The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

The QS World University Rankings for 2025 is a list of universities from all over the world, organized to show which ones are the best in various areas. It is widely recognized as one of the most reliable ways to compare higher education institutions. This ranking helps students, researchers, and decision-makers understand how well universities perform in terms of academics, teaching, research, and global connections. Let’s break it down into simple parts so that you can understand it easily.

What’s in the Ranking? The ranking includes several key pieces of information about each university:

University Name: This is simply the name of the school. For example, Harvard University or Oxford University. Ranking Position: This tells you the university’s position on the list, like 1st, 50th, or 200th. A lower number means the university is ranked higher. Country/Region: This shows where the university is located, like the USA, the UK, or Japan. Academic Reputation Score: This score is based on surveys of professors and researchers. They give their opinions on which universities are best for studying and learning. Employer Reputation Score: Employers are asked which universities produce the most skilled graduates. This score shows how good a university is at preparing students for jobs. Faculty-Student Ratio: This measures how many students there are per teacher. A lower number means smaller classes and more personal attention for students. Citations per Faculty: This is about research. It shows how often the university’s studies are mentioned in other research papers. The more citations, the better. International Faculty & Students: This looks at how many teachers and students come from different countries, showing how global and diverse the university is. Why Is This Ranking Useful? There are many ways this ranking can help people:

For Students: It helps students decide where they might want to study. For example, if someone wants a university with a good reputation for teaching and research, they can use this ranking to find the best options. For Universities: Schools can use the rankings to see how they compare to others. If one university is ranked lower than another, it can look at the scores to find ways to improve. For Researchers: Researchers can study the ranking to learn about trends in global education. For example, they might explore why certain regions, like Asia or Europe, have universities that are improving quickly. For Policymakers: Governments and organizations can use the rankings to decide where to invest in education. They can also study which areas of education are most important for the future. What Can We Learn from It? The QS World University Rankings help us learn which universities are leading in academics and research. It also shows us how important global diversity is in education. By understanding these rankings, people can make smarter decisions about studying, teaching, or improving education systems. It’s like a guidebook for the world of universities, helping everyone find the best options and learn from the best practices.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 28 January 2022.

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

Context

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Content

A data frame with 777 observations on the following 18 variables.

Private A factor with levels No and Yes indicating private or public university

Apps Number of applications received

Accept Number of applications accepted

Enroll Number of new students enrolled

Top10perc Pct. new students from top 10% of H.S. class

Top25perc Pct. new students from top 25% of H.S. class

F.Undergrad Number of fulltime undergraduates

P.Undergrad Number of parttime undergraduates

Outstate Out-of-state tuition

Room.Board Room and board costs

Books Estimated book costs

Personal Estimated personal spending

PhD Pct. of faculty with Ph.D.’s

Terminal Pct. of faculty with terminal degree

S.F.Ratio Student/faculty ratio

perc.alumni Pct. alumni who donate

Expend Instructional expenditure per student

Grad.Rate Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.

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

The Global Longitudinal University Enrolment Dataset (GLUED) has been deaccessioned. Under no circumstances may a list of GLUED hitherto downloaded be disseminated, published or used for commercial purposes.

Analysis of ‘QS World University Rankings 2017 - 2022’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/padhmam/qs-world-university-rankings-2017-2022 on 13 February 2022.

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

Context

QS World University Rankings is an annual publication of global university rankings by Quacquarelli Symonds. The QS ranking receives approval from the International Ranking Expert Group (IREG), and is viewed as one of the three most-widely read university rankings in the world. QS publishes its university rankings in partnership with Elsevier.

Content

This dataset contains university data from the year 2017 to 2022. It has a total of 15 features. - university - name of the university - year - year of ranking - rank_display - rank given to the university - score - score of the university based on the six key metrics mentioned above - link - link to the university profile page on QS website - country - country in which the university is located - city - city in which the university is located - region - continent in which the university is located - logo - link to the logo of the university - type - type of university (public or private) - research_output - quality of research at the university - student_faculty_ratio - number of students assigned to per faculty - international_students - number of international students enrolled at the university - size - size of the university in terms of area - faculty_count - number of faculty or academic staff at the university

Acknowledgements

This dataset was acquired by scraping the QS World University Rankings website with Python and Selenium. Cover Image: Source

Inspiration

Some of the questions that can be answered with this dataset, 1. What makes a best ranked university? 2. Does the location of a university play a role in its ranking? 3. What do the best universities have in common? 4. How important is academic research for a university? 5. Which country is preferred by international students?

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

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

Please cite the following paper when using this dataset:

N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109

Abstract

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

COVID-19

Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus

online learning

online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures

India Number of Students: Colleges data was reported at 26,552,301.000 Person in 2017. This records an increase from the previous number of 26,388,693.000 Person for 2016. India Number of Students: Colleges data is updated yearly, averaging 23,470,323.500 Person from Sep 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 26,552,301.000 Person in 2017 and a record low of 11,551,516.000 Person in 2010. India Number of Students: Colleges data remains active status in CEIC and is reported by Department of Higher Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

There is no description for this dataset.

The Global Terrorism Database™ (GTD) is an open-source database including information on terrorist events around the world from 1970 through 2020 (with annual updates planned for the future). Unlike many other event databases, the GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 200,000 cases.

This dataset presents the 500 first universities of Academic Ranking of World Universities (ARWU), also known as Shanghai Ranking, between 2005 and 2018. It highlights universities scores to ranking indicators, which measure :

• Quality of Education, with Alumni and Award indicators (10% and 20% of the final mark)

• Quality of Faculty, with HiCi and N&S indicators (20% and 20% of the final mark)

• Research Output, with PUB indicator (20% of the final mark)

• Per Capita Performance, with PCP indicator (10% of the final mark)

More information about Shanghai ranking can be found here :

http://www.shanghairanking.com/

The study used an explanatory sequential mixed method design. This method is appropriate for examining the employment status of STEM graduates in terms of gender as well as the time it takes for graduates to secure their first job after graduating. The method is also employed to look at how staff in higher education supports female graduates in their search for employment after graduation. By design, this study collects data in a sequential fashion, starting with quantitative data and moving on to qualitative data that provide context for the quantitative data.Both primary and secondary sources of data were employed in the study (See Figure A). While information from secondary sources was gathered using Eric, Scopus, and Google search engines, information from primary sources was gathered through questionnaires and interviews. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used to conduct the analysis. Using the keywords employment status, duration of job search, and gender-responsive support of higher education, the first 221 articles were collected. Only 15 articles were chosen when PRISMA used the inclusion and exclusion criteria to filter out publications gathered between 2012 and 2024. The information gathered from secondary sources was utilized to triangulate the findings of the primary data sources. The following figure shows the data sources.Figure A: Data sources for the study (see the Description Word Doc. in the dataset)Based on the explanatory sequential mixed method design, quantitative data analysis was first carried out. In order to determine whether there were statistical differences in the employment status and the time it took for male and female STEM engineering graduates to find jobs, the chi square test was employed. An analysis of the degree to which higher education institutions assist female graduates in their job search was also done using an independent samples t-test. The viewpoints of academics from these related universities and prospective employers of STEM graduates were captured through the use of qualitative data.

Green-Schools, known internationally as Eco-Schools, is an environmental education programme run by An Taisce in partnership with local authorities. It promotes long-term, whole-school action for the environment. There are approximately 253 Dublin City Council schools registered with An Taisce Green-Schools. Schools carry out a number of tasks, run educational programs and environmental projects which are incorporated into everyday school-life. Many of them having already achieved Green-School status and proudly fly the Green Flag outside their school throughout the school year. Following the award of their first Green Flag for the Litter & Waste theme, schools renew their Green Flag award every two years by working on a new theme: Energy, Water, Travel, Biodiversity and Global Citizenship. Dublin City Council supports schools by providing ongoing guidance and support, and also carrying out Green-Schools renewal visits.

Global Navigation Dataset (GND) contains multi-modal robot perception and action data across many university campuses with multiple traversability labels (open space, obstacles, stairs, off-road, and roadway).

SAIVT-Campus Dataset

Overview

The SAIVT-Campus Database is an abnormal event detection database captured on a university campus, where the abnormal events are caused by the onset of a storm. Contact or for more information.

Licensing

The SAIVT-Campus database is © 2012 QUT and is licensed under the .

Attribution

To attribute this database, please include the following citation:
Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. available at .

Acknowledging the Database in your Publications

In addition to citing our paper, we kindly request that the following text be included in an acknowledgements section at the end of your publications:
We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-Campus database for our research.

Installing the SAIVT-Campus database

After downloading and unpacking the archive, you should have the following structure:

SAIVT-Campus 
+-- LICENCE.txt 
+-- README.txt 
+-- test_dataset.avi 
+-- training_dataset.avi 
+-- Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf

Notes

The SAIVT-Campus dataset is captured at the Queensland University of Technology, Australia.

It contains two video files from real-world surveillance footage without any actors:

1. training_dataset.avi (the training dataset)
2. test_dataset.avi (the test dataset).

This dataset contains a mixture of crowd densities and it has been used in the following paper for abnormal event detection:

• Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. Available at .
  This paper is also included with the database (Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf) Both video files are one hour in duration.

The normal activities include pedestrians entering or exiting the building, entering or exiting a lecture theatre (yellow door), and going to the counter at the bottom right. The abnormal events are caused by a heavy rain outside, and include people running in from the rain, people walking towards the door to exit and turning back, wearing raincoats, loitering and standing near the door and overcrowded scenes. The rain happens only in the later part of the test dataset.

As a result, we assume that the training dataset only contains the normal activities. We have manually made an annotation as below:

• the training dataset does not have abnormal scenes
• the test dataset separates into two parts: only normal activities occur from 00:00:00 to 00:47:16 abnormalities are present from 00:47:17 to 01:00:00. We annotate the time 00:47:17 as the start time for the abnormal events, as from this time on we have begun to observe people stop walking or turn back from walking towards the door to exit, which indicates that the rain outside the building has influenced the activities inside the building. Should you have any questions, please do not hesitate to contact .

The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.2.0 adds 94 additional coup events. 66 of these came from examining Powell and Thyne’s “discarded” events and 28 of these events were added to the data set in the normal annual review of potential new coup events. This version also updates the coding to events in Brazil in 1945 and the Congo in 1968. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 as a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011) Version 1.0.0 was released in 2013. This version consolidated coup data taken from the following sources: • The Center for Systemic Peace (Marshall and Marshall, 2007) • The World Handbook of Political and Social Indicators (Taylor and Jodice, 1983) • Coup d’Ètat: A Practical Handbook (Luttwak, 1979) • The Cline Center’s Social, Political and Economic Event Database (SPEED) Project (Nardulli, Althaus and Hayes, 2015) • Government Change in Authoritarian Regimes – 2010 Update (Svolik and Akcinaroglu, 2006)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.2.0 Codebook.pdf - This 17-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised January 2025 2. Coup Data v2.2.0.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1094 observations. Revised January 2025 3. Source Document v2.2.0.pdf - This 347-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised January 2025 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised January 2025
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2025. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Michael Martin, Sam Alahi, Norah Fadell, and Maddie Jeralds. 2025. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.2.0. Janurary 30. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V8