This statistic illustrates the answers to a survey question on the usage of big data among SMEs in the Netherlands in 2018, by business unit. As of 2018, 26 percent of the respondents mentioned that they make use of big data with their marketing/sales department, whereas approximately 20 percent of the respondents indicated to use big data for pre-sales. Lowest use of big data is the HR department with six percent of the SME respondents.

The Chinese Big Data market presents a compelling investment landscape, projected to experience robust growth. With a Compound Annual Growth Rate (CAGR) of 30% from 2019 to 2033, the market's value is expected to surge significantly. Several key drivers fuel this expansion. The burgeoning digital economy in China, coupled with increasing government initiatives promoting data-driven decision-making across sectors, is creating substantial demand for big data solutions. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are inextricably linked to big data, fostering innovation and creating new applications across diverse industries, including BFSI, healthcare, retail, and manufacturing. The adoption of cloud-based big data solutions is accelerating, offering scalability and cost-effectiveness for businesses of all sizes. However, challenges remain, including data security concerns, a lack of skilled professionals, and the need for robust data governance frameworks. These restraints, while present, are not expected to significantly impede the overall market trajectory given the substantial opportunities and government support.
The market segmentation reveals diverse investment avenues. The cloud deployment model is projected to dominate due to its advantages, while the large enterprise segment presents the largest revenue pool. Within solutions, customer analytics, fraud detection, and predictive maintenance are currently high-growth areas, offering attractive ROI. Geographically, China itself represents a significant portion of the market, although international players are also gaining traction. Considering the robust CAGR and the diverse segments, strategic investments targeting cloud-based solutions, AI-powered analytics, and specific industry verticals (like BFSI and healthcare) hold significant promise for high returns. Careful consideration of regulatory landscapes and data privacy regulations is crucial for successful investment strategies within this dynamic market. Investment Opportunities of Big Data Technology in China This comprehensive report analyzes the burgeoning investment opportunities within China's Big Data Technology sector, offering a detailed forecast from 2019-2033. The report utilizes 2025 as its base and estimated year, covering the historical period (2019-2024) and forecasting market trends from 2025-2033. It delves into market dynamics, key players, and emerging trends shaping this rapidly expanding industry. This report is crucial for investors, businesses, and analysts seeking to understand and capitalize on the immense potential of China's big data market. Recent developments include: November 2022 - Alibaba announced the Innovative upgrade, and Greener 11.11 runs wholly on Alibaba Cloud, whereas Alibaba Cloud's dedicated processing unit powered 11.11 for the Apsara Cloud operating system. The upgraded infrastructure system significantly improved the efficiency of computing, storage, etc., October 2022 - Huawei Technologies Co.has unveiled its 4-in-1 hyper-converged enterprise gateway NetEngine AR5710, delved into the latest CloudCampus 3.0 + Simplified Solution, and launched a series of products for large enterprises and Small- and Medium-Sized Enterprises (SMEs). With these new offerings, Huawei aims to help enterprises simplify their campus networks and maximize digital productivity.. Key drivers for this market are: 6.1 Data Explosion: Unstructured, Semi-structured and Complex6.2 Improvement in Algorithm Development6.3 Need for Customer Analytics. Potential restraints include: 7.1 Lack of General Awareness And Expertise7.2 Data Security Concerns. Notable trends are: Need for Customer Analytics to Increase Exponentially Driving the Market Growth.

Big data analysis by size class of enterprise Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Big data analysis by NACE Rev. 2 activity Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

The Data Processing Unit (DPU) market is projected to grow from $20.83 billion in 2019 to $44.06 billion by 2033, exhibiting a CAGR of 6.0% during the forecast period. DPUs are specialized processors that offload data processing tasks from CPUs, improving overall system performance and efficiency. They find applications in various sectors, including cloud computing, artificial intelligence, big data analytics, gaming, and IoT devices. The growing demand for data processing capabilities, driven by the proliferation of data-intensive applications, is a major driver of market expansion. Key trends in the DPU market include the adoption of high-performance architectures, such as ARM and RISC-V, which offer enhanced processing power and energy efficiency. Additionally, the integration of DPUs with CPUs into custom chip solutions is gaining traction, enabling optimized performance for specific applications. The increasing deployment of DPUs in end-user industries such as healthcare, finance, and telecommunications is also contributing to market growth. Regional analysis indicates strong growth prospects in North America and Asia Pacific, with the former being driven by the presence of major data center operators and the latter by the rapid adoption of cloud services and AI applications. Key drivers for this market are: Growing demand for AI applications Expansion of cloud computing services Increasing IoT device connectivity Rising need for big data analytics Advancements in semiconductor technology. Potential restraints include: increasing data demand, technological advancements; competition among manufacturers; growing AI applications; cloud computing growth.

The Computer Room Air Conditioning (CRAC) unit market, valued at $5.122 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 16.1% from 2025 to 2033. This surge is driven by the escalating demand for data centers globally, fueled by the exponential growth of data generated by cloud computing, big data analytics, and the Internet of Things (IoT). The increasing adoption of high-density computing equipment, requiring more efficient cooling solutions, is another significant factor. Furthermore, the market is witnessing a shift towards energy-efficient liquid-cooled CRAC units as businesses seek to reduce operational costs and minimize their environmental impact. The large data center segment is expected to dominate the application landscape due to the extensive cooling requirements of large-scale data center infrastructures. However, the small and medium data center segment is also showing promising growth, driven by the rising adoption of cloud services and edge computing technologies by SMEs. Technological advancements leading to more compact, modular, and intelligent CRAC units are further shaping the market's trajectory. Geographical distribution reflects this global expansion. North America and Europe currently hold significant market share, with the United States and the UK emerging as major consumers. However, the Asia-Pacific region is poised for significant growth, driven by rapid digitalization in countries like China and India. Competition in this market is intense, with established players such as Vertiv, Schneider Electric, and Stulz alongside emerging regional manufacturers vying for market dominance. While the market faces potential restraints like supply chain disruptions and the rising cost of raw materials, the strong demand for efficient data center cooling is expected to continue to drive significant market expansion in the forecast period. Strategic partnerships, technological innovations, and geographic expansion will likely be key strategies for success in this dynamic sector.

The global data center rack power distribution unit (PDU) market size was valued at USD 674.52 million in 2025 and is projected to expand at a compound annual growth rate (CAGR) of 9.80% from 2025 to 2033, reaching USD 1,457.28 million by 2033. The market is driven by the growing demand for data centers due to the increasing volume of data generated and processed worldwide. Additionally, the adoption of cloud computing, big data, and artificial intelligence (AI) is also contributing to the growth of the market. The in-row deployment mode segment accounted for the largest share of the market in 2025 and is projected to continue to dominate the market during the forecast period. This is due to the fact that in-row PDUs provide better cooling and power distribution efficiency than other deployment modes. The 1 phase form factor segment is also expected to witness significant growth during the forecast period, owing to the increasing adoption of single-phase power supplies in data centers. The 120V voltage level segment is projected to hold a substantial share of the market during the forecast period. This is due to the fact that 120V is the most common voltage level used in data centers. The 0-5kW power capacity segment is also expected to witness significant growth during the forecast period, owing to the increasing adoption of small and medium-sized data centers. The data center rack power distribution unit (PDU) market is expected to grow from $2.3 billion in 2021 to $3.6 billion by 2026, at a 9.4% CAGR. The growth of the market is attributed to the increasing demand for data centers and the need for efficient power distribution systems. Key drivers for this market are: Growing demand for cloud computing, Edge computing;increasing need for reliable and efficient power distribution; rise in adoption of high-density servers; expansion of data centers. Potential restraints include: Increased cloud computing, AI-adoption; growing data center infrastructure; rising demand for energy efficiency; technological advancements.

The global market for Power Distribution Units (PDUs) for data centers is experiencing robust growth, projected to reach $1759 million in 2025. This signifies a substantial market opportunity, driven primarily by the escalating demand for data center infrastructure to support the burgeoning digital economy. The increasing adoption of cloud computing, big data analytics, and the Internet of Things (IoT) are key factors fueling this demand. Furthermore, the growing need for enhanced power management and monitoring capabilities within data centers is bolstering the adoption of advanced PDUs with intelligent features. The market's compound annual growth rate (CAGR) of 6.3% from 2019 to 2033 reflects a consistent and sustained expansion, indicating a promising long-term outlook for the industry. Leading vendors like Schneider Electric (APC Corp), Eaton Corporation, and Emerson Electric are actively investing in research and development to introduce innovative solutions that cater to the evolving needs of data center operators, which includes efficient energy distribution, remote monitoring and improved safety features. The market segmentation, although not explicitly provided, likely includes various PDU types (basic, metered, intelligent) and deployment models (rack-mounted, floor-standing). This segmentation presents several opportunities for specialized product development and targeted marketing strategies. The projected market growth is expected to continue, fueled by several key factors. The increasing prevalence of edge computing, which necessitates the deployment of smaller, more efficient data centers closer to end-users, will drive demand for smaller, more specialized PDUs. Moreover, ongoing advancements in PDU technology, such as improved energy efficiency, remote monitoring capabilities, and integration with data center infrastructure management (DCIM) systems, will further stimulate market expansion. While challenges such as the initial capital investment required for PDU upgrades and the potential for supply chain disruptions could pose some constraints, the overall market outlook remains positive, driven by the fundamental need for reliable and efficient power management within data centers.

The United States data center power market, valued at approximately $14.28 billion in 2025, is projected to experience robust growth, driven by the increasing adoption of cloud computing, big data analytics, and the Internet of Things (IoT). The market's Compound Annual Growth Rate (CAGR) of 6.60% from 2025 to 2033 indicates a significant expansion, fueled by the rising demand for reliable and efficient power solutions within data centers. Key drivers include the need for high-availability power systems to ensure uninterrupted operations, the proliferation of edge computing deployments requiring localized power infrastructure, and the growing focus on energy efficiency to reduce operational costs and environmental impact. Market segmentation reveals a strong demand for UPS systems, generators, and advanced PDUs (Power Distribution Units) across diverse end-user sectors such as IT and telecommunications, BFSI (Banking, Financial Services, and Insurance), and government. The services segment, encompassing system integration, training, maintenance, and consulting, contributes significantly to the overall market value, reflecting the need for specialized expertise in managing complex data center power infrastructure. While specific regional data for the US is not provided, the overall market growth trajectory suggests strong regional performance within the country, particularly in key technology hubs. Competitive dynamics are characterized by the presence of established players like ABB, Eaton, and Schneider Electric, alongside specialized providers catering to niche segments. The market is poised for sustained growth, driven by technological advancements, expanding data center footprint, and increased digital transformation across various sectors. The continued expansion of hyperscale data centers and colocation facilities will be a significant catalyst for market growth throughout the forecast period. This expansion, combined with the increasing demand for higher power density solutions to accommodate denser server deployments and advancements in energy-efficient technologies like renewable energy integration, will further propel the market’s trajectory. The market is witnessing a shift toward more sophisticated power management systems, including intelligent PDUs capable of real-time monitoring and control, enhancing operational efficiency and reducing energy waste. Furthermore, stringent regulatory requirements regarding energy consumption and environmental sustainability are pushing data center operators to adopt greener power solutions, contributing to the demand for advanced and efficient power infrastructure. The integration of smart technologies and predictive maintenance within power systems is also expected to play a vital role in driving market growth by optimizing operational efficiency and minimizing downtime. Recent developments include: December 2023: Eaton, an intelligent power management company, announced the launch of its new Rack PDU G4 (4th generation) that provides a high security and business continuity data center. It also combines with C39 outlets that securely connect both C14 and C20 power cords, backed by a locking mechanism and a built-in high retention system that secures the power cord., November 2023: ABB Ltd announced the launch of the Protecta Power panel board, designed for industrial, commercial, and institutional buildings. It is integrated with digital monitoring and control technology while enhancing durability and safety.. Key drivers for this market are: Growing Rack Power Density, Increase in the Demand for Energy-efficient and Cost-effective Data Centers. Potential restraints include: Growing Rack Power Density, Increase in the Demand for Energy-efficient and Cost-effective Data Centers. Notable trends are: Switched PDU is Anticipated to be Fastest-growing Segment.

Power Distribution Unit (PDU) for Data Center Market Outlook

The global Power Distribution Unit (PDU) for Data Center market is projected to reach a valuation of USD 3.5 billion by 2032, growing at a robust CAGR of 6.2% from 2024 to 2032. This growth is primarily driven by the rising demand for data centers across various industries and the increasing need for energy efficiency and better power management solutions within these facilities.

One of the key factors fueling the growth of the PDU market for data centers is the exponential increase in data generation and consumption. With the advent of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics, there is a burgeoning need for data centers capable of handling vast amounts of data efficiently. PDUs play a crucial role in managing and distributing power within these centers, ensuring optimal performance and minimizing downtime. Moreover, the growing reliance on cloud computing and digital services has significantly heightened the demand for robust and scalable data center infrastructure, further propelling the PDU market.

Another major growth driver is the emphasis on energy efficiency and sustainability. Data centers are notoriously energy-intensive, and operators are under increasing pressure to reduce their carbon footprints. Advanced PDUs equipped with monitoring and management capabilities enable data center operators to track power usage in real-time, identify inefficiencies, and implement corrective measures. This not only helps in reducing operational costs but also aligns with global sustainability goals, making PDUs an indispensable component of modern data centers. Additionally, regulatory frameworks and government initiatives aimed at promoting energy-efficient practices are likely to boost the adoption of sophisticated PDU solutions.

Technological advancements in PDU design and functionality are also contributing to market growth. The development of intelligent and networked PDUs that offer real-time monitoring, remote management, and automated control is transforming the way data centers operate. These advanced PDUs provide granular insights into power consumption patterns, enabling predictive maintenance and enhancing overall reliability. As data centers continue to evolve, the demand for innovative PDU solutions that can support high-density environments and dynamic power requirements is expected to surge, driving market expansion.

Regional outlook indicates that North America and Asia Pacific are poised to dominate the PDU market for data centers. North America, driven by technological advancements and a high concentration of data centers, is expected to maintain its leadership position. Asia Pacific, on the other hand, is anticipated to witness the highest growth rate, fueled by rapid digitization, increasing investments in data center infrastructure, and the growing adoption of cloud services. Europe and Latin America are also expected to contribute significantly to market growth, with a steady rise in data center deployments and modernization initiatives.

Metering Power Distribution Units have become increasingly vital in modern data centers due to their ability to provide precise power monitoring at the outlet level. These units empower data center operators with detailed insights into power consumption patterns, enabling them to optimize load distribution and prevent potential overloading. The integration of metering capabilities allows for enhanced capacity planning and energy efficiency, which are crucial for maintaining the operational integrity of data centers. As data centers strive to meet sustainability goals and reduce operational costs, the adoption of metering PDUs is expected to rise, offering a strategic advantage in managing power resources effectively.

Type Analysis

The PDU market for data centers can be segmented based on type into Basic, Metered, Monitored, and Switched PDUs. Basic PDUs, being the most traditional and cost-effective option, provide reliable power distribution without any advanced monitoring or control features. They are widely used in small to medium-sized data centers where budget constraints are a primary concern. Despite their simplicity, basic PDUs are essential for ensuring uninterrupted power supply and maintaining the operational efficiency of data centers. However, their lack of advanced features may limit their adoption in highly sophisticated and large-scale dat

The Data Center Power Distribution Unit (PDU) market is experiencing robust growth, driven by the escalating demand for data centers globally. The surge in cloud computing, big data analytics, and the Internet of Things (IoT) necessitates increased power capacity and efficient power management within data centers. This has led to a significant increase in the adoption of PDUs, which provide granular power control and monitoring capabilities, enhancing operational efficiency and reducing energy costs. The market is segmented by type (basic, metered, switched, intelligent), power capacity, and deployment (rack-mounted, wall-mounted). Major players like Schneider Electric, Vertiv, and Eaton dominate the market, leveraging their established brand recognition and extensive product portfolios. However, smaller companies are also emerging, focusing on niche technologies and innovative solutions like remote monitoring and power management software integration. The market's growth is further fueled by increasing investments in data center infrastructure modernization and the adoption of energy-efficient technologies. Growth in the data center PDU market is anticipated to continue at a healthy Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). Factors like increasing data center density, the rise of edge computing, and the growing need for advanced power management solutions are key drivers. However, challenges like the high initial investment cost associated with PDU implementation and the complexities of integrating them with existing infrastructure could act as potential restraints. Nevertheless, the ongoing digital transformation across various industries is expected to overcome these limitations, resulting in sustained market expansion. Regional variations in market growth will be influenced by factors such as the maturity of data center infrastructure, government regulations promoting energy efficiency, and the rate of digital adoption in each region. North America and Europe are currently leading the market, but Asia-Pacific is expected to witness significant growth in the coming years due to rapid economic development and increasing investments in digital infrastructure.

Data Center Rack Power Distribution Unit (PDU) Market Outlook

The global data center rack power distribution unit (PDU) market size is anticipated to witness substantial growth from USD 1.6 billion in 2023 to an estimated USD 2.9 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7% during the forecast period. The market is primarily driven by the rapid expansion of data centers worldwide, which is fueled by the increasing demand for cloud computing, big data analytics, and IoT (Internet of Things) applications. As enterprises continue to digitize their operations, the need for efficient power management solutions in data centers has become a critical factor propelling the growth of the PDU market.

A significant growth factor for the PDU market is the evolution of data centers to accommodate the burgeoning data traffic. The proliferation of digital services has led to the establishment of new data centers, and the expansion of existing ones, to handle the vast amounts of data being generated every day. Additionally, the shift towards high-density computing environments necessitates efficient power distribution solutions. PDUs play a crucial role in enhancing the power usage efficiency (PUE) of data centers, which is a key metric for assessing their energy efficiency. As data centers strive for sustainable operations, the implementation of advanced PDU solutions that minimize energy losses and optimize power distribution becomes imperative.

Technological advancements in PDU design and functionality are further augmenting the market's growth trajectory. Modern PDUs offer features such as remote monitoring and management, power metering, and switchable outlets, which enable data center operators to monitor and control energy consumption at granular levels. These capabilities are vital for ensuring the reliability and uptime of data center operations, which are essential for businesses that rely on uninterrupted digital services. As enterprises prioritize efficiency and scalability, the adoption of advanced PDUs that offer enhanced control over power distribution is expected to increase, leading to robust market growth.

The increasing focus on renewable energy and sustainability practices within data centers is also driving the demand for innovative PDU solutions. Data centers are significant consumers of energy, and there is a growing impetus to reduce their carbon footprint by integrating renewable energy sources such as solar and wind. PDUs that can support hybrid energy setups and provide real-time energy consumption data are becoming increasingly popular. Such PDUs enable data centers to optimize the use of renewable energy, thereby contributing to sustainability goals and reducing operational costs.

Regionally, North America holds a significant share of the data center rack PDU market due to its early adoption of advanced technologies and the presence of key industry players. However, the Asia Pacific region is expected to exhibit the highest growth rate, driven by the rapid digital transformation in countries like China and India. The expansion of cloud service providers and the establishment of new data centers in these regions further bolster the market. Meanwhile, in Europe, stringent regulations on energy efficiency are compelling data centers to adopt more efficient PDU solutions, contributing to the market's growth.

Product Type Analysis

The data center rack PDU market is segmented into several product types, each catering to specific needs within data centers. Basic PDUs are the most fundamental type, providing reliable power distribution without additional monitoring or control features. These are often used in small to mid-sized data centers or in applications where cost-efficiency is a primary concern. Despite their simplicity, basic PDUs are valued for their reliability and durability, making them a staple in many data center environments. However, as data centers evolve, there is a growing trend towards more sophisticated PDU solutions.

Metered PDUs offer an additional layer of functionality by providing insights into power consumption. They feature built-in meters that allow data center operators to monitor the power usage of individual outlets or entire units. This capability is crucial for optimizing energy efficiency and capacity planning. As organizations strive to reduce their energy costs and improve their sustainability credentials, the demand for metered PDUs is expected to increase. These devices enable more precise energy management, helping data centers to operate more efficiently and sustainably.

1. Summary

Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", Geophysical Research Letters (accepted).

2. File Description

Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km². Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki et al., 2019).

3. Attribute Description

• COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019);
• Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km²;
• Area: Upstream drainage basin area in km²;
• Sin: Sinuosity of the river segment (unitless);
• Slp: mean slope of the river segment (unitless);
• Elev: mean elevation of the river segment;
• K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);
• LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013);
• SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018);
• WTD: mean water table depth (m below surface) of the unit catchment; data extracted from Fan et al. (2013);
• HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016)
• DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019)
• QMEAN: mean annual discharge (m³/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019);
• Q2: 2-year return period flood discharge (m³/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019);
• Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;
• Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study

4. References

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636

Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881

Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748

Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860

Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287

Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055

Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3

Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873

Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs5020927

This is the updated version of the dataset from 10.5281/zenodo.6320761 Information The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144648 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design. The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation. This dataset belongs to the publication: https://doi.org/10.3390/molecules27082513 Structure and content of the dataset Dataset structure ChEMBL ID PubChem ID IUPHAR ID Target Activity type Assay type Unit Mean C (0) ... Mean PC (0) ... Mean B (0) ... Mean I (0) ... Mean PD (0) ... Activity check annotation Ligand names Canonical SMILES C ... Structure check (Tanimoto) Source The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file. Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format. Column content: ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases Target: biological target of the molecule expressed as the HGNC gene symbol Activity type: for example, pIC50 Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified Unit: unit of bioactivity measurement Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence no comment: bioactivity values are within one log unit; check activity data: bioactivity values are not within one log unit; only one data point: only one value was available, no comparison and no range calculated; no activity value: no precise numeric activity value was available; no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration Ligand names: all unique names contained in the five source databases are listed Canonical SMILES columns: Molecular structure of the compound from each database Structure check (Tanimoto): To denote matching or differing compound structures in different source databases match: molecule structures are the same between different sources; no match: the structures differ. We calculated the Jaccard-Tanimoto similarity coefficient from Morgan Fingerprints to reveal true differences between sources and reported the minimum value; 1 structure: no structure comparison is possible, because there was only one structure available; no structure: no structure comparison is possible, because there was no structure available. Source: From which databases the data come from

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Ecuador Internet Usage: Number of Companies: Big data was reported at 1,137.000 Unit in 2015. This records an increase from the previous number of 1,135.000 Unit for 2014. Ecuador Internet Usage: Number of Companies: Big data is updated yearly, averaging 1,104.500 Unit from Dec 2012 (Median) to 2015, with 4 observations. The data reached an all-time high of 1,137.000 Unit in 2015 and a record low of 854.000 Unit in 2012. Ecuador Internet Usage: Number of Companies: Big data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.TB004: Internet Usage: by Connection Type and by Company Size.

The Data Center Rack Power Distribution Unit (PDU) market is experiencing robust growth, projected to reach a market size of $2301 million in 2025, with a Compound Annual Growth Rate (CAGR) of 5.1% from 2025 to 2033. This expansion is driven by the increasing adoption of cloud computing and big data analytics, necessitating advanced power management solutions within data centers. The rising demand for high-density computing infrastructure and the need for efficient power distribution are key factors fueling market growth. Growth is further propelled by the increasing focus on data center optimization, energy efficiency initiatives, and the deployment of advanced monitoring and management tools integrated within PDUs. The market is segmented by PDU type (Basic, Metering, Monitoring, Switch, and Others) and application (Large and Small/Medium Data Centers), reflecting diverse user needs and deployment scenarios. Leading vendors like Schneider Electric APC, ABB, Cisco, Eaton, and Vertiv are actively competing through product innovation and strategic partnerships to cater to the growing market demand. The regional distribution of the market reveals strong growth potential across North America, Europe, and Asia Pacific. These regions are characterized by high data center density and a significant presence of major technology companies. The robust growth in these regions is primarily fueled by increased investment in data center infrastructure and a growing focus on energy-efficient solutions, especially in light of rising energy costs and environmental sustainability concerns. Further market expansion will likely be driven by continued advancements in PDU technology, integration with intelligent data center management systems, and the adoption of sustainable and eco-friendly designs. The competitive landscape is dynamic, with established players and emerging technology companies vying for market share through technological innovation and strategic partnerships.

The global Data Communication Unit (DCU) market is experiencing robust growth, driven by the increasing demand for high-speed data transmission across various sectors. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the proliferation of IoT devices necessitating efficient data communication, the expanding adoption of cloud computing and big data analytics, and the ongoing digital transformation across industries like manufacturing, healthcare, and transportation. The rising need for reliable and secure data transmission in enterprise settings and government infrastructure further bolsters market expansion. Key segments driving growth include WLAN and Ethernet technologies, owing to their widespread compatibility and scalability. Within applications, the enterprise and government sectors are major contributors, with significant investment in advanced communication networks and infrastructure upgrades. While the market presents significant opportunities, certain restraints exist. These include the high initial investment costs associated with DCU implementation, the complexity of integrating different DCU technologies within existing infrastructure, and the potential security vulnerabilities associated with large-scale data transmission networks. However, advancements in technology, particularly in areas like 5G and edge computing, are anticipated to mitigate these challenges and drive further market expansion. The competitive landscape is marked by a mix of established players and emerging technology companies, with key players focusing on product innovation, strategic partnerships, and geographical expansion to gain a competitive edge. The market is expected to witness further consolidation in the coming years as companies strive to meet the evolving needs of businesses and governments worldwide.

The Chinese big data technology market presents significant investment opportunities, fueled by a robust 30% CAGR and a substantial market size. Driven by government initiatives promoting digital transformation, rapid technological advancements, and the increasing adoption of cloud-based solutions across diverse sectors like BFSI, healthcare, and manufacturing, the market is poised for continued expansion. Key trends include the growing demand for advanced analytics, including predictive maintenance and fraud detection, coupled with the increasing deployment of big data solutions in the cloud. While data privacy regulations and a potential skills gap pose challenges, the immense potential of the Chinese market outweighs these restraints. The concentration of major technology players like Alibaba Cloud, Tencent, and Huawei within China, alongside established international companies like IBM and Microsoft, indicates a fiercely competitive yet lucrative landscape. Investment strategies should focus on companies offering cutting-edge analytics solutions, particularly those catering to the rapidly expanding cloud and mobile segments. Furthermore, investments in companies specializing in data security and compliance solutions will be crucial given the increasing focus on data privacy. The segmentation of the market offers diverse investment avenues. Large enterprises are likely to lead adoption, but the SME segment presents significant growth potential as more companies embrace data-driven decision-making. Within solutions, customer analytics and fraud detection will maintain high demand, while predictive maintenance and asset management in sectors like manufacturing and automotive will witness substantial growth. Geographical focus should consider the economic powerhouses within China, with Tier-1 cities expected to lead adoption rates, followed by a gradual expansion into Tier-2 and Tier-3 cities. The forecasted market growth for the next decade indicates a substantial return on investment for strategically positioned players. A detailed understanding of regulatory landscapes and the evolving technological landscape will prove critical for successful investment in this dynamic market. Recent developments include: November 2022 - Alibaba announced the Innovative upgrade, and Greener 11.11 runs wholly on Alibaba Cloud, whereas Alibaba Cloud's dedicated processing unit powered 11.11 for the Apsara Cloud operating system. The upgraded infrastructure system significantly improved the efficiency of computing, storage, etc., October 2022 - Huawei Technologies Co.has unveiled its 4-in-1 hyper-converged enterprise gateway NetEngine AR5710, delved into the latest CloudCampus 3.0 + Simplified Solution, and launched a series of products for large enterprises and Small- and Medium-Sized Enterprises (SMEs). With these new offerings, Huawei aims to help enterprises simplify their campus networks and maximize digital productivity.. Key drivers for this market are: 6.1 Data Explosion: Unstructured, Semi-structured and Complex6.2 Improvement in Algorithm Development6.3 Need for Customer Analytics. Potential restraints include: 6.1 Data Explosion: Unstructured, Semi-structured and Complex6.2 Improvement in Algorithm Development6.3 Need for Customer Analytics. Notable trends are: Need for Customer Analytics to Increase Exponentially Driving the Market Growth.