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 Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included. The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment): - Sample Size by Country, Area and Consumption Segment (Number of Households) - Population 2010 by Country, Area and Consumption Segment - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population - Population 2010 by Country, Age Group, Sex and Consumption Segment - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million) - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent) - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent) - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent) - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

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.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported at the county level level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-County-Energy-Use-/46pe-aat9. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Blockchain data query: Marginfi - stablecoin usage database

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.

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.

Sensor measuring benches utilisation in Argyle Square

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported by utilities at the county level. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

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.

October 31, 2025 (Final DWR Data)

The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year.

UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf).

The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include:

• Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo.

• Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University.

• Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.

Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data was reported at 0.000 % in 09 Mar 2025. This records a decrease from the previous number of 0.030 % for 08 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data is updated daily, averaging 0.070 % from Mar 2025 (Median) to 09 Mar 2025, with 9 observations. The data reached an all-time high of 0.070 % in 05 Mar 2025 and a record low of 0.000 % in 09 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Belarus – Table BY.SC.IU: Internet Usage: Search Engine Market Share.

Aedes aegypti is the primary vector of several viruses of international public health concern, including Zika, dengue, yellow fever, and chikungunya. Their synanthropic ecology and establishment in tropical, sub-tropical, and temperate areas make Ae. aegypti one of the most medically relevant mosquito species in the world. While they have been reported to be highly anthropophilic, several studies indicate a broader host range. They are also reported to take multiple bloodmeals between gonotrophic cycles. This consumption of multiple bloodmeals makes determination of host usage difficult when using typical Sanger sequencing methods due to sequence overlap. In this study, we examined host usage of Ae. aegypti in Maricopa County, Arizona and Harris County, Texas, using a Nanopore-based third-generation sequencing protocol to alleviate this issue.This repository contains files related to the methods described in this study, sequencing output metadata files, and the database files and the bioinformatic process used for the analysis of the sequencing data.

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts. This layer has been populated with utility energy consumption data procured directly from Seattle City Light (electricity), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true".For more information please visit the One Seattle Climate Portal item description page.

Water use and supply data for 2015 joined to spatial boundaries. GPCD = Gallons Per Capita Day or Gallons Per Person Per Day. Supply and Use numbers are in Acre Feet Per Year (ACFT).

This dataset provides information surrounding bus patronage in the city of Leicester. The data runs from 2009/2010 and is sourced from the Department for Transport.This data is also part of a dashboard that has been produced displaying various transport related datasets. The dashboard can be viewed here.

This statistic shows the types of data that organizations protect by using data backups worldwide as of 2019. Around ** percent of respondents stated that they used backups to protect their business' databases, while only ** percent stated that they used backups to protect their SaaS data.

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 through the XSEDE operational period, which ended August 31, 2022. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation. Because the XSEDE program has ended, the allocation_award_history file includes all allocations activity initiated via XSEDE processes through August 31, 2022. The Resource Providers and successor program to XSEDE agreed to honor all project allocations made during XSEDE. Thus, allocation awards that extend beyond the end of XSEDE may not reflect all activity that may ultimately be part of the project award. Similarly, allocation usage data only reflects usage reported through August 31, 2022, and may not reflect all activity that may ultimately be conducted by projects that were active beyond XSEDE.

Germany Internet Usage: Search Engine Market Share: Desktop: Dogpile data was reported at 0.000 % in 15 Mar 2025. This records a decrease from the previous number of 0.010 % for 14 Mar 2025. Germany Internet Usage: Search Engine Market Share: Desktop: Dogpile data is updated daily, averaging 0.010 % from Mar 2025 (Median) to 15 Mar 2025, with 6 observations. The data reached an all-time high of 0.020 % in 13 Mar 2025 and a record low of 0.000 % in 15 Mar 2025. Germany Internet Usage: Search Engine Market Share: Desktop: Dogpile data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Germany – Table DE.SC.IU: Internet Usage: Search Engine Market Share.

Dataset and details can be found at the US Energy Information Admininstration (EIA)'s RBECs website

Context

EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2015 survey cycle, EIA used Web and mail forms, in addition to in-person interviews, to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design.

First conducted in 1978, the fourteenth RECS collected data from more than 5,600 households in housing units statistically selected to represent the 118.2 million housing units that are occupied as a primary residence. Data from the 2015 RECS are tabulated by geography and for particularly characteristics, such as housing unit type and income, that are of particular interest to energy analysis.

The results of each RECS include data tables, a microdata file, and a series of reports. Data tables are generally organized across two headings; "Household Characteristics" and "Consumption & Expenditures." See RECS data tables.

The RECS and many of the EIA supplier surveys are integral ingredients for some of EIA's more comprehensive data products and reports, such as the Annual Energy Outlook (AEO) and Monthly Energy Review (MER). These products allow for broader comparisons across sectors, as well as projections of future consumption trends.

Content

The Residential Energy Consumption Survey (RECS) is a periodic study conducted by the U.S. Energy Information Administration (EIA) that provides detailed information about energy usage in U.S. homes. RECS is a multi-year effort (Figure 1) consisting of a Household Survey phase, data collection from household energy suppliers, and end-use consumption and expenditures estimation.

The Household Survey collects data on energy-related characteristics and usage patterns of a national representative sample of housing units. The Energy Supplier Survey (ESS) collects data on how much electricity, natural gas, propane/LPG, fuel oil, and kerosene were consumed in the sampled housing units during the reference year. It also collects data on actual dollar amounts spent on these energy sources.

EIA uses models (energy engineering-based models in the 2015 survey and non-linear statistical models in past RECS) to produce consumption and expenditures estimates for heating, cooling, refrigeration, and other end uses in all housing units occupied as a primary residence in the United States. Originally conducted by trained interviewers with paper and pencil, the 2015 study used a combination of computer-assisted personal interview (CAPI), web, and mail modes to collect data for the Household and Energy Supplier Surveys.

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