The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Daily Information Market Outlook

The global daily information market size was valued at approximately USD 120 billion in 2023 and is projected to reach around USD 300 billion by 2032, growing at a compound annual growth rate (CAGR) of 10%. This rapid growth is driven primarily by the increasing need for real-time data, advancements in technology, and the proliferation of internet-connected devices.

One of the most significant growth factors for this market is the exponential increase in data generation by various industries. As businesses across sectors such as healthcare, finance, and retail increasingly rely on data analytics to drive decision-making, the demand for robust information systems has surged. Moreover, the rise of Internet of Things (IoT) devices has contributed to a substantial increase in the volume of data being generated daily, necessitating advanced solutions for data collection, storage, and analysis.

Another critical driver for the daily information market is the growing importance of data-driven insights in enhancing customer experience and operational efficiency. In sectors like retail and finance, real-time data analytics enable businesses to personalize offerings, optimize supply chains, and detect fraud more effectively. As a result, companies are investing heavily in sophisticated information systems that can process and analyze vast amounts of data efficiently, thus fueling market growth.

The advent of artificial intelligence (AI) and machine learning (ML) technologies has also significantly contributed to the market's expansion. These technologies empower organizations to derive actionable insights from complex datasets with higher accuracy and speed. AI-powered analytics tools are being increasingly adopted by businesses to predict market trends, automate decision-making processes, and improve overall productivity. This trend is expected to continue, further propelling market growth over the forecast period.

Regionally, North America holds a significant share of the global daily information market, primarily due to the presence of numerous tech giants and a highly developed IT infrastructure. The region's early adoption of advanced technologies and significant investments in research and development contribute to its dominance. Europe and Asia Pacific are also expected to witness substantial growth, driven by the increasing digital transformation initiatives and the rise of smart cities in these regions.

Component Analysis

The daily information market can be segmented into three primary components: software, hardware, and services. Each of these components plays a vital role in the overall functionality and efficiency of information systems. The software segment includes various applications and platforms designed for data management, analytics, and visualization. As businesses seek to leverage big data for strategic advantage, the demand for advanced software solutions has increased. This segment is expected to witness substantial growth, driven by continuous innovations in AI and ML algorithms that enhance data processing capabilities.

The hardware segment encompasses the physical devices and infrastructure required to support daily information systems. This includes servers, storage devices, and networking equipment. With the rising volume of data generated daily, there is a growing need for efficient and scalable hardware solutions. Innovations in storage technologies, such as solid-state drives (SSDs) and cloud storage, are driving the growth of this segment. Additionally, the development of high-performance computing systems and edge computing devices further boosts the hardware market.

The services segment comprises various professional services related to the implementation, maintenance, and optimization of daily information systems. These services include consulting, system integration, and managed services. As organizations adopt more complex information systems, the demand for specialized services to ensure seamless integration and optimal performance has risen. The services segment is expected to grow significantly, driven by the increasing need for expert support in deploying and managing advanced data solutions.

One of the key drivers of growth in the software segment is the increasing adoption of cloud-based solutions. Cloud computing offers several advantages, including scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. As a result, there is a growing preference for cl

By 2025, forecasts suggest that there will be more than ** billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019. What is the Internet of Things? The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach *** trillion U.S. dollars. How does Internet of Things work? IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of **** zettabytes. Privacy and security concerns   Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

Outline

This dataset is originally created for the Knowledge Graph Reasoning Challenge for Social Issues (KGRC4SI)

Video data that simulates daily life actions in a virtual space from Scenario Data.

Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).

Knowledge Graph Embedding Data are created for reasoning based on machine learning

This data is open to the public as open data

Details

Videos

mp4 format

203 action scenarios

For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos

Videos with slowly moving characters simulate the movements of elderly people.

Knowledge Graphs

RDF format

203 knowledge graphs corresponding to the videos

Includes schema and location supplement information

The schema is described below

SPARQL endpoints and query examples are available

Script Data

txt format

Data provided to VirtualHome2KG to generate videos and knowledge graphs

Includes the action title and a brief description in text format.

Embedding

Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (https://dglke.dgl.ai/doc/)

Embedding Vectors created with jRDF2vec (https://github.com/dwslab/jRDF2Vec).

Specification of Ontology

Please refer to the specification for descriptions of all classes, instances, and properties: https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm

Related Resources

KGRC4SI Final Presentations with automatic English subtitles (YouTube)

VirtualHome2KG (Software)

VirtualHome-AIST (Unity)

VirtualHome-AIST (Python API)

Visualization Tool (Software)

Script Editor (Software)

USGS researchers with the Patterns in the Landscape – Analyses of Cause and Effect (PLACE) project are releasing a collection of high-frequency surface water map composites derived from daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Using Google Earth Engine, the team developed customized image processing steps and adapted the Dynamic Surface Water Extent (DSWE) to generate surface water map composites in California for 2003-2019 at a 250-m pixel resolution. Daily maps were merged to create 6, 3, 2, and 1 composite(s) per month corresponding to approximately 5-day, 10-day, 15-day, and monthly products, respectively. The resulting maps are available as downloadable files for each year. Each file includes 72, 36, 24, or 12 bands that coincide with the number of maps generated in the 5-day, 10-day, 15-day, and monthly products, respectively. The bands are ordered chronologically, with the first band representing the beginning of the calendar year and the last band representing the end of the year. Each set of maps is labeled according to year and product type. There are 17 GeoTIF (.tif) raster data files for each composite product.

This data set is generated for the purpose of representing Turkish Electricity Day Ahead Market bidding data. The sample consists of 25 single-day bidding data for hourly, block and flexible bids. Along with the data set, a description of the data generation procedure is also provided.

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in Italy. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

📈 Daily Historical Stock Price Data for Generation Bio Co. (2020–2025)

A clean, ready-to-use dataset containing daily stock prices for Generation Bio Co. from 2020-06-12 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Generation Bio Co. Ticker Symbol: GBIO Date Range: 2020-06-12 to 2025-05-28 Frequency: Daily Total Records: 1246 rows (one per trading day)… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-generation-bio-co-20202025.

Big Data Infrastructure Market Outlook

The global Big Data Infrastructure market size was valued at approximately $98 billion in 2023 and is projected to grow to around $235 billion by 2032, exhibiting a compound annual growth rate (CAGR) of about 10.1% during the forecast period. This impressive growth can be attributed to the increasing demand for big data analytics across various sectors, which necessitates robust infrastructure capable of handling vast volumes of data effectively. The need for real-time data processing has also been a significant driver, as organizations seek to harness data to gain competitive advantages, improve operational efficiencies, and enhance customer experiences.

One of the primary growth factors driving the Big Data Infrastructure market is the exponential increase in data generation from digital sources. With the proliferation of connected devices, social media, and e-commerce, the volume of data generated daily is staggering. Organizations are realizing the value of this data in gaining insights and making informed decisions. Consequently, there is a growing demand for infrastructure solutions that can store, process, and analyze this data effectively. Additionally, developments in cloud computing have made big data technology more accessible and affordable, further fueling market growth. The ability to scale resources on-demand without significant upfront capital investment is particularly appealing to businesses.

Another critical factor contributing to the growth of the Big Data Infrastructure market is the advent of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies require sophisticated data management solutions capable of handling complex and large-scale data sets. As industries across the spectrum from healthcare to manufacturing integrate these technologies into their operations, the demand for capable infrastructure is scaling correspondingly. Moreover, regulatory requirements around data management and security are prompting organizations to invest in reliable infrastructure solutions to ensure compliance and safeguard sensitive information.

The role of data analytics in shaping business strategies and operations has never been more pertinent, driving organizations to invest in Big Data Infrastructure. Businesses are keenly focusing on customer-centric approaches, understanding market trends, and innovating based on data-driven insights. The ability to predict trends, consumer behavior, and potential challenges offers a significant strategic advantage, further pushing the demand for robust data infrastructure. Additionally, strategic partnerships between technology providers and enterprises are fostering an ecosystem conducive to big data initiatives.

From a regional perspective, North America currently holds the largest share in the Big Data Infrastructure market, driven by the early adoption of advanced technologies and the presence of major technology companies. The region's strong digital economy and a high degree of IT infrastructure sophistication are further bolstering its market position. Europe is expected to follow suit, with significant investments in data infrastructure to meet regulatory standards and drive digital transformation. The Asia Pacific region, however, is anticipated to witness the highest growth rate, attributed to rapid digitalization, the proliferation of IoT devices, and increasing awareness of the benefits of big data analytics among businesses. Other regions like Latin America and the Middle East & Africa are also poised for growth, albeit at a relatively moderate pace, as they continue to embrace digital technologies.

Component Analysis

In the realm of Big Data Infrastructure, the component segment is categorized into hardware, software, and services. The hardware segment consists of the physical pieces needed to store and process big data, such as servers, storage devices, and networking equipment. This segment is crucial because the efficiency of data processing depends significantly on the capabilities of these physical components. With the rise in data volumes, there’s an increased demand for scalable and high-performance hardware solutions. Organizations are investing heavily in upgrading their existing hardware to ensure they can handle the data influx effectively. Furthermore, the development of advanced processors and storage systems is enabling faster data processing and retrieval, which is critical for real-time analytics.

The software segment of Big Data Infrastructure encompasses analytics soft

Costa Rica Wastewater Generation: Point Sources: Total Phosphorus: 1000 kg O2 per Day data was reported at 0.154 kg th in 2015. Costa Rica Wastewater Generation: Point Sources: Total Phosphorus: 1000 kg O2 per Day data is updated yearly, averaging 0.154 kg th from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 0.154 kg th in 2015 and a record low of 0.154 kg th in 2015. Costa Rica Wastewater Generation: Point Sources: Total Phosphorus: 1000 kg O2 per Day data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Costa Rica – Table CR.OECD.ESG: Environmental: Wastewater Generation: OECD Member: Annual.

Slovakia Wastewater Generation: Point Sources: Industry: Chemical Products and Refined Petroleum: Biochemical Oxygen Demand: 1000 kg O2 per Day data was reported at 6.579 kg th in 2023. This records a decrease from the previous number of 7.786 kg th for 2022. Slovakia Wastewater Generation: Point Sources: Industry: Chemical Products and Refined Petroleum: Biochemical Oxygen Demand: 1000 kg O2 per Day data is updated yearly, averaging 13.867 kg th from Dec 2006 (Median) to 2023, with 16 observations. The data reached an all-time high of 23.000 kg th in 2011 and a record low of 5.030 kg th in 2021. Slovakia Wastewater Generation: Point Sources: Industry: Chemical Products and Refined Petroleum: Biochemical Oxygen Demand: 1000 kg O2 per Day data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovakia – Table SK.OECD.ESG: Environmental: Wastewater Generation: OECD Member: Annual.

Data Centre Equipment Market Outlook

As of 2023, the global data centre equipment market size stands at approximately USD 150 billion and is projected to reach around USD 300 billion by 2032, growing at a robust CAGR of 8%. This market's significant growth is driven by increasing data generation, rapid technological advancements, and heightened demand for cloud-based services. The demand for high-performance computing, alongside the proliferation of the Internet of Things (IoT) and edge computing, is catalyzing the expansion of data centre infrastructure across various industries.

The exponential growth in data generation from various sources such as social media, online transactions, and IoT devices is one of the primary drivers of the data centre equipment market. With global internet users surpassing 5 billion, the volume of data generated daily is unprecedented and continually rising, necessitating advanced and scalable data centre solutions. This insatiable need for data processing, storage, and security is fostering the development and deployment of sophisticated data centre equipment.

Technological advancements are another crucial factor stimulating the market's growth. Innovations such as high-density server hardware, advanced networking solutions, and efficient power distribution units are enhancing the capabilities and efficiency of data centres. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in data centre operations is leading to predictive maintenance, optimized energy consumption, and improved overall performance. These technological strides are essential in meeting the escalating demands for data processing and storage.

The increasing adoption of cloud-based services is significantly propelling the data centre equipment market. As businesses transition from traditional on-premises IT infrastructure to cloud solutions, there is a burgeoning demand for data centres to support these cloud services. Cloud providers are investing heavily in building and expanding data centres to accommodate the growing needs of their customers. This trend is particularly pronounced in sectors such as IT and telecommunications, BFSI, and healthcare, where data security and accessibility are paramount.

Regionally, North America dominates the data centre equipment market due to the early adoption of advanced technologies and the presence of major cloud service providers. The region's well-established IT infrastructure and substantial investments in data centre expansion are driving market growth. Asia Pacific is emerging as a lucrative market, attributed to the rapid digitization, increasing internet penetration, and significant investments in IT infrastructure in countries like China and India. Europe is also witnessing substantial growth, spurred by stringent data protection regulations and the increasing need for data storage and processing capabilities.

Component Analysis

The data centre equipment market is segmented by component into servers, storage devices, power distribution units (PDUs), networking equipment, and others. Servers constitute the backbone of data centres, handling the processing and management of data. With the rise in data-intensive applications and services, the demand for high-performance, scalable servers continues to grow. Innovations in server technology, such as blade servers and microservers, offer enhanced performance and energy efficiency, making them integral to modern data centres.

Storage devices are another vital component, catering to the burgeoning need for data storage and retrieval. With data volumes reaching unprecedented levels, there is a substantial demand for advanced storage solutions such as solid-state drives (SSDs) and network-attached storage (NAS). These devices offer high speed, reliability, and scalability, making them indispensable in handling large datasets efficiently. Innovations in storage technology, including storage virtualization and cloud storage, are further augmenting the market.

Power distribution units (PDUs) play a critical role in ensuring the efficient distribution and management of power within data centres. With the increasing power density of data centres, there is a growing need for intelligent PDUs that offer remote monitoring, energy usage tracking, and load balancing. The adoption of energy-efficient PDUs is driven by the desire to reduce operational costs and improve sustainability. As data centres aim to minimize their carbon footprint, the demand for advanced PDUs is expected to rise significantly.

The City of Chicago has published trip-level data for every TNC trip since November 1, 2018. To the best of our knowledge, this dataset is the only one that includes trip fare variables. As we wrote this paper in Oct 2022, the dataset includes approximately 263 million trip records (rows) and 21 features (columns) for trips dated from November 1, 2018, through October 1, 2022. The features of this data include Trip ID, Trip Start Timestamp (rounded to the nearest 15 minutes), Trip End Timestamp (rounded to the nearest 15 minutes), Trip Seconds, Trip Miles, Pickup Census Tract, Dropoff Census Tract, Pickup Community Area, Drop Off Community Area, Trip Fare, Tip, Additional Charges, Total Trip Fare, Shared Trip Authorized, Trips Pooled, Pickup Centroid Latitude, Pickup Centroid Longitude, Pickup Centroid Location, Dropoff Centroid Latitude, Dropoff Centroid Longitude, Dropoff Centroid Location. As the dataset is too large to be processed without a supercomputer, we generated a random sample of 2 million trips from Nov 2018 to June 2022 with valid pickup and drop-down area information. To explore the data, we processed the features to extract date information from the timestamp. We created new variables, including each trip's average fare per mile (excluding tips and additional charges, mainly taxes). In dataset (1), the sampled TNC trips data was processed and summarized to include the average daily fare per mile (USD/mile), and exogenous variables that impact the price were added to the data including holidays (Christmas, thanksgiving, Independence Day, easter and new year) and other variables including gas prices, and climate (snow, precipitation, and average daily temperature). The City of Chicago also publishes taxi trips from 2013 to the present. To protect privacy but allow for aggregate analyses, the Taxi ID is consistent for any given taxi medallion number but does not show the number, and times are rounded to the nearest 15 minutes. Due to the data reporting process, not all but most trips are reported. Taxicabs in Chicago, Illinois, are operated by private companies and licensed by the city. About seven thousand licensed cabs are operating within the city limits. As the dataset is too large to be processed without a supercomputer, we generated a random sample of 2 million trips from Nov 2018 to June 2022 with valid pickup and drop-down area information. To explore the data, we processed the features to extract date information from the timestamp. We created new variables, including each trip's average fare per mile (excluding tips and additional charges, mainly taxes). In dataset (2), the taxi trips data was processed and summarized to include the average daily fare per mile (USD/mile), and exogenous variables that impact the price were added to the data including holidays (Christmas, thanksgiving, Independence Day, easter and new year) and other variables including gas prices, and climate (snow, precipitation, and average daily temperature).

Data Lake Storage Market Outlook

The global data lake storage market size was estimated at $14.2 billion in 2023 and is projected to reach $52.0 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The significant growth factors driving this market include the increasing volume of data generated by various sectors, advancements in data analytics technologies, and the growing need for real-time analytics. Companies are increasingly leveraging data lake storage solutions to handle massive amounts of unstructured data, which traditional storage solutions struggle to manage efficiently.

One of the primary growth factors for the data lake storage market is the exponential increase in data generation across various industries, including healthcare, finance, and retail. With the proliferation of the Internet of Things (IoT), social media, and digital transactions, the volume of data being created daily has skyrocketed. Traditional data storage solutions have become inadequate for processing and managing this voluminous and diverse data, thus driving the demand for scalable and flexible data lake storage solutions. This trend is expected to continue as more devices become interconnected, further contributing to market growth.

Another significant factor contributing to market growth is the advancement in data analytics technologies. Modern businesses are increasingly relying on data-driven decision-making processes, which require efficient and swift data storage and retrieval systems. Data lakes offer a solution by allowing businesses to store vast amounts of raw data in its native format until it is needed for analysis. This flexibility enables more efficient and cost-effective data management, making it an attractive option for companies looking to harness the power of big data. Moreover, advancements in artificial intelligence (AI) and machine learning (ML) are further propelling the adoption of data lake storage systems.

The need for real-time analytics is also fueling the growth of the data lake storage market. In today's fast-paced business environment, organizations require immediate insights to stay competitive. Data lakes enable real-time data processing and analytics, allowing businesses to respond quickly to changing market conditions and customer demands. This capability is particularly crucial in sectors such as finance, healthcare, and retail, where timely information can significantly impact decision-making and operational efficiency. As the demand for real-time analytics continues to grow, the adoption of data lake storage solutions is expected to increase correspondingly.

On a regional level, North America currently dominates the data lake storage market, driven by the presence of major technology players and high adoption rates of advanced data management solutions. However, significant growth opportunities also exist in the Asia Pacific region, where rapid digital transformation and increasing investments in big data and analytics are driving the market. Europe is also expected to witness substantial growth due to stringent data protection regulations and the increasing need for data-driven decision-making. These regional dynamics are further shaping the global data lake storage market landscape.

Component Analysis

In the data lake storage market, the component segment is broadly categorized into software, hardware, and services. The software component is crucial as it encompasses the various platforms and solutions that enable the creation, management, and utilization of data lakes. This includes data ingestion tools, processing frameworks, and analytical tools. The increasing focus on cloud-based solutions and the integration of advanced analytics capabilities are driving the demand for robust software solutions in the data lake storage market. Companies are continuously innovating to offer more scalable and efficient software to handle large datasets and complex queries.

The hardware component of the data lake storage market primarily includes storage devices and computing infrastructure necessary to support data lake environments. This segment is witnessing growth due to the increased need for high-capacity and high-performance storage solutions. As data volumes continue to grow, organizations are investing in advanced storage technologies such as solid-state drives (SSDs) and high-density storage systems to meet their data storage requirements. The hardware segment's growth is also fueled by the need for efficient data retrieval and processing capabilities, which are essential for real-time

This data is a compilation of the status of food waste generation by city and county in Gyeonggi Province, and provides information such as the amount of food waste generated (tons), population (people), and average amount generated per person per day (kg). The data can be used to establish resource circulation and waste reduction policies, set food waste reduction goals, and create environmental education materials. The data is updated annually and managed by the Gyeonggi Province Resource Circulation Division. Food waste is one of the items subject to separate disposal, and can be recycled through energy recovery or composting during the processing process, so continuous monitoring of the amount generated is directly related to environmental protection. The data is provided as an API in XML and JSON formats. - Food waste: Generally includes food scraps and food ingredient waste generated in households or restaurants, and is waste that can be recycled through composting or feed conversion. - Daily amount generated per person: This refers to the average amount of food waste generated by one person per day, and is used as an indicator of waste management efficiency.

This data set provides daily gridded brightness temperatures derived from passive microwave sensors and distributed in a polar stereographic projection. NSIDC produces daily gridded brightness temperatures from orbital swath data generated by the Special Sensor Microwave/Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F8, F11, and F13 platforms and the Special Sensor Microwave Imager/Sounder (SSMIS) aboard DMSP F17 and F18. The SSM/I and SSMIS channels used to calculate brightness temperatures include 19.3 GHz vertical and horizontal, 22.2 GHz vertical, 37.0 GHz vertical and horizontal, 85.5 GHz vertical and horizontal (on SSM/I), and 91.7 GHz vertical and horizontal (on SSMIS). Data at 85.5 GHz and 91.7 GHz are gridded at a resolution of 12.5 km, with all other frequencies at a resolution of 25 km. Orbital data for each 24-hour period are mapped to respective grid cells using a simple sum-and-average method, also known as the drop-in-the-bucket method. Data coverage began on 09 July 1987 and is ongoing through the most current processing, with updated data processed several times annually.

With the internet becoming increasingly accessible, the number of e-mails sent and received globally has increased each year since 2017. In 2022, there were an estimated 333 billion e-mails sent and received daily around the world. This figure is projected to increase to 392.5 billion daily e-mails by 2026.

E-Mail marketing Despite the increasing popularity of messengers, chat apps and social media, e-mail has managed to remain central to digital communication and continues to grow in uptake. By 2025, the number of global e-mail users is expected to reach a total of 4.6 billion - an approximate six hundred thousand increase in users, up from 4 billion in 2020. Not only that, when it comes to online advertising e-mail has seen higher click-through-rates than on social media. In Belgium and Germany, these were 5.5 and 4.3 percent respectively - compared to the 1.3 percent global average CTR for social media during the same time period.

Gmail Launched in April 2004, Google’s Gmail has earned its spot as one of the most popular freemail services in the world. According to a 2019 survey, its popularity worldwide was trumped only by Apple’s native iPhone Mail app with 26 percent of all e-mail opens worldwide taking place on the platform. Millennials surveyed in the United Kingdom listed Gmail among their top 5 most important mobile apps, while a similar survey carried out in Sweden saw Gmail tie with WhatsApp for a spot among the top mobile apps nationwide.

Data Center Colocation Market Outlook

The global data center colocation market size was valued at USD 50.3 billion in 2023 and is expected to reach USD 92.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.8% during the forecast period. The primary growth factors for this market include the increasing demand for data storage and management solutions, rapid technological advancements, and the scalability offered by colocation services. As businesses continue to generate large amounts of data, the need for robust, secure, and scalable data center solutions has become more critical than ever before.

The growing adoption of cloud computing is one of the major drivers of the data center colocation market. Companies are increasingly opting for colocation services to manage their data storage and processing needs without the significant investment required for building and maintaining their own data centers. Furthermore, colocation services offer enhanced security, redundant systems, and scalability, which are essential for businesses looking to protect their data and ensure seamless operations. As a result, the shift towards cloud-native applications and hybrid IT environments is expected to propel the market growth substantially.

Another significant growth factor is the increasing penetration of the Internet of Things (IoT) and the subsequent rise in data generation. IoT devices generate massive volumes of data that need to be processed, stored, and analyzed in real-time. Colocation data centers provide an ideal environment for managing this data due to their advanced infrastructure, reliable connectivity, and efficient power utilization. Additionally, the proliferation of 5G technology is expected to further drive the demand for colocation services, as it will enable faster data transmission and lower latency, making real-time data analysis more feasible.

Environmental sustainability and energy efficiency are also key factors influencing the growth of the data center colocation market. With increasing awareness of environmental issues, companies are under pressure to adopt greener practices. Colocation providers are investing in energy-efficient technologies and renewable energy sources to reduce their carbon footprint. This focus on sustainability not only helps businesses meet regulatory requirements but also enhances their corporate social responsibility (CSR) profiles, making colocation services an attractive option for eco-conscious organizations.

The role of a Data Centre in the modern digital landscape cannot be overstated. As the backbone of the internet, data centres are critical in supporting the vast amounts of data generated daily. They provide the necessary infrastructure for storing, processing, and managing data efficiently. With the rise of big data, artificial intelligence, and machine learning, the demand for advanced data centre solutions has surged. These facilities are equipped with state-of-the-art technology to ensure optimal performance, reliability, and security. As businesses continue to rely on data-driven insights, the importance of well-maintained and strategically located data centres will only increase, making them indispensable assets in the digital economy.

Regional outlook for the data center colocation market indicates robust growth across all major regions. North America currently holds the largest market share due to the high concentration of technology companies and the widespread adoption of cloud computing. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing digitalization, rising investments in data center infrastructure, and favorable government initiatives. Europe and Latin America are also projected to experience significant growth, supported by the expansion of IT services and the growing demand for scalable data management solutions.

Type Analysis

The data center colocation market can be segmented by type into retail colocation and wholesale colocation. Retail colocation refers to the leasing of smaller spaces within a data center, often measured in racks or cabinets. This option is typically preferred by small to medium-sized businesses (SMBs) that require a limited amount of space and have moderate data storage and processing needs. Retail colocation offers several advantages, including cost-effectiveness, flexibility, and ease of scalability. SMBs can benefit from the shared infrastructure and services provi

The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.

The dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5) CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval.

This JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth’s hydrological cycle, ocean circulation, and climate change.