In 2025, there were around 1.53 billion people worldwide who spoke English either natively or as a second language, slightly more than the 1.18 billion Mandarin Chinese speakers at the time of survey. Hindi and Spanish accounted for the third and fourth most widespread languages that year. Languages in the United States The United States does not have an official language, but the country uses English, specifically American English, for legislation, regulation, and other official pronouncements. The United States is a land of immigration, and the languages spoken in the United States vary as a result of the multicultural population. The second most common language spoken in the United States is Spanish or Spanish Creole, which over than 43 million people spoke at home in 2023. There were also 3.5 million Chinese speakers (including both Mandarin and Cantonese),1.8 million Tagalog speakers, and 1.57 million Vietnamese speakers counted in the United States that year. Different languages at home The percentage of people in the United States speaking a language other than English at home varies from state to state. The state with the highest percentage of population speaking a language other than English is California. About 45 percent of its population was speaking a language other than English at home in 2023.

In 2023, around 43.37 million people in the United States spoke Spanish at home. In comparison, approximately 998,179 people were speaking Russian at home during the same year. The distribution of the U.S. population by ethnicity can be accessed here. A ranking of the most spoken languages across the world can be accessed here.

As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

Analysis of ‘Languages spoken across various nations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shubhamptrivedi/languages-spoken-across-various-nations on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

I was fascinated by this type of data as this gives a slight peek on cultural diversity of a nation and what kind of literary work to be expected from that nation

Content

This dataset is a collection of all the languages that are spoken by the different nations around the world. Nowadays, Most nations are bi or even trilingual in nature this can be due to different cultures and different groups of people are living in the same nation in harmony. This type of data can be very useful for linguistic research, market research, advertising purposes, and the list goes on.

Acknowledgements

This dataset was published on the site Infoplease which is a general information website.

Inspiration

I think this dataset can be useful to understand which type of literature publication can be done for maximum penetration of the market base

--- Original source retains full ownership of the source dataset ---

Mexico is the country with the largest number of native Spanish speakers in the world. As of 2024, 132.5 million people in Mexico spoke Spanish with a native command of the language. Colombia was the nation with the second-highest number of native Spanish speakers, at around 52.7 million. Spain came in third, with 48 million, and Argentina fourth, with 46 million. Spanish, a world language As of 2023, Spanish ranked as the fourth most spoken language in the world, only behind English, Chinese, and Hindi, with over half a billion speakers. Spanish is the official language of over 20 countries, the majority on the American continent, nonetheless, it's also one of the official languages of Equatorial Guinea in Africa. Other countries have a strong influence, like the United States, Morocco, or Brazil, countries included in the list of non-Hispanic countries with the highest number of Spanish speakers. The second most spoken language in the U.S. In the most recent data, Spanish ranked as the language, other than English, with the highest number of speakers, with 12 times more speakers as the second place. Which comes to no surprise following the long history of migrations from Latin American countries to the Northern country. Moreover, only during the fiscal year 2022. 5 out of the top 10 countries of origin of naturalized people in the U.S. came from Spanish-speaking countries.

Does the person speak a language other than English at home? This map takes a look at answers to this question from Census Night.Colour:For each SA1 geography, the colour indicates which language 'wins'.SA1 geographies not coloured are either tied between two languages or not enough data Colour Intensity:The colour intensity compares the values of the winner to all other values and returns its dominance over other languages in the same geographyNotes:Only considers top 6 languages for VICCensus 2016 DataPacksPredominance VisualisationsSource CodeNotice that while one language level appears to dominate certain geographies, it doesn't necessarily mean it represents the majority of the population. In fact, as you explore most areas, you will find the predominant language makes up just a fraction of the population due to the number of languages considered.

Papua New Guinea is the most linguistically diverse country in the world. As of 2025, it was home to 840 different languages. Indonesia ranked second with 709 languages spoken. In the United States, 335 languages were spoken in that same year.

Polygons delineating the linguistic homelands of most of the language-in-country entries in the Ethnologue, 15th edition

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Vietnamese part of GlobalPhone was collected in summer 2009. In total 160 speakers were recorded, 140 of them in the cities of Hanoi and Ho Chi Minh City in Vietnam, and an additional set of 20 speakers were recorded in Karlsruhe, Germany. All speakers are Vietnamese native speakers, covering the main dialectal variants from South and North Vietnam. Of these 160 speakers, 70 were female and 90 were male. The majority of speakers are well educated, being graduated students and engineers. The age distribution of the speakers ranges from 18 to 65 years. Each speaker read between 50 and 200 utterances from newspaper articles, corresponding to roughly 9.5 minutes of speech or 138 utterances per person, in total we recorded 22.112 utterances. The speech was recorded using a close-talking microphone Sennheiser HM420 in a push-to-talk scenario using an inhouse developed modern laptop-based data collection toolkit. All data were recorde...

Historical Dataset of World Language High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2008-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2008-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (2006-2023),Two or More Races Student Percentage Comparison Over Years (2013-2022),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2010-2021),Overall School Rank Trends Over Years (2011-2022),Graduation Rate Comparison Over Years (2011-2022)

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Spanish (Latin America) corpus was produced using the La Nacion newspaper. It contains recordings of 100 speakers (44 males, 56 females) recorded in Heredia and San Jose, Costa Rica. The following age distribution has been obtained: 20 speakers are below 19, 54 speakers are between 20 and 29, 13 speakers are between 30 and 39, 5 speakers are between 40 and 49, and 8 speakers are over 50.

This dataset tracks annual two or more races student percentage from 2017 to 2023 for World Languages Institute vs. Texas and Fort Worth Independent School District

While English is the official language, it is typically used for governmental, business, and media purposes. In day to day life most people in the country speak Krio, which is a style of Pidgin English or English-based creole language. Krio is the lingua franco for the country and the formal language for those who do not speak English. With the number of different ethnic groups, Krio unites these groups with a common language. The citizens who are fluent in English are among the elite minority and often experience privileges such as economic opportunities that non-English speakers are excluded from. Other common indigenous languages used in the country are Mende, Temne, and Limba. As the official language, English is the only language used in education. It is reported that school children who speak indigenous languages on school premises are punished. Students who fail English classes are not granted admission into college. Attribute Table Field DescriptionsISO3-International Organization for Standardization 3-digit country codeADM0_NAME-Administration level zero identification / nameLANG_FAM-Language familyLANG_SUBGR-Language subgroupALT_NAMES-Alternate namesCOMMENTS-Comments or notes regarding languageSOURCE_DT-Source one creation dateSOURCE-Source oneSOURCE2_DT-Source two creation dateSOURCE2-Source twoCollectionThis feature class was created using Anthromapper consisting of linguistic layers that have been primarily based on The World Language Mapping System (WMLS). Geographical terrain features, combined with a watershed model, were also used to predict the likely extent of linguistic influence. The metadata was supplemented with anthropological and linguistic information from peer-reviewed journals and published books. It should be noted that this feature class only depicts the majority first level languages spoken in a given area; there might be significant populations of other minority language speakers not shown in this dataset.The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, November 2014.Ethnologue, “Languages of the World." 2012. Accessed November 2014. http://www.ethnologue.com.World Language Mapping System (WLMS) Version 16. World GeoDatasets, November 2014.Sources (Metadata)Antimoon, “English, French, and Arabic languages in Sierra Leone”. December 2009. Accessed December 2014. http://www.antimoon.com.Central Intelligence Agency. The World FactBook, “Serra Leone”. June 2014. Accessed November 2014. https://www.cia.gov/library/publications/the-world-factbook.DePauw University. Sierra Leone, “Language”. January 2014. Accessed December 2014. http://www.depauw.edu.National African Language Resource Center (NALRC), “Krio”. January 2014. Accessed December 2014. http://www.nalrc.indiana.edu.

Analysis of ‘Extinct Languages’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/the-guardian/extinct-languages on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

A recent Guardian blog post asks: "How many endangered languages are there in the World and what are the chances they will die out completely?" The United Nations Education, Scientific and Cultural Organisation (UNESCO) regularly publishes a list of endangered languages, using a classification system that describes its danger (or completion) of extinction.

Content

The full detailed dataset includes names of languages, number of speakers, the names of countries where the language is still spoken, and the degree of endangerment. The UNESCO endangerment classification is as follows:

• Vulnerable: most children speak the language, but it may be restricted to certain domains (e.g., home)
• Definitely endangered: children no longer learn the language as a 'mother tongue' in the home
• Severely endangered: language is spoken by grandparents and older generations; while the parent generation may understand it, they do not speak it to children or among themselves
• Critically endangered: the youngest speakers are grandparents and older, and they speak the language partially and infrequently
• Extinct: there are no speakers left

Acknowledgements

Data was originally organized and published by The Guardian, and can be accessed via this Datablog post.

Inspiration

• How can you best visualize this data?
• Which rare languages are more isolated (Sicilian, for example) versus more spread out? Can you come up with a hypothesis for why that is the case?
• Can you compare the number of rare speakers with more relatable figures? For example, are there more Romani speakers in the world than there are residents in a small city in the United States?

--- Original source retains full ownership of the source dataset ---

Dataset Card for [Dataset Name]

  Dataset Summary

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the… See the full description on the dataset page: https://huggingface.co/datasets/Ruth-Ann/jampatoisnli.

Our groundbreaking translation dataset represents a monumental advancement in the field of natural language processing and machine translation. Comprising a staggering 785 million records, this corpus bridges language barriers by offering translations from English to an astonishing 548 languages. The dataset promises to be a cornerstone resource for researchers, engineers, and developers seeking to enhance their machine translation models, cross-lingual analysis, and linguistic investigations.

Size of the dataset – 41GB(Uncompressed) and Compressed – 20GB

Key Features:

Scope and Scale: With a comprehensive collection of 785 million records, this dataset provides an unparalleled wealth of translated text. Each record consists of an English sentence paired with its translation in one of the 548 target languages, enabling multi-directional translation applications.

Language Diversity: Encompassing translations into 548 languages, this dataset represents a diverse array of linguistic families, dialects, and scripts. From widely spoken languages to those with limited digital representation, the dataset bridges communication gaps on a global scale.

Quality and Authenticity: The translations have been meticulously curated, verified, and cross-referenced to ensure high quality and authenticity. This attention to detail guarantees that the dataset is not only extensive but also reliable, serving as a solid foundation for machine learning applications. Data is collected from various open datasets for my personal ML projects and looking to share it to team.

Use Case Versatility: Researchers and practitioners across a spectrum of domains can harness this dataset for a myriad of applications. It facilitates the training and evaluation of machine translation models, empowers cross-lingual sentiment analysis, aids in linguistic typology studies, and supports cultural and sociolinguistic investigations.

Machine Learning Advancement: Machine translation models, especially neural machine translation (NMT) systems, can leverage this dataset to enhance their training. The large-scale nature of the dataset allows for more robust and contextually accurate translation outputs.

Fine-tuning and Customization: Developers can fine-tune translation models using specific language pairs, offering a powerful tool for specialized translation tasks. This customization capability ensures that the dataset is adaptable to various industries and use cases.

Data Format: The dataset is provided in a structured json format, facilitating easy integration into existing machine learning pipelines. This structured approach expedites research and experimentation. Json format contains the English word and equivalent word as single record. Data was exported from MongoDB database to ensure the uniqueness of the record. Each of the record is unique and sorted.

Access: The dataset is available for academic and research purposes, enabling the global AI community to contribute to and benefit from its usage. A well-documented API and sample code are provided to expedite exploration and integration.

The English-to-548-languages translation dataset represents an incredible leap forward in advancing multilingual communication, breaking down barriers to understanding, and fostering collaboration on a global scale. It holds the potential to reshape how we approach cross-lingual communication, linguistic studies, and the development of cutting-edge translation technologies.

Dataset Composition: The dataset is a culmination of translations from English, a widely spoken and understood language, into 548 distinct languages. Each language represents a unique linguistic and cultural background, providing a rich array of translation contexts. This diverse range of languages spans across various language families, regions, and linguistic complexities, making the dataset a comprehensive repository for linguistic research.

Data Volume and Scale: With a staggering 785 million records, the dataset boasts an immense scale that captures a vast array of translations and linguistic nuances. Each translation entry consists of an English source text paired with its corresponding translation in one of the 548 target languages. This vast corpus allows researchers and practitioners to explore patterns, trends, and variations across languages, enabling the development of robust and adaptable translation models.

Linguistic Coverage: The dataset covers an extensive set of languages, including but not limited to Indo-European, Afroasiatic, Sino-Tibetan, Austronesian, Niger-Congo, and many more. This broad linguistic coverage ensures that languages with varying levels of grammatical complexity, vocabulary richness, and syntactic structures are included, enhancing the applicability of translation models across diverse linguistic landscapes.

Dataset Preparation: The translation ...

This fileset contains majority maps for all languages in the World Color Survey data. Material produced for J.P. Correia, R. Ocelák, and J. Mašek's "Towards more realistic modeling of linguistic color categorization" (to appear).

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks. The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.The Japanese corpus was produced using the Nikkei Shinbun newspaper. It contains recordings of 149 speakers (104 males, 44 females, 1 unspecified) recorded in Tokyo, Japan. The following age distribution has been obtained: 22 speakers are below 19, 90 speakers are between 20 and 29, 5 speakers are between 30 and 39, 2 speakers are between 40 and 49, and 1 speaker is over 50 (28 speakers age is unknown).

According to Cognitive Market Research, the global language services market size was estimated at USD 58.9 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 6.2% from 2023 to 2030. Which Factors Drives the Language Services Market Growth?

Cross-border contact has become more intense due to globalization, increasing the need for translation, localization, and interpretation services. Language solutions are required by growing multinational businesses, e-commerce, and multilingual customer service. Growth is also fueled by government programs that support accessibility and multilingualism. Technology advancements, including AI-driven translation tools, increase productivity and widen the market.

These developments empower businesses to offer better-tailored solutions and services, which, in turn, contribute to the growth of the Language Services industry.

For instance, A well-known international provider of language services, BIG Language Solutions, revealed in April 2022 that it had acquired the Milan-based company Lawlinguists, which offers legal translation services. With the addition of Italy, Germany, and Spain to BIG's European footprint through the purchase, its clients now have access to a wider range of excellent legal translation services, resources, and technology.

(Source:biglanguage.com/blog/big-acquires-lawlinguists-expands-legal-offering-and-european-presence/)

Globalization and Internationalization to Provide Viable Market Output

A significant market driver for language services has been globalization. Communication in various languages is becoming increasingly important as firms grow internationally. The expansion of international trade, e-commerce, and cross-border investments all contribute to this trend. Companies must translate, localize, and adapt their products and services to local languages and cultures to remain competitive in the global market.

There are approximately 7,139 languages spoken in the world today. However, many of these languages are endangered, with experts estimating that around 40% of languages are at risk of extinction.

(Source:www.ohchr.org/en/stories/2019/10/many-indigenous-languages-are-danger-extinction)

Multinational corporations with diverse workforces and clients from various language backgrounds have become popular due to globalization. These enterprises rely on translation services to eliminate language barriers to guarantee efficient internal communication and seamless relations with external parties. Language solutions, including document, website, and marketing material translation and conference and meeting interpretation services, greatly aid international collaboration and understanding.

Technological Advancements to Propel Market Growth





Localization of Digital Content

Factors Restraining Growth of the Language Services Market

Machine Translation Limitations to Hinder Market Growth

The constraints of machine translation constrain the language services market. While machine translation quality has increased due to technological developments in AI, especially for complicated or specialized information, it still falls short of human translation in accuracy and nuance. The context and idiomatic idioms that machine translation systems frequently struggle with might cause translations to sound uncomfortable or inaccurate to native speakers. This restriction is especially important for fields like law, medicine, and marketing, where accuracy and cultural appropriateness are key.

How COVID-19 Impacted the Language Services Market?

To reach a worldwide audience, the pandemic drove digital transformation and remote labor, driving up demand for translation and localization services. Translations in the medical and scientific fields increased as information sharing became essential. Travel restrictions hampered on-site interpreting services simultaneously, increasing the demand for remote interpreting services. Due to the pandemic's emphasis on efficient intercultural communication, businesses, the medical community, and governments have all prioritized language services to enable proper information flow and support during the crisis What is Language Services?

Language services means it is a professional service used for communication and understanding between different cultural groups. It facilitates effective comm...

Historical Dataset of Robert Randall World Languages is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1988-2006),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1988-2020),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2009-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1992-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Overall School Rank Trends Over Years (2010-2022)