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 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.

During the fourth quarter of 2024, data breaches exposed more than a million user data records in the United Kingdom (UK). The figure decreased significantly from nearly 41 million in the quarter prior. Overall, the time between the first quarter of 2022 and the fourth quarter of 2023, saw the lowest number of exposed user data accounts.

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

Population, female (% of total population) in World was reported at 49.71 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on May of 2025.

Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of probable COVID-19 deaths among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown. Description The MD COVID-19 - Probable Deaths by Race and Ethnicity Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by categories of race and ethnicity. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Race and Ethnicity Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This is a comprehensive point theme that incorporates land categories associated with a given parcel - one or more records per parcel account number. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_ComputerAssistedMassAppraisal/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

As of June 2024, 71 percent of countries worldwide had data privacy legislation in place. Furthermore, nine percent had the legislation drafted. Overall, 15 percent of markets worldwide had no data privacy legislation yet, and five percent have not provided any data on such laws.

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

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.

About this dataset

Context

The dataset tabulates the New York population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of New York.

Content

The dataset constitues the following two datasets across these two themes

• New York Population Breakdown by Gender
• New York Population Breakdown by Gender and Age

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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

Under the Freedom of Information Act 2000, I request the following information: The number of individuals of all ages who were prescribed contraceptives in the financial years 2019-2020, 2021-2020, 2020-2021, 2021-2022 and 2022-2023 in community settings (GP surgeries and pharmacies) broken down by contraceptive method. I would also like the proportion these represent of contraception users. For example, X proportion of those on contraception are using the Mirena coil. If possible, I would also appreciate if this were broken down by age of those prescriptions too. To clarify, I mean patients. I also mean both contraceptive drugs and appliances/devices Response A copy of the information is attached. Please read the following information to ensure correct understanding of the data. Fewer than five Please be aware that I have decided not to release the full details where the total number of individuals falls below five. This is because the individuals could be identified, when combined with other information that may be in the public domain or reasonably available. This information falls under the exemption in section 40 subsections 2 and 3 (a) of the Freedom of Information Act (FOIA). This is because it would breach the first data protection principle as: a - It is not fair to disclose individual’s personal details to the world and is likely to cause damage or distress. b - These details are not of sufficient interest to the public to warrant an intrusion into the privacy of the individual. Please click the weblink to see the exemption in full: www.legislation.gov.uk/ukpga/2000/36/section/40 NHS Business Services Authority (NHSBSA) - NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors, and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over one billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care (DHSC) users. Data Source: ePACT2 - Data in ePACT2 is sourced from the NHSBSA Data Warehouse and is derived from products prescribed on prescriptions and dispensed in the Community. The data captured from prescription processing is used to calculate reimbursement and remuneration. It includes items prescribed in England, Wales, Scotland, Northern Ireland, Guernsey/Alderney, Jersey, and Isle of Man which have been dispensed in the community in England. English prescribing that has been dispensed in Wales, Scotland, Guernsey/Alderney, Jersey, and Isle of Man is also included. The data excludes: • Items not dispensed, disallowed and those returned to the contractor for further clarification. • Prescriptions prescribed and dispensed in prisons, hospitals, and private prescriptions. • Items prescribed but not presented for dispensing or not submitted to NHS Prescription Services by the dispenser. Dataset - The data is limited to presentations prescribed in BNF sections 0703 Contraceptives and BNF section 2104 Contraceptive Devices. Data is presented at BNF Sub Paragraph and BNF Presentation level. Time Period - Financial years 2019/20, 2020/21, 2021/22, 2022/23 and 2023/24 (April 2023 - January 2024). Data is currently available up to and including January 2024. Organisation Data - The data is for prescribing in England regardless of where dispensed in the community. British National Formulary (BNF) Sub Paragraph and Presentation Code – The BNF Code is a 15-digit code in which the first seven digits are allocated according to the categories in the BNF, and the last eight digits represent the medicinal product, form, strength and the link to the generic equivalent product. NHS Prescription Services has created pseudo BNF chapters, which are not published, for items not included in BNF chapters 1 to 15. Most of such items are dressings and appliances which NHS Prescription Services has classified into four pseudo BNF chapters (20 to 23). Patient Identification - Where patient identifiable figures have been reported they are based on the information captured during the prescription processing activities. Please note, patient details cannot be captured from every prescription form and based on the criteria used for this analysis, patient information (NHS number) was only available for 98.28% of prescription items. The unique patient count figures are based on a distinct count of NHS number as captured from the prescription image. Patient ages are based on the age as captured from the prescription image and relates to the patient's age at the time of prescribing/dispensing. Please note it is possible that a single patient may be included in the results for more than one age band where a patient has received prescribing at different ages during a financial year. The figures for the number of identifiable patients should not be combined and reported at any other level than provided as this may result in the double counting of patients. For example, a single patient could appear in the results for multiple presentations or both financial years. Patient Age - Shows the age of the patient, if recorded. Data Quality for patient age - NHSBSA stores information on the age of the recipient of each prescription as it was read by computer from images of paper prescriptions or as attached to messages sent through the electronic prescription system. The NHSBSA does not validate, verify or manually check the resulting information as part of the routine prescription processing. There are some data quality issues with the ages of patients prescribed the products. The NHSBSA holds prescription images for 18 months. A sample of the data was compared to the images of the paper prescription forms from which the data was generated where these images are still available. These checks revealed issues in the reliability of age data, in particular the quality of the stored age data was poor for patients recorded as aged two years and under. When considering the accuracy of age data, it is expected that a small number of prescriptions may be allocated against any given patient age incorrectly. Application of Disclosure Control to information services (prescriptions) products- ePACT 2 data is not published statistics - it is available to authorised NHS users who are subject to Caldicott Guardian approval. We have no plans to apply disclosure control to data released to ePACT 2 users. These users are under an obligation to protect the anonymity of any patients when reusing this data or releasing derived information publicly. All requests that fall under the FOI process are subject to the NHSBSA Anonymisation and Pseudonymisation Standard. The application of the techniques described in the standard is judged on a case-by-case basis (by NHSBSA Information Governance) in respect of what techniques should be applied. The ICO typically rules on a case-by-case basis too so each case or challenge or appeal is judged on its own merits. FOI rules apply to data that we hold as part of our normal course of business.

This table contains 696 series, with data for years 1998 - 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 (Flemish speaking); Canada; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age groups (3 items: 11 years; 15 years;13 years ...), Frequency (4 items: Not at all; Twice; Three or more times; Once ...).

This data package aims to pilot an approach for providing usable data for analyses related to drought planning and management for urban water suppliers--ultimately contributing to improvements in communication around drought. This project was convened by the California Water Data Consortium in partnership with the Department of Water Resources (DWR) and the State Water Resources and Control Board (SWB) and is one of two use cases of this working group that aim to improve data submitted by urban water suppliers in terms of accessibility and useability. The datasets from DWR and the SWB are compiled in a standard format to allow interested parties to synthesize and analyze these data into a cohesive message. This package includes a data management plan describing its development and maintenance. All code related to preparing this data package can be found on GitHub. Please note that the "org_id" (DWR's Organization ID) and the "pwsid" (SWB's Public Water System ID) can be used to connect to the various data tables in this package.

We acknowledge that data quality issues may exist. Making these data available in a usable format will help identify and address data quality issues. If you identify any data quality issues, please contact the data steward (see contact information). We plan to iteratively update this data package to incorporate new data and to update existing data with quality fixes. The purpose of this project is to demonstrate how data from two agencies, when made publicly available, can be used in relevant analyses; if you found this data package useful, please contact the data steward (see contact information) to share your experience.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

About this dataset

Context

The dataset tabulates the Clifton population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Clifton. The dataset can be utilized to understand the population distribution of Clifton by age. For example, using this dataset, we can identify the largest age group in Clifton.

Key observations

The largest age group in Clifton, NJ was for the group of age 30 to 34 years years with a population of 6,802 (7.62%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Clifton, NJ was the 80 to 84 years years with a population of 1,440 (1.61%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Clifton is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Clifton total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Clifton Population by Age. You can refer the same here

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

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.