. This automation leads to significant time and cost savings, thereby driving the market. Additionally, the software minimizes human errors, which can be costly and detrimental to business operations, further fueling its adoption.

Another critical growth driver is the increasing complexity of database environments. The surge in big data, IoT, and artificial intelligence applications has led to more complex and large-scale database systems. Managing these vast and complex databases manually can be incredibly challenging and prone to errors. Database automation software simplifies these processes by providing automated solutions for database configuration, monitoring, and maintenance, thereby making it easier to manage and optimize database performance.

Furthermore, the rapid adoption of cloud computing is significantly boosting the database automation software market. Cloud-based databases are becoming increasingly popular due to their scalability, flexibility, and cost-effectiveness. Database automation software provides seamless integration with cloud services, enabling businesses to efficiently manage their cloud databases. The capabilities of database automation tools to offer real-time analytics and ensure data accuracy in cloud environments are some of the other factors driving the market growth.

As organizations continue to navigate the complexities of modern data environments, the role of Database Development and Management Tools Software becomes increasingly vital. These tools are designed to streamline the process of database creation, modification, and maintenance, allowing businesses to focus on strategic objectives rather than routine database tasks. By leveraging such software, companies can ensure that their databases are not only efficient but also scalable and secure. This is particularly important in today's data-driven world, where the ability to quickly adapt to changing data requirements can provide a competitive edge. The integration of these tools with database automation software further enhances their capabilities, providing a comprehensive solution for managing complex database environments.

Regionally, North America holds a significant share of the database automation software market due to the early adoption of advanced technologies and the presence of key market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by the rapid industrialization, increasing investments in IT infrastructure, and the growing adoption of cloud-based solutions in countries like China and India.

Component Analysis

The database automation software market can be segmented into two primary components: software and services. The software segment includes tools and platforms specifically designed for automating database tasks. These tools typically feature functionalities such as automated provisioning, configuration, patching, upgrades, and monitoring. The growing need for efficient database management solutions that can handle complex and large-scale database environments is driving the demand for database automation software. Companies are increasingly investing in advanced software solutions to optimize their database performance and ensure data accuracy.

On the other hand, the services segment encompasses various services associated with the implementation, integration, and maintenance of database automation software. This includes consulting services, managed services, and training and support services. As organizations seek to leverage the full

Analyze the landscape of protein complex nomenclature resources, species-agnostic.

Database Market Outlook

The global database market size was valued at approximately USD 67 billion in 2023 and is projected to reach USD 138 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3%. The market is poised for significant growth due to the increasing demand for data storage solutions and the rapid digital transformation across various industries. As businesses continue to generate massive volumes of data, the need for efficient and scalable database solutions is becoming more critical than ever. This growth is further propelled by advancements in cloud computing and the increasing adoption of artificial intelligence and machine learning technologies, which require robust database management systems to handle complex data sets.

One of the primary growth factors for the database market is the exponential increase in data generation from various sources, including social media, IoT devices, and enterprise applications. As organizations strive to leverage data for competitive advantage, the demand for sophisticated database technologies that can manage, process, and analyze large volumes of data is on the rise. These technologies enable businesses to gain actionable insights, improve decision-making, and enhance customer experiences. Additionally, the proliferation of connected devices and the Internet of Things (IoT) are contributing to the surge in data volume, necessitating the deployment of advanced database systems to handle the influx of information efficiently.

The cloud computing revolution is another significant growth driver for the database market. With the increasing adoption of cloud-based services, organizations are shifting from traditional on-premises database solutions to cloud-based database management systems. This transition is driven by the need for scalability, flexibility, and cost-effectiveness, as cloud solutions offer the ability to scale resources up or down based on demand. Cloud databases also provide enhanced data security, disaster recovery, and backup solutions, making them an attractive option for businesses of all sizes. Moreover, cloud service providers continuously innovate by offering managed database services, reducing the burden on IT departments and allowing organizations to focus on core business activities.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also playing a crucial role in shaping the future of the database market. These technologies require robust and dynamic database systems capable of handling complex algorithms and large data sets. Databases optimized for AI and ML applications enable organizations to harness the power of predictive analytics, automation, and data-driven decision-making. The integration of AI and ML with database systems enhances the ability to identify patterns, detect anomalies, and predict future trends, further driving the demand for advanced database solutions.

From a regional perspective, North America is expected to dominate the database market, owing to the presence of established technology companies and the rapid adoption of advanced technologies. The region's mature IT infrastructure and the increasing need for data-driven insights in various industries contribute to the market's growth. Asia Pacific is anticipated to witness the highest growth rate during the forecast period, driven by the increasing digitization efforts, rising internet penetration, and the growing popularity of cloud-based solutions. Europe is also expected to experience significant growth due to the expanding IT sector and the increasing adoption of data analytics solutions across industries.

Type Analysis

The database market can be segmented by type into relational, non-relational, cloud, and others. Relational databases are among the oldest and most established types of database systems, widely used across industries due to their ability to handle structured data efficiently. These databases rely on structured query language (SQL) for managing and manipulating data, making them suitable for applications that require complex querying and transaction processing. Despite their maturity, relational databases continue to evolve, with advancements such as NewSQL and distributed SQL databases enhancing their scalability and performance for modern applications.

Non-relational databases, also known as NoSQL databases, have gained popularity in recent years due to their flexibility and ability to handle unstructured data. These databases are designed to accommodate a diverse range of data types, making them ideal for applications involving large v

The database NPIDB (Nucleic acid Protein Interaction DataBase) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from Protein Data Bank (PDB) (1932 complexes in the end of 2007). It is equipped with a web-interface and a set of tools for extracting biologically meaningful characteristics of complexes. They are committed to satisfy all potential database users in order to: 1. Provide an essential information on structural features of DNA-protein and RNA-protein interaction for the users who need to get acquainted with the problem. 2. Give an effective access to the reasonably structured information about all DNA-protein and RNA-protein complexes containing in PDB. 3. Allow all visitors a quick access to our own research.

The past few decades have witnessed rapid progresses in the research and development of complex metallic alloys such as metallic glasses and multi-principal element alloys, which offer new solutions to tackle engineering problems of materials such as the strength-toughness conflict and deployment in harsh environments and/or for long-term service. A fatigue database (FatigueData-CMA2022) is compiled from the literature by the end of 2022. Data for both metallic glasses and multi-principal element alloys are included and analyzed for their statistics and patterns. Automatic extraction and manual examination are combined in the workflow to improve the efficiency of processing, the quality of published data, and the reusability. The database contains 272 fatigue datasets of S-N (the stress-life relation), ε-N (the strain-life relation), and da/dN-ΔK (the relation between the fatigue crack growth rate and the stress intensity factor range) data, together with the information of materials, processing and testing conditions, and mechanical properties. The database and scripts are released in open repositories, which are designed in formats that can be continuously expanded and updated.

Article DOI: 10.1038/s41597-023-02354-1

Database Performance Monitoring Software Tools Market Outlook

In 2023, the global database performance monitoring software tools market size was valued at approximately USD 1.8 billion. With a robust compound annual growth rate (CAGR) of 10.5%, it is projected to reach an impressive USD 4.5 billion by 2032. The growth of this market is primarily driven by the increasing demand for real-time data analytics and the need to maintain optimal database performance across various industries. The proliferation of data generated by businesses, along with the rising adoption of cloud computing technologies, acts as a catalyst for the expansion of this market.

One of the significant growth factors for the database performance monitoring software tools market is the increasing complexity of database environments. As organizations transition from traditional databases to more complex, distributed systems, the need for advanced monitoring tools has become essential. These tools provide critical insights into database performance, helping businesses optimize operations, reduce downtime, and ensure efficient data management. The rise of technologies like artificial intelligence and machine learning further enhances the capabilities of these software tools, allowing for predictive analytics and automated performance optimization, which are crucial in today's fast-paced business environment.

Another driving force behind the market's growth is the escalating demand for better user experience and service delivery. Enterprises are increasingly focusing on improving their database performance to ensure faster data retrieval and processing, which directly impacts customer satisfaction and retention. Additionally, regulatory compliance requirements across various sectors necessitate the use of sophisticated monitoring solutions to maintain data integrity and security. The integration of IoT devices and the explosion of big data analytics are also contributing to the demand for comprehensive database performance monitoring solutions.

Furthermore, the ongoing digital transformation initiatives across industries are fostering the growth of the database performance monitoring software tools market. Organizations are investing in digital technologies to enhance their operational efficiency and gain competitive advantages. As part of these initiatives, the need to monitor and manage database performance has become more pronounced. The shift towards cloud-based solutions and the increasing adoption of DevOps practices are also encouraging enterprises to deploy advanced monitoring tools that can seamlessly integrate with their existing IT infrastructure, thereby driving market growth.

In the realm of database management, Database Comparison Software plays a pivotal role in ensuring data consistency and integrity across various platforms. As organizations increasingly rely on complex database systems, the ability to compare and synchronize data becomes essential. This software facilitates the identification of discrepancies between databases, enabling IT teams to rectify issues swiftly and maintain seamless operations. By automating the comparison process, businesses can save time and resources, reducing the risk of human error and enhancing overall efficiency. As the demand for robust data management solutions grows, the integration of Database Comparison Software into existing IT infrastructures is becoming a strategic priority for many enterprises.

The regional outlook of the database performance monitoring software tools market underscores a strong growth trajectory in North America, which holds the largest market share due to the presence of key industry players and advanced technological infrastructure. The Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid industrialization, increasing IT investments, and a surge in cloud computing adoption. Europe and Latin America are also expected to experience significant growth as enterprises in these regions continue to adopt digital solutions to optimize their database management processes.

Component Analysis

The database performance monitoring software tools market is segmented into two primary components: software and services. Within the software segment, there is an increasing demand for comprehensive solutions that offer real-time monitoring, advanced analytics, and automated alerts to proactively address performance issues. As databases become more complex, organizations a

A database of proteins and RNAs that have been identified in various purified splicing complexes. Various names, orthologs and gene identifiers of spliceosome proteins have been cataloged to navigate the complex nomenclature of spliceosome proteins. Links to gene and protein records are also provided for the spliceosome components in other databases. To navigate spliceosome assembly dynamics, tools were created to compare the association of spliceosome proteins with complexes that form at specific stages of spliceosome assembly based on a compendium of mass spectrometry experiments that identified proteins in purified splicing complexes.

.xlsx file for the replication of the Paper The Complex Crises Database: 70 years of Macroeconomic Crises. It contains the term frequencies of 20 crises sentiment indexes computed from the IMF country report for the period 1956-2016 for 181 countries. (2021-07-02)

Database providing automatic test cases for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches. Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of the test cases, e.g. for testing docking algorithms. In unbound protein--protein docking, the complex of two proteins is predicted using the unbound conformations of the proteins (Halperin et al.,2002). For testing of docking algorithms, two unbound proteins which form a known complex have to be identified, so that the result of the docking algorithm can be compared to the known complex. For the identification of test cases, the structures taken from the PDB have to be classified as unbound proteins or complexes and unbound proteins with a 100% sequence identity to one complex part have to be searched. By now, most groups use handpicked test sets. The largest collection of test cases used so far is described by Chen et al. (Chen et al.,2003) and contains 31 test cases for unbound docking. Because of the exponential growth of available protein structures in the PDB, automatic generation of test cases will become more and more important in the future.

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

Object-Oriented Databases Software Market Outlook

The global object-oriented databases software market size was valued at approximately USD 5 billion in 2023 and is projected to reach around USD 10 billion by 2032, marking a robust compound annual growth rate (CAGR) of 8% over the forecast period. The growth trajectory of this market is fueled by increasing demand for efficient data management solutions across various industries, which is driving the adoption of object-oriented databases. These databases are gaining traction due to their ability to handle complex data types and relationships, a necessity in today's data-driven landscape where the volume, velocity, and variety of data are continuously expanding.

One of the primary growth factors for the object-oriented databases software market is the increasing complexity of data being generated in industries such as BFSI, healthcare, and IT. These sectors require advanced database solutions that can manage intricate data structures and relationships more effectively than traditional relational databases. With the rise of big data and the Internet of Things (IoT), enterprises need systems capable of handling large datasets with complex interconnections, driving the adoption of object-oriented databases. Additionally, the transition to digital platforms in sectors like retail and government, where customer interactions and services are increasingly digitized, further bolsters demand for these sophisticated database systems.

Another significant driver is the advancement and integration of technologies such as artificial intelligence (AI) and machine learning (ML) within database systems. Object-oriented databases are particularly suited to support AI and ML applications due to their flexible schema design and ability to model real-world entities more naturally. This adaptability facilitates more efficient data processing and storage solutions, which are essential for harnessing insights from AI and ML initiatives. As organizations strive to become more data-driven, the need for databases that can seamlessly integrate with AI and ML technologies is becoming critical, thereby propelling the growth of the object-oriented databases software market.

The proliferation of cloud-based solutions is also a significant factor contributing to market growth. Cloud deployment offers scalability, cost-efficiency, and accessibility, making it an attractive option for enterprises of all sizes. As businesses increasingly migrate their operations to the cloud to leverage these benefits, the demand for cloud-compatible object-oriented databases continues to rise. This shift not only supports the scalability requirements of large enterprises but also offers small and medium enterprises (SMEs) a cost-effective avenue to leverage advanced database technologies, thereby democratizing access to sophisticated data management tools.

Regionally, North America leads the object-oriented databases software market, driven by the early adoption of cutting-edge technologies and a strong presence of major IT and software companies. The region's market is characterized by high investment in research and development, which supports the continuous evolution of database technologies. In Asia Pacific, rapid industrialization and digital transformation initiatives are accelerating market growth, with countries like China and India emerging as significant contributors. Europe follows closely, with a focus on technological innovation and regulatory support driving adoption. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual adoption, supported by increasing investments in IT infrastructure and digitalization efforts.

Component Analysis

In the object-oriented databases software market, the component segment is categorized into software and services. The software component is at the forefront, encompassing the core database solutions that facilitate data storage, retrieval, and management. These software solutions are designed to handle complex data types such as multimedia, spatial, and temporal data, which are increasingly prevalent in modern applications. The ability to represent real-world entities more naturally within the database architecture is a distinctive advantage that supports the growing preference for object-oriented databases over traditional models. As industries continue to evolve and digitalize, the demand for robust software solutions that can cater to complex application requirements is expected to remain strong, driving growth within this segment.

Complementing the software component is the critical

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

The global document database market is experiencing robust growth, driven by the increasing demand for flexible, scalable, and easily manageable data solutions. The market's appeal stems from its ability to handle unstructured and semi-structured data efficiently, making it a crucial technology for diverse applications, including content management, e-commerce, and real-time analytics. Considering a plausible market size of $15 billion in 2025 and a conservative Compound Annual Growth Rate (CAGR) of 20% for the forecast period (2025-2033), the market is projected to reach approximately $70 billion by 2033. This substantial growth is fueled by several key trends, including the rise of cloud computing, the increasing adoption of NoSQL databases, and the need for improved data management capabilities across various industries. Major players such as Couchbase, MongoDB, Amazon, and others are actively contributing to this expansion through continuous innovation and competitive pricing strategies. However, challenges remain. The market faces constraints like data security concerns, the complexity of integrating document databases into existing systems, and the need for skilled professionals to manage and maintain these complex database solutions. Despite these hurdles, the market's inherent advantages, particularly its flexibility and scalability, are expected to outweigh the constraints, ensuring continuous growth throughout the forecast period. The segment breakdown will likely favor cloud-based solutions due to their inherent scalability and ease of access. Geographical expansion, particularly in developing economies with burgeoning digital infrastructure, will also contribute significantly to the overall market expansion. Companies are investing heavily in research and development to improve performance, security, and ease of use, ultimately bolstering market adoption and further accelerating growth.

Comprehensive dataset of 23,148 Housing complexes in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.