As of 2023, the average data consumption per user per month in India was at **** gigabytes. 4G data traffic contributes to ** percent of the overall data traffic while 5G was launched in India in October 2022. Increased online education, remote working for professionals and higher OTT viewership contributed to the data traffic growth.

In the first quarter of 2021, the average Singaporean mobile internet user consumed about 12.7 GB of data per month. With the increasing demand of online video and social media content this figure is expected to further grow over the next few years.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

In May 2023, 905 million gigabytes of data were uploaded and downloaded via mobile networks in the United Kingdom. This was around a 25 percent increase on May 2022, with increased data use driven by shifting consumer habits and the adoption of artificial intelligence.

PurposeTo help search and rescue (SAR) volunteer teams and their partners collect mission data. By collecting information in a consistent manner and with spatially explicit tools, SAR agencies can better answer key questions:How many incidents have we responded to?Where are there high concentrations of incidents by type? How many total hours were volunteered in a given year?Audience Public Safety GIS Specialists who are deploying mission data collection solutions.What Is It? The Mountain Rescue Association has provided their Mission Data Collection Schema as a public resource. This is a zip file that contains the XLSForm, example look up tables, and schema fields in a text document. If you want to use ArcGIS Online and Survey123 Connect to deploy this form, please see documentation provided here https://doc.arcgis.com/en/survey123/desktop/create-surveys/xlsformessentials.htmFor DevelopersSee the GitHub repository https://github.com/cmrRose/sar-mission-data-entry

Data files for the NWEI Azura grid-connected deployment at the 30-meter berth of the US Navys Wave Energy Test Site (WETS 30m Site) at the Kaneohe Marine Corps Base Hawaii (MCBH) on the windward (northeast) coast of the island of Oahu, HI. See general documentation describing specifics of the data files and formats in a separate submission.

A series of short video clips illustrating how to use the Community and Education Data Portal (https://portal.ga.gov.au/persona/education). The Community and Education data portal is one of many data delivery portals available from Geoscience Australia, giving users access to a wealth of useful data and tools. It has been designed specifically for non-technical users, so that general community members, including educators, can access themed surface and subsurface datasets or images with …Show full descriptionA series of short video clips illustrating how to use the Community and Education Data Portal (https://portal.ga.gov.au/persona/education). The Community and Education data portal is one of many data delivery portals available from Geoscience Australia, giving users access to a wealth of useful data and tools. It has been designed specifically for non-technical users, so that general community members, including educators, can access themed surface and subsurface datasets or images with enhanced capabilities including 3D visualisation, and online analysis tools. The User Guide Video complements the help menu in the portal. The User guide is broken into a series of topics Introduction Toolbar Map layers Multiple Layers Background Layers and Sharing 3D Layers Tools Custom Layers The step by step guides were produced by James Cropper.

Generating synthetic population data from multiple raw data sources is a fundamental step for many data science tasks with a wide range of applications. However, despite the presence of a number of ap- proaches such as iterative proportional fitting (IPF) and combinatorial optimization (CO), an efficient and standard framework for handling this type of problems is absent. In this study, we propose a multi-stage frame- work called SynC (short for Synthetic Population via Gaussian Copula) to fill the gap. SynC first removes potential outliers in the data and then fits the filtered data with a Gaussian copula model to correctly capture dependencies and marginals distributions of sampled survey data. Fi- nally, SynC leverages neural networks to merge datasets into one. Our key contributions include: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of- the-art machine learning and statistical techniques, 2) design a metric for validating the accuracy of generated data when the ground truth is hard to obtain, 3) release an easy-to-use framework implementation for repro- ducibility and demonstrate its effectiveness with the Canada National Census data, and 4) present two real-world use cases where datasets of this nature can be leveraged by businesses.

Off-the-shelf parallel corpus data (Translation Data) covers many fields including spoken language, traveling, medical treatment,news, and finance. Data cleaning, desensitization, and quality inspection have been carried out.

Space Data Analytics Market Outlook

The global space data analytics market size was valued at approximately $3.2 billion in 2023 and is projected to reach around $11.8 billion by 2032, reflecting a robust CAGR of 15.6% over the forecast period. Driven by the increasing deployment of satellites and growing advancements in machine learning and data analytics technologies, the market is poised for substantial growth. The convergence of these technologies allows for more efficient data collection, processing, and utilization, which fuels the demand for space data analytics across various sectors.

The primary growth factor for the space data analytics market is the exponential increase in satellite deployments. Governments and private entities are launching satellites for diverse purposes such as communication, navigation, earth observation, and scientific research. This surge in satellite launches generates vast amounts of data that require sophisticated analytical tools to process and interpret. Consequently, the need for advanced analytics solutions to convert raw satellite data into actionable insights is driving the market forward. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of space data analytics, making them more accurate and efficient.

Another significant growth driver is the escalating demand for real-time data and analytics in various industries. Sectors such as agriculture, defense, and environmental monitoring increasingly rely on satellite data for applications like precision farming, border surveillance, and climate change assessment. The ability to obtain real-time data from satellites and analyze it promptly allows organizations to make informed decisions swiftly, thereby improving operational efficiency and outcomes. Furthermore, the growing awareness about the advantages of space data analytics in proactive decision-making is expanding its adoption across multiple sectors.

Moreover, international collaborations and government initiatives aimed at space exploration and satellite launches are propelling the market. Many countries are investing heavily in space missions and satellite projects, creating a fertile ground for the space data analytics market to thrive. These investments are accompanied by supportive regulatory frameworks and funding for research and development, further encouraging innovation and growth in the sector. Additionally, the commercialization of space activities and the emergence of private space enterprises are opening new avenues for market expansion.

Artificial Intelligence in Space is revolutionizing the way we approach space exploration and data analysis. By integrating AI technologies with space missions, scientists and researchers can process vast amounts of data more efficiently and accurately. This integration allows for real-time decision-making and predictive analytics, which are crucial for successful space missions. AI's ability to learn and adapt makes it an invaluable tool for navigating the complex and unpredictable environment of space. As AI continues to evolve, its applications in space exploration are expected to expand, offering new possibilities for understanding our universe and enhancing the capabilities of space data analytics.

From a regional perspective, North America holds the largest market share due to the presence of leading space agencies, like NASA, and prominent private space companies, such as SpaceX and Blue Origin. Europe follows closely, driven by robust investments in space research and development by the European Space Agency (ESA). The Asia Pacific region is expected to witness the fastest growth rate, attributed to increasing satellite launches by countries like China and India, alongside growing investments in space technology and analytics within the region.

Component Analysis

The space data analytics market can be segmented by component into software, hardware, and services. The software segment commands a significant share of the market due to the development of sophisticated analytics tools and platforms. These software solutions are crucial for processing and interpreting the vast amounts of data collected from satellites. Advanced algorithms and AI-powered analytics enable users to extract meaningful insights from raw data, driving the adoption of these solutions across various sectors. The continuous innovation in software capabilities, such as enhanced visualization t

This file contains the prediction accuracy for subway and bus. Prediction accuracy is determined by the number of accurate predictions vs the number of total predictions for each "bin" or timeframe. Data is not guaranteed to be complete for any line or date. There is a known gap in Orange Line data from 09/02/2022 to 09/16/2022.NameDescriptionData TypeExampleweeklyDate representing one week's worth of data. For both bus and subway, the week is labeled as a Friday and represents data from the previous Friday up till the Thursday the day before. (05/23/2025 represents data from 05/16/2025 to 05/22/2025.) The date is based on "service day", so "May 1" means May 1, 3:00am ET until May 2, 2:59am ET.Date05/23/2025modeEither "bus" for bus predictions, or "subway" for Red, Orange, Green-[B/C/D/E], Blue, and Mattapan predictions.Stringbusroute_idThe subway route the data is for. Our bus data provider does not have this data at a per-route level.StringGreen-Barrival_departureFor bus, whether the data is about the timing of an arrival at a bus stop, or the departure from that bus stop. Bus only supports "departure". Absent on subway data because subway uses a "blended" approach of departure predictions at terminals, and arrival predictions otherwise.StringdeparturebinThe bin a prediction belongs to based on how far in the future the predicted event is for. The options are "0-3 min", "3-6 min", "6-12 min", and "12-30 min".String0-3 minnum_predictionsThe count of predictions sampled that meet the criteria of the other fields.Integer50000num_accurate_predictionsOf the num_predictions, how many of them were considered accurate, where "accurate" means the predicted number of seconds was within a threshold of the actual number of seconds, based on the bin. For a given bin, the passing threshold is if a vehicle arrives: 0-3 min: 60 seconds early to 60 seconds late, 3-6 min: 90 seconds early to 120 seconds late, 6-12 min: 150 seconds early to 210 seconds late, 12:30 min: 240 seconds early to 360 seconds late.Integer30000MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

This dataset provides information about the number of properties, residents, and average property values for Ann Street cross streets in Many, LA.

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The Saudi Arabia Data Center Market report segments the industry into Hotspot (Riyadh, Rest Of Saudi Arabia), Data Center Size (Large, Massive, Medium, Mega, Small), Tier Type (Tier 1 And 2, Tier 3, Tier 4), and Absorption (Non-Utilized, Utilized). Get five years of historical data alongside five-year market forecasts.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

New Zealand NZ: Internet Users: Individuals: % of Population data was reported at 88.470 % in 2016. This records an increase from the previous number of 88.223 % for 2015. New Zealand NZ: Internet Users: Individuals: % of Population data is updated yearly, averaging 61.405 % from Dec 1990 (Median) to 2016, with 26 observations. The data reached an all-time high of 88.470 % in 2016 and a record low of 0.000 % in 1990. New Zealand NZ: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

This table contains 174 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (29 items: Austria; Belgium (French speaking); Canada; Belgium (Flemish speaking) ...) Sex (2 items: Males; Females ...) Age groups (3 items: 11 years; 15 years; 13 years ...).

This dataset contains crash information from the last five years to the current date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, crash data becomes more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.

United States US: Population: as % of Total: Female: Aged 65 and Above data was reported at 16.925 % in 2017. This records an increase from the previous number of 16.550 % for 2016. United States US: Population: as % of Total: Female: Aged 65 and Above data is updated yearly, averaging 14.035 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 16.925 % in 2017 and a record low of 10.023 % in 1960. United States US: Population: as % of Total: Female: Aged 65 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Population and Urbanization Statistics. Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.