Database Platform as a Service Market Outlook

The Database Platform as a Service (DBPaaS) market is poised for substantial growth, with a market size that was valued at USD 9.5 billion in 2023 and is projected to reach USD 25.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. This remarkable growth is driven by factors such as the increasing adoption of cloud-based solutions, the surge in data generation across various sectors, and the need for scalable and efficient database management systems. Furthermore, the growing demand for real-time data analytics to derive actionable insights and the rising trend of digital transformation across industries are further propelling the market's expansion.

One of the critical growth drivers of the DBPaaS market is the widespread embrace of cloud technology across businesses of all sizes. As organizations increasingly migrate their operations to the cloud, the demand for flexible and cost-effective database management solutions has surged. DBPaaS allows companies to manage databases without the need for complex on-premises infrastructure, enabling them to focus more on their core business objectives. This cloud-first approach is particularly appealing to small and medium enterprises (SMEs) that may lack the resources to maintain robust IT infrastructures, thereby fueling market growth across this segment.

Moreover, the acceleration of digital transformation initiatives across various industries is another pivotal factor influencing the growth of the DBPaaS market. Industries such as BFSI, healthcare, IT and telecommunications, and retail are increasingly relying on digital solutions to optimize their operations, improve customer experiences, and gain competitive advantages. As these sectors generate vast amounts of data, the need for efficient and scalable database management systems becomes paramount. DBPaaS offers these industries the agility and scalability required to handle their data needs effectively, thereby contributing significantly to market expansion.

The ongoing advancements in real-time data analytics and the increasing importance of data-driven decision-making are also boosting the DBPaaS market. Organizations today are keen on leveraging big data and analytics to enhance business operations and customer satisfaction. DBPaaS solutions provide the necessary infrastructure and tools to manage and analyze large datasets efficiently, allowing businesses to derive insights that can drive strategic initiatives. The ability to access real-time data analytics is crucial for industries like retail and BFSI, where timely decisions can significantly impact performance and profitability.

As the DBPaaS market continues to evolve, the concept of a Database Private Cloud is gaining traction among organizations seeking enhanced security and control over their data. Unlike public cloud solutions, a Database Private Cloud offers dedicated resources and infrastructure, ensuring higher levels of data privacy and compliance with industry regulations. This model is particularly appealing to sectors such as healthcare and BFSI, where data sensitivity and confidentiality are paramount. By opting for a Database Private Cloud, businesses can maintain greater oversight of their data environments, tailoring their database management strategies to meet specific security and operational requirements. This approach not only enhances data protection but also allows for more customized and efficient database solutions, aligning with the growing demand for secure cloud-based services.

Regionally, North America dominates the DBPaaS market due to the early adoption of innovative technologies and the presence of major cloud service providers. The region's mature IT infrastructure, coupled with a strong focus on digital transformation across verticals, creates a conducive environment for DBPaaS growth. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as increasing investments in cloud infrastructure, rapid economic development, and the rising uptake of cloud services by SMEs in countries like India and China contribute to this regional surge. Europe also demonstrates steady growth, driven by stringent data protection regulations that encourage cloud adoption and database management solutions.

Service Type Analysis

The DBPaaS market is segmented based on service types into managed services and pr

SPACK is a spatio-temporal database dedicated to whaling, sealing and fishing history. It aims to gather miscellaneous and scattered sources about whaling, sealing and fishing voyages that visited Saint-Paul, Amsterdam, Crozet and Kerguelen Islands between 1780’s and 1930’s.

SPACK has been defined and populated during a PhD thesis in history. The main purpose is to assess the attendance of whaling, sealing and fishing ships around the French Southern Islands from the late 18th century. The goal is also to shed light on the issues arising from the first public policies for managing natural resources once French sovereignty was affirmed in the late 19th.

The data collected in SPACK are stored in the object-relational database, PostgreSQL, plus its spatial extension PostGIS. This repository can be used to create a new instance of the SPACK database. It contains 7 SQL files that represent the main tables of the SPACK model.

- attested_presence_areas: this table shows the dates on which the vessel is present in the area.

- code_areas: this table indicates the codes used to identify each covered area.

- code_sealing_gangs: this table shows the code used to indicate when a gang of hunters has been dropped off or relieved on shore by the ship.

- natural_resources: this table provides the codes used to classify vessel activity by 'area'.

- shipment_origin: this table lists the codes for the main shipowner's geographical origin.

- stop_over_voyages: this table describes the date of arrival and departure by 'area'. It also indicates the degree of interpolation of the data, month or day.

- voyages_areas: this table contains a list of vessels involved in whaling, fishing and sealing activities that crossed Saint-Paul, Amsterdam, Crozet or/and Kerguelen islands. It provides information such as vessel name, rig type, tonnage, port, shipment origin, natural resource exploited, agent, dates of presence, primary and secondary sources.

The main entity of this database is a ship attached to a voyage and a geographical area. This entity is described by a set of properties: ship’s and master’s names, geographical origin, shipowner, port, arrival and departure dates. Those data are featured in the voyages_areas table. The database also provides other helpful information, such as the dates of attendance on the island, the type of natural resource exploited and the sources used to identify a voyage.

The SPACK database takes profit from the Whaling History Database (https://whalinghistory.org/). It does not contain any data imported from WHDB, but it is still possible to link the two sources. Indeed, the voyages_areas table stores the identifier used by the WHDB to describe each voyage.

The WHDB provides the vessel's location in lat/lon for several voyages. Those locations have been processed to populate the voyages_areas table and check when a voyage crossed a study area: Saint-Paul, Amsterdam, Crozet or Kerguelen Islands. However, no spatial information is saved in the SQL files. You can contact the authors if you want more information about the spatial analysis techniques used.

The ArtSymbioCyc database is a collection of genomic resources for insect hosts and their obligate symbiotic or commensal bacteria annotated with the CycADS information system. Dedicated to metabolism, ArtSymbioCyc uses the powerful tools of the BioCyc community to analyze, compare and model the metabolic networks of the symbiotic partners on a genome-wide scale (including pathways, reactions or metabolites). The ArtSymbioCyc database collection is a dedicated resource that provides tools to study and compare the adaptive metabolic capacities of symbiotic organisms and to identify specific targets that may destabilize symbiotic equilibria and be useful for pest control. Here we put the functional annotation data used to reconstruct the metabolic networks from the different databases, as well as the reactions and metabolic networks that were inferred.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.1 https://doi.org/10.5066/P92QM3NT. 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 public and nonprofit lands and waters. Most are public lands owned in fee; however, 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 public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas with over twenty-five attributes in nine feature classes to support data management, queries, web mapping services, and analyses. NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.1 https://doi.org/10.5066/P92QM3NT This PAD-US Version 2.0 dataset includes a variety of updates and changes from the previous Version 1.4 dataset. The following list summarizes major updates and changes: 1) Expanded database structure with new layers: the geodatabase feature class structure now includes nine feature classes separating fee owned lands, conservation (and other) easements, management designations overlapping fee lands, marine areas, proclamation boundaries and various 'Combined' feature classes (e.g. 'Fee' + 'Easement' + 'Designation' feature classes); 2) Major update of the Federal estate including data from 8 agencies, developed in collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 3) Major updates to 30 States and limited additions to 16 other States; 4) Integration of The Nature Conservancy's (TNC) Secured Lands geodatabase; 5) Integration of Ducks Unlimited's (DU) Conservation and Recreation Lands (CARL) database; 6) Integration of The Trust for Public Land's (TPL) Conservation Almanac database; 7) The Nature Conservancy (TNC) Lands database update: the national source of lands owned in fee or managed by TNC; 8) National Conservation Easement Database (NCED) update: complete update of non-sensitive (suitable for publication in the public domain) easements; 9) Complete National Marine Protected Areas (MPA) update: from the NOAA MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; 10) First integration of Bureau of Energy Ocean Management (BOEM) managed marine lands: BOEM submitted Outer Continental Shelf Area lands managed for natural resources (minerals, oil and gas), a significant and new addition to PAD-US; 11) Fee boundary overlap assessment: topology overlaps in the PAD-US 2.0 'Fee' feature class have been identified and are available for user and data-steward reference (See Logical_Consistency_Report Section).
For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the “Data Manual for PAD-US” available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

The Issues extension for CKAN, though now unmaintained, historically provided a mechanism for users to report and manage issues related to datasets and resources within a CKAN data portal. It aimed to improve data quality and metadata accuracy by enabling community feedback and tracking. While it's no longer actively developed, its original intent was to facilitate communication between data providers and consumers regarding data errors, access problems, or suggestions for improvement. Key Features (based on assumed functionality of an issue tracker): Issue Reporting: Enabled users to directly report issues related to specific datasets and resources. This might have included tagging specific fields within dataset/resource metadata that were problematic. Issue Tracking: Provided a system for tracking the status of reported issues, potentially allowing maintainers to assign issues, set priorities, and mark them as resolved. Comment/Discussion Thread: Supported a discussion thread or comment section within each issue, permitting users and maintainers to exchange clarification or additional information regarding each issue. Email Notifications: Possibly offered email notifications to relevant parties (e.g., dataset maintainers, issue reporters) when issues are created, updated, or resolved. Integration with User Accounts: This presumably integrated with the CKAN user account system, allowing authenticated users to report issues and track their submissions. Admin Interface: Provide an admin interface to manage and review reported issues and oversee the issue resolution process. Use Cases (based on generic issues extension capabilities): Data Quality Improvement: Allows data consumers to quickly report data quality issues (e.g., incorrect data formats, missing values) directly to data publishers, enabling rapid identification resolution of these issues. Increase Data Discoverability: Enables users to inform administrators of poor metadata descriptions, broken links, outdated resources etc., helping to enhance the discoverability of resources inside the CKAN install. Technical Integration (inferred, as not explicitly documented): The Issues extension likely integrated with CKAN through plugins, hooks, and potentially new API endpoints. A dedicated database schema likely existed for storing the information. The user interface probably added new sections to dataset and resource pages to display and manage related issues. Benefits & Impact (inferred, based on general knowledge of issue trackers): By providing a dedicated system for reporting and tracking issues, this extension, when maintained, would have enhanced the overall quality and reliability of the CKAN data portal. It would have improved communication between data providers and consumers making dataset owners responsible for issues in a streamlined manner. The extension would also have improved user satisfaction and reduced the burden on data portal administrators.

The global dedicated host market is projected to reach a value of USD 21.6 billion by 2033, exhibiting a CAGR of 15.2% during the forecast period (2023-2033). The increasing adoption of cloud computing services and the growing need for data security and compliance are driving the market growth. Moreover, the rise of artificial intelligence (AI) and machine learning (ML) workloads is further fueling the demand for dedicated hosts. The market is segmented based on application, type, and region. Large enterprises are the major users of dedicated hosts, owing to their need for high-performance computing and data security. In terms of type, managed hosts are expected to hold a larger market share, as they offer a hassle-free experience and 24/7 technical support. Geographically, North America is the largest market for dedicated hosts, followed by Europe and Asia Pacific. The increasing adoption of cloud computing services in these regions is driving the market growth.

Data Center Dedicated Precision Air Conditionings Market Outlook

The Data Center Dedicated Precision Air Conditionings market is anticipated to experience notable growth, with its market size projected to rise from USD 1.5 billion in 2023 to an estimated USD 2.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 7.2%. This growth is driven by the increasing demand for efficient cooling solutions essential for the optimal operation of data centers, which are becoming larger and more complex. The evolution of cloud computing, big data, and IoT has further spurred the expansion of data centers globally, necessitating advanced precision air conditioning systems that ensure temperature and humidity control, thus safeguarding sensitive IT equipment and enhancing operational efficiency.

A significant growth factor in the Data Center Dedicated Precision Air Conditionings market is the exponential rise in data generation and the subsequent demand for data processing capabilities. With the proliferation of digital transformation across industries, businesses are increasingly relying on data centers to manage their operations, leading to a surge in the deployment of these facilities worldwide. The robust growth in data center capacity necessitates precise and reliable cooling solutions to maintain system reliability and efficiency. Additionally, the increasing emphasis on energy efficiency and sustainability is pushing the demand for precision air conditioning systems that not only provide fine-tuned environmental control but also reduce energy consumption and minimize operational costs.

Technological advancements in precision air conditioning systems are another pivotal factor driving market growth. Innovations such as advanced control systems, integration with IoT for intelligent monitoring, and the development of energy-efficient cooling technologies are enhancing the appeal of these systems. The use of AI and machine learning in optimizing air conditioning performance is also becoming prominent, allowing for predictive maintenance and real-time adjustments that further ensure the stability and efficiency of data centers. As these technologies become more accessible and cost-effective, their adoption across various data center scales is expected to see a significant uptick.

Additionally, the regulatory landscape is increasingly favoring the growth of precision air conditioning systems. Governments and industry bodies are setting stringent standards regarding data center energy efficiency and environmental impact, which is propelling the adoption of precision cooling solutions capable of meeting these regulations. Moreover, as businesses strive to achieve carbon neutrality and reduce their environmental footprint, precision air conditioning systems that are designed to be energy efficient and environmentally friendly will see heightened demand, thus driving market expansion.

The integration of Data Center Environment Sensors has become increasingly crucial in the modern data center landscape. These sensors play a pivotal role in monitoring and maintaining the optimal environmental conditions necessary for the efficient operation of data centers. By providing real-time data on temperature, humidity, and airflow, these sensors enable data center operators to make informed decisions about cooling strategies and energy consumption. The ability to detect and respond to environmental changes promptly helps prevent potential equipment failures and ensures the longevity of critical IT infrastructure. As data centers continue to expand and evolve, the deployment of advanced environment sensors is expected to become a standard practice, contributing to enhanced operational efficiency and sustainability.

Type Analysis

In the Data Center Dedicated Precision Air Conditionings market, the type segment is primarily divided into Chilled Water Precision Air Conditionings and Direct Expansion Precision Air Conditionings. Chilled water systems are widely favored in large data centers due to their efficiency in cooling large spaces and the ability to maintain precise temperature control over extensive operational zones. Their operational flexibility and scalability make them ideal for large-scale implementations, where cooling needs are substantial. These systems' ability to incorporate free cooling techniques further reduces energy expenditures, making them economically viable over long-term operation. The growing preference for chilled water systems is also supported

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://ngda-cadastre-geoplatform.hub.arcgis.com/ ). 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. PAD-US provides a full inventory geodatabase, spatial analysis, statistics, data downloads, web services, poster maps, and data submissions included in efforts to track global progress toward biodiversity protection. PAD-US integrates spatial data to ensure public lands and other protected areas from all jurisdictions are represented. PAD-US version 4.0 includes new and updated data from the following data providers. All other data were transferred from previous versions of PAD-US. Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in regular PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. Revisions associated with the federal estate in this version include updates to the Federal estate (fee ownership parcels, easement interest, management designations, and proclamation boundaries), with authoritative data from 7 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), and the U.S. Forest Service (USFS). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup/ ). This includes improved the representation of boundaries and attributes for 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. Additionally, National Cemetery boundaries were added using geospatial boundary data provided by the U.S. Department of Veterans Affairs and NASA boundaries were added using data contained in the USGS National Boundary Dataset (NBD). State Updates - USGS is committed to building capacity in the state data steward network and the PAD-US Team to increase the frequency of state land and NGO partner updates, as resources allow. State Lands Workgroup ( https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/state-lands-workgroup ) is focused on improving protected land inventories in PAD-US, increase update efficiency, and facilitate local review. PAD-US 4.0 included updates and additions from the following seventeen states and territories: California (state, local, and nonprofit fee); Colorado (state, local, and nonprofit fee and easement); Georgia (state and local fee); Kentucky (state, local, and nonprofit fee and easement); Maine (state, local, and nonprofit fee and easement); Montana (state, local, and nonprofit fee); Nebraska (state fee); New Jersey (state, local, and nonprofit fee and easement); New York (state, local, and nonprofit fee and easement); North Carolina (state, local, and nonprofit fee); Pennsylvania (state, local, and nonprofit fee and easement); Puerto Rico (territory fee); Tennessee (land trust fee); Texas (state, local, and nonprofit fee); Virginia (state, local, and nonprofit fee); West Virginia (state, local, and nonprofit fee); and Wisconsin (state fee data). Additionally, the following datasets were incorporated from NGO data partners: Trust for Public Land (TPL) Parkserve (new fee and easement data); The Nature Conservancy (TNC) Lands (fee owned by TNC); TNC Northeast Secured Areas; Ducks Unlimited (land trust fee); and the National Conservation Easement Database (NCED). All state and NGO easement submissions are provided to NCED. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/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 9) Revised - April 2024 (Version 4.0) https://doi.org/10.5066/P96WBCHS 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.

A dedicated database for the storage, browsing and data mining of whole-genome, single-base-pair resolution methylomes.

The rEGEN-B (rrn operons Extracted from GENomes of Bacteria) database is dedicated to the ribosomal operon sequences of bacteria. The database contains 523,869 sequences, representing 16,217 species, with an average length of 4,580 bp. The database was filtered according to “high-confidence curation” criteria that were defined: (i) the sequences in the database only come from genomes with confident assembly levels (i.e. “chromosome” or “complete genome” status, but not “contig” nor “scaffold”), (ii) only sufficiently recent genomes were retained for operon sequence extraction (nothing before 2005), and (iii) the database was curated using the DB4Q2 pipeline (Dubois et al., 2022) to discard low-quality and misidentified sequences. To enable users with lower computational capabilities to utilize the rEGEN-B database in a more efficient way, a lighter version of the database has also been compiled by extracting only the first copy of the rrn operon in each genome (see the “uniq” label in the database files). This lighter database contains 115,032 rrn opeorn sequences.Database update (2025-01-15): rEGEN-B: 542,371 sequences, 15,903 speciesrEGEN-B_uniq: 115,727 sequences, 15,903 speciesThe rEGEN-B database was constructed as part of the PRONAME pipeline, which has been developed to process Nanopore metabarcoding data and to significantly increase its accuracy and usability. Thanks to an innovative approach combining different quality filtering steps, read clustering, error-correction with a tool specifically dedicated to Nanopore data and the valorization of duplex reads, the generated consensus sequences display at least 99.5% accuracy with default settings.Please refer to the project GitHub repository for detailed information: https://github.com/benn888/PRONAMEDubois, B., Debode, F., Hautier, L., Hulin, J., Martin, G. S., Delvaux, A., et al. (2022). A detailed workflow to develop QIIME2-formatted reference databases for taxonomic analysis of DNA metabarcoding data. BMC Genom Data 23, 53. doi: 10.1186/s12863-022-01067-5

The ArtSymbioCyc database is a collection of genomic resources for insect hosts and their obligate symbiotic or commensal bacteria annotated with the CycADS information system. Dedicated to metabolism, ArtSymbioCyc uses the powerful tools of the BioCyc community to analyze, compare and model the metabolic networks of the symbiotic partners on a genome-wide scale (including pathways, reactions or metabolites). The ArtSymbioCyc database collection is a dedicated resource that provides tools to study and compare the adaptive metabolic capacities of symbiotic organisms and to identify specific targets that may destabilize symbiotic equilibria and be useful for pest control. Here we put the functional annotation data used to reconstruct the metabolic networks from the different databases, as well as the reactions and metabolic networks that were inferred.

As of third quarter 2018, there were **** million dedicated mobile data subscriptions in Saudi Arabia. The total number of mobile broadband subscriptions in Saudi Arabia was ** million, corresponding to ** percent of the population.

Bibliographic databases are essential research tools. In medicine, key databases are MEDLINE/PubMed, Embase, and Cochrane Central (MEC). In education, the Education Resource Information Center (ERIC) is a major database. Medical education, situated between medicine and education, has no dedicated database of its own. Many medical education researchers use MEC, some use ERIC and some do not. We performed a descriptive analysis using search strategies to retrieve medical education references from MEC and ERIC. ERIC references which were duplicates with MEC references were removed. Unique ERIC references were tallied. Between 1977 and 2022, MEC has 359,354 unique references relevant to medical education. ERIC provided 3925 unique references for the same period, all of which would be missed by searching only MEC. The mean unique ERIC medical education references per year for all 46 years is 85 (SD = ±29), or 119 (SD = ±15) for the last 10 years from 2013 to 2022. ERIC consistently offered a small yet significant number of unique references relevant to medical education for decades. We recommend the use of ERIC for medical education research when comprehensive literature searches are required, such as in systematic reviews, scoping reviews, evidence synthesis, or guideline development.

This database contains the results of 146 experimental evacuation drills. Several configurations are proposed: from a single room to a multi-compartment configuration. For each test, a unique file contains all the evacuation times in seconds from the drill start when people go through the doorways. These raw data will be handled by future users to calibrate or validate their own evacuation models.

The managed hosting services market is experiencing robust growth, driven by increasing demand for scalable, reliable, and secure IT infrastructure solutions. Businesses of all sizes, from small startups to large enterprises, are increasingly outsourcing their IT infrastructure management to managed hosting providers to focus on core competencies and reduce operational costs. The market's expansion is fueled by several key factors, including the rising adoption of cloud computing, the growing need for data center consolidation, and the increasing prevalence of hybrid and multi-cloud environments. Furthermore, the escalating demand for enhanced security features, disaster recovery solutions, and compliance with stringent industry regulations is pushing organizations towards managed hosting services. The competitive landscape is marked by a mix of large multinational corporations and smaller specialized providers, each catering to specific market niches and offering diverse service portfolios. This leads to intense competition based on pricing, service quality, and innovative features. The forecast period (2025-2033) suggests continued growth, albeit potentially at a slightly moderated pace compared to recent years. Factors such as economic fluctuations and evolving technological landscapes will likely influence the market's trajectory. However, the underlying drivers of demand, including the ongoing digital transformation and the need for agility in IT infrastructure, are expected to sustain significant growth throughout the forecast period. Key players are continually innovating and expanding their service offerings to meet the evolving demands of their customers, fostering ongoing competition and driving overall market advancement. Geographic expansion, particularly into emerging markets, also presents a significant opportunity for growth in the coming years.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

The bare metal server hosting market is experiencing robust growth, driven by increasing demand for high performance, customization, and control over IT infrastructure. This market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. The rising adoption of cloud-native applications and the need for high-performance computing in sectors like gaming, artificial intelligence, and big data analytics are significant drivers. Furthermore, the enhanced security and compliance features offered by bare metal servers are attracting enterprises seeking greater control over their data. The market's segmentation reflects diverse user needs, with options ranging from single servers to large-scale deployments. Leading providers like OVHcloud, Oracle, and DigitalOcean cater to a wide spectrum of clients, fostering competition and innovation. However, challenges such as high initial investment costs and the need for specialized expertise in server management could potentially restrain market growth to some extent. The competitive landscape is dynamic, with established players vying for market share alongside emerging cloud providers offering bare metal solutions. The geographic distribution of the market is expected to be diverse, with North America and Europe holding significant shares initially, followed by growth in Asia-Pacific and other regions driven by increased digital transformation initiatives. Over the forecast period, the market will likely see increased consolidation among providers as smaller firms are acquired by larger players, leading to greater focus on efficiency and expansion into new markets. Continued technological advancements, particularly in areas like network connectivity and server virtualization, will play a pivotal role in shaping the future trajectory of this market. The focus on sustainability and energy-efficient data centers will also influence both technology choices and market competition.

Ecuador Internet Access: Dedicated data was reported at 1,727,067.000 Unit in Sep 2017. This records an increase from the previous number of 1,699,361.000 Unit for Jun 2017. Ecuador Internet Access: Dedicated data is updated quarterly, averaging 709,670.000 Unit from Sep 2006 (Median) to Sep 2017, with 42 observations. The data reached an all-time high of 1,727,067.000 Unit in Sep 2017 and a record low of 54,287.000 Unit in Sep 2006. Ecuador Internet Access: Dedicated data remains active status in CEIC and is reported by Regulation and Control of the Telecommunication Agency. The data is categorized under Global Database’s Ecuador – Table EC.TB002: Internet Users.

ZiFDB is a database of zinc finger arrays and zinc finger proteins organized for use by molecular biologists. ZiFDB organizes information on both individual zinc finger modules and engineered ZFAs. There are currently four sets of zinc finger modules available: 1) Sangamo BioScience researchers have identified fingers recognizing all 5''-GNN-3'' and a few of 5''-ANN-3'', 5''-CNN-3'' and 5''-TNN-3'' triplets using phage display, targeted mutagenesis and SELECT methods (Liu et al., 2002); 2) the Barbas group constructed another set of models, which recognize all 5''-GNN-3'', most 5''-ANN-3'', 5''-CNN-3'' and a few 5''-TNN-3'' triplets (Dreier et al., 2001; Dreier et al., 2005; Dreier et al., 2000;); 3) Toolgen, Inc. isolated a set of naturally-occurring zinc finger modules from human transcription factors (Bae et al., 2003); 4) the Joung lab has made a large number of ZFAs by OPEN, and the constituent zinc fingers are included in the database. For the engineered ZFAs, we have collected information on 3-finger ZFAs, since this is the architecture advocated by the Zinc Finger Consortium (http://www.zincfingers.org), a group of academic laboratories dedicated to the development of improved methods to engineer zinc finger proteins. Currently, all ZFAs in ZiFDB are described in the published literature. In the future, unpublished ZFAs will also be included. It is hoped that the information in this database will help molecular biologists develop zinc finger reagents that meet their needs for genome modification. Further, we hope the analysis of the collected information will aid in improving modular design.

In order to qualify the level of service, a census of all BHNS networks was therefore initiated by Cerema. The creation of a dedicated database, called "Panorama BHNS", makes it possible to identify a large number of characteristics classified by theme (infrastructure, vehicles, passenger information, costs, etc.). This database was built on the basis of the declarations of the AOMs and the operators. Thus, no selection of BHNS lines, on the basis of service level criteria, is carried out by Cerema. As a result, some BHNS lines in the database have a low level of service while other lines, with an equivalent or higher level of service, will not have been considered as "BHNS" by the operators and AOM and will therefore be absent from the database. The database data will be collected and updated regularly from AOMs and operators. The base currently comprises nearly 40 networks representing more than 100 lines. For more information: https://www.cerema.fr/en/actualites/panorama-detaille-bus-haut-level-service-bhns-france