The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for all MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsProficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021.Definition: How well people whose main language is not English (English or Welsh in Wales) speak English.This dataset provides details for the MSOAs of Leicester city.

This statistic shows the usage of English muffins in the United States from 2011 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans used English muffins in 2020. This figure is projected to increase to ****** million in 2024.

This document summarizes research into the problem of small population civil parishes in England. (File Size - 715 KB)

About this dataset

Context

The dataset tabulates the population of North English by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for North English. The dataset can be utilized to understand the population distribution of North English by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in North English. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for North English.

Key observations

Largest age group (population): Male # 5-9 years (51) | Female # 10-14 years (81). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the North English population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the North English is shown in the following column.
• Population (Female): The female population in the North English is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in North English for each age group.

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 North English Population by Gender. You can refer the same here

Wiktionary Data on Hugging Face Datasets

wiktionary-data is a sub-data extraction of the English Wiktionary that currently supports the following languages:

Deutsch - German Latinum - Latin Ἑλληνική - Ancient Greek 한국어 - Korean 𐎠𐎼𐎹- Old Persian 𒀝𒅗𒁺𒌑(𒌝) - Akkadian Elamite संस्कृतम् - Sanskrit, or Classical Sanskrit

wiktionary-data was originally a sub-module of wilhelm-graphdb. While the dataset it's getting bigger, I noticed a wave of more exciting potentials this… See the full description on the dataset page: https://huggingface.co/datasets/paion-data/wiktionary-data.

Table from the American Community Survey (ACS) 5-year series on languages spoken and English ability related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B16004 Age by Language Spoken at Home by Ability to Speak English, C16002 Household Language by Household Limited English-Speaking Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B16004, C16002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This service shows the percentage of population, excluding institutional residents, with knowledge of English and French for Canada by 2016 census subdivision. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001. Knowledge of official languages refers to whether the person can conduct a conversation in English only, French only, in both languages or in neither language. For a child who has not yet learned to speak, this includes languages that the child is learning to speak at home. For additional information refer to 'Knowledge of official languages' in the 2016 Census Dictionary. For additional information refer to 'Knowledge of official languages' in the 2016 Census Dictionary. To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census subdivision” web service, accessible in the data resources section below.

In 2023, students who were in grade 12 in Thailand scored an average of ***** percent in English proficiency. English scores for students under this level of education had slightly increased compared to the previous year, which averaged at around ***** percent.

Description

French/English parallel texts for training translation models. Over 22.5 million sentences in French and English. Dataset created by Chris Callison-Burch, who crawled millions of web pages and then used a set of simple heuristics to transform French URLs onto English URLs, and assumed that these documents are translations of each other. This is the main dataset of Workshop on Statistical Machine Translation (WML) 2015 Dataset that can be used for Machine Translation and Language Models. Refer to the paper here: http://www.statmt.org/wmt15/pdf/WMT01.pdf

Citation

@InProceedings{bojar-EtAl:2015:WMT,
 author  = {Bojar, Ond\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
 title   = {Findings of the 2015 Workshop on Statistical Machine Translation},
 booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
 month   = {September},
 year   = {2015},
 address  = {Lisbon, Portugal},
 publisher = {Association for Computational Linguistics},
 pages   = {1--46},
 url    = {http://aclweb.org/anthology/W15-3001}
}

Image Credits: Unsplash - chriskaridis

Contents of https://www.statice.is and https://hagstofa.is/ websites downloaded, aligned and converted into parallel corpus

We work with the Ministry of Education to organize the "Overseas Chinese Youth English Service Camp" to promote international youth exchange and enhance the English learning interest of children in remote or disadvantaged areas. We invite overseas Chinese youth who are willing to serve to come to Taiwan to engage in voluntary English teaching services in remote areas in order to benefit students in those areas. This initiative has been carried out since 2006.

Financial overview and grant giving statistics of Western And English Sales Association

Financial overview and grant giving statistics of Arizona English Teachers Association Inc.

In London, **** percent of students at English schools had a first language that was believed to not be English in 2024/25, the most of any region in this year. By contrast, in North East England, just *** percent of school students had English as an additional language, the lowest percentage in England.

This dataset contains four types of neural language models trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens. The language model architectures include static (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the two static models, and four instances considering different time slices for BERT.

Github repository: https://github.com/Living-with-machines/histLM

Financial overview and grant giving statistics of English House of Brevard Inc.