Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..In 2016, changes were made to the languages and language categories presented in tables B16001, C16001, and B16002. For more information, see: 2016 Language Data User note..Geographical restrictions have been applied to Table B16001 - LANGUAGE SPOKEN AT HOME BY ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER for the 5-year data estimates. These restrictions are in place to protect data privacy for the speakers of smaller languages. Geographic areas published for the 5-year B16001 table include: Nation (010), States (040), Metropolitan Statistical Area-Metropolitan Divisions (314), Combined Statistical Areas (330), Congressional Districts (500), and Public Use Microdata Sample Areas (PUMAs) (795). For more information on these geographical delineations, see the Metropolitan Statistical Area Reference Files. County and tract-level data are no longer available for table B16001; for specific language data for these smaller geographies, please use table C16001. Additional languages are also available in the Public Use Microdata Sample (PUMS), at the State and Public Use Microdata Sample Area (PUMA) levels of geography..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2020, about 93.8 percent of the Mexican population was monolingual in Spanish. Around five percent spoke a combination of Spanish and indigenous languages. Spanish is the third-most spoken native language worldwide, after Mandarin Chinese and Hindi.

Mexican Spanish

Spanish was first being used in Mexico in the 16th century, at the time of Spanish colonization during the Conquest campaigns of what is now Mexico and the Caribbean. As of 2018, Mexico is the country with the largest number of native Spanish speakers worldwide. Mexican Spanish is influenced by English and Nahuatl, and has about 120 million users. The Mexican government uses Spanish in the majority of its proceedings, however it recognizes 68 national languages, 63 of which are indigenous.

Indigenous languages spoken

Of the indigenous languages spoken, two of the most widely used are Nahuatl and Maya. Due to a history of marginalization of indigenous groups, most indigenous languages are endangered, and many linguists warn they might cease to be used after a span of just a few decades. In recent years, legislative attempts such as the San Andréas Accords have been made to protect indigenous groups, who make up about 25 million of Mexico’s 125 million total inhabitants, though the efficacy of such measures is yet to be seen.

This layer shows language group of language spoken at home by age. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percentage of the population age 5+ who speak Spanish at home. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): B16007 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: March 17, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=527ea2b5ba814c8ca1c34a2945e1b751

In the 2024/2025 school year, ******* was the most common foreign language studied by students in higher education institutions in Poland. The German language followed it.

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.

The data in this repository were collected in France, the Netherlands and the UK between 2020 and 2022 to inform the validation of the Q-BEx questionnaire (). Children between the ages of 5 and 9 were tested individually to assess their proficiency in the societal language (i.e., French, Dutch or English), as well as their non-verbal intelligence and working memory. One of their parents or caregivers also filled in the full version of the Q-BEx questionnaire. The respository includes data from 299 children (FR: n=78, NL: n=117, UK: n=104), although some measures are not available for all children. In France, children were recruited in ordinary schools and in private clinics for Speech & Language Therapy (37 children were recruited via clinics). The consent form for schools asked parents if the child had previous or current SLT, and the reason. In the Netherlands, recruitment took place in schools and via social media advertisement. Language disorder (reported by parent/teacher/remedial teacher) was an exclusionary criterion. In the UK, all children were recruited in schools; no exclusionary criteria were applied, and not SLT information was collected. Language experience data* Language experience data was collected using the full version of the Q-BEx questionnaire (), which was completed by one of the child’s parents or caregivers. This includes all the following modules (except that Language mixing wasn’t included in France): - Background (languages the child is exposed to, adults and children the child lives with - Risk factors (early language milestones, early parental concerns) - Language exposure and use (current and cumulative estimates; onset of exposure to each language) - Estimates of proficiency in each language (listening, speaking, reading, writing) - Richness of experience in each language (activities, diversity of interlocutors, parental education) - Language mixing - Attitudes The questionnaire was administered either in the societal language (French, Dutch or English) or in one of the child’s home languages (Arabic, Dutch, English, French, German, Italian, Polish, Romanian, Russian, Spanish, Turkish). The choice of administration language was constrained by the translated versions available at the time of testing. The translation protocol used to create the versions in different languages can be found at . Direct outcome measures We collected measures of language and cognitive outcomes during individual, face-to-face sessions with each child (two sessions per child, lasting approximatively 45 minutes each). Most of the testing was done in the child’s school. In France, the children recruited via speech & language therapy clinics were tested in the clinic. In the UK and the NL, some testing sessions took place in a different location (e.g., university premises), and on rare occasions online via Zoom. Language proficiency Outcomes in the societal language (i.e., Dutch, English, or French) include phonology, morphosyntax and vocabulary. Phonology Phonological competence was assessed with the LITMUS Quasi-Universal Non-Word Repetition task. See dos Santos, C., and Ferré, S. (2016) “A Nonword Repetition Task to Assess Bilingual Children’s Phonology”. Language Acquisition 41: 1–14. Morphosyntax Morphosyntax outcomes were assessed with the LITMUS test in each societal language. See Marinis, T. and S. Armon-Lotem (2015). “Sentence Repetition. Methods for assessing multilingual children: disentangling bilingualism from Language Impairment.” in S. Armon-Lotem, J. de Jong and N. Meir, Methods for assessing multilingual children: disentangling bilingualism from Language Impairment. Amsterdam: Multilingual Matters). The English and Dutch versions included 30 items. The French version included 16 items. We created two blocks in the English and Dutch versions so that the first block be comparable to the 16-item French version. Subsequent analyses demonstrated that there was no block effect in EN and NL. For each child, we report three overall scores: Identical Repetitions, Target Repetitions, and Grammatical Attempts. These overall scores correspond to the mean across items (n=30 in English and Dutch; n=16 in French), excluding NAs (i.e., the mean is calculated on the items for which the child did provide a response). Vocabulary Vocabulary breadth was measured in the UK with the British Picture Vocabulary Scale (BPVS), in France with the the Échelle de vocabulaire en images Peabody (EVIP), and in the Netherlands with the Peabody Picture Vocabulary Test (PPVT). Vocabulary depth was measured with the Word Classes component of the Clinical Evaluation of Language Fundamentals CELF-V (in its Dutch, English, or French version). Cognitive measures The tasks used to evaluate cognitive skills were administered in the child’s societal language. Memory Short-term memory was assessed through Forward Digit Recall; working memory was assessed through Backward Digit Recall. Most children were tested using the digit span protocols described in Hill et al (2021), implemented in Psychopy (to allow randomisation of the digit sequences and facilitate the acquisition of detailed data). Children were presented with sequences of numbers (through headphones) and asked to repeat these numbers either in the same order (in the FDR task) or in reverse order (in the BDR task). The length of the sequence increased by one digit after 4 trials, starting with 3 digits in the first block of the FDR task, and 2 digits in the first block of the BDR task. The maximum sequence length was 6 digits in the FDR task, and 5 digits in the BDR task.

Children recruited via the Speech and Language Therapy clinics in France (n=37) experienced more difficulty with this task, so it was decided to use the WISC-V protocol instead for these children, as it included a discontinuation rule.

To allow comparison across groups, we created a WISC-like score for the data collected via the Hill et al (2021) protocol. This consisted in the digit span for which the child had at least one fully accurate response (i.e., all the digits in the span, in the right order) for at least one out of the first two trials for that span (as the WISC-V protocol only features 2 trials per digit span).

FDR_overall and BDR_overall correspond to the total accuracy scores, as per the Hill et al (2021) protocol. FDS_Q and BDS_Q correspond to either the WISC-V score (for children recruited via clinics) or the WISC-like score created as explained above, depending on which protocol the child was tested with. All children have a FDR_Q and a BDR_Q score in the dataset.

Non-verbal intelligence The matrices task from either the Wechsler Intelligence Scale for Children–Fifth Edition (WISC–V) or the Wechsler Preschool & Primary Scale of Intelligence - Fourth UK Edition (WPPSI-IV) was used to measure non-verbal intelligence - depending on the age of the child: children below the age of 6 were tested with the WPPSI.

Depending on the age of the child at the time of testing, we used the WPPSI protocol (for children younger than 6 years of age, n= 77) or the WISC-V protocol (for all the other children).

Table from the American Community Survey (ACS) B16003 of age by language spoken at home for the population 5 years and over in limited English-speaking households. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): B16003Data downloaded from: <a href='https://data.c

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..In 2016, changes were made to the languages and language categories presented in tables B16001, C16001, and B16002. For more information, see: 2016 Language Data User note..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

There were more than ************* speakers of indigenous languages in Mexico as of 2020. Nahuatl was the most spoken indigenous language (although it is also considered a group of languages), with more than **** million speakers. Both the Mayan languages Tseltal and Tsotsil were spoken by over ******* persons. Furthermore, about ******* of all the indigenous language speakers were located in just two states: Chiapas and Oaxaca.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by primary language spoken (English, Spanish, and all other languages). Results are shown overall; by state; and by five subpopulation topics: race and ethnicity, age group, scope of Medicaid and CHIP benefits, urban or rural residence, and eligibility category. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with data quality issues with the primary language variable in TAF are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown overall (where subpopulation topic is "Total enrollees") exclude enrollees younger than age 5 and enrollees in the U.S. Virgin Islands. Results for states with TAF data quality issues in the year have a value of "Unusable data." Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Primary language spoken by the Medicaid and CHIP population in 2020." Enrollees are assigned to a primary language category based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

State wise language data drawn from the 2011 government censuses. This layer also Includes mother tongue languages and literacy rates for men and women.Data source: https://data.humdata.org/dataset/india-languagesThis map layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

The Language Barometer has measured the quality of language services in bilingual Finnish municipalities since 2004. The aim of the study was to find out how satisfied minority language speakers in bilingual municipalities were with the services offered in their mother tongue. 36 bilingual municipalities in Finland were included in the study. The study first examined the respondents' language skills and whether the respondents' children went to a Swedish- or Finnish-language daycare or school. The respondents' use of Swedish, Finnish or both in different types of contexts (e.g. at home, at school, with friends, at the store, with municipal and state authorities, at work) was also investigated. The respondents' familiarity with their linguistic rights was charted, along with how important these rights were for them. They were also asked to evaluate the attitudes towards minority language speakers in the municipality and change in this relationship within the past few years. Harassment or discrimination experienced by the respondents because of their language were also surveyed. The next set of questions focused on the availability and quality of municipal and state services in the respondents' mother tongue. The respondents were asked to evaluate municipal services (e.g. social and healthcare services, libraries, sports facilities) in their home municipality from the perspective of minority language users. State services (e.g. police, tax offices, emergency response central agency) were also evaluated. For both municipal and state services, the respondents were asked whether the services had developed for the better or for the worse in the previous year, and if special measures should be taken to preserve Swedish-language service in the future. Additionally, the functionality of online municipal and state services in the respondents' mother tongue was charted. Finally, the respondents' reactions in the case that they did not receive service in their own language were surveyed, and the respondents were asked whether they had noticed an increase in the prevalence of English as a customer service language when accessing public services in Finland. Background variables included the respondent's age (categorised), gender, mother tongue, municipality of residence, level of education, and occupational status.

The Sign Language Barometer 2020 data was used to examine how Finnish and Finnish-Swedish sign language users feel that their linguistic rights have been implemented. The questionnaire first asked respondents about their language skills and their use of sign language in different environments and contexts, such as at home, during studies and at work. Respondents were also asked about any sign language education in their children's day care and school. Then questions were asked about the language climate, prejudice, harassment and discrimination in relation to sign language use. This was followed by questions about access to information and services in sign language in the municipality where the respondent lived. Respondents were also asked how well they are served in their own language in public services, such as health and medical care, day care, TE services and police services. The next question asked about the interpretation services provided by Kela, for example, what interpretation the respondent had ordered from Kela and whether the interpretation had been provided in accordance with the order. Finally, questions were asked about linguistic rights, including how well the respondent knows his/her linguistic rights and how important he/she considers them to be. Background variables included the respondent's year of birth (categorised), native language, region, level of education and occupational group.

Each person who files bankruptcy is required to attend a meeting of creditors and respond to questions under oath from the trustee and creditors. The meetings are held nationwide. In those locations where the room is controlled by the USTP, if a participant (debtor or creditor) has limited English proficiency, an interpreter is provided free of charge via a conference phone. The number and type of languages interpreted, along with the location where the service was provided, is collected monthly by the USTP for oversight, billing, and statistical purposes. Data are provided in delimited text files. Each entry represents one interpreting session, which may include more than one case.

This layer shows language group of language spoken at home by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population age 5+ who speak Spanish at home. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B16007Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe 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. For more information about ACS layers, visit the FAQ. 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:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.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 2023 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.

Among the programming languages presented here, JavaScript most frequently used open source software within their active public repositories worldwide in 2020, with ** percent. Depending on open source software may lead to vulnerabilities in code, emphasizing the need for open source security.

The task involves three subtasks corresponding to the hierarchical taxonomy of the OLID schema (Zampieri et al., 2019) from OffensEval 2019. The task featured five languages and this upload is for the English language. In addition, English also featured Subtasks B and C. OffensEval 2020 was one of the most popular tasks at SemEval-2020 attracting a large number of participants across all subtasks and also across all languages. A total of 528 teams signed up to participate in the task, 145 teams submitted systems during the evaluation period, and 70 submitted system description papers.

This upload includes a test set used in the paper describing the dataset used in the shared task as well as the official test set used in the shared task.

The evaluation phase for English is available on Codalab: https://competitions.codalab.org/competitions/23285

The Website for the shared task is https://sites.google.com/site/offensevalsharedtask/home

This dataset tracks annual reduced-price lunch eligibility from 2000 to 2020 for Milwaukee School Of Languages vs. Wisconsin and Milwaukee School District

The Language 0-5 project was a multi-methodological longitudinal cohort study that tracked the language development of 90 English-learning children from 6 months to 4;6 years. Its goal was to establish how differences in processing abilities interact with linguistic knowledge, socio-cognitive skills and the environment to predict individual differences in language acquisition. This collection contains all data for which the children’s caregivers gave permission for sharing including summary datasheets for data from questionnaires, diaries and experimental tasks, raw audio and audio-video recordings, and transcripts of (some of) the recordings in CHAT or ELAN format. The collection also includes readme documents both for the project itself and for each of the measures, and copies of materials and publicity images and videos.

Section 48 of the Official Languages Act (OLA) requires the President of the Treasury Board to submit an annual report to Parliament. Additionally, the Act requires the Treasury Board to monitor the status of the Official Languages Program. The purpose of the Official Languages Information System II (OLIS II) is to meet the Treasury Board’s information needs on the status of the Official Languages Program in institutions/organizations subject to the OLA but for whom the Treasury Board does not represent the employer.