The use of imaging systems in protein crystallisation means that the experimental setups no longer require manual inspection to determine the outcome of the trials. However, it leads to the problem of how best to find images which contain useful information about the crystallisation experiments. The adoption of a deeplearning approach in 2018 enabled a four-class machine classification system of the images to exceed human accuracy for the first time. Underpinning this was the creation of a labelled training set which came from a consortium of several different laboratories. The MARCO classification model does not have the same accuracy on local data as it does on images from the original test set; this can be somewhat mitigated by retraining the ML model and including local images. We have characterized the image data used in the original MARCO model, and performed extensive experiments to identify training settings most likely to enhance the local performance of a MARCO-dataset based ML classification model.

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data.

Classification of Mars Terrain Using Multiple Data Sources

Alan Kraut1, David Wettergreen1

ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

Big Data and Data Engineering Services Market Outlook

The global market size for Big Data and Data Engineering Services was valued at approximately USD 45.6 billion in 2023 and is expected to reach USD 136.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This robust growth is primarily driven by the increasing volume of data being generated across industries, advancements in data analytics technologies, and the rising importance of data-driven decision-making. Enterprises of all sizes are progressively leveraging big data solutions to gain strategic insights and maintain competitive advantage, thereby fueling market growth.

One of the pivotal growth factors for the Big Data and Data Engineering Services market is the exponential rise in data generation. With the advent of the Internet of Things (IoT), social media, and digital interactions, the volume of data generated daily is staggering. This data, if harnessed effectively, can provide invaluable insights into consumer behaviors, market trends, and operational efficiencies. Companies are increasingly investing in data engineering services to streamline and manage this data effectively. Additionally, the adoption of advanced analytics and machine learning techniques is enabling organizations to derive actionable insights, further driving the market's expansion.

Another significant growth driver is the technological advancements in data processing and analytics. The development of sophisticated data engineering tools and platforms has made it easier to collect, store, and analyze large datasets. Cloud computing has played a crucial role in this regard, offering scalable and cost-effective solutions for data management. The integration of artificial intelligence (AI) and machine learning (ML) in data analytics is enhancing the ability to predict trends and make informed decisions, thereby contributing to the market's growth. Furthermore, continuous innovations in data security and privacy measures are instilling confidence among businesses to invest in big data solutions.

The increasing emphasis on regulatory compliance and data governance is also propelling the market forward. Industries such as BFSI, healthcare, and government are subject to stringent regulatory requirements for data management and protection. Big Data and Data Engineering Services are essential in ensuring compliance with these regulations by maintaining data accuracy, integrity, and security. The implementation of data governance frameworks is becoming a top priority for organizations to mitigate risks associated with data breaches and ensure ethical data usage. This regulatory landscape is creating a conducive environment for the adoption of comprehensive data engineering services.

Regionally, North America dominates the Big Data and Data Engineering Services market, owing to the presence of major technology companies, high adoption of advanced analytics, and significant investments in R&D. However, the Asia Pacific region is expected to exhibit the highest growth rate due to rapid digital transformation, increasing internet penetration, and growing awareness about the benefits of data-driven decision-making among businesses. Europe also represents a significant market share, driven by the strong presence of industrial and technological sectors that rely heavily on data analytics.

Service Type Analysis

Data Integration is a critical component of Big Data and Data Engineering Services, encompassing the process of combining data from different sources to provide a unified view. This service type is instrumental for organizations aiming to harness data from various departments, applications, and geographies. The increasing complexity of data landscapes, characterized by disparate data sources and formats, necessitates efficient data integration solutions. Companies are investing heavily in data integration technologies to consolidate their data, improve accessibility, and enhance the quality of insights derived from analytical processes. This segment's growth is further fueled by advancements in integration tools that support real-time data processing and seamless connectivity.

Data Quality services ensure the accuracy, completeness, and reliability of data, which is essential for effective decision-making. Poor data quality can lead to misinformed decisions, operational inefficiencies, and regulatory non-compliance. As organizations increasingly recognize the criticality of data quality, there is a growing demand for robust data quality solutions. These services include da

Create a custom pipeline using enterprise data integrators which provides high consistency, reliability and scalability of data.

The process to get this is easy: - analyze data sources (max 2) - create a sample output based on your expectations/needs - start creating the pipeline: our internal workflow that provides the output defined - check and refine

Secondary data and baseline covariates of patients included in DISCOVER CKD.

Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only available for mammals, to birds. In addition, Frugivoria adds newly described species not included in other databases and harmonizes species classifications among databases. Frugivoria and its workflow enable researchers to quantify relationships between traits and the environment, as well as spatial trends in functional diversity, contributing to basic knowledge and applied conservation of frugivores in this region. By harmonizing trait information from disparate sources and providing code to access species occurrence data, this open-access database fills a major knowledge gap and enables more comprehensive trait-based studies of species exhibiting frugivory in this ecologically important region.

The Full-Link Big Data Solution market is experiencing robust growth, driven by the increasing need for real-time data analytics across diverse industries. The market's value is estimated at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors, including the proliferation of interconnected devices (IoT), the rising volume of unstructured data, and the growing demand for advanced analytics capabilities to gain actionable insights. Businesses are increasingly adopting full-link solutions to enhance operational efficiency, improve decision-making, and gain a competitive edge. Key application segments include financial services, healthcare, and retail, while prominent solution types comprise data integration platforms, data visualization tools, and advanced analytics software. The market's growth is further bolstered by ongoing technological advancements, including the adoption of cloud-based solutions and the rise of artificial intelligence (AI) and machine learning (ML) in data analysis. Geographic growth is notably strong in North America and Asia Pacific, driven by early adoption of these technologies and the presence of significant technology hubs. Despite the considerable market potential, certain restraints are present. These include the high initial investment costs associated with implementing full-link big data solutions, the complexity of integrating disparate data sources, and the need for skilled professionals to manage and interpret the insights derived. Data security and privacy concerns also pose challenges that need to be addressed. However, the ongoing development of user-friendly platforms, cost-effective solutions, and robust security measures are expected to mitigate these limitations and further stimulate market growth in the coming years. The overall forecast indicates a substantial expansion of the Full-Link Big Data Solution market, presenting significant opportunities for technology providers and businesses seeking to leverage the power of big data for enhanced performance and strategic advantage.

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and non-cancer aspects of a patient's health.

ObjectiveThis paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.Materials and methodsThe method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer’s disease (AD) across 10 different observational data sources.ResultsBy utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease’s anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity.DiscussionWe provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.ConclusionDiagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs.

This report compares adult mental health prevalence estimates generated from the 2009 National Survey on Drug Use and Health (NSDUH) with estimates of similar measures generated from other national data sources. It also describes the methodologies of the different data sources and discusses the differences in survey design and estimation that may contribute to differences among these estimates. The other data systems discussed include the 2001 to 2003 National Comorbidity Survey Replication (NCS-R), 2001 to 2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), 2007 Behavioral Risk Factor Surveillance System (BRFSS), 2008 National Health Interview Survey (NHIS), 2008 Medical Expenditure Panel Survey (MEPS), and 2008 Uniform Reporting System (URS).

This data release is a compilation of construction depth information for 12,383 active and inactive public-supply wells (PSWs) in California from various data sources. Construction data from multiple sources were indexed by the California State Water Resources Control Board Division of Drinking Water (DDW) primary station code (PS Code). Five different data sources were compared with the following priority order: 1, Local sources from select municipalities and water purveyors (Local); 2, Local DDW district data (DDW); 3, The United States Geological Survey (USGS) National Water Information System (NWIS); 4, The California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Groundwater Information System (SWRCB); and 5, USGS attribution of California Department of Water Resources well completion report data (WCR). For all data sources, the uppermost depth to the well's open or perforated interval was attributed as depth to top of perforations (ToP). The composite depth to bottom of well (Composite BOT) field was attributed from available construction data in the following priority order: 1, Depth to bottom of perforations (BoP); 2, Depth of completed well (Well Depth); 3; Borehole depth (Hole Depth). PSW ToPs and Composite BOTs from each of the five data sources were then compared and summary construction depths for both fields were selected for wells with multiple data sources according to the data-source priority order listed above. Case-by-case modifications to the final selected summary construction depths were made after priority order-based selection to ensure internal logical consistency (for example, ToP must not exceed Composite BOT). This data release contains eight tab-delimited text files. WellConstructionSourceData_Local.txt contains well construction-depth data, Composite BOT data-source attribution, and local agency data-source attribution for the Local data. WellConstructionSourceData_DDW.txt contains well construction-depth data and Composite BOT data-source attribution for the DDW data. WellConstructionSourceData_NWIS.txt contains well construction-depth data, Composite BOT data-source attribution, and USGS site identifiers for the NWIS data. WellConstructionSourceData_SWRCB.txt contains well construction-depth data and Composite BOT data-source attribution for the SWRCB data. WellConstructionSourceData_WCR.txt contains contains well construction depth data and Composite BOT data-source attribution for the WCR data. WellConstructionCompilation_ToP.txt contains all ToP data listed by data source. WellConstructionCompilation_BOT.txt contains all Composite BOT data listed by data source. WellConstructionCompilation_Summary.txt contains summary ToP and Composite BOT values for each well with data-source attribution for both construction fields. All construction depths are in units of feet below land surface and are reported to the nearest foot.

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Enterprise Data Management Market Size 2024-2028

The enterprise data management market size is estimated to grow by USD 126.2 billion, at a CAGR of 16.83% between 2023 and 2028. The market is experiencing significant growth, driven by the increasing demand for data integration and visual analytics to support informed business decision-making. Technological developments, such as cloud computing, artificial intelligence, and machine learning, are revolutionizing data management processes, enabling organizations to handle large volumes of data more efficiently. However, integration challenges persist, particularly with unscalable applications and disparate data sources. Addressing these challenges requires strong EDM solutions that ensure data accuracy, security, and accessibility. The market is expected to continue its expansion, fueled by the growing recognition of data as a strategic asset and the need for organizations to derive actionable insights from their data to gain a competitive edge.

What will be the Size of the Enterprise Data Management Market During the Forecast Period?

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Enterprise Data Management Market Segmentation

The enterprise data management market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024 to 2028, as well as historical data from 2018 to 2022 for the following segments.

End-user Outlook 

  BFSI
  Healthcare
  Manufacturing
  Retail
  Others





Deployment Outlook 

  On-premise
  Cloud-based





Region Outlook

  North America

    The U.S.
    Canada





  Europe

    U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa


  South America

    Chile
    Brazil
    Argentina

By End User

The market share growth by the BFSI segment will be significant during the forecast period. The BFSI segment dominated the market and will continue to hold a major share of the market during the forecast period. The complete digitization of core processes, the adoption of customer-centric approaches, and the rising volume of data drive the growth of the segment. The enterprise data management market is growing with advancements in data governance, master data management (MDM), and cloud-based data management. Solutions such as data integration, big data management, and data security ensure seamless operations. Enterprise data analytics, data warehousing, and real-time data processing enhance decision-making. With data quality management, business intelligence tools, and data as a service (DaaS), businesses achieve robust insights and efficient data handling.

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The BFSI segment was valued at USD 18.30 billion in 2018. The deployment allows financial institutions to manage data generated from diverse systems and processes such as loan processing, claims management, customer data management, and financial transactions electronically. Hence, it improves customer-centricity. The deployment also allows financial institutions to address sectoral challenges, which range from compliance requirements to data management, data security, transparency, and availability across platforms, time, and geographies. The growth of the BFSI segment is also driven by the need to reduce processing costs, improve operational efficiency, and ensure adherence to compliance standards.

Moreover, solutions such applications provide enterprises with financial planning, budgeting, forecasting, and financial and operational reporting abilities. BFSI companies adopt to streamline their financial planning and budgeting processes in line with their business strategies and plans. The adoption enables funds transfer pricing and provides suitable applications for the accurate calculation of the profitability of the enterprise. Thus, the growth of the BFSI will positively impact enterprise data management market growth during the forecast period.

Regional Analysis

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North America is estimated to contribute 38% to the growth of the global enterprise data management market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. Several industries, especially in the US and Canada, are early adopters of advanced technologies. Hence, the volume of data generated is high, which necessitates its use in North America. The US is the leading market in North America. It is the technological capital of the world and is one of the early adopters of cutting-edge innovations. The increase in

Master Data Management Market Outlook

The global Master Data Management (MDM) market size is estimated to reach approximately USD 18.5 billion by 2032, growing from USD 9.5 billion in 2023, with a compound annual growth rate (CAGR) of 8.5% during the forecast period. This market's growth is fueled by the increasing need for data compliance and data quality management across various industries. The proliferation of data sources and rise in digital transformation initiatives among enterprises are acting as major growth drivers for the MDM market. As businesses aim to streamline their operations and enhance customer experiences, the demand for robust master data management solutions is expected to surge.

A key growth factor for the MDM market is the escalating volume of data generated across various industry verticals. Organizations are inundated with data from diverse sources, including IoT devices, social media, transactional data, and customer interactions. This influx of data necessitates efficient management solutions to ensure data accuracy, consistency, and reliability. Master data management solutions help organizations establish a single, trusted source of data, enabling them to make informed business decisions. Additionally, stringent regulatory compliance requirements, such as GDPR and CCPA, are pushing organizations to adopt comprehensive data management solutions, further propelling market growth.

Another significant driver is the rising trend of digital transformation across the globe. Enterprises are increasingly adopting digital technologies to enhance their operational efficiency, improve customer engagement, and drive innovation. Master data management plays a crucial role in these transformation initiatives by providing a unified view of enterprise data, thus enabling improved analytics and decision-making capabilities. As businesses continue to prioritize digital maturity, the demand for advanced MDM solutions that support real-time data integration and analytics is expected to witness substantial growth.

The growing importance of customer experience management is also contributing to the expansion of the MDM market. In a highly competitive marketplace, organizations are striving to deliver personalized customer experiences to gain a competitive edge. MDM solutions enable businesses to gain a comprehensive understanding of their customers by integrating data from multiple sources and providing a 360-degree view of customer interactions. This holistic approach to data management allows organizations to anticipate customer needs, optimize marketing strategies, and enhance customer satisfaction, thereby driving market growth.

The role of MDM in modern enterprises extends beyond mere data organization. It acts as a strategic enabler, helping businesses to harness the full potential of their data assets. By implementing MDM, organizations can break down data silos, ensuring that all departments have access to consistent and accurate information. This unified approach not only enhances operational efficiency but also supports strategic initiatives such as mergers and acquisitions, where the integration of disparate data systems is crucial. As companies continue to expand globally, the ability to manage master data effectively becomes a key differentiator in maintaining competitive advantage.

Regionally, North America continues to maintain its dominant position in the master data management market, attributed to the early adoption of advanced technologies and the presence of key market players in the region. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period. This growth is driven by the rapid digital transformation of industries, increasing cloud adoption, and a growing emphasis on data governance. Additionally, the expansion of IT infrastructure and the increasing focus on customer experience in emerging economies like India and China are expected to boost the demand for MDM solutions in this region.

Component Analysis

The Master Data Management market can be bifurcated into two primary components: software and services. The software segment holds a significant share in the market due to the increasing demand for platforms that can effectively organize, manage, and utilize master data. With advancements in artificial intelligence and machine learning, software solutions are becoming more sophisticated, allowing for real-time data processing, cleansing, and integration. The adoption of cloud-ba

The PowerView extension for CKAN enables users to configure data sources in order to power views for one or more resources. This extension provides the required infrastructure and actions to define and manage how CKAN visualizes data from various sources. By leveraging this extension, CKAN administrators can define specific configurations tailored to presenting datasets effectively. Key Features: Data Source Configuration: Allows administrators to configure data sources that drive views within CKAN, enabling seamless integration of external data sources. Action API: All actions related to PowerView, facilitating automated management and integration within CKAN workflows are exposed via the CKAN Action API. Extensible View Management: Supports creation of configurable views based on defined data sources for one or more available resources. Technical Integration: The PowerView extension integrates directly with the CKAN Action API, which allows for seamless incorporation into custom workflows and automation processes. By adding powerview to the ckan.plugins setting in the CKAN configuration file, the extension is activated within a CKAN instance. This modification allows for PowerView actions to be managed. Also, this Extension will require SQL tables to be created within CKAN to properly record data mappings and associated data. Benefits & Impact: By using the PowerView extension, users can display different types of views, powered by specific data sources (e.g., databases, APIs, etc.). This facilitates better data presentation and simplifies dataset curation to visualize data from multiple data entities within the ecosystem.

Objective The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health r..., Aggregated data from 2020-05-16 to 2022-01-17 regarding Bordeaux Hospital EHR. Bordeaux hospital data warehouse was used, during the pandemic, to describe the current state of the epidemic at the hospital level on a daily basis. Those data were then used in the forecast model including: hospitalizations, hospital and ICU admission and discharge, ambulance service notes and emergency unit notes. Concepts related to COVID-19 were extracted from notes by dictionary-based approaches (e.g. cough, dyspnoea, covid-19). Dictionaries were manually created based on manual chart review to identify terms used by practitioners. Then, the number and proportion of ambulance service calls or hospitalization in emergency units mentioning concepts related to covid-19 were extracted. Due to different data acquisition mechanisms, there was a delay between the occurrence of events and the data acquisition. It was of 1 day for EHR data, 5 days for department hospitalizations and RT-PCR, 4 days for weather, 2..., Data are stored in a .rdata file. Please use R (https://www.r-project.org/) software to open the data.

Integration Platform as a Service (iPaaS) Market Outlook

According to our latest research, the global Integration Platform as a Service (iPaaS) market size reached USD 6.8 billion in 2024, demonstrating robust growth momentum. The market is poised to expand at a CAGR of 20.7% from 2025 to 2033, with revenues forecasted to reach approximately USD 44.1 billion by 2033. This remarkable growth trajectory is primarily driven by the accelerating digital transformation initiatives across enterprises, the increasing need for seamless cloud integration, and the proliferation of hybrid IT environments. As organizations strive to connect disparate applications, data sources, and business processes, iPaaS solutions have emerged as a critical enabler of agility and innovation in the modern enterprise landscape.

The rapid adoption of cloud technologies is a significant growth factor fueling the iPaaS market. Enterprises are increasingly migrating their workloads to public, private, and hybrid clouds, creating a complex ecosystem of applications and data sources that must interact efficiently. iPaaS platforms offer a unified integration solution that bridges on-premises and cloud-based systems, enabling organizations to orchestrate business processes, automate workflows, and ensure data consistency across environments. As a result, iPaaS is becoming essential for organizations seeking to streamline operations, reduce integration costs, and accelerate time-to-market for new digital services.

Another key driver for the iPaaS market is the growing demand for real-time data integration and analytics. Businesses are increasingly leveraging data-driven decision-making to gain competitive advantages, necessitating seamless integration between various data sources, applications, and analytics platforms. iPaaS solutions facilitate this by providing pre-built connectors, API management capabilities, and low-code integration tools that empower both IT and business users to create, manage, and monitor integrations with minimal coding effort. This democratization of integration capabilities is fostering greater agility, enabling organizations to respond swiftly to market changes and customer demands.

The evolution of application architectures, such as the rise of microservices, APIs, and SaaS applications, is also contributing to the growth of the iPaaS market. Traditional integration approaches are often ill-suited to the dynamic and scalable nature of modern IT environments. iPaaS platforms are designed to support these new paradigms by offering flexible, scalable, and secure integration capabilities. This adaptability is particularly valuable for organizations undergoing digital transformation, as it enables them to integrate legacy systems with new cloud-native applications, ensuring business continuity and maximizing return on investment in technology.

The emergence of Data Integration Platform as a Service (iPaaS) has revolutionized how organizations manage their data ecosystems. By offering a comprehensive suite of tools for data aggregation, transformation, and synchronization, these platforms enable businesses to seamlessly integrate disparate data sources, whether they are located on-premises or in the cloud. This capability is particularly crucial as organizations increasingly rely on data-driven insights to inform strategic decisions and enhance operational efficiency. With the ability to handle large volumes of data in real-time, Data Integration iPaaS solutions are becoming indispensable for enterprises aiming to maintain a competitive edge in today's fast-paced digital landscape.

From a regional perspective, North America continues to lead the iPaaS market, accounting for the largest share of global revenues in 2024. This dominance is attributed to the early adoption of cloud technologies, the presence of major iPaaS vendors, and a highly digitized business landscape. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid economic development, increasing cloud adoption, and a surge in digital transformation initiatives among enterprises and governments. Europe, Latin America, and the Middle East & Africa are also witnessing significant uptake of iPaaS solutions, each with unique growth driver

Brand Data Management Software Market Outlook

The global brand data management software market size was valued at USD 3.5 billion in 2023 and is expected to reach approximately USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2032. This remarkable growth is driven by the increasing need for businesses to manage and leverage vast amounts of data associated with their brand presence across multiple channels. Companies are increasingly recognizing the importance of robust brand data management systems in optimizing their brand strategies, improving customer engagement, and enhancing operational efficiencies. The rising digital transformation across industries has necessitated platforms that can streamline and integrate disparate data sources, allowing for a more cohesive brand strategy. As the market continues to evolve, the advancements in artificial intelligence and machine learning are also anticipated to play a pivotal role, offering more sophisticated data analytics tools and insights.

One of the primary growth factors driving the brand data management software market is the exponential increase in digital content and touchpoints. With the rise of social media platforms, e-commerce sites, and mobile applications, companies are inundated with brand-related data. This surge in data has created an urgent demand for sophisticated software systems that can manage, analyze, and utilize this information effectively. Companies are investing heavily in technology that can offer them a competitive edge by providing insights into customer preferences, market trends, and brand performance metrics. Moreover, the ability to personalize customer interactions based on these insights is a significant value proposition for these software solutions, further fueling their adoption across various sectors.

Another crucial factor contributing to the growth of the market is the increasing emphasis on regulatory compliance and data security. As data privacy laws become stricter around the globe, companies are under pressure to manage their brand data in a secure and compliant manner. Brand data management software helps organizations ensure that they are not only compliant with local and international data privacy regulations but also able to protect their brand integrity. This has led to a significant uptick in demand for solutions that offer robust security features and comprehensive compliance tools, especially in industries such as healthcare and finance, where data sensitivity is paramount. Consequently, organizations are looking for software vendors who can not only provide cutting-edge technology but also ensure the highest standards of data protection and compliance.

The burgeoning demand for enhanced customer experience and engagement is also a significant growth driver for the brand data management software market. As businesses strive to create seamless and personalized customer experiences, understanding customer behavior and preferences has become crucial. Brand data management software enables companies to gather, analyze, and interpret customer data from multiple sources, resulting in a comprehensive understanding of customer journeys. This insight allows businesses to tailor their marketing strategies and product offerings, thus enhancing customer satisfaction and loyalty. The ability to create unified customer profiles and deliver consistent brand messages across all touchpoints is becoming an industry standard, pushing organizations to invest in these software solutions to stay competitive.

Component Analysis

The component analysis of the brand data management software market is broadly segmented into software and services. The software segment is gaining significant traction as businesses seek comprehensive solutions to manage their brand data effectively. These software solutions offer various capabilities, such as data integration, analytics, and visualization, which empower organizations to derive actionable insights from complex datasets. With the rising trend of digital marketing and online brand presence, companies are increasingly adopting advanced software solutions that can handle real-time data processing and provide predictive analytics to enhance brand strategies. Moreover, the advent of cloud-based solutions has made these software tools more accessible and scalable, catering to the diverse needs of businesses across different industries.

The services segment within the component analysis encompasses a range of professional services that support the implementation, customization, and maintenance of brand data management software. These services are cru

These are the different datasets used in the analysis of the relation between protest, protest campaigns, and armed conflict in Colombia and South Africa. Different files are included. 1. Excell file containing a description of the different variables and their sources 2. Stata file of the data (appended data) and stata file for each hypothesis 3. Do file for the analysis used for undertaking the statistical analysis.