United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Chemistry Search Engine Market Outlook

The global chemistry search engine market size is set to witness robust growth, expanding from $1.2 billion in 2023 to an impressive $3.4 billion by 2032, driven by a compound annual growth rate (CAGR) of 12.1%. This substantial growth is primarily fueled by the increasing demand for efficient and accurate chemical data retrieval and the rising investment in scientific research and technological advancements in the field of chemistry.

One major growth factor for the chemistry search engine market is the burgeoning need for streamlined data management in academic and industrial research. With the exponential increase in scientific data, researchers require sophisticated tools to efficiently retrieve, analyze, and utilize chemical information. Chemistry search engines provide a centralized platform that simplifies the search process, enhances data accuracy, and saves valuable research time, thus driving their adoption in research institutions and laboratories worldwide.

Another significant driver is the pharmaceutical industryÂ’s increasing reliance on advanced search engines to expedite drug discovery and development processes. As pharmaceutical companies continue to innovate and develop new drugs, the need for quick and accurate access to chemical databases and scientific literature becomes paramount. Chemistry search engines facilitate this by offering comprehensive and specific search capabilities, enabling researchers to draw insights from vast datasets and make informed decisions more rapidly.

The chemical manufacturing sector also significantly contributes to the market's growth. With the industry's constant evolution, companies require robust search engines to stay updated with the latest chemical compounds, safety data sheets, and regulatory guidelines. By leveraging chemistry search engines, chemical manufacturers can enhance their research and development efforts, optimize production processes, and ensure regulatory compliance, thereby improving operational efficiency and innovation capabilities.

Regionally, North America is expected to dominate the market, owing to its strong research infrastructure, significant investment in R&D, and the presence of major pharmaceutical and chemical companies. The Asia Pacific region is anticipated to witness the highest growth rate, attributed to the expanding industrial base, increasing government funding for scientific research, and the rising number of academic institutions. Europe will also showcase substantial growth due to its well-established chemical and pharmaceutical sectors.

In the realm of chemical engineering, the integration of advanced software tools is becoming increasingly vital. Chemical Engineering Software plays a crucial role in enhancing the efficiency and accuracy of chemical processes. These software solutions are designed to simulate and analyze complex chemical reactions, optimize production processes, and ensure compliance with safety and environmental regulations. By leveraging these tools, chemical engineers can improve process design, reduce operational costs, and enhance product quality. The growing adoption of Chemical Engineering Software is driven by the need for innovation and efficiency in the chemical manufacturing sector, as companies strive to stay competitive in a rapidly evolving market.

Component Analysis

The chemistry search engine market is segmented by components into software and services. The software segment encompasses various types of chemistry search engines, including molecular search, spectral search, and structural search tools. These software solutions are designed to cater to the specific needs of researchers and scientists, offering features such as advanced search algorithms, data integration, and user-friendly interfaces. The increasing demand for efficient data retrieval and high accuracy in search results is driving the adoption of sophisticated software solutions in academia and industry.

The services segment includes implementation, training, maintenance, and support services provided by vendors to assist users in effectively deploying and utilizing chemistry search engines. These services are crucial for ensuring the optimal performance of search engines and for providing users with the necessary technical support and training. The growing complexity of chemical data and the need for seamless integration with existing systems have he

Vector Database as a Service Market Outlook

According to our latest research, the global Vector Database as a Service (DBaaS) market size reached USD 1.12 billion in 2024, driven by surging demand for AI-powered applications and data-intensive workloads. The market is expected to grow at a robust CAGR of 27.4% from 2025 to 2033, with the market size projected to reach USD 9.41 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of machine learning, generative AI, and advanced semantic search technologies across industries, as organizations seek scalable, real-time data solutions to power next-generation applications.

The primary growth factor for the Vector Database as a Service market is the exponential rise in unstructured and high-dimensional data generated by enterprises. Organizations across sectors such as BFSI, healthcare, e-commerce, and telecommunications are increasingly leveraging AI-driven applications that require rapid, accurate similarity search and retrieval from massive datasets. Traditional relational databases are ill-suited for these workloads, prompting a shift toward vector databases that can handle embeddings and facilitate real-time semantic search. This technological shift is further amplified by the proliferation of large language models (LLMs) and generative AI, both of which inherently depend on vector representations and require robust, scalable vector data infrastructure.

Another significant driver is the growing adoption of cloud-based solutions and managed services. Enterprises are rapidly moving away from on-premises database management due to the high costs, complexity, and lack of scalability associated with traditional systems. Vector Database as a Service enables organizations to deploy, scale, and manage high-performance vector databases with minimal operational overhead, allowing them to focus on core business and innovation. The pay-as-you-go pricing models and seamless integration with cloud-native AI/ML workflows further enhance the appeal of DBaaS offerings. This trend is particularly pronounced among small and medium enterprises (SMEs) that lack the resources for in-house data infrastructure but require advanced capabilities to stay competitive.

The increasing focus on personalized user experiences and intelligent automation is also propelling the Vector Database as a Service market. Recommendation engines, semantic search platforms, fraud detection systems, and advanced analytics all rely on the ability to process and analyze high-dimensional vectors in real time. As organizations strive to deliver hyper-personalized content and services, the need for scalable, low-latency vector search capabilities becomes paramount. This demand is further bolstered by the rise of hybrid and multi-cloud environments, where DBaaS solutions offer flexibility, reliability, and seamless integration across diverse IT landscapes. As a result, the market is witnessing heightened investment from both established technology vendors and innovative startups aiming to capture a share of this rapidly expanding landscape.

Regionally, North America remains at the forefront of the Vector Database as a Service market, accounting for the largest share in 2024 due to the early adoption of AI technologies, strong presence of leading cloud providers, and a mature digital ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, burgeoning AI research, and increasing investments in cloud infrastructure. Europe is also witnessing significant growth, supported by stringent data regulations and a growing focus on enterprise AI adoption. Latin America and the Middle East & Africa are gradually catching up, with local enterprises and governments recognizing the value of advanced vector data solutions for economic modernization and digital competitiveness.

Offering Analysis

The Vector Database as a Service market is segmented by offering into Solutions and Services. Solutions encompass the core vector database platforms, APIs, and software tools that enable organizations to store, index, and search high-dimensional vectors at scale. These offerings are rapidly evolving to support advanced features such as hybrid search (combining vector and keyword search), real-time analytics, and integration with popular AI/ML frameworks. As enterprises increasingly demand seamless, end-to-end data pipelin

The cardiovascular disease (C/VD) database is an integrated and clustered information resource that covers multi-omic studies (microRNA, genomics, proteomics and metabolomics) of cardiovascular-related traits with special emphasis on coronary artery disease (CAD). This resource was built by mining existing literature and public databases and thereafter manual biocuration was performed. To enable integration of omic data from distinct platforms and species, a specific ontology was applied to tie together and harmonise multi-level omic studies based on gene and protein clusters (CluSO) and mapping of orthologous genes (OMAP) across species. CAD continues to be a leading cause of death in the population worldwide, and it is generally thought to be an age-related disease. However, CAD incidence rates are now known to be highly influenced by environmental factors and interactions, in addition to genetic determinants. With the complexity of CAD aetiology, there is a difficulty in research studies to elucidate general elements compared to other cardiovascular diseases. Data from 92 studies, covering 13945 molecular entries (4353 unique molecules) is described, including data descriptors for experimental setup, study design, discovery-validation sample size and associated fold-changes of the differentially expressed molecular features (p-value

Library Discovery Services Market Outlook

According to our latest research, the global Library Discovery Services market size reached USD 1.28 billion in 2024, with a robust CAGR of 8.6% projected for the period from 2025 to 2033. By 2033, the market is expected to attain a value of USD 2.71 billion. This growth is primarily driven by the increasing digitization of library resources, the rising demand for seamless access to a wide range of digital and physical content, and the ongoing transformation of educational and research institutions worldwide.

The primary growth factor for the Library Discovery Services market is the accelerating shift towards digital transformation in libraries, both academic and public. Libraries are increasingly moving away from traditional catalog systems to embrace comprehensive discovery platforms that integrate diverse resources, including e-books, journals, multimedia, and open-access materials. This trend is further fueled by the proliferation of cloud computing, which enables libraries to offer users unified, remote access to vast repositories of information. The integration of artificial intelligence and machine learning into discovery services is also enhancing search relevancy and user experience, making these platforms indispensable for modern libraries. As a result, institutions are investing heavily in upgrading their library infrastructure to remain competitive and meet evolving patron expectations.

Another significant driver is the growing emphasis on interoperability and collaboration among libraries and consortia. Library Discovery Services are designed to support resource sharing and interlibrary loan functionalities, which are critical for expanding access to specialized collections and reducing acquisition costs. The ability to integrate with various library management systems, digital archives, and third-party databases is a key requirement for institutions seeking to maximize the value of their investments. Additionally, the rise of open educational resources (OER) and the push for open access publishing are prompting libraries to adopt discovery solutions that can seamlessly index and expose a broader spectrum of content types. This, in turn, is fostering innovation and competition among solution providers, further propelling market growth.

The demand for personalized and data-driven library services is also shaping the market landscape. Modern Library Discovery Services leverage analytics and user behavior insights to deliver tailored recommendations, improve resource utilization, and inform collection development strategies. Educational institutions, in particular, are leveraging these capabilities to enhance student engagement, support research activities, and demonstrate the impact of library investments on learning outcomes. The growing adoption of mobile devices and the expectation for anytime, anywhere access to library resources are compelling vendors to prioritize responsive design and mobile app integration in their offerings. These factors collectively contribute to the sustained expansion of the global Library Discovery Services market.

Regionally, North America continues to dominate the Library Discovery Services market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high concentration of academic and research libraries, coupled with substantial investments in digital infrastructure, underpins the market’s strength in North America. Europe is also witnessing significant growth, driven by government initiatives to promote digital literacy and open access. Meanwhile, Asia Pacific is emerging as a lucrative market, propelled by rapid educational expansion and increasing government support for library modernization. Latin America and the Middle East & Africa are experiencing steady growth, albeit from a smaller base, as libraries in these regions accelerate their digital transformation efforts.

Component Analysis

The Library Discovery Services market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment encompasses the core discovery platforms, search engines, and integration tools that form the backbone of modern library systems. These solutions are designed to index, retrieve, and present a wide array of resources, from print materials to digital content and multimedia. The increasing demand for intuitive, feature-rich interfaces and

The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.

Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.

• The data we collect consists of two Primary files:

UltimateArabic: A file containing more than 193,000 original Arabic news texts, without pre-processing. The texts contain words, numbers, and symbols that can be removed using pre-processing to increase accuracy when using the dataset in various Arabic natural language processing tasks such as text classification.

UltimateArabicPrePros: It is a file that contains the data mentioned in the first file, but after pre-processing, where the number of data became about 188,000 text documents, where stop words, non-Arabic words, symbols and numbers have been removed so that this file is ready for use directly in the various Arabic natural language processing tasks. Like text classification.

• We add two samples of data collected by web scraping techniques:

Sample_Youm7_Politic: An example of news in the "Politic" category collected from the Youm7 website.

Sample_alarabiya_Sport: An example of news in the "Sport" category collected from the Al-Arabiya website.

• The data is divided into 10 different categories: Culture, Diverse, Economy, Sport, Politic, Art, Society, Technology, Medical and Religion.

Personal Data Removal Services Market Outlook

As per our latest research, the global personal data removal services market size reached USD 1.65 billion in 2024, reflecting a robust momentum driven by increasing privacy concerns and stringent data protection regulations worldwide. The market is exhibiting a strong CAGR of 18.2% and is forecasted to reach USD 8.17 billion by 2033. This remarkable growth is underpinned by rising consumer awareness about digital footprints, a surge in cyber threats, and evolving regulatory frameworks mandating stricter data privacy compliance for organizations and individuals alike.

The primary growth driver for the personal data removal services market is the exponential rise in data breaches and cyber-attacks, which have significantly heightened the need for robust data privacy solutions. Organizations and individuals are increasingly realizing the risks associated with personal data exposure, including identity theft, financial loss, and reputational damage. This awareness, coupled with the proliferation of digital platforms and social networks, has led to a higher demand for services that can effectively remove or anonymize personal information from online databases, directories, and search engines. Businesses are also leveraging these services to comply with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which enforce strict guidelines for data handling and deletion, further accelerating market adoption.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) have also catalyzed the growth of the personal data removal services market. AI-powered solutions enable automated identification and removal of personal information across a multitude of online sources, enhancing efficiency and accuracy. These innovations have made it easier for service providers to offer scalable, customizable, and cost-effective solutions to a diverse client base. As the digital ecosystem continues to expand with the Internet of Things (IoT), wearable devices, and smart applications, the volume of personal data generated and stored online is expected to surge, further fueling the demand for comprehensive data removal services.

Another significant growth factor is the rising emphasis on consumer rights and transparency in data usage. Governments and regulatory bodies across the globe are enacting new laws and updating existing frameworks to empower individuals with greater control over their personal data. This shift is compelling organizations to adopt personal data removal services as a proactive measure to build trust with customers, avoid legal penalties, and maintain a positive brand image. The increasing adoption of remote work and digital transactions in the post-pandemic era has also contributed to the marketÂ’s expansion, as both enterprises and individuals seek to safeguard sensitive information from unauthorized access.

Takedown Services have become increasingly vital in the realm of personal data removal, providing a crucial layer of protection against unauthorized online content. These services specialize in identifying and removing unwanted or harmful information from the internet, which can include anything from defamatory content to unauthorized personal data. As the digital landscape becomes more complex, individuals and businesses are finding it challenging to manage their online presence effectively. Takedown Services offer a proactive approach to maintaining privacy and reputation by swiftly addressing potential threats. By collaborating with legal experts and leveraging advanced technology, these services ensure that sensitive information is not only removed but also prevented from resurfacing, thereby offering peace of mind to clients concerned about their digital footprint.

Regionally, North America continues to dominate the personal data removal services market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The presence of leading technology firms, early adoption of privacy-centric solutions, and stringent regulatory requirements have cemented North America's leadership position. EuropeÂ’s growth is propelled by the enforcement of GDPR and a strong culture of data privacy, while the Asia Pacific is witnessing rapid expansion due to

911 Public Safety Answering Point (PSAP) service area boundaries in New Mexico According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such system from within a military base it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPS are (i.e. load balancing), operational status (i.e. redundancy), or time of date / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases this was geospatial information, in others it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g. city and county boundaries). In some cases existing boundaries had to be modified to reflect coverage areas (e.g. "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county, and the homes and businesses along a road which is partly in another county. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

Historical Aerial Photography Archive Project (HAPAP) is a geodatabase that interfaces with a Map Viewer Search engine of all aerial photography projects completed in the Southwestern Region of the Forest Service. This database contains metadata on each project indicating the original project specifications and information on how to locate the associated rolls of film. The database consist of two feature classes and one table. ProjectAreas is a footprint polygon depicting the approximate area of each project. PhotoPoints is a point feature class that depicts the approximate location of the center of each photo where photo indexes are available. PhotoProjects is a tabular table that has project level information for each project. The three datasets link and relate to each other through a primary key of ProjectID. The Southwestern Regional Office (RO) photogrammetry program was initiated in the mid-1950’s and has collected thousands of aerial photographs of forest system lands for the region. The aerial photography supports projects that include mapping and surveying of administrative sites, campgrounds, recreational sites, infrastructure, timber sales, archeology, and forest resource inventory and monitoring. There are two types of aerial photography projects – 1) resource photography which is large extent, smaller scale and 2) photogrammetry projects – small extent large scale projects used for the mapping and surveying. A majority of the records are in hard copy format making it extremely difficult and time consuming for retrieval of records and historical data requested by forest service personnel and members of the public. The GIS/Photogrammetry group has identified existing databases of all aerial photography projects completed in the region and plans to convert these historical records and data into a GIS database system. This new database will allow forest service personnel to query the database, identify potential historical records of aerial photography and retrieve the records to be used in their field of work. All of the aerial photography that is acquired by agencies within the Department of Agriculture is archived at the Aerial Photography Field Office (APFO) of the Farm Service Agency (FSA). The FS is mandated to employ APFO for archival of the regions aerial photography and contracting of aerial photography acquisition projects. There are over 280,000 exposures of aerial photography that date back to 1956 which are associated with thousands of projects completed in the region. Due to the age and degradation of certain film rolls, the region is concerned of losing historical aerial photography of the region’s forest system lands.Metadata and Data

The dataset represents Emergency Medical Services (EMS) locations in the United States and its territories. EMS Stations are part of the Fire Stations / EMS Stations HSIP Freedom sub-layer, which in turn is part of the Emergency Services and Continuity of Government Sector, which is itself a part of the Critical Infrastructure Category. The EMS stations dataset consists of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Ambulance services are included even if they only provide transportation services, but not if they are located at, and operated by, a hospital. If an independent ambulance service or EMS provider happens to be collocated with a hospital, it will be included in this dataset. The dataset includes both private and governmental entities. A concerted effort was made to include all emergency medical service locations in the United States and its territories. This dataset is comprised completely of license free data. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 12/29/2004 and the newest record dates from 01/11/2010.This dataset represents the EMS stations of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. An assessment of whether or not the total emergency medical services capability in a given area is adequate. 2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can determine those entities that are able to respond the quickest. 3. A resource for Emergency Management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for Federal Government events.

Managed Milvus Services Market Outlook

According to our latest research, the global Managed Milvus Services market size reached USD 412 million in 2024, fueled by the surging adoption of vector databases for AI and machine learning workloads. The market is growing at a robust CAGR of 28.7% and is projected to attain USD 3.86 billion by 2033. This remarkable growth is driven by increasing enterprise demand for scalable, high-performance vector search capabilities, seamless cloud integration, and the need for managed solutions to reduce operational complexity and accelerate AI deployment cycles.

A primary growth factor for the Managed Milvus Services market is the exponential increase in unstructured data generated by enterprises across industries. As organizations pivot towards AI-driven applications, the need for robust vector databases such as Milvus has become paramount. Managed Milvus Services offer a compelling proposition by providing streamlined deployment, integration, and maintenance, thereby allowing enterprises to focus on extracting value from their data rather than managing infrastructure. The rise in adoption of AI-powered search, recommendation engines, and anomaly detection systems is further propelling the demand for managed Milvus services, as these solutions enable real-time, scalable vector similarity search with minimal latency.

Another significant driver is the growing complexity of AI model lifecycle management and the associated infrastructure requirements. Enterprises are increasingly challenged by the need to handle large-scale vector data, ensure high availability, and maintain performance benchmarks. Managed Milvus Services address these pain points by offering end-to-end support, from consulting and deployment to ongoing maintenance and optimization. This comprehensive service model is particularly attractive to organizations with limited in-house expertise in vector database management, enabling them to accelerate AI innovation while mitigating operational risks. The proliferation of cloud-native architectures and hybrid deployments has further amplified the attractiveness of managed services, as they provide agility, scalability, and cost-efficiency.

The integration of Milvus with popular AI and machine learning frameworks is also catalyzing market growth. Managed Milvus Services are increasingly being adopted in sectors such as BFSI, healthcare, retail, and IT & telecommunications, where real-time data processing and advanced analytics are mission-critical. The ability to seamlessly connect Milvus with existing data pipelines and analytics tools enhances enterprise agility and fosters innovation. Furthermore, the rise of regulatory requirements for data security and privacy is prompting organizations to opt for managed solutions that offer robust compliance and governance features, thereby ensuring adherence to industry standards while leveraging the power of vector search.

From a regional perspective, North America currently leads the Managed Milvus Services market, accounting for the largest revenue share in 2024. This dominance is attributed to the region’s advanced digital infrastructure, strong presence of technology-driven enterprises, and early adoption of AI and vector database technologies. However, the Asia Pacific region is poised for the fastest growth during the forecast period, fueled by rapid digital transformation, expanding startup ecosystems, and strategic investments in AI infrastructure. Europe is also witnessing significant traction, particularly in sectors such as healthcare and manufacturing, where the demand for AI-powered data solutions is accelerating.

Service Type Analysis

The Managed Milvus Services market is segmented by service type into Deployment & Integration, Consulting, Support & Maintenance, and Training & Education. Among these, Deployment & Integration services hold the largest share, reflecting the complexity and criticality of initial setup and seamless integration with existing enterprise systems. Organizations ar

A set of online services created in support of MIRIAM, a set of guidelines for the annotation and curation of computational models. The core of MIRIAM Resources is a catalogue of data types (namespaces corresponding to controlled vocabularies or databases), their URIs and the corresponding physical URLs or resources. Access to this data is made available via exports (XML) and Web Services (SOAP). MIRIAM Resources are developed and maintained under the BioModels.net initiative, and are free for use by all. MIRIAM Resources are composed of four components: a database, some Web Services, a Java library and this web application. * Database: The core of the system is a MySQL database. It allows us to store the data types (which can be controlled vocabularies or databases), their URIs and the corresponding physical URLs, and other details such as documentation and resource identifier patterns. Each entry contains a diverse set of details about the data type: official name and synonyms, root URI, pattern of identifiers, documentation, etc. Moreover, each data type can be associated with several resources (or physical locations). * Web Services: Programmatic access to the data is available via Web Services (based on Apache Axis and SOAP messages). In addition, REST-based services are currently being developed. This API allows one to not only resolve model annotations, but also to generate appropriate URIs, based upon the provision of a resource name and accession number. A list of available web services, and a WSDL are provided. A browser-based online demonstration of the Web Services is also available to try. * Java Library: A Java library is provided to access the Web Services. The documentation explains where to download it, its dependencies, and how to use it. * Web Application: A Web application, using an Apache Tomcat server, offers access to the whole data set via a Web browser. It is possible to browse by data type names as well as browse by tags. A search engine is also provided.

BackgroundSurvivors of intimate partner violence (IPV) often face increased incidents of violence during stressful life events (SLEs) such as economic recessions, environmental disasters, and pandemics. These events can diminish the effectiveness of both formal (e.g., health, social, justice, labor, community) and informal (e.g., friends, family, neighbors) support systems. Additionally, SLEs exacerbate existing health and social inequities, making it necessary to understand the accessibility of support services during these times. This scoping review investigates access to services by individuals experiencing IPV during SLEs in high-income countries.ApproachA comprehensive search was conducted across several electronic databases including MEDLINE (OVID), Embase (OVID), PsychInfo (OVID), CINAHL (EBSCO), Global Health (EBSCO), Gender Watch (ProQuest), Web of Science, and Applied Social Sciences Index & Abstracts (ProQuest), along with the search engine Google Scholar. This search, which imposed no date restrictions, was extended through May 22nd, 2024. Key search terms were developed from prior literature and in consultation with an expert librarian, focusing on ‘stressful life events,’ ‘intimate partner violence,’ and ‘access to services.’. Each study was screened and extracted by two reviewers and conflicts were resolved through discussion or a third reviewer.ResultsThe search across eight databases and citation searching resulted in a total of 7396 potentially relevant articles. After removing 1968 duplicates and screening 5428 based on titles and abstracts, 200 articles underwent full abstract review. Ultimately, 74 articles satisfied the inclusion criteria and were selected for further analysis. The analysis focused on barriers and facilitators to access, identifying challenges within Survivors’ support systems, redirected resources during crises, and complex control dynamics and marginalization. Over 90% of the literature included covered the recent COVID-19 pandemic. Addressing these challenges requires innovative strategies, sustained funding, and targeted interventions for high-risk subgroups.ConclusionThis scoping review systematically outlined the challenges and enabling factors influencing the availability of support services for Survivors of IPV during SLEs. It underscores the need for robust, culturally sensitive health and social support mechanisms, and policies. Such measures are essential to better protect and assist IPV Survivors and their service providers during these critical times. Furthermore, it is imperative to integrate the insights and expertise of the violence against women (VAW) sector into emergency planning and policy-making to ensure comprehensive and effective responses that address the unique needs of Survivors in crises.