The dataset contains ranking-and-academic-year-, state-, and university/institute-wise compiled data on the total number of admitted, graduated and placed students of post and graduate students, along with additional details such as number of first year students intaken, number of admitted students through lateral entry, number of students selected for higher studies and median salary of students selected for placements, etc., as per the National Institutional Ranking Framework (NIRF) data

This statistic provides a ranking of universities in the United States in 2015, by their number of Hispanic Master's degree graduates. In 2015, Florida International University conferred ***** Master's degrees to Hispanic students.

Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

Data on the top universities for Computer Science in 2025.

Data on the top universities for Law in 2025.

Data on the top universities for Engineering in 2025, including disciplines such as Chemical Engineering, Civil Engineering, and Mechanical and Aerospace Engineering.

In 2022, Canada had the highest share of adults with a university degree, at over 60 percent of those between the ages of 25 and 64. India had the smallest share of people with a university degree, at 13 percent of the adult population. University around the world Deciding which university to attend can be a difficult decision for some and in today’s world, people are not left wanting for choice. There are thousands of universities around the world, with the highest number found in India and Indonesia. When picking which school to attend, some look to university rankings, where Harvard University in the United States consistently comes in on top. Moving on up One of the major perks of attending university is that it enables people to move up in the world. Getting a good education is generally seen as a giant step along the path to success and opens up doors for future employment. Future earnings potential can be determined by which university one attends, whether by the prestige of the university or the connections that have been made there. For instance, graduates from the Stanford Graduate School of Business can expect to earn around 250,000 U.S. dollars annually.

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

Analysis of ‘The Ultimate Halloween Candy Power Ranking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fivethirtyeight/the-ultimate-halloween-candy-power-ranking on 28 January 2022.

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

Context

What’s the best (or at least the most popular) Halloween candy? That was the question this dataset was collected to answer. Data was collected by creating a website where participants were shown presenting two fun-sized candies and asked to click on the one they would prefer to receive. In total, more than 269 thousand votes were collected from 8,371 different IP addresses.

Content

candy-data.csv includes attributes for each candy along with its ranking. For binary variables, 1 means yes, 0 means no. The data contains the following fields:

• chocolate: Does it contain chocolate?
• fruity: Is it fruit flavored?
• caramel: Is there caramel in the candy?
• peanutalmondy: Does it contain peanuts, peanut butter or almonds?
• nougat: Does it contain nougat?
• crispedricewafer: Does it contain crisped rice, wafers, or a cookie component?
• hard: Is it a hard candy?
• bar: Is it a candy bar?
• pluribus: Is it one of many candies in a bag or box?
• sugarpercent: The percentile of sugar it falls under within the data set.
• pricepercent: The unit price percentile compared to the rest of the set.
• winpercent: The overall win percentage according to 269,000 matchups.

Acknowledgements:

This dataset is Copyright (c) 2014 ESPN Internet Ventures and distributed under an MIT license. Check out the analysis and write-up here: The Ultimate Halloween Candy Power Ranking. Thanks to Walt Hickey for making the data available.

Inspiration:

• Which qualities are associated with higher rankings?
• What’s the most popular candy? Least popular?
• Can you recreate the 538 analysis of this dataset?

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

ATP Tennis Rankings, Results, and Stats

This contains my master ATP player file, historical rankings, results, and match stats.

The player file columns are player_id, first_name, last_name, hand, birth_date, country_code, and height (cm).

The columns for the ranking files are ranking_date, ranking, player_id, and ranking_points (where available).

ATP rankings are mostly complete from 1985 to the present. 1982 is missing, and rankings from 2001-2022020 are only intermittent.

Results and stats: There are up to three files per season: One for tour-level main draw matches (e.g. 'atp_matches_2014.csv'), one for tour-level qualifying and challenger main-draw matches, and one for futures match.

Most of the columns in the results files are self-explanatory. I've also included a matches_data_dictionary.txt file to spell things out a bit more.

To make the results files easier for more people to use, I've included a fair bit of redundancy with the biographical and ranking files: each row contains several columns of biographical information, along with ranking and ranking points, for both players. Ranking data, as well as age, areas of tourney_date, which is almost always the Monday at or near the beginning of the event.

MatchStats a are included where I have them. In general, that means 1991-present for tour-level matches, 2008-present for challengers, and 2011-present for tour-level qualifying. The MatchStats columns should be self-explanatory, but they might not be what you're used to seeing; it's all integer totals (e.g. 1st serves in, not 1st serve percentage), from which traditional percentages can be calculated.

There are some tour-level matches with missing stats. Some are missing because ATP doesn't have them. Others I've deleted because they didn't pass some sanity check (loser won 60% of points, or match time was under 20 minutes, etc). Also, Davis Cup matches are included in the tour-level files, but there are no stats for Davis Cup matches until the last few seasons.

Doubles I've added tour-level doubles back to 2000. Filenames follow the convention atp_matches_doubles_yyyy.csv. I may eventually be able to add tour-level doubles from before 2000, as well as lower-level doubles for some years. Most of the columns are the same, though in a different order.

Doubles updates are temporarily suspended as of late 2020.

Contributing If you find a bug, please file an issue, and be as specific as possible.

Feel free to correct bugs or fill in missing data via pull requests, but be aware that I will not merge PRs. But if that's the most convenient way for you to submit improvements to the data, that's fine; I can work with that.

If you'd like to contribute to the project, I post "help wanted" issues, starting with a plea to fill in biographical data such as date of birth.

Also, I encourage everyone to pitch into the Match Charting Project by charting pro matches. It's not a direct contribution to this repo, but it is a great way to improve the existing state of tennis data.

Attention Please read, understand, and abide by the license below. It seems like a reasonable thing to ask, given the hundreds of hours I've put into amassing and maintaining this dataset. Unfortunately, a few bad apples have violated the license, and when people do that, it makes me considerably less motivated to continue updating.

Also, if you're using this for academic/research purposes (great!), take a minute and cite it properly. It's not that hard, it helps others find a useful resource, and let's face it, you should be doing it anyway.

License Creative Commons License Tennis databases, files, and algorithms by Jeff Sackmann / Tennis Abstract is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Based on a work at https://github.com/JeffSackmann.

Historical Dataset of Lexington School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2009-2023),Comparison of Students By Grade Trends

In the academic year 2023/24, there were 331,602 international students from India studying in the United States. International students The majority of international students studying in the United States are originally from India and China, totaling 331,602 students and 277,398 students respectively in the 2023/24 school year. In 2022/23, there were 467,027 international graduate students , which accounted for over one third of the international students in the country. Typically, engineering and math & computer science programs were among the most common fields of study for these students. The United States is home to many world-renowned schools, most notably, the Ivy League Colleges which provide education that is sought after by both foreign and local students. International students and college Foreign students in the United States pay some of the highest fees in the United States, with an average of 24,914 U.S. dollars. American students attending a college in New England paid an average of 14,900 U.S. dollars for tuition alone and there were about 79,751 international students in Massachusetts . Among high-income families, U.S. students paid an average of 34,700 U.S. dollars for college, whereas the average for all U.S. families reached only 28,026 U.S. dollars. Typically, 40 percent of families paid for college tuition through parent income and savings, while 29 percent relied on grants and scholarships.

Lithuania had the highest tuition fees of the OECD countries in 2022, charging an average of over ****** U.S. dollars per national student at the master's level. The United States followed behind with ****** U.S. dollars per student. The Nordic countries as well as Estonia had no tuition fees.

Considering the three different types presented in this statistics, 'Pouches' leads the ranking with 6 million people. Contrastingly, 'Foil trays' is ranked last, with 2.4 million people.

According to latest figures, around ***** million undergraduate students were enrolled in degree programs at public colleges and universities in China in 2024. In 2023, around ***** million students were studying in bachelor's degree programs, while ***** million were enrolled in more practically oriented short-cycle degree programs. The number of graduates from these programs reached around ***** million in 2023. On a postgraduate level, there were almost *** million master's and doctor's degree students studying at public institutions in China in 2023. Development of enrollment figures Since the beginning of the reform era in 1979, the number of students enrolled at institutions of tertiary education in China has increased tremendously. While the gross enrollment rate in tertiary education ranged at only *** percent in 1990, it reached ** percent of related age groups in 2023. This is the result of a thorough governmental plan aimed at increasing the number of specialists China needed for its economic development. The quality of university education in China also increased a lot throughout these years. Nowadays, two Chinese institutions, Tsinghua University and Peking University, regularly reach the highest positions in international university rankings, while a broad group of institutions are continuously improving into the midfield of international universities. However, competition for admission to the elite universities is fierce and the quality of many lower level colleges is not comparable to higher international standards. Types of tertiary education in China China generally differentiates between universities, providing four-year bachelor, master and doctorate programs, and higher vocational colleges, providing more practically oriented three-year, short-cycle degree programs. In addition, it is possible to obtain degrees at public institutes for adult education and from online and self-learning courses provided by public institutions. The number of students enrolled in degree programs at all different levels of public tertiary education in China reached more than **** million in 2023. In addition to public institutions, there is also a growing number of students enrolled at private colleges and universities. However, these private institutions are generally not as esteemed and work on a lower level than their public counterparts.

Considering the three different types presented in this statistics, 'Tins' leads the French ranking with 1.6 million people. Contrastingly, 'Pouches' is ranked last, with 738.41 thousand people. Their difference, compared to the Types, lies at 850.73 thousand people.

About this dataset

Context

This list ranks the 60 cities in the Worcester County, MA by Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Asian Population: This column displays the rank of cities in the Worcester County, MA by their Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Asian Population: The Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Worcester County Asian Population: This tells us how much of the entire Worcester County, MA Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

About this dataset

Context

This list ranks the 53 cities in the Middlesex County, MA by White population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by White Population: This column displays the rank of cities in the Middlesex County, MA by their White population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• White Population: The White population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Middlesex County White Population: This tells us how much of the entire Middlesex County, MA White population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.