One Health Data Integration Platforms Market Outlook

According to our latest research, the global One Health Data Integration Platforms market size in 2024 stands at USD 1.92 billion, reflecting the growing demand for integrated data solutions that unify human, animal, and environmental health information. The market is projected to expand at a robust CAGR of 17.5% from 2025 to 2033, reaching an estimated USD 8.23 billion by 2033. This impressive growth trajectory is primarily driven by the increasing recognition of the interconnectedness between human, animal, and environmental health, as well as the need for comprehensive data platforms to support collaborative disease surveillance, policy-making, and research.

The surge in zoonotic diseases, such as COVID-19, avian influenza, and Ebola, has underscored the critical importance of the One Health approach, which integrates data from multiple sectors to better predict, prevent, and respond to public health threats. Governments, international organizations, and research institutes are increasingly investing in One Health Data Integration Platforms to facilitate real-time data sharing, advanced analytics, and cross-sectoral collaboration. The advent of advanced technologies, including artificial intelligence, machine learning, and big data analytics, is further enabling the collection and analysis of vast datasets from disparate sources, allowing for more effective disease surveillance and response strategies. As a result, the market is witnessing a significant influx of funding and innovation, particularly in the development of user-friendly and interoperable platforms that can bridge the gap between health domains.

Another key growth factor is the rising adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness for organizations managing large volumes of health data. Cloud-based deployment enables seamless integration of data from various sources, such as electronic health records, veterinary databases, environmental monitoring systems, and public health surveillance networks. This trend is particularly pronounced in developed regions, where digital infrastructure is well-established, but is also gaining traction in emerging markets as governments and organizations modernize their health information systems. The shift towards cloud technology is expected to accelerate market growth by reducing operational barriers and facilitating cross-border data exchange, essential for addressing global health challenges.

The growing emphasis on collaborative research and policy development is also fueling demand for One Health Data Integration Platforms. Academic institutions, research organizations, and public health agencies are increasingly working together to address complex health challenges that transcend traditional boundaries. Integrated data platforms enable these stakeholders to share information, conduct joint analyses, and develop evidence-based interventions that consider the interplay between human, animal, and environmental health. This collaborative approach is being reinforced by international initiatives and funding programs aimed at strengthening global health security and pandemic preparedness, further propelling the market forward.

From a regional perspective, North America currently dominates the One Health Data Integration Platforms market, owing to its advanced healthcare infrastructure, strong government support, and high adoption of digital health technologies. Europe follows closely, driven by robust regulatory frameworks and significant investments in research and innovation. The Asia Pacific region is emerging as a high-growth market, fueled by increasing awareness of zoonotic diseases, rapid digitalization, and government initiatives to enhance public health surveillance. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as they work to strengthen their health information systems and improve cross-sectoral collaboration.

Component Analysis

The Component segment of the One Health Data Integration Platforms market is categorized into software, hardware, and services. Software forms the backbone of these platforms, encompassing data integration tools, analytics engines, visualization dashboards, and interoperability modules. The demand for advanced software solutions is being driven by the need for real-time data processing, sophis

Clinical Data Integration Platforms Market Outlook

According to our latest research, the global clinical data integration platforms market size reached USD 2.85 billion in 2024, driven by the increasing demand for interoperable healthcare solutions and the rapid digital transformation across healthcare systems worldwide. The market is expected to grow at a robust CAGR of 12.4% from 2025 to 2033, reaching a forecasted value of USD 8.13 billion by 2033. This growth is primarily fueled by the rising need for efficient data management, regulatory compliance, and the adoption of advanced healthcare analytics for improved patient outcomes.

The primary growth factor for the clinical data integration platforms market is the exponential increase in healthcare data volumes generated from various sources such as electronic health records (EHRs), wearable devices, diagnostic tools, and administrative databases. Healthcare providers are increasingly recognizing the value of integrating disparate clinical data to gain a holistic view of patient health, streamline operations, and facilitate evidence-based decision-making. This integration not only enhances patient care quality but also supports healthcare organizations in meeting stringent regulatory requirements such as HIPAA and GDPR. Moreover, the growing emphasis on value-based care models is compelling providers to adopt platforms that can aggregate, normalize, and analyze data from multiple sources, thereby improving care coordination and patient outcomes.

Another significant driver is the surge in demand for personalized medicine and precision healthcare. As clinical research and genomics become more central to treatment protocols, there is a critical need for platforms that can seamlessly integrate complex datasets, including genetic information, lifestyle data, and clinical history. Clinical data integration platforms enable healthcare professionals to harness the power of big data and advanced analytics, facilitating tailored treatment plans and predictive modeling. Furthermore, the proliferation of health information exchanges (HIEs) and the expansion of telemedicine services have accelerated the adoption of integration solutions, ensuring that patient data is readily accessible and actionable across the care continuum.

The market is also benefiting from increased investments in healthcare IT infrastructure, particularly in emerging economies. Governments and private sector stakeholders are prioritizing digital health initiatives to enhance accessibility, efficiency, and quality of care. These investments are fostering the development and deployment of comprehensive data integration platforms that support interoperability and data standardization. Additionally, the growing trend of mergers and acquisitions among healthcare providers and technology vendors is driving the need for scalable integration solutions that can accommodate diverse IT environments and legacy systems. However, challenges such as data privacy concerns, high implementation costs, and the complexity of integrating heterogeneous data sources continue to pose hurdles to market growth.

API Platforms for Healthcare Integration are becoming increasingly vital as healthcare systems strive for seamless interoperability. These platforms enable disparate healthcare applications and systems to communicate effectively, facilitating the exchange of data across various stakeholders. By leveraging APIs, healthcare organizations can integrate new technologies with existing systems, enhancing the efficiency of data management and reducing the time required for data exchange. This is particularly important in the context of electronic health records (EHRs) and telemedicine, where timely access to patient data is crucial for delivering quality care. As the demand for real-time data integration grows, API platforms are expected to play a pivotal role in advancing healthcare interoperability and improving patient outcomes.

Regionally, North America dominates the clinical data integration platforms market, accounting for the largest revenue share in 2024, followed by Europe and the Asia Pacific. The presence of advanced healthcare infrastructure, favorable regulatory frameworks, and a high adoption rate of digital health technologies contribute to North America's leadership position. In contrast, the Asi

A data warehouse that integrates information on patients from multiple sources and consists of patient information from all the visits to Cincinnati Children''''s between 2003 and 2007. This information includes demographics (age, gender, race), diagnoses (ICD-9), procedures, medications and lab results. They have included extracts from Epic, DocSite, and the new Cerner laboratory system and will eventually load public data sources, data from the different divisions or research cores (such as images or genetic data), as well as the research databases from individual groups or investigators. This information is aggregated, cleaned and de-identified. Once this process is complete, it is presented to the user, who will then be able to query the data. The warehouse is best suited for tasks like cohort identification, hypothesis generation and retrospective data analysis. Automated software tools will facilitate some of these functions, while others will require more of a manual process. The initial software tools will be focused around cohort identification. They have developed a set of web-based tools that allow the user to query the warehouse after logging in. The only people able to see your data are those to whom you grant authorization. If the information can be provided to the general research community, they will add it to the warehouse. If it cannot, they will mark it so that only you (or others in your group with proper approval) can access it.

Bird Strike Database Integration Market Outlook

According to our latest research, the global Bird Strike Database Integration market size reached USD 621.5 million in 2024, demonstrating a robust trajectory driven by increased aviation safety mandates and technological advancements. The market is projected to grow at a CAGR of 9.2% from 2025 to 2033, reaching a forecasted value of USD 1,353.7 million by 2033. This impressive growth is primarily fueled by the rising incidences of bird strikes, the expanding global aviation sector, and the growing emphasis on real-time data integration for proactive risk mitigation. As per our latest research, stakeholders across airports, airlines, and regulatory bodies are increasingly investing in advanced bird strike database integration solutions to enhance operational safety and regulatory compliance.

A significant factor propelling the growth of the Bird Strike Database Integration market is the increasing frequency of bird strikes globally. The expansion of air traffic, both commercial and military, has led to a higher probability of wildlife encounters, making it imperative for aviation stakeholders to adopt robust data management and integration systems. These systems facilitate the collection, analysis, and dissemination of bird strike data, enabling timely interventions and preventive measures. Moreover, the integration of artificial intelligence and machine learning has further enhanced the predictive capabilities of these databases, allowing for more accurate risk assessments and resource allocation. As a result, the demand for comprehensive bird strike database integration solutions continues to surge, especially in regions with high air traffic density and migratory bird patterns.

Another key growth driver is the stringent regulatory framework governing aviation safety. Regulatory authorities such as the Federal Aviation Administration (FAA), European Union Aviation Safety Agency (EASA), and International Civil Aviation Organization (ICAO) have mandated the reporting and analysis of bird strikes, compelling airports and airlines to adopt sophisticated database integration solutions. These regulations not only ensure compliance but also promote the sharing of critical data across stakeholders, fostering a collaborative approach to wildlife hazard management. The integration of bird strike databases with airport management systems and air traffic control platforms further streamlines operations, reduces downtime, and minimizes the risk of costly incidents. This regulatory impetus, coupled with the increasing adoption of digital technologies, is expected to sustain the market’s upward trajectory throughout the forecast period.

Technological advancements in data analytics, cloud computing, and Internet of Things (IoT) are also playing a pivotal role in shaping the Bird Strike Database Integration market. Modern solutions offer real-time data collection from multiple sources, including radar systems, surveillance cameras, and wildlife monitoring devices, allowing for holistic situational awareness. The integration of these technologies enables predictive modeling, automated reporting, and seamless communication between stakeholders. Furthermore, the growing focus on sustainability and environmental stewardship has prompted airports and airlines to invest in wildlife management programs supported by advanced database integration. This convergence of technology, regulation, and environmental responsibility is creating a fertile landscape for innovation and market expansion.

From a regional perspective, North America continues to dominate the Bird Strike Database Integration market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of major airports, proactive regulatory frameworks, and early adoption of advanced technologies have positioned North America as a leader in this domain. Europe is witnessing steady growth, driven by increasing investments in airport infrastructure and wildlife management initiatives. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rapid aviation sector growth, rising air passenger traffic, and heightened awareness of aviation safety. Latin America and the Middle East & Africa are also showing promising potential, supported by government initiatives and international collaborations aimed at enhancing aviation safety standards.

Component Analysis

The Bird Strike Database Integrati

REVISED 1/2/2019. SEE UPDATE LINK BELOW. This database contains unit cost information for different components that may be used to integrate distributed photovotaic D-PV systems onto distribution systems. Some of these upgrades and costs may also apply to integration of other distributed energy resources DER. Which components are required and how many of each is system-specific and should be determined by analyzing the effects of distributed PV at a given penetration level on the circuit of interest in combination with engineering assessments on the efficacy of different solutions to increase the ability of the circuit to host additional PV as desired. The current state of the distribution system should always be considered in these types of analysis. The data in this database was collected from a variety of utilities PV developers technology vendors and published research reports. Where possible we have included information on the source of each data point and relevant notes. In some cases where data provided is sensitive or proprietary we were not able to specify the source but provide other information that may be useful to the user e.g. year location where equipment was installed. NREL has carefully reviewed these sources prior to inclusion in this database. Additional information about the database data sources and assumptions is included in the Unit_cost_database_guide.doc file included in this submission. This guide provides important information on what costs are included in each entry. Please refer to this guide before using the unit cost database for any purpose.

Information integration has gained new importance since the widespread success of the World Wide Web. The simplicity of data publishing on the web and the promises of the emerging eCommerce markets pose a strong incentive for data providers to offer their services on the Internet. Due to the exponential growth rate of the number of web sites, users are already faced with an overwhelming amount of accessible information. Finding the desired piece of information is difficult and time-consuming due to the inherently chaotic organisation of the Web. For this reason, information integration services are becoming increasingly important. The idea of such a service is to offer to a user a single point of access that provides him or her ex-actly with the information he or she is interested in. To achieve this goal, the service dynami-cally integrates and customises data from various data providers. For instance, a business in-formation service would integrate news tickers, specialised business databases, and stock in-formation. However, integration services face serious technical problems. Two of them are particularly hard to overcome: The heterogeneity between data sources and the high volatility that interfaces to data providers on the web typically exhibit. Mediator based information systems (MBIS) offer remedies for those problems. A MBIS tackles heterogeneity on two levels: Mediators carry out structural and semantic integration of information stemming from different origins, whereas wrappers solve technical and syntacti-cal problems. Users only communicate with mediators, which use wrappers to access data sources. To this end, mediators and wrappers are connected by declarative rules that semanti-cally describe the content of data sources. This decoupling supports the stability of interfaces and thus increases the maintainability of the overall system. This thesis discusses query-centred MBIS, i.e., MBIS in which mediators represent their domain through a schema. The core functionality of a mediator is to receive and answer user queries against its schema. Two ingredients are essential to accomplish this task: First, it re-quires a powerful language for specifying the rules that connect mediators and wrappers. The higher the expressiveness of this these rules, the more types of heterogeneity can be overcome declaratively. Second, the mediator must be equipped with algorithms that are - guided by the semantic rules - capable of efficiently rewriting user queries into queries against wrappers. We contribute to both issues. We introduce query correspondence assertions (QCA) as a flexible and expressive language to describe the content of heterogeneous data sources with respect to a given schema. QCAs are able to bridge more types of conflicts between schemas than previous languages. We describe algorithms that rewrite queries against a mediator into sequences of queries against wrappers, based on the knowledge encoded in QCAs. Our algo-rithms are considerably more efficient than previously published algorithms for query rewrit-ing in MBIS. Furthermore, we define a formal semantics for queries in MBIS, which allows us to derive statements about properties of rewriting algorithms. Based on this semantics, we prove that our algorithm is sound and complete. Finally, we show how to reduce the main cost factor of query answering in MBIS, i.e., the number of accesses to remote data sources. To this end, we device algorithms that are capable of detecting and removing redundant remote accesses.

Operational Database Management Market Outlook

The global operational database management market size was valued at approximately USD 39.1 billion in 2023 and is projected to reach around USD 82.6 billion by 2032, growing at a CAGR of 8.7% during the forecast period. This market is driven by the increasing need for real-time data analytics, enhanced data security, and the rising adoption of cloud-based solutions. As businesses continue to digitize their operations, the demand for robust database management systems that can handle large volumes of data in real time has surged, positioning this market for significant growth.

One of the primary growth factors for this market is the proliferation of data across various industries. With the advent of IoT, social media, and other digital platforms, organizations are generating an unprecedented amount of data that needs to be managed efficiently. This has led to the adoption of advanced database management systems that can handle diverse data types and provide real-time insights. Additionally, advancements in AI and machine learning have further fueled the demand for operational databases that can support predictive analytics and automated decision-making processes.

Another major driver is the increasing necessity for enhanced data security and compliance. As data breaches and cyber threats become more sophisticated, organizations are under immense pressure to ensure the security and integrity of their data. Modern operational database management systems offer advanced security features such as encryption, access controls, and regular audits, which help organizations comply with stringent regulatory requirements and protect their sensitive information from unauthorized access and attacks.

The growing adoption of cloud-based solutions is also a significant contributor to market growth. Cloud-based operational databases offer numerous advantages, including reduced infrastructure costs, scalability, and accessibility from anywhere with an internet connection. This has made them particularly appealing to small and medium enterprises (SMEs) that may lack the resources to invest in on-premises solutions. Moreover, the integration of cloud services with AI and machine learning capabilities allows organizations to leverage their data for more strategic decision-making, further driving the demand for cloud-based database management systems.

The rise of Open Source Database solutions has been a game-changer in the operational database management market. These databases offer a cost-effective alternative to traditional proprietary systems, making them particularly attractive to small and medium enterprises (SMEs) and startups. Open source databases are not only budget-friendly but also provide the flexibility to customize and adapt the software to meet specific business needs. The robust community support and continuous innovation associated with open-source projects ensure that these databases remain at the forefront of technological advancements. As a result, many organizations are increasingly adopting open-source databases to leverage their scalability, reliability, and comprehensive feature sets, which are comparable to those of their proprietary counterparts.

From a regional perspective, North America remains a dominant player in the operational database management market, thanks to its advanced IT infrastructure and the presence of major technology companies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in IT infrastructure, and the rising adoption of cloud services in countries like China and India. Europe and Latin America are also anticipated to experience steady growth due to the increasing focus on data security and compliance with regulations such as GDPR.

Component Analysis

The operational database management market can be segmented into software and services. The software segment is anticipated to hold the larger market share during the forecast period. This is primarily due to the continuous advancements in database technologies that offer enhanced performance, scalability, and security. Companies are increasingly investing in sophisticated database management software that can support their growing data requirements and provide real-time analytics. Moreover, the integration of AI and machine learning capabilities into database software is enabling predictive analytic

According to Cognitive Market Research, the global Data Integration Market size was USD 15.24 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 12.31% from 2024 to 2031. Key Dynamics of

Data Integration Market

Key Drivers of

Data Integration Market

Explosion of Data Across Disparate Systems: Organizations are producing enormous quantities of data across various platforms such as CRMs, ERPs, IoT devices, social media, and third-party services. Data integration tools facilitate unified access, allowing businesses to obtain comprehensive insights by merging both structured and unstructured data—thereby enhancing analytics, reporting, and operational decision-making.

Demand for Real-Time Business Intelligence: Contemporary enterprises necessitate real-time insights to maintain their competitive edge. Real-time data integration enables the smooth synchronization of streaming and batch data from diverse sources, fostering dynamic dashboards, tailored user experiences, and prompt reactions to market fluctuations or operational interruptions.

Adoption of Hybrid and Multi-Cloud Environments: As organizations embrace a combination of on-premise and cloud applications, the integration of data across these environments becomes essential. Data integration solutions guarantee seamless interoperability, facilitating uninterrupted data flow across platforms such as Salesforce, AWS, Azure, SAP, and others—thereby removing silos and promoting collaboration.

Key Restraints for

Data Integration Market

Complexity of Legacy Systems and Data Silos: Many organizations continue to utilize legacy databases and software that operate with incompatible formats. The integration of these systems with contemporary cloud tools necessitates extensive customization and migration strategies—rendering the process laborious, prone to errors, and demanding in terms of resources.

Data Governance and Compliance Challenges: Achieving secure and compliant data integration across various borders and industries presents significant challenges. Regulations such as GDPR, HIPAA, and CCPA impose stringent requirements on data management, thereby heightening the complexity of system integration without infringing on privacy or compromising sensitive information.

High Cost and Technical Expertise Requirements: Implementing enterprise-level data integration platforms frequently demands considerable financial investment and the expertise of skilled professionals for ETL development, API management, and error resolution. Small and medium-sized enterprises may perceive the financial and talent demands as obstacles to successful adoption.

Key Trends in

Data Integration Market

The Emergence of Low-Code and No-Code Integration Platforms: Low-code platforms are making data integration accessible to non-technical users, allowing them to design workflows and link systems using intuitive drag-and-drop interfaces. This movement enhances time-to-value and lessens reliance on IT departments—making it particularly suitable for agile, fast-growing companies.

AI-Driven Automation for Data Mapping and Transformation: Modern platforms are increasingly utilizing machine learning to automatically identify schemas, propose transformation rules, and rectify anomalies. This minimizes manual labor, improves data quality, and accelerates integration processes—facilitating more effective data pipelines for analytics and artificial intelligence.

Heightened Emphasis on Data Virtualization and Federation: Instead of physically transferring or duplicating data, organizations are embracing data virtualization. This strategy enables users to access and query data from various sources in real time, without the need for additional storage—enhancing agility and lowering storage expenses. Introduction of the Data Integration Market Market

Data Integration Market is the increasing need for seamless access and analysis of diverse data sources to support informed decision-making and digital transformation initiatives. As organizations accumulate vast amounts of data from various systems, applications, and platforms, integrating this data into a unified view becomes crucial. Data integration solutions enable businesses to break down data silos, ensuring consistent, accurate, and real-time data availability acr...

2001 forward. The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient stays. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by indicator, and they include CVDs (e.g., heart failure). The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

In order to improve the capacity of storage, exploration and processing of sensor data, a spatial DBMS was used and the Aquopts system was implemented.

In field surveys using different sensors on the aquatic environment, the existence of spatial attributes in the dataset is common, motivating the adoption of PostgreSQL and its spatial extension PostGIS. To enable the insertion of new data sets as well as new devices and sensing equipment, the database was modeled to support updates and provide structures for storing all the data collected in the field campaigns in conjunction with other possible future data sources. The database model provides resources to manage spatial and temporal data and allows flexibility to select and filter the dataset.

The data model ensures the storage integrity of the information related to the samplings performed during the field survey in an architecture that benefits the organization and management of the data. However, in addition to the storage specified on the data model, there are several procedures that need to be applied to the data to prepare it for analysis. Some validations are important to identify spurious data that may represent important sources of information about data quality. Other corrections are essential to tweak the data and eliminate undesirable effects. Some equations can be used to produce other factors that can be obtained from the combination of attributes. In general, the processing steps comprise a cycle of important operations that are directly related to the characteristics of the data set. Considering the data of the sensors stored in the database, an interactive prototype system, named Aquopts, was developed to perform the necessary standardization and basic corrections and produce useful data for analysis, according to the correction methods known in the literature.

The system provides resources for the analyst to automate the process of reading, inserting, integrating, interpolating, correcting, and other calculations that are always repeated after exporting field campaign data and producing new data sets. All operations and processing required for data integration and correction have been implemented from the PHP and Python language and are available from a Web interface, which can be accessed from any computer connected to the internet. The data access cab be access online (http://sertie.fct.unesp.br/aquopts), but the resources are restricted by registration and permissions for each user. After their identification, the system evaluates the access permissions and makes available the options of insertion of new datasets.

The source-code of the entire Aquopts system are available at: https://github.com/carmoafc/aquopts

The system and additional results were described on the official paper (under review)

CBP Data Integration Platform Market Outlook

According to our latest research, the global CBP Data Integration Platform market size reached USD 2.35 billion in 2024, demonstrating robust growth momentum driven by increasing digitalization and modernization of cross-border processes. The market is expected to advance at a CAGR of 12.4% from 2025 to 2033, reaching a projected value of USD 6.72 billion by 2033. This impressive growth trajectory is primarily fueled by the rising need for seamless data exchange across customs, border protection, and trade facilitation agencies worldwide, as well as the proliferation of cloud-based solutions and heightened security requirements at international borders.

One of the key growth factors propelling the CBP Data Integration Platform market is the intensifying demand for real-time data sharing and interoperability between customs agencies, border security authorities, and trade organizations. As global trade volumes expand and supply chains become increasingly complex, the necessity for unified platforms that can integrate disparate data sources, automate risk assessments, and streamline customs management has become paramount. Governments and private sector stakeholders are investing heavily in advanced integration platforms to enhance operational efficiency, minimize manual interventions, and ensure compliance with international trade regulations. Furthermore, the adoption of artificial intelligence, machine learning, and predictive analytics within these platforms is enabling more accurate risk profiling and faster decision-making, thus significantly reducing bottlenecks at borders.

Another critical driver is the escalating focus on border security and the need to combat evolving threats such as smuggling, trafficking, and illegal immigration. The integration of sophisticated surveillance technologies, biometric identification, and blockchain-based tracking solutions within CBP data integration platforms is revolutionizing how authorities monitor and secure borders. With governments prioritizing national security, there is a growing emphasis on deploying platforms that can seamlessly aggregate data from multiple sources, including IoT devices, surveillance cameras, and public databases. This convergence of technologies not only strengthens security protocols but also improves the accuracy and reliability of border control operations, thereby fostering greater trust among international trade partners and regulatory bodies.

The market is also witnessing substantial growth due to the increasing adoption of cloud-based deployment models, which offer scalability, flexibility, and cost-efficiency. Cloud-native CBP data integration platforms enable agencies to rapidly deploy new functionalities, integrate with legacy systems, and support remote operations, which has become especially important in the wake of global disruptions such as the COVID-19 pandemic. Additionally, the proliferation of cross-border e-commerce and the digital transformation of logistics and transportation sectors are creating new opportunities for platform vendors to expand their offerings and cater to a broader range of end-users. As a result, the competitive landscape is evolving rapidly, with both established players and emerging startups vying for market share through innovation and strategic partnerships.

From a regional perspective, North America continues to dominate the CBP Data Integration Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, has been at the forefront of adopting advanced integration platforms, driven by substantial government investments in border security and customs modernization initiatives. Meanwhile, the Asia Pacific region is poised for the fastest growth during the forecast period, supported by increasing cross-border trade, rising investments in smart border infrastructure, and the rapid digitalization of customs operations in emerging economies such as China and India. Europe also presents significant growth prospects, given its highly integrated trade environment and ongoing efforts to harmonize customs procedures across member states.

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstancesa problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/.

Lightning Talk at the International Digital Curation Conference 2025. The presentation examines OpenAIRE's solution to the “entity disambiguation” problem, presenting a hybrid data curation method that combines deduplication algorithms with the expertise of human curators to ensure high-quality, interoperable scholarly information. Entity disambiguation is invaluable to building a robust and interconnected open scholarly communication system. It involves accurately identifying and differentiating entities such as authors, organisations, data sources and research results across various entity providers. This task is particularly complex in contexts like the OpenAIRE Graph, where metadata is collected from over 100,000 data sources. Different metadata describing the same entity can be collected multiple times, potentially providing different information, such as different Persistent Identifiers (PIDs) or names, for the same entity. This heterogeneity poses several challenges to the disambiguation process. For example, the same organisation may be referenced using different names in different languages, or abbreviations. In some cases, even the use of PIDs might not be effective, as different identifiers may be assigned by different data providers. Therefore, accurate entity disambiguation is essential for ensuring data quality, improving search and discovery, facilitating knowledge graph construction, and supporting reliable research impact assessment. To address this challenge, OpenAIRE employs a deduplication algorithm to identify and merge duplicate entities, configured to handle different entity types. While the algorithm proves effective for research results, when applied to organisations and data sources, it needs to be complemented with human curation and validation since additional information may be needed. OpenAIRE's data source disambiguation relies primarily on the OpenAIRE technical team overseeing the deduplication process and ensuring accurate matches across DRIS, FAIRSharing, re3data, and OpenDOAR registries. While the algorithm automates much of the process, human experts verify matches, address discrepancies and actively search for matches not proposed by the algorithm. External stakeholders, such as data source managers, can also contribute by submitting suggestions through a dedicated ticketing system. So far OpenAIRE curated almost 3 935 groups for a total of 8 140 data sources. To address organisational disambiguation, OpenAIRE developed OpenOrgs, a hybrid system combining automated processes and human expertise. The tool works on organisational data aggregated from multiple sources (ROR registry, funders databases, CRIS systems, and others) by the OpenAIRE infrastructure, automatically compares metadata, and suggests potential merged entities to human curators. These curators, authorised experts in their respective research landscapes, validate merged entities, identify additional duplicates, and enrich organisational records with missing information such as PIDs, alternative names, and hierarchical relationships. With over 100 curators from 40 countries, OpenOrgs has curated more than 100,000 organisations to date. A dataset containing all the OpenOrgs organizations can be found on Zenodo (https://doi.org/10.5281/zenodo.13271358). This presentation demonstrates how OpenAIRE's entity disambiguation techniques and OpenOrgs aim to be game-changers for the research community by building and maintaining an integrated open scholarly communication system in the years to come.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. LinkHub is a software system using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. It leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data. For efficiency, it is also provided with relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; LinkHub was used to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as connecting glue between different identifier spaces. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code. Sponsors: Funding for this work comes from NIH/NIGMS grant P50 GM62413-01, NIH grant K25 HG02378, and NSF grant DBI-0135442.

IMPORTANT NOTE: This is the current version of NREL's Distribution System Unit cost Database and should be considered the most up-to-date. Compared to the previous version (https//data.nrel.gov/submissions/77) this database has additional data points and has been modified for improved usability. More information on the changes that have been made can be found in the attached file Unit_cost_database_guide_v2.docx. This guide also has important information about data sources and quality as well as intended use of the database. Please consult this database guide before using this data for any purpose. This database contains unit cost information for different components that may be used to integrate distributed photovotaic DPV systems onto distribution systems. Some of these upgrades and costs may also apply to integration of other distributed energy resources DER. Which components are required and how many of each is system-specific and should be determined by analyzing the effects of distributed PV at a given penetration level on the circuit of interest in combination with engineering assessments on the efficacy of different solutions to increase the ability of the circuit to host additional PV as desired. The current state of the distribution system should always be considered in these types of analysis. The data in this database was collected from a variety of utilities PV developers technology vendors and published research reports. Where possible we have included information on the source of each data point and relevant notes. In some cases where data provided is sensitive or proprietary we were not able to specify the source but provide other information that may be useful to the user e.g. year location where equipment was installed. NREL has carefully reviewed these sources prior to inclusion in this database. - Originated 01/02/2019 by National Renewable Energy Laboratory

SUE Data Management Platforms Market Outlook

According to our latest research, the global SUE Data Management Platforms market size reached USD 4.12 billion in 2024, and is expected to grow at a robust CAGR of 13.6% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a value of USD 12.65 billion in 2033. This impressive growth trajectory is primarily driven by the escalating need for efficient data unification, advanced analytics, and regulatory compliance across diverse industries, as organizations worldwide continue to embrace digital transformation and data-centric strategies.

One of the primary growth factors fueling the expansion of the SUE Data Management Platforms market is the exponential surge in data generation from multiple sources, including social media, IoT devices, enterprise applications, and customer touchpoints. As organizations strive to harness actionable insights from vast and disparate data sets, the demand for robust data management solutions has intensified. SUE Data Management Platforms offer a centralized approach to aggregating, cleansing, and harmonizing data, thereby enabling businesses to improve decision-making, personalize customer experiences, and optimize operational efficiency. The increasing adoption of cloud technology and the proliferation of big data analytics are further amplifying the need for comprehensive data management platforms that can seamlessly integrate with existing IT infrastructures.

Another significant driver is the growing emphasis on regulatory compliance and data privacy across various sectors, particularly in industries such as BFSI, healthcare, and retail. With stringent data protection laws like GDPR, CCPA, and HIPAA coming into force, enterprises are under immense pressure to ensure data accuracy, security, and traceability. SUE Data Management Platforms provide essential features such as data lineage, audit trails, and automated compliance reporting, which help organizations mitigate risks associated with non-compliance and data breaches. This regulatory landscape is compelling businesses to invest in advanced data management solutions that not only streamline compliance processes but also foster trust among customers and stakeholders.

Furthermore, the increasing reliance on data-driven marketing and customer engagement strategies is propelling the adoption of SUE Data Management Platforms. Modern enterprises are leveraging these platforms to create unified customer profiles, segment audiences, and deliver targeted campaigns across multiple channels. The ability to integrate and analyze data from diverse sources—such as CRM systems, web analytics, and third-party databases—enables marketers to gain a holistic view of customer behavior and preferences. This, in turn, drives higher conversion rates, enhances customer loyalty, and maximizes marketing ROI. As businesses continue to prioritize personalized engagement and omnichannel experiences, the role of SUE Data Management Platforms in supporting these objectives is becoming increasingly indispensable.

Regionally, North America remains the dominant market for SUE Data Management Platforms, accounting for the largest share in 2024. The region's leadership is attributed to the high concentration of technology-driven enterprises, rapid digital transformation initiatives, and early adoption of cloud-based solutions. However, Asia Pacific is emerging as the fastest-growing region, driven by the expanding digital economy, increasing investments in IT infrastructure, and rising awareness of data management best practices among enterprises in countries such as China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, fueled by evolving regulatory requirements and the growing need for data-driven business strategies.

Component Analysis

The SUE Data Management Platforms market is segmented by component into software and services, each playing a pivotal role in the ecosystem. The software segment comprises the core platforms that facilitate data integration, cleansing, transformation, and visualization. These solutions are designed to support a wide range of data formats and sources, enabling organizations to centralize their data management processes. The increasing complexity of enterprise data environments and the need for real-time analytics are driving continuous innovation in software offerings. Vendors are focusing on

Biologists and biochemists have at their disposal a number of excellent, publicly available data resources such as UniProt, KEGG, and NCBI Taxonomy, which catalogue biological entities. Despite the usefulness of these resources, they remain fundamentally unconnected. While links may appear between entries across these databases, users are typically only able to follow such links by manual browsing or through specialised workflows. Although many of the resources provide web-service interfaces for computational access, performing federated queries across databases remains a non-trivial but essential activity in interdisciplinary systems and synthetic biology programmes. What is needed are integrated repositories to catalogue both biological entities and–crucially–the relationships between them. Such a resource should be extensible, such that newly discovered relationships–for example, those between novel, synthetic enzymes and non-natural products–can be added over time. With the introduction of graph databases, the barrier to the rapid generation, extension and querying of such a resource has been lowered considerably. With a particular focus on metabolic engineering as an illustrative application domain, biochem4j, freely available at http://biochem4j.org, is introduced to provide an integrated, queryable database that warehouses chemical, reaction, enzyme and taxonomic data from a range of reliable resources. The biochem4j framework establishes a starting point for the flexible integration and exploitation of an ever-wider range of biological data sources, from public databases to laboratory-specific experimental datasets, for the benefit of systems biologists, biosystems engineers and the wider community of molecular biologists and biological chemists.

Terrain and Obstacle Database Market Outlook

According to our latest research, the global Terrain and Obstacle Database market size reached USD 4.82 billion in 2024, reflecting robust demand across key industries such as aviation, defense, and automotive. The market is expanding at a CAGR of 8.7% and is forecasted to attain a value of USD 10.09 billion by 2033. This impressive growth trajectory is primarily driven by the increasing adoption of advanced navigation and situational awareness systems, coupled with stringent regulatory mandates for safety and operational efficiency worldwide.

One of the most significant growth factors propelling the Terrain and Obstacle Database market is the rapid evolution of the global aviation sector. As air traffic continues to surge, airlines and air navigation service providers are increasingly relying on precise digital terrain and obstacle data to enhance flight safety, optimize routes, and support next-generation air traffic management systems. The integration of terrain and obstacle databases into avionics systems has become a regulatory requirement in many regions, further accelerating market growth. Additionally, the rise of unmanned aerial vehicles (UAVs) and their commercial applications has created new demand for real-time, high-resolution terrain and obstacle information, ensuring safe flight operations even in complex environments.

Another pivotal driver is the expanding use of terrain and obstacle data in the defense sector. Military organizations worldwide are leveraging these databases for mission planning, threat assessment, and the safe navigation of manned and unmanned platforms. The increasing complexity of modern warfare, which often involves operations in unfamiliar or hostile terrains, necessitates highly accurate and up-to-date geospatial intelligence. As governments invest in advanced defense technologies and digital transformation initiatives, the demand for robust, interoperable terrain and obstacle databases continues to climb, further fueling market expansion.

The automotive and maritime industries are also contributing to the growth of the Terrain and Obstacle Database market. The advent of autonomous vehicles, both on land and at sea, requires sophisticated environmental perception systems that rely heavily on detailed terrain and obstacle data. In automotive applications, these databases enable advanced driver assistance systems (ADAS) and autonomous navigation, enhancing safety and efficiency. Similarly, in maritime navigation, accurate obstacle and terrain data are vital for avoiding collisions and optimizing shipping routes. The convergence of these trends across multiple sectors is creating a strong, sustained demand for comprehensive terrain and obstacle databases.

From a regional perspective, North America held the largest market share in 2024, driven by the presence of leading technology providers, robust regulatory frameworks, and significant investments in aviation and defense infrastructure. However, the Asia Pacific region is expected to witness the fastest growth through 2033, supported by rapid urbanization, expanding transportation networks, and increasing defense budgets. Europe also remains a key market, underpinned by strong regulatory oversight and a focus on safety and innovation in the aviation and automotive sectors. The Middle East & Africa and Latin America are emerging as promising markets, with growing investments in infrastructure and technology adoption.

Component Analysis

The Terrain and Obstacle Database market is segmented by component into Software, Hardware, and Services, each playing a critical role in the delivery and utilization of accurate geospatial data. The software segment represents the backbone of the market, encompassing solutions for data processing, visualization, integration, and analytics. Advanced software platforms are designed to ingest vast volumes of terrain and obstacle data from multiple sources, perform real-time updates, and deliver actio

BACKGROUNDThe INFRASTRUCTURE (View) is a beta-version view-only layer representing a collection of different stormwater infrastructure datasets curated by federal, state, regional, and local government agencies and organizations combined into a unified data schema for stormwater management. This layer is made available for public use and is intended to provide an approximate location of different storm infrastructure assets and common attributes compiled from different data sources. Additional data from each layer may be limited for public visibility. Future phases of this project include adding new/updated datasets as they become available from partners, coordinating with federal, state, and local government agencies to create stormwater data schema standards within stormwater infrastructure datasets for improved data management across jurisdictions, and incorporating temporal data functionality for efficient reporting of operations and maintenance activities for different stormwater infrastructure. LAYERSThis dataset is comprised of the layers listed below: Regional Infrastructure Layers- Lakes and Reservoirs- Dams- Irrigation Canals and Ditches- Diversion StructuresStormwater Facility Layers- Stormwater Facilities- Stormwater Facility Boundary (Tributary Area, Footprint Area, Design Ponding Area)Storm Drainage Layers - Storm Outfalls- Channel Protection Measures- Surface Channels and Open Channels- Culverts- Storm Structures (Miscellaneous)- Storm Mains and Pipes- Storm Manholes- Storm InletsMonitoring Layers - Monitoring Stations- Monitoring Wells ADDITIONAL NOTESThis beta version view-only layer intends to present a collection of multiple databases in a unified stormwater data systems structure. It is one of several databases developed for the Stormwater Infrastructure Management System (SWIMS) project. All layers and data are provided as a resource, subject to constant changes and updates, and should never be used for engineering design purposes. If data is missing or incorrect, or if there are new or similar datasets you would like to be incorporated into one of these layers, please contact us to incorporate them into the stormwater infrastructure database. By using this layer, you understand and agree to the Terms of Use provided below. For additional questions, please contact us at gis@mhfd.org. For quicker responses, please include "GIS Layer - INFRASTRUCTURE (Public View)" in the subject line of your email. Thanks!