We introduce a method for scaling two data sets from different sources. The proposed method estimates a latent factor common to both datasets as well as an idiosyncratic factor unique to each. In addition, it offers a flexible modeling strategy that permits the scaled locations to be a function of covariates, and efficient implementation allows for inference through resampling. A simulation study shows that our proposed method improves over existing alternatives in capturing the variation common to both datasets, as well as the latent factors specific to each. We apply our proposed method to vote and speech data from the 112th U.S. Senate. We recover a shared subspace that aligns with a standard ideological dimension running from liberals to conservatives while recovering the words most associated with each senator's location. In addition, we estimate a word-specific subspace that ranges from national security to budget concerns, and a vote-specific subspace with Tea Party senators on one extreme and senior committee leaders on the other.

This resource contains 1 pdf document dated December 13th 2018. This resource will be a part of the collection "CUAHSI Legacy documents". The abstract is as follows:

Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata.

Data Streaming as a Service Market Outlook

According to our latest research, the global Data Streaming as a Service market size reached USD 6.2 billion in 2024, and is anticipated to grow at a robust CAGR of 24.7% from 2025 to 2033. By the end of the forecast period, the market is projected to reach USD 48.4 billion by 2033. The surge in demand for real-time data analytics, combined with the proliferation of IoT devices and the increasing adoption of cloud-based solutions, are key factors propelling this market's growth trajectory.

The Data Streaming as a Service market is witnessing exponential growth, primarily driven by the escalating need for real-time data processing across diverse industries. Organizations today are increasingly reliant on instant insights to make informed decisions, optimize operational efficiency, and enhance customer experiences. As digital transformation accelerates, enterprises are migrating from traditional batch processing to real-time data streaming to gain a competitive edge. The ability to process, analyze, and act on data instantaneously is becoming a critical differentiator, especially in sectors such as BFSI, healthcare, and retail, where time-sensitive decisions can directly impact business outcomes. The rapid expansion of connected devices, sensors, and IoT infrastructure is further amplifying the demand for scalable and reliable data streaming solutions.

Another significant growth factor for the Data Streaming as a Service market is the increasing adoption of cloud technologies. Cloud-based data streaming platforms offer unparalleled scalability, flexibility, and cost advantages, making them attractive for organizations of all sizes. Enterprises are leveraging these platforms to handle massive volumes of data generated from multiple sources, including mobile applications, social media, and IoT devices. The cloud deployment model not only reduces the burden of infrastructure management but also accelerates time-to-market for new analytics-driven services. Additionally, advancements in AI and machine learning are enabling more sophisticated real-time analytics, driving further demand for robust data streaming services that can seamlessly integrate with intelligent applications.

The growing emphasis on data security, regulatory compliance, and data sovereignty is also shaping the evolution of the Data Streaming as a Service market. As organizations handle sensitive information and comply with stringent data privacy regulations, there is a heightened focus on secure data streaming solutions that offer end-to-end encryption, access controls, and audit trails. Vendors are responding by enhancing their platforms with advanced security features and compliance certifications, thereby expanding their appeal to regulated industries such as finance and healthcare. The convergence of data streaming with edge computing is another emerging trend, enabling real-time analytics closer to the data source and reducing latency for mission-critical applications.

Streaming Data Integration is becoming increasingly vital as organizations strive to unify disparate data sources into a cohesive, real-time analytics framework. This integration facilitates seamless data flow across various platforms and applications, enabling businesses to harness the full potential of their data assets. By adopting streaming data integration, companies can ensure that their data is always up-to-date, providing a solid foundation for real-time decision-making and operational efficiency. This capability is particularly crucial in today's fast-paced digital landscape, where timely insights can significantly impact competitive advantage. As enterprises continue to embrace digital transformation, the demand for robust streaming data integration solutions is expected to grow, driving innovation and development in this area.

From a regional perspective, North America continues to dominate the Data Streaming as a Service market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the presence of leading technology providers, high cloud adoption rates, and a mature digital infrastructure. Meanwhile, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitalization, expanding IT investments, and the proliferation of smart

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Climate data is a vital resource for understanding and addressing the complexities of climate change. With the advent of digital technology, accessing and utilizing climate datasets has become increasingly important for researchers, policymakers, and the general public alike. In this era of data-driven decision-making, the availability of comprehensive climate datasets empowers stakeholders to analyze trends, assess risks, and develop informed strategies for climate resilience and mitigation.

The Climate Data Online platform serves as a gateway to a wealth of climate datasets, offering users the opportunity to explore, analyze, and extract valuable insights from a diverse array of environmental data sources. By providing access to a wide range of datasets encompassing various climatic variables, geographic regions, and temporal scales, Climate Data Online facilitates interdisciplinary research, fosters collaboration, and supports evidence-based decision-making in climate science and related fields.

One of the key features of Climate Data Online is its user-friendly interface, which allows users to easily navigate through different datasets and access detailed information about each dataset. By clicking on the name of a dataset, users can expand and view comprehensive descriptions, including metadata, data formats, temporal coverage, spatial resolution, and relevant links to related tools and resources. This intuitive interface enhances the usability of the platform, enabling users to quickly find and retrieve the data they need for their specific research or analysis purposes.

Moreover, Climate Data Online offers various download options, including FTP access and downloadable samples, enabling users to obtain the data in the format and resolution that best suits their requirements. Whether users need raw data for advanced analysis or pre-processed data for visualization and modeling purposes, Climate Data Online provides the flexibility and scalability to meet diverse data needs.

One of the strengths of Climate Data Online is its extensive coverage of different climatic variables, ranging from temperature and precipitation to atmospheric pressure and wind speed. By aggregating data from multiple sources, including weather stations, satellites, and climate models, Climate Data Online offers a comprehensive view of the Earth's climate system, enabling users to explore spatial and temporal patterns, identify trends, and detect anomalies.

For example, researchers studying the impact of climate change on agriculture may utilize temperature and precipitation datasets to assess changes in growing season length, drought frequency, and crop yields. Similarly, urban planners may use data on temperature and air quality to evaluate heat island effects, assess health risks, and design resilient infrastructure. By providing access to such diverse datasets, Climate Data Online facilitates interdisciplinary research and supports evidence-based decision-making across various sectors.

In addition to its rich collection of climate datasets, Climate Data Online also serves as a valuable repository of tools and resources for data analysis and visualization. From interactive maps and charting tools to statistical analysis software and programming libraries, Climate Data Online offers a variety of options for exploring and interpreting the data. Moreover, the platform provides documentation, tutorials, and user support to help users navigate the datasets and leverage the available tools effectively.

Furthermore, Climate Data Online encourages collaboration and knowledge sharing among users by facilitating community forums, workshops, and collaborative projects. By connecting researchers, practitioners, and policymakers with shared interests in climate data analysis and interpretation, Climate Data Online fosters a vibrant community of practice, where ideas are exchanged, best practices are shared, and innovative solutions are developed.

Overall, Climate Data Online plays a crucial role in advancing climate science and supporting evidence-based decision-making in response to the challenges of climate change. By providing access to comprehensive climate datasets, user-friendly tools, and a supportive community, Climate Data Online empowers stakeholders to explore, analyze, and ...

The Park Units Enhanced layer – first and foremost contains additional attributes. Some of these attributes can be found below. The primary reason for creating the layer is to assist staff with getting park information quickly. This layer retains the status of existing and pending units of parkland for local, state, and federal park systems within Montgomery County, Maryland.Management AreaManagement RegionCongressional BoundariesPolice BeatsCommunity Equity IndexAnd many more.Click here for the complete list of attributes. For information on how this layer was created click here. NotesContact the Data Analytics Section of Montgomery Parks for more information via email: dataanalytics@montgomeryparks.org.Update CycleLayer is updated every Monday and Thursday morning on an automated basis. The Analytics Team programs the Azure cloud to source data from the data incorporated and ArcGIS Data Pipelines pulls said data back into this layer on an automated basis.BenefitsFoundational for Key Applications - The comprehensive Park Units layer serves as core data for various mapping applications, supporting decision-making and enhancing public engagement across multiple platforms.Improved Accessibility – The Park Units Points layer offers enhanced accessibility, particularly when polygon data is difficult to interpret at larger scales. This ensures that all users, regardless of the map's scale, can easily access and interpret the data.Integrated Data and Reduced Silos - By integrating data from multiple sources, the Park Units layer provides users with more comprehensive insights, minimizing the confusion of dealing with scattered data across systems and layers. This leads to faster analysis and better decision-making.Improved Analysis – The Park Units layer contains numerous valuable attributes for analysis purposes and is the product of many common analyses involving the Park Units.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

Enterprise Data Catalog Market Outlook

According to our latest research, the global Enterprise Data Catalog market size reached USD 1.82 billion in 2024. The market is projected to grow at a robust CAGR of 22.7% from 2025 to 2033, reaching a forecasted value of USD 13.22 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing reliance on data-driven decision-making, regulatory compliance mandates, and the growing complexity of enterprise data landscapes. As organizations strive to harness the full value of their data assets, the demand for advanced and scalable enterprise data catalog solutions continues to surge across all major industries.

One of the most significant growth factors propelling the enterprise data catalog market is the exponential rise in data volume and diversity. Enterprises today generate and collect vast amounts of structured and unstructured data from multiple sources, including IoT devices, social media, customer transactions, and cloud applications. This data sprawl creates challenges in data discovery, accessibility, and governance. Enterprise data catalogs offer a centralized repository that enables organizations to efficiently organize, classify, and manage their data assets, making it easier for data scientists, analysts, and business users to discover and utilize relevant information. The growing adoption of big data analytics, artificial intelligence, and machine learning further amplifies the need for robust data cataloging tools that enhance data visibility and trustworthiness.

Another key driver is the increasing emphasis on regulatory compliance and data governance. With stringent data privacy regulations such as GDPR, CCPA, and other regional mandates, enterprises face mounting pressure to maintain accurate, auditable records of their data assets and their usage. Enterprise data catalogs play a pivotal role in ensuring compliance by providing comprehensive metadata management, data lineage tracking, and automated policy enforcement. These capabilities help organizations mitigate risks associated with data breaches, unauthorized access, and non-compliance penalties. As regulatory landscapes evolve and become more complex, the adoption of enterprise data catalog solutions is expected to accelerate, especially in highly regulated industries such as BFSI, healthcare, and government.

The rapid shift towards cloud adoption and digital transformation initiatives also significantly boosts the enterprise data catalog market. Organizations are increasingly migrating their workloads and data assets to cloud environments to achieve greater agility, scalability, and cost efficiency. This transition, however, introduces new challenges in data integration, interoperability, and security. Enterprise data catalogs that support hybrid and multi-cloud deployments enable seamless data discovery and management across on-premises and cloud platforms. The integration of advanced features such as AI-driven data classification, self-service analytics, and automated metadata enrichment further enhances the value proposition of modern data catalog solutions, making them indispensable for enterprises seeking to unlock actionable insights from their data ecosystems.

Regionally, North America continues to dominate the enterprise data catalog market, driven by early technology adoption, a mature digital infrastructure, and a high concentration of data-centric enterprises. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, increased IT investments, and the proliferation of digital transformation initiatives across industries. Europe also holds a significant market share, supported by stringent data privacy regulations and a strong focus on data-driven innovation. As organizations worldwide recognize the strategic importance of effective data management, the enterprise data catalog market is poised for sustained growth across all major regions.

Component Analysis

The enterprise data catalog market is segmented by component into software and services, each playing a crucial role in the overall ecosystem. The software segment dominates the market, accounting for the largest share in 2024, primarily due to continuous advancements in cataloging features, automation, and integration capabilities. Modern enterprise data catalog software solutions offer a comprehensive suite of functionalities, including metadata management, data lineage, automat

Data Warehousing for Insurance Market Outlook

According to our latest research, the global Data Warehousing for Insurance market size reached USD 4.7 billion in 2024, with a robust compound annual growth rate (CAGR) of 10.2% expected from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of approximately USD 12.3 billion. This growth is primarily driven by the insurance sector’s escalating demand for advanced data analytics, regulatory compliance, and digital transformation initiatives, as well as the need for seamless integration of disparate data sources to improve operational efficiency and customer experience.

The primary growth factor for the Data Warehousing for Insurance market is the increasing volume and complexity of data generated across insurance operations. Insurers are handling vast amounts of structured and unstructured data from claims, customer interactions, policy management, and regulatory reporting. As digital channels proliferate and customer expectations for real-time services rise, insurance companies are compelled to invest in robust data warehousing solutions that enable centralized data storage, rapid data retrieval, and comprehensive analytics. This, in turn, supports more informed decision-making, personalized product offerings, and enhanced risk assessment capabilities, making data warehousing a critical enabler of competitive advantage in the insurance industry.

Another significant driver is the stringent regulatory landscape governing the insurance sector. Data warehousing solutions are increasingly adopted to facilitate compliance with evolving regulations such as Solvency II, GDPR, HIPAA, and other local mandates. These platforms provide insurers with the ability to consolidate and audit data efficiently, ensuring transparency and traceability throughout the data lifecycle. Moreover, the integration of artificial intelligence, machine learning, and advanced analytics within data warehouses enables insurers to detect fraud, monitor risk, and predict future trends more accurately. These capabilities are crucial in an environment where regulatory scrutiny is intensifying and the consequences of non-compliance are severe.

The rapid adoption of cloud-based solutions and hybrid deployment models is also fueling market expansion. Cloud data warehousing offers scalability, cost-effectiveness, and flexibility, allowing insurers to manage data growth without significant upfront infrastructure investments. Hybrid models, which combine on-premises and cloud deployments, are gaining traction as insurers seek to balance data security, regulatory requirements, and operational agility. The shift towards digital transformation, accelerated by the COVID-19 pandemic, has further highlighted the importance of agile and resilient data architectures, cementing the role of data warehousing as a cornerstone of modern insurance IT strategy.

Regionally, North America dominates the Data Warehousing for Insurance market due to the presence of large insurance providers, advanced IT infrastructure, and early adoption of digital technologies. Europe follows closely, driven by stringent regulatory requirements and a mature insurance landscape. The Asia Pacific region is poised for the fastest growth, fueled by rapid insurance sector expansion, increasing digitalization, and rising investments in technology infrastructure. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by insurance market liberalization and growing awareness of the benefits of data-driven operations.

Component Analysis

The Component segment of the Data Warehousing for Insurance market is composed of ETL Tools, Data Management, Metadata Management, Data Mining, and Others. ETL (Extract, Transform, Load) tools are fundamental to the operation of data warehouses, as they enable the seamless extraction of data from multiple sources, its transformation into usable formats, and subsequent loading into the warehouse. As insurers increasingly integrate data from legacy systems, third-party sources, and digital platforms, the demand for advanced ETL tools has surged. These tools are being enhanced with automation, artificial intelligence, and real-time processing capabilities, enabling insurers to accelerate data integration and support time-sensitive analytics such as fraud detection and claims processing.

A platform for semantic data integration through RDF warehousing and efficient reasoning that helps to resolve conflicts in the data. Search and explore over 5 billion RDF statements from various sources including UniProt, PubMed, EntrezGene and 20 more... Perform complex SPARQL queries and retrieve more than one billion RDF resources. One of the major problems that biotechnology and pharmaceutical industries face today is how to combine data from multiple sources and make their research more productive. Data integration takes much time and often leads to errors and redundancies that require more time and resources to resolve. LinkedLifeData is a data warehouse that syndicates tons of heterogeneous biomedical knowledge in a common data model. The platform uses an extension of the RDF model that is able to track the provenance of each individual fact in the repository and thus update the information. Data Sources include: Disease Ontology, LinkedCT, Reactome, HPRD, DBPedia, UniProt, CellMap, NCBI Entrez-Gene, UMLS, IMID, MINT, DrugBank, LHGDN, Gene Ontology, HumanCYC, PubMed, NCI Nature, Human Phenotype Ontology, BioGRID, IntAct, HapMap, Symptom Ontology, DailyMed, ChEBI, Diseasome, Freebase, SIDER

Retail Media Data Onboarding Market Outlook

According to our latest research, the global Retail Media Data Onboarding market size reached USD 2.18 billion in 2024, reflecting robust adoption across the retail and advertising sectors. The market is projected to expand at a CAGR of 13.6% during the forecast period, reaching a value of USD 6.38 billion by 2033. This impressive growth is driven primarily by the escalating demand for omnichannel marketing strategies, increased focus on personalized customer experiences, and the growing importance of first-party data in a privacy-centric digital landscape.

One of the primary growth factors fueling the Retail Media Data Onboarding market is the rapid digital transformation of the retail industry. As retailers strive to bridge the gap between online and offline consumer touchpoints, data onboarding solutions have become essential for integrating disparate customer data sources. The proliferation of e-commerce platforms and the surge in digital advertising investments are compelling brands and retailers to leverage data onboarding to create unified customer profiles, enabling more precise audience targeting and measurement. Additionally, the shift towards cookieless advertising and stringent data privacy regulations have underscored the value of first-party data, further accelerating the adoption of data onboarding solutions among retailers and their partners.

Another significant driver is the heightened focus on customer personalization and experience optimization. Retailers and brands are increasingly utilizing data onboarding to enrich their understanding of customer behaviors, preferences, and purchase journeys. By connecting offline transaction data with digital identifiers, organizations can deliver highly relevant content, offers, and advertisements across channels. This not only improves marketing ROI but also enhances customer loyalty and engagement. The evolution of advanced analytics and artificial intelligence within onboarding platforms is enabling deeper insights and more granular segmentation, making personalization efforts more impactful and measurable.

The expanding ecosystem of retail media networks, particularly those operated by large retailers, is also contributing to market growth. These networks are leveraging data onboarding to monetize their audience data, offering advertisers the ability to reach shoppers both within and outside their owned properties. As retail media becomes a critical component of the advertising mix, partnerships between retailers, brands, agencies, and technology providers are intensifying. This collaborative approach is fueling innovation in onboarding technologies, driving the development of more scalable, secure, and privacy-compliant solutions tailored to the unique needs of the retail sector.

From a regional perspective, North America continues to dominate the Retail Media Data Onboarding market, accounting for the largest revenue share in 2024. This leadership is attributed to the mature digital advertising landscape, high adoption of advanced marketing technologies, and the presence of major retail and e-commerce players. Europe follows closely, with significant investments in data privacy and regulatory compliance driving the need for sophisticated onboarding solutions. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding retail infrastructure, and a burgeoning middle-class consumer base. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a relatively nascent stage, as retailers in these regions increasingly recognize the benefits of integrated data strategies.

In this dynamic landscape, the role of a Reference Data Management Platform becomes increasingly crucial. As retailers and brands navigate the complexities of data onboarding, these platforms offer a structured approach to manage and integrate diverse data sources. By providing a centralized repository for reference data, these platforms ensure consistency and accuracy across all marketing channels. This capability is particularly valuable in the context of retail media, where the alignment of data from multiple sources is essential for effective audience targeting and personalization. The integration of Reference Data Management Platforms wi

Test-Maker

The Test-Maker dataset is a curated collection of question-answer pairs derived from multiple sources, designed for training AI models to generate questions for question-answering tasks. This dataset combines and deduplicates entries from three primary sources and offers a diverse range of question types and contexts.

  Dataset Composition

Dataset Source Number of Rows

BatsResearch/ctga-v1 1 628 295… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/test-maker.

Area exposed to one or more surfacial hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.

All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.

Multi-Link Operation Analytics Market Outlook

According to our latest research, the global Multi-Link Operation Analytics market size reached USD 2.1 billion in 2024, and is projected to grow at a robust CAGR of 13.9% from 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 6.6 billion. This significant growth is driven by the rising complexity of network infrastructures, the proliferation of IoT devices, and the increasing demand for real-time analytics to ensure optimal network performance and security.

The growth of the Multi-Link Operation Analytics market is largely attributed to the increasing digital transformation initiatives across diverse industries. As enterprises deploy more sophisticated and distributed network architectures, the need for advanced analytics platforms that can seamlessly monitor, optimize, and secure multi-link operations becomes paramount. The surge in cloud adoption, coupled with the exponential growth in data traffic, has compelled organizations to invest in robust analytics tools capable of handling multi-path network environments. These tools not only enhance network visibility but also empower IT teams to proactively identify and resolve issues before they impact business operations, thus fueling market expansion.

Another significant factor propelling market growth is the escalating threat landscape in the digital world. Cybersecurity has become a top priority for organizations, especially with the rise in remote work and the increasing sophistication of cyber-attacks targeting network infrastructures. Multi-Link Operation Analytics solutions offer comprehensive security management by providing real-time threat detection, traffic analysis, and anomaly identification across multiple network links. This capability is crucial for businesses aiming to safeguard sensitive data and maintain regulatory compliance, thereby driving the adoption of these analytics platforms across sectors such as BFSI, healthcare, and manufacturing.

Moreover, the evolution of next-generation technologies such as 5G, edge computing, and AI is creating new opportunities for the Multi-Link Operation Analytics market. As network environments become more dynamic and decentralized, traditional monitoring tools are no longer sufficient. Advanced analytics platforms equipped with AI and machine learning algorithms can process vast volumes of data from multiple sources, offering actionable insights for network optimization and predictive maintenance. This technological shift is encouraging both large enterprises and SMEs to invest in scalable and intelligent analytics solutions, further accelerating market growth.

From a regional perspective, North America remains the dominant market for Multi-Link Operation Analytics, driven by the presence of leading technology vendors, high adoption rates of cloud and IoT technologies, and stringent regulatory requirements. However, Asia Pacific is emerging as the fastest-growing region, owing to rapid industrialization, increasing investments in digital infrastructure, and the growing need for network optimization in countries like China, India, and Japan. Meanwhile, Europe is witnessing steady growth, supported by digital transformation initiatives and the expansion of 5G networks. Latin America and the Middle East & Africa are also showing promising potential, albeit at a slower pace, as enterprises in these regions gradually modernize their network infrastructures.

Component Analysis

The Multi-Link Operation Analytics market is segmented by component into Software, Hardware, and Services. The software segment holds the largest market share, accounting for a significant portion of global revenues in 2024. This dominance is attributed to the crucial role that analytics software plays in aggregating, processing, and visualizing network data from multiple sources. Advanced software platforms are designed to support real-time monitoring, predictive analytics, and automated reporting, which are essential for managing complex multi-link environments. As network architectures become more intricate, organizations are increasingly prioritizing investments in flexible and scalable software solutions that can seamlessly integrate with existing IT systems.

The hardware segment, while smaller compared to software, remains vital for the deployment of Multi-Link Operation Analytics so

AI in Diversity & Inclusion Market Outlook

As per our latest research, the AI in Diversity & Inclusion market size reached USD 1.38 billion in 2024 globally, reflecting rapid adoption of AI-driven solutions in human resources and organizational management. The market is set to expand at a robust CAGR of 37.2% from 2025 to 2033, with the forecasted market size expected to reach USD 21.38 billion by 2033. This remarkable growth is primarily fueled by increasing corporate commitment to diversity, equity, and inclusion (DEI) initiatives, combined with the accelerating integration of artificial intelligence across enterprise functions to mitigate bias and foster inclusive workplaces.

The primary growth factor driving the AI in Diversity & Inclusion market is the mounting pressure on organizations to demonstrate measurable progress in DEI. Regulatory scrutiny, social movements, and stakeholder demand for transparent, equitable practices have all converged to make diversity and inclusion a boardroom priority. AI-powered platforms are increasingly leveraged to analyze workforce data, identify patterns of unconscious bias, and recommend actionable interventions, helping organizations not only comply with evolving legal requirements but also build more innovative and productive teams. As companies seek to attract and retain top talent in a competitive global market, the ability to foster a genuinely inclusive culture is becoming a critical differentiator, further accelerating the adoption of AI in this space.

Another significant growth driver is the rapid evolution of AI technologies themselves, which are now capable of handling complex, unstructured data from multiple sources – including employee feedback, recruitment pipelines, and performance reviews. These advanced analytics enable real-time monitoring of diversity metrics, sentiment analysis, and predictive modeling to forecast the impact of policy changes. The scalability and efficiency of AI tools also allow organizations of all sizes to implement DEI initiatives that were previously only feasible for large enterprises with substantial resources. As a result, the democratization of AI-driven diversity solutions is opening up new opportunities across small and medium enterprises (SMEs), fueling market expansion.

Furthermore, the increasing integration of AI in HR processes such as recruitment, training, and performance management is transforming traditional approaches to diversity and inclusion. AI-enabled recruitment tools can reduce bias in job descriptions, screen candidates more objectively, and ensure diverse hiring panels, while AI-driven training platforms can personalize learning experiences to address specific inclusion gaps. These innovations are not only enhancing compliance and reporting capabilities but are also contributing to tangible improvements in employee engagement and retention. As organizations continue to invest in digital transformation, the synergy between AI and DEI is expected to drive sustained market growth over the next decade.

From a regional perspective, North America currently dominates the AI in Diversity & Inclusion market, accounting for over 38% of global revenue in 2024, followed by Europe and Asia Pacific. This leadership is attributed to the region’s advanced technological infrastructure, progressive regulatory environment, and strong corporate focus on diversity. However, Asia Pacific is projected to exhibit the fastest CAGR of 40.1% through 2033, driven by rapid digitalization, increasing awareness of DEI benefits, and rising investments from multinational corporations. Europe continues to see steady growth, underpinned by stringent DEI mandates and a mature HR tech ecosystem, while emerging markets in Latin America and the Middle East & Africa are gradually catching up as local enterprises recognize the value of inclusive practices.

Component Analysis

The AI in Diversity & Inclusion market is segmented by component into software and services, with each segment playing a pivotal role in shaping the industry landscape. The software segment comprises AI-powered analytics platforms, recruitment tools, sentiment analysis engines, and compliance management systems. These solutions are designed to automate the collection, analysis, and visualization of diversity metrics, enabling organizations to monitor and improve their DEI performance in real time. The growing sophistication of AI algorithms, particularl

Techsalerator's Job Openings Data for Poland: A Comprehensive Resource for Employment Insights

Techsalerator's Job Openings Data for Poland is an essential tool for businesses, job seekers, and labor market analysts. This dataset offers a detailed overview of job openings across various sectors in Poland, consolidating and categorizing job-related information from multiple sources, including company websites, job boards, and recruitment agencies.

Key Data Fields

• Job Posting Date: Captures the date a job is listed. This helps job seekers and HR professionals stay updated on the latest opportunities and trends.
• Job Title: Specifies the position being advertised. This aids in categorizing and filtering job openings based on industry roles and career interests.
• Company Name: Lists the hiring company. This information assists job seekers in targeting their applications and helps businesses track competitors and market trends.
• Job Location: Provides the job's geographic location within Poland. Job seekers use this to find regional opportunities, while employers analyze regional talent and market conditions.
• Job Description: Includes details about responsibilities, required qualifications, and other relevant aspects. This is vital for candidates to determine if they meet the requirements and for recruiters to communicate expectations clearly.

Top 5 Job Categories in Poland

1. Information Technology (IT): High demand for software developers, IT support specialists, and cybersecurity experts as the digital sector continues to expand.
2. Healthcare: Opportunities for doctors, nurses, medical technicians, and public health workers, driven by both public and private sector needs.
3. Finance and Accounting: Roles in financial analysis, accounting, and investment, reflecting Poland's growing financial services sector.
4. Engineering and Manufacturing: High demand for engineers and technicians across various fields, including mechanical, civil, and electrical engineering, due to ongoing industrial growth.
5. Education: Positions for teachers, administrators, and educational consultants, reflecting the country’s commitment to improving educational standards.

Top 5 Employers in Poland

• Polska Grupa Energetyczna (PGE): A leading energy provider with frequent openings in engineering, operations, and project management.
• PKN Orlen: The largest oil refiner in Poland, offering roles in operations, engineering, and management.
• Allegro: A major e-commerce platform with opportunities in IT, marketing, and logistics.
• Capgemini: A global consulting firm with positions in IT consulting, project management, and business analysis.
• University of Warsaw: One of the largest and most prestigious universities in Poland, regularly hiring for academic, administrative, and research roles.

Accessing Techsalerator’s Data

To access Techsalerator’s Job Openings Data for Poland, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields: - Job Posting Date - Job Title - Company Name - Job Location - Job Description - Application Deadline - Job Type (Full-time, Part-time, Contract) - Salary Range - Required Qualifications - Contact Information

Techsalerator’s dataset is a valuable tool for staying informed about job openings and employment trends in Poland, assisting businesses, job seekers, and analysts in making informed decisions.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.

All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1m Data: Raw 1m historical data from multiple exchanges, covering the entire trading history of BNBUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken BNBUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/aqtuPay.png" alt="BNBUSD Dataset Summary">

https://i.imgur.com/mnzs2f4.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)