Total Total Roberta Willis Scholarship need-based grant and need-merit scholarships awarded for school years 2016/2017 to 2020/2021. Includes both the total amount awarded and total number of awards.

This dataset shows the number of award recipients and award dollars by college for the Excelsior Scholarship program. Academic year 2017-18 was the first award year for this program. Refer to data dictionary for details.

Analysis of ‘Students enrolled in Higher Education ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5d388014454ae3468a4e4358 on 19 January 2022.

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

Students enrolled in Higher Education, by Organic Unit, NUTS, District, Municipality, Nature of Establishment, Teaching Type, Course, Cycle of Studies, CNAEF, Sex, Nationality, Student/worker, Regime and Scholarship. Several datasets with information per academic year.

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

The College Bound Scholarship was created to provide state financial aid to low-income students who may not consider college a possibility due to the cost. The scholarship covers tuition (at comparable public college rates), some fees, and a small book allowance.

This dataset contains the counts of 7th or 8th grade students whose family meets the income requirements (CBS_Eligible), those who submit and complete an application by June 30 of the student’s 8th grade year(CBS_Applications), and the Sign-Up Rate (CBS_Rate) calculated as a percentage.

Dataset from Public Service Division. For more information, visit https://data.gov.sg/datasets/d_b4aeb6f39b50ebbe499bb4c3894a2a06/view

This table gives an overview of government expenditure on regular education in the Netherlands since 1900. All figures presented have been calculated according to the standardised definitions of the OECD.

Government expenditure on education consists of expenditure by central and local government on education institutions and education. Government finance schools, colleges and universities. It pays for research and development conducted by universities. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances to households as well as subsidies to companies and non-profit organisations.

Total government expenditure is broken down into expenditure on education institutions and education on the one hand and government expenditure on student grants and loans and allowances for school costs to households on the other. If applicable these subjects are broken down into pre-primary and primary education, special needs primary education, secondary education, senior secondary vocational and adult education, higher professional education and university education. Data are available from 1900. Figures for the Second World War period are based on estimations due to a lack of source material.

The table also includes the indicator government expenditure on education as a percentage of gross domestic product (GDP). This indicator is used to compare government expenditure on education internationally. The indicator is compounded on the basis of definitions of the OECD (Organisation for Economic Cooperation and Development). The indicator is also presented in the StatLine table education; Education expenditure and CBS/OECD indicators. Figures for the First World War and Second World War period are not available for this indicator due to a lack of reliable data on GDP for these periods.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP has been adjusted upwards as a result of the revision. The revision has not been extended to the years before 1995. In the indicator “Total government expenditure as % of GDP”, a break occurs between 1994 and 1995 as a result of the revision.

Data available from: 1900

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes on 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

Context

The dataset used can be found on the UCI Machine Learning Repository at the following location:

Heart Disease Dataset

There are several copies of this dataset to be found on Kaggle, with people focusing on different types of analyses of the data. This specific copy can be analysed by anyone interested, but is primarily used by a study group from the Udacity Bertelsmann Technology Scholarship to practice analysis of association between variables as well as implementation and comparison of various Machine Learning models.

Content

According to the paper by (Detrano et al., 1989) as found on the UCI Dataset webpage, the data represents data collected for 303 patients referred for coronary angiography at the Cleveland Clinic between May 1981 and September 1984. The 13 independent/ features variables can be divided into 3 groups as follows:

Routine evaluation (based on historical data):

• ECG at rest
• Serum Cholesterol
• Fasting blood sugar

Non-invasive test data (informed consent obtained for data as part of research protocol):

• Exercise ECG
  • ST-segment peak slope (upsloping, flat or downsloping)
  • ST-segment depression
• Excercise Thallium scintigraphy (fixed, reversible or none)
• Cardiac fluoroscopy (number of vessels appeared to contain calcium)

Other demographic and clinical variables (based on routine data):

• Age
• Sex
• Chest pain type
• Systolic blood pressure
• ST-T-wave abnormality (T-wave abnormality)
• Probably or definite ventricular hypertrophy (Este's criteria)
• The dependent/ response variable was the angiographic test result indicating a >50% diameter narrowing.

Data Dictionary

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3632459%2Fa01747fb0158dc51c12bc0824c9c4ae4%2Fdata_dictionary2.png?generation=1609522473018549&alt=media" alt="">

Acknowledgements

UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Donor:

David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

The objective of the analysis is to use statistical learning to identify factors associated with Coronary Artery Disease as indicated by a coronary angiography interpreted by a Cardiologist (as per paper written by Detrano et al cited before).

Note: Let's collaborate to make this dataset bigger! Connect with me if you're interested.

The “Educational Backgrounds of Successful People” dataset brings together comprehensive academic profiles for over 30 distinguished figures across entrepreneurship, science, politics, entertainment, sports, and activism. Each record captures the individual’s highest completed degree (or enrollment status), field of study, awarding institution, graduation year, location, institutional ranking, academic performance, and notable scholarships or honors. By aggregating these educational trajectories in one structured CSV, the dataset enables clear, cross‑comparable insights into the academic foundations behind world‑renowned achievement.

Key Features

• Broad Representation: Profiles span diverse professions—from Nobel laureates and Fortune 500 CEOs to Olympic champions and social activists.
• Rich Academic Metadata: Includes institution country, global ranking, GPA (or equivalent), and prestigious scholarships/awards.
• Complete Educational Paths: Notes both completed degrees and in‑progress or “dropped out” statuses to illustrate alternative success routes.
• CSV‑Friendly Structure: Ten well‑labeled columns facilitate quick import into any analytics tool or database.

Columns and Descriptions

Column	Description
Name	Full name of the individual.
Profession	Primary field or role (e.g., Entrepreneur, Scientist).
Degree	Highest completed degree (e.g., PhD, MBA) or enrollment status.
Field	Major or area of study.
Institution	Name of the university or school.
Graduation Year	Year degree was conferred (or expected).
Country	Country where the institution resides.
Global Ranking	Approximate world ranking (QS/The Times).
GPA (or Equivalent)	Grade point average or comparable metric.
Scholarship/Award	Notable academic honors received.

Why This Dataset Matters

• Educational Research: Compare how different academic paths correlate with later accomplishments.
• Policy & Guidance: Inform educators and career counselors about common institutional and field‑of‑study patterns among high achievers.
• Visualization & Storytelling: Create compelling charts—e.g., the frequency of Ivy League degrees or average GPAs—highlighting trends in elite education.
• Machine Learning & Clustering: Cluster profiles by educational attributes to uncover hidden groupings (e.g., STEM vs. business‑oriented leaders).

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Grants. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Grants, the median income for all workers aged 15 years and older, regardless of work hours, was $33,061 for males and $24,485 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 26% between the median incomes of males and females in Grants. With women, regardless of work hours, earning 74 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Grants.

- Full-time workers, aged 15 years and older: In Grants, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,449, while females earned $43,438, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Grants, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Grants median household income by race. You can refer the same here

The Canadian College Student Survey Consortium (the Consortium, CCSSC) includes the Association of Canadian Community Colleges (ACCC), individual participating colleges and the Canada Millennium Scholarship Foundation (CMSF). Established in late 2001, the Consortium conducted its first survey of college students in the spring of 2002. In 2003, it conducted a second survey, involving 27 colleges and approximately 9,900 students. This report summarizes the findings of the second annual survey. The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. Approximately 9,900 students completed the survey. Of which most students who responded to the survey are enrolled full-time in programs that take two years or longer to complete. Students' financial situations and time use vary greatly by program type as well as region. Many of the differences arise because of students' personal characteristics are correlated with the program they are enrolled in. The fact that some programs are more predominant in certain regions adds another dimension to this variation. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

The Measuring the Effectiveness of Student Aid (MESA) dataset comprises a sample of low income students receiving student financial aid in 2006-07. Students were contacted first (Cycle I) in February-May of that academic year (the precise date varying by province), and were then followed up in 2007-08 (Cycle II), contacted in February April of that year. Students will be contacted again in 2008-09 for the last time. The dataset represents a national sample, including all provinces-except for Prince Edward Island. In the spring and summer of 2005, the Canada Millennium Scholarship Foundation negotiated a series of agreements with provincial governments to deliver a set of bursaries (known as “Access Bursaries”) to first-time, first-year undergraduates from low-income families. These agreements are all broadly similar though eligibility criteria vary slightly by jurisdiction (section 1, below, describes the Access Bursaries as they exist in each province). Students do not need to apply for the award separately; instead, they are automatically considered for the award through their application for provincial student assistance. The sample represents a particular subset of the students who received student financial aid in their first year of postsecondary education in 2006 07. In the majority of the provinces, this subset consists of the students who received a Low Income Bursary from the Millennium Scholarship Foundation. In British Columbia and Nova Scotia, a control group made up of students who received financial aid but not the Millennium Bursary was surveyed as well. The Ontario sample is made up those Millennium Bursary recipients who also received a Canada Access Grant and those who did not, with sub-samples selected from each group (all appear together in the data but can be separately identified). The Bursary and the Grant are awarded in similar amounts, but the eligibility requirements are different. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

Analysis of ‘Medical Transportation Unit Types for Ryan White Grants’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2dfa77b0-d057-41ad-81e2-a4a09ce3b6ef on 26 January 2022.

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

The Ryan White HIV/AIDS Program provides a comprehensive system of care that includes primary medical care and essential support services for people living with HIV who are uninsured or underinsured. The Ryan White Grants are Federal funds which offer services to HIV clients in the Austin area and surrounding 10 counties. Medical Transportation is a service available to Ryan White eligible clients to use to get to and from medical appointments. This data incudes Grant Year, Grant Name, Service Category(Medical Transportation), the Unit Type, Unduplicated Clients and Service Units for the grant year. Unduplicated clients are counted to know how many clients are helped with the service. The services units represent how many unit types were used in a year.

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

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Grants Pass. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Grants Pass, the median income for all workers aged 15 years and older, regardless of work hours, was $38,332 for males and $26,418 for females.

These income figures highlight a substantial gender-based income gap in Grants Pass. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Grants Pass.

- Full-time workers, aged 15 years and older: In Grants Pass, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,267, while females earned $46,744, resulting in a 11% gender pay gap among full-time workers. This illustrates that women earn 89 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Grants Pass.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Grants Pass.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Grants Pass median household income by race. You can refer the same here

The datasets found below contain information regarding investments in health research made by the Canadian Institutes of Health Research (CIHR) for projects that received funding within specified time periods (fiscal year or quarters within) since the 2000-2001 fiscal year. Fiscal years at CIHR run from April 1st to March 31st. Please note that much of the information in the files below is provided in the language in which it was submitted by the researcher. Significant changes to the way information is reported on this page were made in September 2024 and are applicable to all files published henceforth. Changes were made to improve the user experience and increase data accessibility. A more robust and informative metadata file was introduced as well as additional data fields. There is now one amalgamated file provided per fiscal year, organized into the following five tabs: Grants & Awards - This dataset contains general information about the funded projects. There is one row per project with the exception of situations when information pertaining to that project (e.g. the nominated principal investigator, the institution receiving funds) changes within a fiscal year. Research Team (formerly “Co-Applicants”) - This dataset contains information about the investigators on funded projects and their affiliations. There is one row per investigator, and multiple investigators can exist per project. Partners - This dataset contains information about the partners (both applicant-level partners and CIHR competition-level partners) aligned with a funded project. There is one row per partner and multiple partners can exist per project. Research Categories [NEW] - This dataset contains information about the various research area classifications that applicants can identify their projects as relevant to. There is one row per research area and there are usually multiple research areas listed per project. Institutions [NEW] - This dataset contains further information about the institutions linked to the projects identified in the other tabs. The file mentioned above is now updated on a quarterly basis, as opposed to annually: Quarter 1 update – Dataset contains information regarding projects that received funding between April 1st and June 30th (quarter 1). Quarter 2 update – Dataset contains information regarding projects that received funding between April 1st and September 30th (quarters 1 and 2). Quarter 3 update – Dataset contains information regarding projects that received funding between April 1st and December 31st (quarters 1, 2 and 3). Quarter 4 update [entire fiscal year] – Dataset contains information regarding projects that received funding between April 1st and March 31st (quarters 1, 2, 3 and 4). Each update will replace the previous quarterly dataset to include data associated with all projects that received funding up to and including the most recent quarter. For example, the quarter 3 update will include all previous information provided in the quarter 1 and 2 updates, as well as new information for quarter 3. The full fiscal year file – provided following the close of the fiscal year – will replace the quarterly file for that year and will contain information for the entire April 1st to March 31st fiscal period. The quarter 4 dataset is considered final for reporting purposes. The 60+ files published prior to September 2024 no longer appear on this page. However, the data are available in the previous format (e.g. with the old field names), but collapsed to amalgamate all fiscal years from 2000-01 to 2022-23 into one large dataset with three tabs (Grants & Awards, Co-Applicants, Partners). This file contains the word “ARCHIVE” in its name and is found at the bottom of this page. The individual fiscal year files have also been recreated in alignment with the new format and are available below for continuity of reporting purposes. The individual fiscal year files can be joined using the FundingReferenceNumber_NumeroReferenceFinancement, which now serves as the unique identifier for all funded projects. The former unique identifier (Key-Clé) provided in the archived datasets will remain present for continuity, as required. Note that this field is now called FundingCode_CodeFinancement. Any project that appeared in the archived datasets – and therefore had a Funding Code generated – will carry that key forward to all future datasets in which they appear. New projects will not contain the former Funding Code field, and only the FundingReferenceNumber_NumeroReferenceFinancement will be included. The FundingCode_CodeFinancement field will be retired once funding for projects with a Funding Code has ended. The datasets are encoded in Latin-1. This was chosen to allow proper display of French accents in Microsoft Windows programs (e.g. Excel). On other operating systems, the encoding may need to be specified as Latin-1 for the files to be read properly. Grant: Support for the direct costs of research projects including for the training of researchers and/or activities that support the translation of research findings, conducted by either an investigator working alone or by a group of investigators working together. Award: Generally intended as direct salary support to individual research personnel or stipend support to individual research trainees. Occasionally awards can carry funding intended for use on research expenditures (e.g. Canada Research Chair awards).

The University of Miami Libraries Special Collections is home to the Pan American World Airways, Inc. Records. From Pan Am’s founding in 1927 through its closing in 1991, Pan Am was a pioneer in the development of aviation equipment, air routes, commercial passenger service, navigation techniques, and communication systems. The collection is comprised of fifteen hundred boxes of administrative, legal, financial, technical and promotional materials, as well as internal publications, photographs, audiovisual material and graphic material. A cataloging effort supported by a grant awarded in 2012 from the National Historical Publications and Records Commission (NHPRC) has allowed these materials to be organized thematically. A subsequent NHPRC grant awarded in 2016 later enabled the library to digitize the Printed Materials Series from this vast collection and make it available online.

This dataset comprises periodicals produced by Pan Am throughout its history, both for internal and external audiences, and with a wide variety of subject matter. We are making the dataset available in the form of plain text files, for use in textual analysis and digital scholarship research, inspired by the work of the Collections as Data project.

Each periodical is provided in the form of a zip file, which includes the text files, as well as a file roster and a readme file. The readme files for the majority of the periodicals include potential starting points: interesting keywords or queries customized for each periodical. These readme files were produced by Lilianne Lugo Herrera and Alexandria Morgan, the UM Libraries Digital Humanities UGrow Fellows for 2018-19, working with Digital Scholarship Librarian Paige Morgan.

While this dataset only includes the plain text, full scans of the periodicals and other materials from the Pan Am Collection are available for viewing, and in many cases, for download, through the University of Miami Libraries Digital Collections.

Information on scholarships intended to help male and female prisoners, regardless of age, obtain a higher education diploma (for information, only 20% of prisoners receive general training provided by teachers from the National Education Department made available to the Ministry of Justice).

In addition, this system is supplemented by:

• free registration fees in agreement with partner academic institutions such as the University of Paris-Est Marne-la-Vallée
• assistance with the acquisition of books, university books and computer equipment
• multi-year regional support for aid and support programmes for incarcerated students carried out in Île-de-France by universities in the Ile-de-France region

N.B.:

• SPIP: Prison Service for Integration and Probation
• UPR: Regional Pedagogical Unit
• The "unique ID" column is only there to secure the quality of the dataset; it does not carry any particular information and is not part of the initial dataset

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on s tudent access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average". In January 2003, the Foundation engaged Prairie Research Associates (PRA) Inc. to oversee this research. This dataset was freely received by the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking documentation.

This dataset tracks annual total students amount from 2019 to 2023 for Scholarship Prep - Oceanside

Increase the percentage of eligible students who enroll in the Oklahoma’s Promise (OHLAP) scholarship program from 48.8% in 2014 to 50.8% by 2018.

Historical Dataset of Acad-scholarship & Entrepreneurship is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2011-2023),Total Classroom Teachers Trends Over Years (2011-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),American Indian Student Percentage Comparison Over Years (2012-2023),Asian Student Percentage Comparison Over Years (2012-2023),Hispanic Student Percentage Comparison Over Years (2012-2023),Black Student Percentage Comparison Over Years (2012-2023),White Student Percentage Comparison Over Years (2012-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2012-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022),Graduation Rate Comparison Over Years (2012-2022)