North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

This statistic shows the average price of cellular data per gigabyte in the United States from 2018 to 2023. In 2018, the average price of cellular data was estimated to amount to 4.64 U.S. dollars per GB.

One gigabyte for mobile internet in Nigeria cost on average ** U.S. cents as of August 2023. The country ranked **** in a list of *** countries worldwide, from the cheapest to the most expensive for mobile data. In the regional comparison, Nigeria was among the nations with lower costs for mobile data in Africa. Out of 55 plans analyzed in the country, the lowest price observed was **** U.S. dollars per *** for a ** days plan. In the most expensive plan, *** cost **** U.S. dollars.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.

In December 2023, the volume of mobile internet data that internet users consumed in Nigeria amounted to ******* terabytes. Compared to the previous year, this was a considerable increase of almost ***** percent.

The Hadoop Big Data Analytics Market Report is Segmented by Solution (Data Discovery and Visualization (DDV), Advanced Analytics (AA), and More), End-Use Industry (BFSI, Retail, IT and Telecom, Healthcare and Life Sciences, and More), Deployment Mode (On-Premise, Cloud, and More), Organization Size (Large Enterprises and Small and Medium Enterprises), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

This paper investigates the relationship between housing prices and the quality of public schools in the Australian Capital Territory. To disentangle the effects of schools and other neighbourhood characteristics on the value of residential properties, we compare sale prices of homes on either side of high school attendance boundaries. We find that a 5 percent increase in test scores (approximately one standard deviation) is associated with a 3.5 percent increase in house prices. Our result is in line with private school tuition costs, and accords with prior research from Britain and the United States. Estimating the effect of school quality on house prices provides a possible measure of the extent to which parents value better educational outcomes.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

The Big Data Security Market is estimated to be valued at USD 26.3 billion in 2025 and is projected to reach USD 87.9 billion by 2035, registering a compound annual growth rate (CAGR) of 12.8% over the forecast period.

The Big Data Market is projected to grow at a CAGR of around 14.7% during 2023-28, says MarkNtel Advisors. (Top Companies - Accenture PLC, Cloudera Inc., Teradata Corporation, Microsoft Corporation, Splunk Inc., Amazon Web Services, and Cisco Systems Inc)

Internet Data Center Market Outlook

According to our latest research, the global Internet Data Center market size stood at USD 68.3 billion in 2024, registering a robust growth trajectory. The market is forecasted to reach USD 165.7 billion by 2033, expanding at a healthy CAGR of 10.4% during the 2025-2033 period. The key growth factor driving this surge is the exponential rise in data generation, cloud computing adoption, and the proliferation of digital transformation initiatives across industries worldwide. As organizations increasingly prioritize business continuity, security, and scalability, the demand for advanced data center infrastructure is at an all-time high, shaping the future of the Internet Data Center market.

One of the primary drivers fueling the growth of the Internet Data Center market is the rapid expansion of digital services and applications, which has led to an unprecedented surge in global data traffic. The proliferation of Internet of Things (IoT) devices, video streaming, e-commerce, and social media platforms has necessitated the deployment of high-capacity, low-latency data centers capable of handling massive workloads. Enterprises and service providers are investing heavily in data center modernization, focusing on energy efficiency, automation, and robust connectivity to support these evolving digital ecosystems. The growing emphasis on hybrid and multi-cloud strategies further amplifies the need for flexible and scalable data center solutions, propelling market growth.

Another significant growth factor is the increasing adoption of artificial intelligence (AI), machine learning, and big data analytics across various sectors, including healthcare, finance, and retail. These technologies require substantial computational power and storage capabilities, driving demand for advanced data center infrastructure. Modern data centers are being designed to support high-density computing, GPU acceleration, and edge computing, enabling real-time data processing and analytics at scale. Additionally, the shift toward software-defined data centers (SDDC) and virtualization is transforming traditional data center architectures, enabling greater agility, cost-efficiency, and operational resilience. This evolution is further supported by advancements in network technologies such as 5G, which facilitate faster data transmission and improved user experiences.

Sustainability and energy efficiency have emerged as crucial considerations in the Internet Data Center market, as organizations and governments worldwide prioritize environmental responsibility. Data centers are significant consumers of electricity, prompting the adoption of green technologies, renewable energy sources, and innovative cooling solutions to minimize carbon footprints. Regulatory mandates and industry standards are driving investments in energy-efficient hardware, intelligent power management, and sustainable building practices. Leading market players are increasingly focusing on achieving carbon neutrality and leveraging circular economy principles, which not only reduce operational costs but also enhance brand reputation and stakeholder trust. This sustainable approach is expected to shape investment decisions and technological advancements in the coming years.

As the demand for data processing and storage continues to grow, the concept of a Hyperscale Data Center has emerged as a pivotal solution to meet these needs. Hyperscale data centers are designed to efficiently scale up resources, accommodating the vast amounts of data generated by modern digital activities. These facilities are characterized by their ability to support thousands of servers and millions of virtual machines, ensuring seamless performance and reliability. The architecture of hyperscale data centers focuses on maximizing energy efficiency and optimizing cooling systems, making them a sustainable choice for large-scale operations. As businesses increasingly rely on cloud services and big data analytics, the role of hyperscale data centers becomes ever more critical in providing the necessary infrastructure to support these advanced technologies.

Regionally, the Asia Pacific market is witnessing remarkable growth, outpacing other regions due to rapid digitalization, government initiatives, and increasing internet penetration. Countries such as China, India, and Singapo

Context

USASpending.gov is the government's official tool for tracking spending, it shows where money goes and who benefits from federal funds.

The Federal Funding Accountability and Transparency Act of 2006 required that federal contract, grant, loan awards over $25k be searchable online to give the American public access to government spending. The data that is collected in USAspending.gov is derived from data gathered at more than a hundred agencies, as well as other government systems. Federal agencies submit contracts, grants, loans and other awards information to be uploaded on USAspending.gov at least twice a month.

Content

The United States spends a lot of money on contracts every year but where does it all go? This data set has information about how much different agencies have spent on awards for the fiscal year 2021. More data can be downloaded, for other years, on USAspending.gov.

Contracts are published to the GSA's Federal Procurement Data System within five days of being awarded, with contract reporting automatically getting posted on USAspending.gov by 9 AM the next day and going live at 8:00 am EST two mornings later

Learn more about the contents here: https://www.usaspending.gov/data-dictionary

The Bureau of the Fiscal Service, United States Department of the Treasury, is dedicated to making government spending data available to everyone.

Data Description

This data starts off separated into smaller files that need to be joined.

Data Overview

The federal government buys a lot of things, like office furniture and aircraft. It also buys services, like telephone and Internet access. The Federal Government and its sub-agencies use contracts to buy these things. They use Product and Service Codes (PSC) to classify the items and services they purchase.

An obligation is a promise to spend money. An outlay is when the government spends money. When the government enters into a contract or grant, it promises to spend all of the money. This is so it can pay people who do what they agreed to do. When the government actually pays someone, then it counts as an outlay.

Data Items that Help get Started

There are many different variables in this database, which are spread across multiple files. The most important ones to start learning are:

1. The contractor who won the award - recipient_name
2. The agency issuing the award - awarding_agency_name
3. The product or service code (PSC) - product_or_service_code
4. The industry classification code (NAICS) of the vendor - naics_code
5. How much was obligated - total_dollars_obligated or total_obligated_amount
6. The contract modification number - modification_number
7. The description of the award - award_description
8. The date of award - action_date or award_base_action_date

Data Dictionary and Analyst Guide

To learn more about the data, you can reference the data dictionary. The data dictionary includes information on outlays, which are not included in the data provided here. https://www.usaspending.gov/data-dictionary

Please see the analysts guide for more information: https://datalab.usaspending.gov/analyst-guide/

License

The U.S. Department of the Treasury, Bureau of the Fiscal Service is committed to providing open data to enable effective tracking of federal spending. The data is available to copy, adapt, redistribute, or otherwise use for non-commercial or for commercial purposes, subject to the Limitation on Permissible Use of Dun & Bradstreet, Inc. Data noted on the homepage. https://www.usaspending.gov/db_info

Acknowledgements

USAspending.gov collects data from all over the government to provide information to the public. Special thanks for the Data Transparency Team within the Office of the Chief Data Officer at the Bureau of Fiscal Services.

Inspiration

Can we find any patterns to help the public? How about predicting future spending needs or opportunities? Test out your ideas here!

The replication file includes the raw data extracted from the NCES NPSAS PowerStats batch processor, CPI inflation data from the Bureau of Labor Statistics, Nominal Personal Consumption Expenditures on Postsecondary Education from the Bureau of Economic Analysis, a Stata do file (replication.do) which uses these datasets to produce the figures in the text, and an archive of those visuals.

Fossil-based estimates of diversity and evolutionary dynamics mainly rely on the study of morphological variation. Unfortunately, organism remains are often altered by post-mortem taphonomic processes such as weathering or distortion. Such a loss of information often prevents quantitative multivariate description and statistically controlled comparisons of extinct species based on morphometric data. A common way to deal with missing data involves imputation methods that directly fill the missing cases with model estimates. Over the last several years, several empirically determined thresholds for the maximum acceptable proportion of missing values have been proposed in the literature, whereas other studies showed that this limit actually depends on several properties of the study dataset and of the selected imputation method, and is by no way generalizable. We evaluate the relative performances of seven multiple imputation techniques through a simulation-based analysis under three distinct patterns of missing data distribution. Overall, Fully Conditional Specification and Expectation-Maximization algorithms provide the best compromises between imputation accuracy and coverage probability. Multiple imputation (MI) techniques appear remarkably robust to the violation of basic assumptions such as the occurrence of taxonomically or anatomically biased patterns of missing data distribution, making differences in simulation results between the three patterns of missing data distribution much smaller than differences between the individual MI techniques. Based on these results, rather than proposing a new (set of) threshold value(s), we develop an approach combining the use of multiple imputations with procrustean superimposition of principal component analysis results, in order to directly visualize the effect of individual missing data imputation on an ordinated space. We provide an R function for users to implement the proposed procedure.

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UK Data Center Market Size 2024-2028

The uk data center market size is forecast to increase by USD 37.87 billion, at a CAGR of 21.8% between 2023 and 2028.

The Data Center Market in the UK is experiencing significant shifts, driven by the increasing adoption of multi-cloud solutions and the necessity to upgrade networks to support the rollout of 5G technology. These trends reflect the evolving digital landscape, with businesses seeking greater agility, scalability, and efficiency in their IT infrastructure. Simultaneously, the consolidation of data centers continues, as organizations aim to optimize resources and reduce operational costs. However, these advancements come with challenges, including the growing power consumption demands of data centers, which necessitate sustainable energy solutions and innovative cooling technologies to mitigate environmental concerns and maintain cost competitiveness. Companies in the UK data center market must navigate these trends and challenges to capitalize on opportunities for growth and maintain a competitive edge in the rapidly evolving digital economy.

What will be the size of the UK Data Center Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The UK data center market is witnessing significant advancements, driven by the increasing demand for power-hungry tower servers and the shift towards renewable energy sources. With the expansion of data center footprints, IP addressing and data center certifications have become crucial for ensuring efficient network management. Green data centers, integrating cooling technology and energy management, are gaining traction as businesses prioritize sustainability. Server clustering, virtual machines (VMs), solid-state drives (SSDs), and storage arrays are key technologies enhancing data center performance. Location selection, risk management, and business continuity plans are essential considerations for organizations. Data analytics, wireless networking, and big data applications are fueling the adoption of high-performance computing (HPC) solutions, including blade servers and load balancing. Fiber optic cables and Ethernet cables are vital components for seamless connectivity, while tape libraries and optical storage cater to data archiving needs. Sustainable data centers, employing energy-efficient rackmount servers and advanced cooling systems, are the future of the industry.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentIT infrastructurePower managementMechanical constructionGeneral constructionSecurity solutionsTypeOn-premiseHyperscaleHPCColocationEdgeOn-premiseHyperscaleHPCColocationEdgeDesignTraditionalContainerizedModularTraditionalContainerizedModularGeographyEuropeUK

By Component Insights

The it infrastructure segment is estimated to witness significant growth during the forecast period.

The data center IT infrastructure market in the UK is experiencing significant growth due to the increasing demand for computing power and storage to accommodate expanding data traffic. Enterprises are transitioning from traditional on-premises data centers to cloud-based solutions, including hyperscale data centers and edge computing. Modular data centers and containerized data centers are gaining popularity for their flexibility and scalability. Network security and access control are essential considerations, with high-availability systems and disaster recovery solutions ensuring business continuity. Energy efficiency and liquid cooling are key trends, as are managed services, remote monitoring, and data center automation. IT infrastructure solutions encompass server racks, network switches, cooling systems, and performance monitoring tools. Software-defined networking (SDN) and data center virtualization are crucial components, enabling temperature control, power capacity, and storage virtualization. Data storage and capacity planning require technical expertise, while backup and recovery solutions ensure business continuity. The market is also witnessing the adoption of hybrid cloud and maintenance contracts for network management. Data center design and construction are ongoing processes, with a focus on reducing carbon footprint and optimizing airflow management. Cloud migration is a significant trend, with the need for seamless integration and efficient data transfer.

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The IT infrastructure segment was valued at USD 11191.10 million in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, alo

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

Big Data and Society CiteScore 2024-2025 - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

We propose simple diagnostics to assess the validity of modeling assumptions in multiplicative interaction models and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation.