This repository contains all the input and output data (including maps) related to Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm. In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled Machine learning for large-scale crop yield forecasting, accessible at https://doi.org/10.1016/j.agsy.2020.103016. The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting.

Notes:

The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details).

Data

The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline.

1. Crop Area Fractions (NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).
2. Centroids (NUTS2): Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.
3. Meteo Daily Data and Meteo Dekadal Data (NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.
4. Remote Sensing Data (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.
5. Soil Data: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).
6. WOFOST data (NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al. 2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).
7. National yield statistics (NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).
8. Regional yield statistics (NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).
9. Past MCYFS Yield Forecasts (NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.

Crop ID and name mapping

2 : grain maize

6 : sugar beets

7 : potatoes

90 : soft wheat

93 : sunflower

95 : spring barley

Acknowledgements

We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.

The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around *** billion U.S. dollars by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning. What is artificial intelligence? Artificial intelligence refers to the capability of a machine that is able to replicate or simulate intelligent human behaviours such as analysing and making judgments and decisions. Originated in the computer sciences and a contested area in philosophy, artificial intelligence has evolved and developed rapidly in the past decades and AI use cases can now be found in all corners of our society: the digital voice assistants that reside in our smartphones or smart speakers, customer support chatbots, as well as industrial robots. Investments in AI Many of the biggest names in the tech industry have invested heavily into both AI acquisitions and AI related research and development. When it comes to AI patent applications by company, Microsoft, IBM, Google, and Samsung have each submitted thousands of such applications, and funding for AI related start-ups are raking in dozens of billions of dollars each year.

The global Electronic Chart Display and Information System (ECDIS) market size is estimated at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growth of the market is attributed to the increasing adoption of ECDIS systems in the maritime industry due to its enhanced safety, efficiency, and compliance with international regulations. The key drivers of the market include the increasing demand for improved navigational safety, the growing adoption of digital technologies in the maritime industry, and the rising awareness of environmental regulations. The market is also witnessing emerging trends such as the adoption of cloud-based ECDIS systems, the integration of artificial intelligence and machine learning technologies, and the development of autonomous navigation systems. However, the market faces challenges such as the high cost of implementation and maintenance of ECDIS systems, the lack of skilled personnel, and the cybersecurity concerns associated with digital navigation systems.

Gantt Chart Software Market Outlook

The global Gantt chart software market size was valued at approximately USD 300 million in 2023 and is expected to reach around USD 600 million by 2032, expanding at a compound annual growth rate (CAGR) of 7.5%. This growth is driven by the increasing demand for efficient project management tools that streamline planning, scheduling, and collaboration across various industries. The increasing complexity and scale of projects, coupled with the need for real-time progress tracking, are key growth factors propelling the adoption of Gantt chart software. With businesses striving for improved productivity and efficiency, the market is poised for significant growth over the forecast period.

The growing emphasis on the digitization of project management processes is a major factor fueling the growth of the Gantt chart software market. As organizations become more complex and geographically dispersed, the need for tools that facilitate seamless communication and coordination among team members and stakeholders has become crucial. Gantt chart software offers a visual representation of project timelines, resources, and dependencies, enabling project managers to efficiently allocate resources and foresee potential bottlenecks. This efficiency in project execution helps businesses meet deadlines and optimize resource utilization, thereby driving the demand for such software solutions globally.

Another significant growth driver for the Gantt chart software market is the increasing adoption of cloud-based solutions. Cloud technology offers multiple benefits, including accessibility, scalability, and reduced infrastructure costs, which make it attractive for enterprises of all sizes. Cloud-based Gantt chart software allows teams to collaborate in real-time, regardless of their geographical location, providing flexibility and fostering innovation. Moreover, the integration of advanced technologies such as artificial intelligence and machine learning into Gantt chart software aids in predictive analytics, offering insights into project timelines and resource management, further enhancing the software's value proposition.

The rising trend of remote work and the need for virtual collaboration tools also significantly contribute to market growth. As more organizations embrace remote work policies, the demand for software that supports distributed teams and facilitates seamless project management has surged. Gantt chart software caters to this need by providing a centralized platform for tracking project progress, assigning tasks, and ensuring accountability among team members. This shift towards remote work environments has accelerated the adoption rate of Gantt chart software, as it addresses the challenges posed by geographic dispersion and fosters efficient project execution.

Regionally, North America remains a dominant player in the Gantt chart software market, driven by the presence of a large number of technology companies and a well-established IT infrastructure. The region's focus on innovation and the adoption of advanced project management tools contribute significantly to market growth. Meanwhile, the Asia Pacific region is expected to experience the highest growth rate due to rapid industrialization, increased IT investments, and the burgeoning startup ecosystem in countries like China and India. As organizations in these regions seek to enhance their project management capabilities, the demand for Gantt chart software is anticipated to rise substantially.

Deployment Type Analysis

The deployment type segment of the Gantt chart software market is categorized into on-premises and cloud-based solutions. On-premises deployment involves the installation of software on local servers within the organization. This type of deployment offers increased control over data security and customization, making it a preferred choice for large enterprises with strict compliance requirements. However, it often comes with higher upfront costs and maintenance responsibilities, which can be a deterrent for smaller businesses. Despite these challenges, on-premises solutions remain relevant for industries requiring stringent data privacy and security measures, such as banking and healthcare.

Cloud-based deployment, on the other hand, has gained significant traction due to its flexibility, scalability, and cost-effectiveness. It offers businesses the advantage of accessing project management tools from anywhere, at any time, facilitating seamless collaboration among team members. Cloud-based solutions eliminate the need for extensive infrastructure inv

Graph Analytics Market Outlook

According to our latest research, the global graph analytics market size reached USD 2.9 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 31.2% during the forecast period, reaching USD 26.5 billion by 2033. This accelerated growth is driven by the increasing need for advanced analytics to uncover complex relationships within large and interconnected datasets, as organizations seek to enhance decision-making, detect fraud, and improve customer experiences with actionable insights.

A primary growth factor in the graph analytics market is the exponential rise in data complexity and volume generated by digital transformation initiatives. Modern enterprises are leveraging IoT devices, social media, and transactional systems that produce vast amounts of structured and unstructured data. Traditional analytics tools often fall short in identifying intricate relationships and patterns within these datasets. Graph analytics, with its ability to map and analyze complex networks, offers a powerful solution for extracting valuable insights from interconnected data points. As organizations recognize the limitations of conventional analytics and the advantages of graph-based approaches, demand for graph analytics platforms continues to surge across sectors such as BFSI, healthcare, and retail.

Another significant driver for the graph analytics market is the increasing adoption of AI and machine learning technologies. Graph analytics enhances the performance of machine learning models by providing contextual information and relationship mapping, which are crucial for applications like fraud detection, recommendation engines, and risk management. The integration of graph analytics with AI workflows enables more accurate predictions and real-time decision-making, especially in industries where timely insights are critical. This synergy between AI and graph analytics is encouraging enterprises to invest in advanced analytics solutions, further fueling market expansion.

The proliferation of cloud computing is also playing a pivotal role in the growth of the graph analytics market. Cloud-based deployment offers scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to organizations of all sizes. As businesses increasingly migrate their data and analytics workloads to the cloud, the adoption of cloud-native graph analytics solutions is accelerating. These solutions facilitate seamless integration with existing IT infrastructure and support real-time analytics at scale, enabling organizations to respond swiftly to evolving business needs and market dynamics.

Regionally, North America continues to lead the graph analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of major technology players, early adoption of advanced analytics, and high investment in digital transformation initiatives are key factors driving market growth in these regions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding e-commerce, and increasing focus on data-driven decision-making in emerging economies. As organizations worldwide recognize the strategic value of graph analytics, the market is poised for significant expansion across all major regions during the forecast period.

Component Analysis

The graph analytics market by component is segmented into software and services. The software segment currently dominates, accounting for the majority of market revenue in 2024. This dominance is attributed to the growing adoption of advanced graph analytics platforms that enable organizations to visualize, query, and analyze complex relationships within their data. These software solutions are becoming increasingly sophisticated, offering features such as real-time analytics, integration with AI and machine learning, and support for large-scale datasets. As

The market for artificial intelligence grew beyond *** billion U.S. dollars in 2025, a considerable jump of nearly ** billion compared to 2023. This staggering growth is expected to continue, with the market racing past the trillion U.S. dollar mark in 2031. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together, these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on various factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

The Knowledge Area Mapping Map market is experiencing robust growth, driven by the increasing need for efficient knowledge management and improved organizational learning within enterprises. The market's expansion is fueled by the rising adoption of digital transformation initiatives across various industries, leading to a surge in data volume and complexity. Organizations are increasingly recognizing the value of effectively mapping their knowledge assets to optimize resource allocation, enhance collaboration, and drive innovation. This market is segmented by application (e.g., training and development, research and development, strategic planning) and type (e.g., software, services). While precise market sizing requires specific data, considering a similar technology market's average CAGR of 15% and a current market size estimate of $500 million (2025), we can project a steady increase in market value, reaching approximately $700 million by 2026 and continuing this upward trend throughout the forecast period. This growth is influenced by factors such as advancements in artificial intelligence (AI) and machine learning (ML), enhancing knowledge mapping capabilities. However, challenges remain, including the integration of knowledge mapping solutions with existing enterprise systems and the need for skilled professionals to effectively manage and interpret the resulting maps. The geographic distribution of the market shows significant potential across various regions. North America and Europe currently hold a dominant market share due to the higher adoption of advanced technologies and a strong focus on knowledge management practices. However, Asia Pacific is expected to show substantial growth in the coming years due to increasing digitalization and a large pool of potential users in countries like China and India. Competitive landscape analysis reveals a mix of established players and emerging startups offering various solutions. The market’s growth will be shaped by ongoing technological advancements, regulatory changes influencing data privacy, and the evolving needs of different industry verticals. Further research into specific application areas and regional markets will provide a more granular understanding of the growth trajectory and opportunities within this dynamic space.

According to Cognitive Market Research, the global AI Server Market size will be USD 143524.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 35.20% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 53104.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 41622.19 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 34445.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 37.2% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 5453.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 34.2% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 5740.99 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 3157.55 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.9% from 2025 to 2033.
Inference server category is the fastest growing segment of the AI Server industry

Market Dynamics of AI Server Market

Key Drivers for AI Server Market

Increasing Demand for High-Performance AI Computing Infrastructure Across Key Sectors

A major driver of the AI server market is the rising demand for advanced computing infrastructure as artificial intelligence becomes central to sectors like healthcare, finance, and autonomous technology. With the growing integration of generative AI and machine learning across industries, there is an urgent need for high-performance servers to support complex AI workloads. This trend is prompting major players such as Dell, HPE, and Lenovo to ramp up investments in AI server solutions. Lenovo, for instance, recently saw a significant boost in revenue—up by 20% in a single quarter—largely due to increased spending on AI infrastructure, helping the company outperform profit expectations and support a broader recovery in the tech sector.

Growing Demand for Cloud-Based AI Services and Its Impact on the AI Server Market

The expansion of cloud-based AI services is a key driver propelling the AI server market forward. As organizations increasingly adopt AI for business intelligence, automation, and customer engagement, cloud platforms offer a scalable and cost-effective solution to deploy these technologies without the need for extensive on-premise infrastructure. Major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are investing heavily in AI-optimized server hardware to support the growing demand for machine learning and inference capabilities on the cloud. These platforms allow enterprises of all sizes to access powerful AI tools and infrastructure on a pay-as-you-go basis, democratizing the use of advanced AI. This trend is accelerating the deployment of AI servers in hyperscale data centres globally, fueling the growth of the overall market. Moreover, with the increasing integration of AI into cloud-native applications and services, the need for more efficient and high-performance AI servers is only expected to rise.

Restraint Factor for the AI Server Market

High Costs of Investment and Maintenance for AI Server Infrastructure

A key restraint in the AI server market is the high cost of both initial investment and ongoing maintenance of AI-optimized server infrastructure. Deploying AI solutions, including deep learning and large-scale data processing, demands specialized hardware, such as powerful GPUs and high-performance CPUs, which are often costly. Beyond the upfront capital expenditure, there are continuous operational costs related to energy consumption, cooling systems, and software licenses. While large organizations with substantial budgets can manage these expenses, smaller businesses or those with limited resources may find it difficult to justify the investment, hindering the broader adoption of AI server technologies across various sectors. Introduction of the AI Server Market

The AI server market refers to the specialized computing infr...

The resolution test target market, encompassing a range of products used to assess the resolving power of imaging systems, is experiencing robust growth. While precise market sizing data for 2025 is unavailable, a logical estimation based on typical growth trajectories in the optics and imaging sectors suggests a market value of approximately $150 million in 2025. This market is driven by several key factors, including the increasing demand for high-resolution imaging in various applications such as medical imaging, industrial inspection, and semiconductor manufacturing. Technological advancements in imaging systems, leading to higher resolutions and increased need for accurate testing, further fuel market expansion. The rising adoption of automation and sophisticated testing procedures across diverse industries also contributes significantly to market growth.
Furthermore, emerging trends such as the growth of artificial intelligence (AI) and machine learning (ML) in image analysis are expected to further boost demand for precise resolution test targets. These technologies rely heavily on accurate image resolution for efficient analysis, necessitating high-quality test targets. However, the market faces some restraints, including high initial investment costs associated with advanced testing equipment and potential supply chain disruptions affecting the availability of specialized materials. Despite these challenges, the overall market outlook remains positive, driven by the continuous innovation in imaging technologies and the expanding application areas for high-resolution imaging. The market is segmented by type (e.g., resolution charts, USAF 1951 targets, Siemens stars), application (e.g., microscopy, machine vision, aerial photography), and end-user industry. Key players like Edmund Optics, Thorlabs, and others are constantly innovating and expanding their product lines to meet the growing demand.

The HD Map for Autonomous Vehicle market is poised for significant growth, projected to reach $140.5 million in 2025 and expanding at a Compound Annual Growth Rate (CAGR) of 3.5% from 2025 to 2033. This expansion is driven by the increasing adoption of autonomous vehicles across passenger and commercial car applications. The shift towards cloud-based HD map solutions offers scalability and real-time updates, fueling market growth. Furthermore, advancements in sensor technology and artificial intelligence are enhancing the accuracy and reliability of HD maps, paving the way for safer and more efficient autonomous driving systems. Competitive landscape analysis reveals a diverse range of players, including established map providers, technology giants, and specialized autonomous driving companies, each contributing to innovation and market expansion. The North American market currently holds a substantial share due to early adoption and significant investments in autonomous vehicle technology. However, growing technological advancements and supportive government policies in regions like Asia-Pacific and Europe are expected to stimulate market expansion in these regions over the forecast period. The embedded segment, offering integration directly within the vehicle, is also expected to see considerable growth, driven by enhanced security and reliability. The restraints on market growth primarily stem from the high initial investment costs associated with developing and deploying HD map infrastructure, as well as the complexities involved in data collection, processing, and management. Regulatory hurdles and concerns related to data security and privacy also present challenges. However, ongoing technological breakthroughs, decreasing hardware costs, and increasing collaboration between stakeholders are likely to mitigate these constraints. Future growth will be significantly impacted by the rate of autonomous vehicle adoption, improvements in map accuracy and real-time updates, and the successful development of standardized data formats and protocols across the industry. The market segmentation, encompassing both cloud-based and embedded systems across passenger and commercial vehicle applications, ensures a diverse and adaptable market structure, conducive to sustained growth.

The global data visualization market, valued at $9.84 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various industries necessitates effective visualization tools for insightful analysis and decision-making. Furthermore, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market growth. Advances in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly with data visualization platforms, enhancing automation and predictive capabilities, further stimulating market demand. The BFSI (Banking, Financial Services, and Insurance) sector, along with IT and Telecommunications, are major adopters, leveraging data visualization for risk management, fraud detection, customer relationship management, and network optimization. However, challenges remain, including the need for skilled professionals to effectively utilize these tools and concerns regarding data security and privacy. The market segmentation reveals a strong presence of executive management and marketing departments across organizations, highlighting the strategic importance of data visualization in business operations. The market's competitive landscape is characterized by established players like SAS Institute, IBM, Microsoft, and Salesforce (Tableau), along with emerging innovative companies. This competition fosters innovation and drives down costs, making data visualization solutions more accessible to a broader range of businesses and organizations. Regional variations in market penetration are expected, with North America and Europe currently holding significant shares, but Asia Pacific is poised for substantial growth, driven by rapid digitalization and technological advancements in the region. The on-premise deployment mode still holds a considerable market share, though the cloud/on-demand segment is experiencing faster growth due to its inherent advantages. The ongoing trend towards self-service business intelligence (BI) tools is empowering end-users to access and analyze data independently, increasing the overall market demand for user-friendly and intuitive data visualization platforms. Future growth will depend on continued technological advancements, expanding applications across diverse industries, and addressing the existing challenges related to data skills gaps and security concerns. This report provides a comprehensive analysis of the Data Visualization Market, projecting robust growth from $XX Billion in 2025 to $YY Billion by 2033. It covers the period from 2019 to 2033, with a focus on the forecast period 2025-2033 and a base year of 2025. This in-depth study examines key market segments, competitive landscapes, and emerging trends influencing this rapidly evolving industry. The report is designed for executives, investors, and market analysts seeking actionable insights into the future of data visualization. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Lack of Tech Savvy and Skilled Workforce/Inability. Notable trends are: Retail Segment to Witness Significant Growth.

Quantifying the spatial and temporal footprint of multiple environmental stressors on marine fisheries is imperative to understanding the effects of changing ocean conditions on living marine resources. Pacific Cod (Gadus macrocephalus), an important marine species in the Gulf of Alaska ecosystem, has declined dramatically in recent years, likely in response to extreme environmental variability in the Gulf of Alaska related to anomalous marine heatwave conditions in 2014–2016 and 2019. Here, we evaluate the effects of two potential environmental stressors, temperature variability and ocean acidification, on the growth of juvenile Pacific Cod in the Gulf of Alaska using a novel machine-learning framework called “stress-scapes,” which applies the fundamentals of dynamic seascape classification to both environmental and biological data. Stress-scapes apply a probabilistic self-organizing map (prSOM) machine learning algorithm and Hierarchical Agglomerative Clustering (HAC) analysis to produce distinct, dynamic patches of the ocean that share similar environmental variability and Pacific Cod growth characteristics, preserve the topology of the underlying data, and are robust to non-linear biological patterns. We then compare stress-scape output classes to Pacific Cod growth rates in the field using otolith increment analysis. Our work successfully resolved five dynamic stress-scapes in the coastal Gulf of Alaska ecosystem from 2010 to 2016. We utilized stress-scapes to compare conditions during the 2014–2016 marine heatwave to cooler years immediately prior and found that the stress-scapes captured distinct heatwave and non-heatwave classes, which highlighted high juvenile Pacific Cod growth and anomalous environmental conditions during heatwave conditions. Dominant stress-scapes underestimated juvenile Pacific Cod growth across all study years when compared to otolith-derived field growth rates, highlighting the potential for selective mortality or biological parameters currently missing in the stress-scape model as well as differences in potential growth predicted by the stress-scape and realized growth observed in the field. A sensitivity analysis of the stress-scape classification result shows that including growth rate data in stress-scape classification adjusts the training of the prSOM, enabling it to distinguish between regions where elevated sea surface temperature is negatively impacting growth rates. Classifications that rely solely on environmental data fail to distinguish these regions. With their incorporation of environmental and non-linear physiological variables across a wide spatio-temporal scale, stress-scapes show promise as an emerging methodology for evaluating the response of marine fisheries to changing ocean conditions in any dynamic marine system where sufficient data are available.

Data Asset Map System Market Outlook

The global Data Asset Map System market size was valued at USD 1.8 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a CAGR of 10.4% from 2024 to 2032. The rising demand for efficient data management solutions across various industries acts as a significant growth factor in this market. Data Asset Map Systems are becoming crucial as organizations continue to generate vast amounts of data, driving the need for systems that can ensure data integrity, accessibility, and security.

One of the primary growth drivers for the Data Asset Map System market is the increasing volume of data generated across industries. Companies are recognizing the necessity of organizing and mapping their data assets to extract valuable insights and make informed decisions. This growing emphasis on data-driven decision-making processes is encouraging the adoption of Data Asset Map Systems. Additionally, regulatory requirements regarding data management and compliance are pushing organizations to implement these systems to avoid legal complications and financial penalties.

Technological advancements in data analytics and artificial intelligence are also propelling the market forward. The integration of AI and machine learning technologies with Data Asset Map Systems enhances their capability to process and analyze large datasets efficiently. This synergistic effect not only improves operational efficiency but also provides predictive insights, which are invaluable for strategic planning and decision-making. The continual evolution of these technologies ensures that Data Asset Map Systems will remain relevant and increasingly effective in the coming years.

The growing trend of digital transformation initiatives across various sectors is another significant growth factor. Organizations are transitioning from traditional data management practices to more sophisticated, automated solutions to stay competitive in the digital age. Data Asset Map Systems are integral to these digital transformation efforts, offering scalable and flexible solutions for data management that can adapt to the evolving needs of businesses. This shift is particularly evident in sectors such as healthcare, BFSI, and retail, where data plays a crucial role in operations and strategic planning.

From a regional perspective, North America holds a substantial share of the Data Asset Map System market. The region's advanced technological infrastructure and high adoption rate of innovative data management solutions contribute to this dominance. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period. The rapid digitalization and increasing awareness of data management solutions in countries such as China and India are key factors driving the market in this region.

Component Analysis

The Component segment of the Data Asset Map System market is divided into Software and Services. The Software segment holds the largest market share, driven by the increasing need for robust and scalable software solutions that can efficiently manage and map data assets across various industries. These software solutions offer a range of functionalities, including data cataloging, data lineage tracking, and metadata management, which are essential for comprehensive data asset management. The continuous development and enhancement of software capabilities are expected to further fuel the growth of this segment.

Services, on the other hand, are gaining traction as organizations seek expert assistance to implement and optimize their Data Asset Map Systems. This segment includes professional services such as consulting, system integration, and support & maintenance. The demand for these services is driven by the complexity of data management projects and the need for specialized skills to ensure successful deployment and operation of data asset mapping solutions. As data management becomes more sophisticated, the role of service providers will become increasingly critical, offering tailored solutions and support to meet specific organizational needs.

The integration of artificial intelligence and machine learning within software solutions is a notable trend in this segment. These technologies enhance the capabilities of Data Asset Map Systems, enabling automated data processing, anomaly detection, and predictive analytics. This integration not only improves the efficiency of data management processes but also provides valuable insights that can drive strategic decision-making. As a result, the Softw

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(GTLS) Chart Industries: Poised for Growth in a Cooling World

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking. Here, we propose a map point-of-interest classification method based on machine learning technology and build a dataset of the distribution of Chinese manufacturing firms called the Gridded Manufacturing Dataset. This dataset includes the number and type of manufacturing firms at a 0.01° latitude by 0.01° longitude scale. It includes all manufacturing firms (classified into seven categories) in China in 2015 (4.40 million) and 2019 (6.01 million). This dataset can be used to characterize temporal and spatial patterns in the distribution of manufacturing firms as well as reveal the mechanisms underlying the development of the manufacturing industry and changes in regional economic policies.

The Location Intelligence (LI) Tools market, currently valued at $15.66 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 12.3% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability of location data from various sources, including GPS, mobile devices, and social media, fuels the demand for sophisticated tools to analyze and leverage this information. Businesses across diverse sectors, including retail, logistics, marketing, and urban planning, are increasingly recognizing the strategic value of location intelligence for optimizing operations, improving decision-making, and gaining a competitive edge. Furthermore, advancements in technologies such as AI, machine learning, and cloud computing are enhancing the capabilities of LI tools, making them more accessible and powerful. The rise of location-based services (LBS) and the growing need for real-time insights further contribute to market growth. However, the market also faces certain restraints. The complexity of LI tools and the need for specialized skills to effectively utilize them can pose a barrier to entry for some businesses. Data security and privacy concerns also play a significant role, as the handling of location data requires robust measures to protect sensitive information. Despite these challenges, the long-term outlook for the LI tools market remains positive, driven by ongoing technological advancements and the increasing adoption of data-driven strategies across various industries. The market is segmented by deployment (cloud, on-premise), functionality (geocoding, spatial analysis, route optimization), and end-user (government, enterprises), although specific segment data is not provided. Key players such as Esri, Pitney Bowes, and Google are at the forefront of innovation and market share, constantly developing and refining their offerings to cater to evolving industry needs.

The self-driving 3D high-precision map market is experiencing rapid growth, driven by the escalating demand for autonomous vehicles and advanced driver-assistance systems (ADAS). The market's expansion is fueled by several key factors, including advancements in sensor technologies (LiDAR, radar, cameras), increasing investments in R&D by automotive manufacturers and technology companies, and the growing adoption of connected car technologies. The market is segmented by application (L1/L2+ Driving Automation, L3 Driving Automation, Others) and type (Crowdsourcing Model, Centralized Mode). The centralized mode currently dominates, leveraging sophisticated data processing and management systems, but crowdsourcing is gaining traction due to its cost-effectiveness and ability to rapidly update map data, particularly in dynamic urban environments. Leading players like TomTom, Google, and Baidu are strategically investing in enhancing their mapping capabilities and expanding their geographical coverage to capitalize on this burgeoning market. While challenges exist, such as high initial investment costs for infrastructure and data acquisition, and data security and privacy concerns, the long-term outlook remains exceptionally positive, driven by the inevitable shift towards autonomous driving technologies. The regional market exhibits significant variations. North America and Europe currently hold the largest market share, owing to the presence of established automotive and technology industries, and advanced regulatory frameworks supporting autonomous vehicle development. However, the Asia-Pacific region, particularly China and India, shows strong growth potential due to the rapid expansion of the automotive sector and significant government initiatives promoting autonomous driving technologies. Future market growth will be significantly influenced by advancements in artificial intelligence (AI) and machine learning (ML), enabling more accurate and dynamic map updates. Competition is intensifying, with both established mapping companies and emerging tech giants vying for market dominance through strategic partnerships, acquisitions, and technological innovations. Focus areas for future development include improving map accuracy, enhancing real-time updates, and addressing scalability challenges to support the increasing number of connected and autonomous vehicles.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can Cavendish Financial (CAV) Chart a Course for Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

According to Cognitive Market Research, the global IT spending market size is USD 4251.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.20% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 1700.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.4% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1275.3 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 977.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 212.56 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.6% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 85.02 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.9% from 2024 to 2031.

Increasing AI Investments to Drive the Market Growth

Growth in overall IT spending is being supported by investments in AI more widely, which is projected to drive the market growth during the forecast period. Businesses' investments in projects aimed at optimising organisational efficiency are mostly to blame for this. Furthermore, AI may have an even more profound and quick economic impact on IT spending which is propelling the market growth. Businesses in both established and emerging industries stand to gain from the fusion of human and machine intelligence. AI productivity advances have the potential to increase business profits and wages. By taxing greater salaries of both employees and businesses, it might even strengthen government finances. The innovation of artificial intelligence (AI) may lead to shifts in market leadership, global economic growth, and investment opportunities as organisations throughout the world implement the technology.

Increasing Spending on the Cloud to Propel the Market Growth

Rising spending on cloud by market players anticipated driving the market growth during the forecast period. Growing performance and efficiency, greater flexibility and dependability, and a reduction in IT expenses are all provided by the cloud. Additionally, it enhances innovation, enabling businesses to launch more quickly and integrate AI and machine learning use cases into their plans. In addition, acquire more in-depth knowledge about expenditure and cloud utilisation in a multicloud setting. Market players able to spot chances for cost savings as well as underutilised and wasted resources which is one of the factor which is fuelling the market growth. Comprehensive understanding of how a company employs cloud resources for various business divisions. This makes it possible to centrally tag cloud resources across providers for improved resource management.

Market Restraints of the IT Spending Market

High Implementation and Maintenance Costs:

Despite the long-term benefits of IT systems, the initial capital investment required for infrastructure setup, software licensing, integration, and skilled personnel can be substantial—especially for small and medium enterprises (SMEs). Additionally, ongoing maintenance, cybersecurity upgrades, and technical support add to the total cost of ownership, often leading businesses to delay or scale back their IT spending.

Rapid Technological Obsolescence:

The fast pace of innovation in IT—such as the frequent emergence of new hardware, software, and digital tools—creates a challenge for organizations to keep up. Technology becomes outdated quickly, leading to a shortened lifecycle for IT assets. This rapid obsolescence can deter organizations from making large-scale IT investments, as they fear their systems will become irrelevant or incompatible within a short timeframe.

Impact of Covid-19 on the IT Spending Market

Some industries were affected by the COVID-19 pandemic because of supply chain difficulties, workforce shortages, and lockdowns. The COVID-19 epidemic has severely impacted the Indian economy, bringing with it a host of new challenges that point to a significant shift in the dynamics of the market. People's spending patterns were seen to shift from indulgence to hoarding throughout the pandemic.

COVID...