East Baton Rouge Parish Library computer usage statistics are organized by branch, year, and month. This dataset only includes the count for library patrons who have logged in to the Library’s public computers, located at any of the 14 locations.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

Note: data is continuously updated・ PG&E provides non-confidential, aggregated usage data that are available to the public and updated on a quarterly basis. These public datasets consist of monthly consumption aggregated by ZIP code and by customer segment: Residential, Commercial, Industrial and Agricultural. The public datasets must meet the standards for aggregating and anonymizing customer data pursuant to CPUC Decision 14-05-016, as follows: a minimum of 100 Residential customers; a minimum of 15 Non-Residential customers, with no single Non-Residential customer in each sector accounting for more than 15% of the total consumption. If the aggregation standard is not met, the consumption will be combined with a neighboring ZIP code until the aggregation requirements are met.

The average daily in-home data usage in the United States has increased significantly during the coronavirus (COVID-19) outbreak in March 2020. Compared to the same time in March 2019 the daily average in-home data usage has increased by 38 percent to 16.6 gigabytes, up from 12 gigabytes in March 2019. The increase can be observed across almost all device categories with the data usage of gaming consoles and smartphones increasing the most.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Metrics from the Transportation Data Exchange Initiative, a demonstration deployment that is part of ITS4US, from the University of Washington's Taskar Center for Accessible Technology (TCAT).

As of 2024, the average data consumption per user per month in India was at **** gigabytes. 5G data traffic contributes to ***percent of the overall data traffic. It was launched in India in October 2022. Increased online education, remote working for professionals, and higher OTT viewership contributed to the data traffic growth.

Although ** percent of the respondents reported no use of database as a service (DBaaS) in their organizations in 2020, the use of this cloud database technology appears to have increased from 2019 and is expected to increase significantly in the coming years, as the need for streamlining and commoditizing services grows.

This dataset reports downloads of metadata records and documents from the Development Experience Clearinghouse (DEC).

The Food Standards Agency uses a system called Wisdom for document and Record Management. Wisdom is the default system where official records should be added, managed and stored. We produce reports on usage levels by directorate to monitor system usage and ensure that staff are complying with the FSA Information Management policy. The reports are ad hoc in terms of timing and relate to usage in the month that they are titled by. The reports show the percentage of staff (represented as a decimal) using Wisdom against headcount figures.

Compiled count of database access for all branches, by month, by fiscal year. Numbers represent “any action performed by the user in relation to a content item," i.e., number of times databases were used to access materials such as articles, videos, PDFs, abstracts etc. Updated annually.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This data is purely random and created for learning purpose.

In situations where data is not readily available but needed, you'll have to resort to building up the data yourself. There are many methods you can use to acquire this data from web scraping to APIs. But sometimes, you'll end up needing to create fake or “dummy” data. Dummy data can be useful in times where you know the exact features you’ll be using and the data types included but, you just don’t have the data itself.

Features Description

• ID — a unique string of characters to identify each user.
• Gender — string data type of three choices.
• Subscriber — a binary True/False choice of their subscription status.
• Name — string data type of the first and last name of the user.
• Email —string data type of the email address of the user.
• Last Login — string data type of the last login time.
• Date of Birth — string format of year-month-day.
• Education — current education level as a string data type.
• Bio — short string descriptions of random words.
• Rating — integer type of a 1 through 5 rating of something.

Note - This Data is Purely Random (Dummy Data). if you wish, you can perform some data visualization and model building part into it.

Reference - https://towardsdatascience.com/build-a-your-own-custom-dataset-using-python-9296540a0178

Sensor measuring benches utilisation in Argyle Square

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Keeping track of your health is, for many people, a continuous task. Monitoring what you eat, how often you exercise and how much water you drink can be time-consuming, fortunately there are tens of...

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

Data.SA dataset analytics for 2018. Dataset views, visits and resource downloads by month.

Record-level usage and geolocation statistics by country and organization for the Drug Repurposing Central portal.

Covered time frame is from October 2021 to August 2024.

MDWU98 is a point coverage that represents the locations of wells for the State of Maryland that are permitted to withdraw 10,000 gallons or more per day by the Maryland Department of the Environment (MDE). Each site has the permit number, permit amount, reported withdrawal, aquifer code, and type of use. Information contained in the dataset comes from the U.S.Geological Survey site-specific water-use database (SWUDS).

This site provides interactive access to data from NASS, as part of a cooperative effort among USDA, the USDA Regional Pest Management Centers and the NSF Center for Integrated Pest Management (CIPM). All data available have been previously published by NASS and have been consolidated at the state level. Commodity acreages and active ingredient agricultural chemical use (% acres treated, ai/acre/treatment, average number of treatments, ai/acre, total ai used) data are available. All data can be searched by commodity, year, state and active ingredient. For more details on methodology, please see NASS website. Search results can be obtained in web format and as downloadable Excel files. For each individual active ingredient, commodity, year and statistic, dynamic U.S. maps of each use statistic can be generated. Agricultural chemical usage statistic data can also be seen in a graphical format. Currently, this site contains the data from 1990. We will continue to update the database annually. As this site is enhanced, we will also provide means and totals of the statistics over years, states, and commodities. This project is funded by USDA, The Cooperative State Research, Education, and Extension Service (CSREES), project award No. 2001-34366-10324. Resources in this dataset:Resource Title: Agricultural Chemical Use Program Data. File Name: Web Page, url: https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Chemical_Use/#data Since 2009, the release of chemical use surveys is available through Quick Stats. The following materials are available for each survey: highlights fact sheet, a methodology paper, and a set of data tables featuring commonly requested information.

In 2021, the global annual cellular data usage is projected to reach roughly *** thousand petabytes (PB), with approximately *** thousand petabytes coming from the use of mobile handsets, in other words, mobile phones. Tablets and cellular IoT devices currently do not compare to mobile phones in terms of data usage, but they are expected to grow in the upcoming years.