There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

Updated 30 January 2023

Version 14 of Dataset

License Update:

There has been some confusion around licensing for this data set. Dr. Carla Patalano and Dr. Rich Huebner are the original authors of this dataset.

We provide a license to anyone who wishes to use this dataset for learning or teaching. For the purposes of sharing, please follow this license:

CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Codebook

https://rpubs.com/rhuebner/hrd_cb_v14

PLEASE NOTE -- I recently updated the codebook - please use the above link. A few minor discrepancies were identified between the codebook and the dataset. Please feel free to contact me through LinkedIn (www.linkedin.com/in/RichHuebner) to report discrepancies and make requests.

Context

HR data can be hard to come by, and HR professionals generally lag behind with respect to analytics and data visualization competency. Thus, Dr. Carla Patalano and I set out to create our own HR-related dataset, which is used in one of our graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. We created this data set ourselves. We use the data set to teach HR students how to use and analyze the data in Tableau Desktop - a data visualization tool that's easy to learn.

This version provides a variety of features that are useful for both data visualization AND creating machine learning / predictive analytics models. We are working on expanding the data set even further by generating even more records and a few additional features. We will be keeping this as one file/one data set for now. There is a possibility of creating a second file perhaps down the road where you can join the files together to practice SQL/joins, etc.

Note that this dataset isn't perfect. By design, there are some issues that are present. It is primarily designed as a teaching data set - to teach human resources professionals how to work with data and analytics.

Content

We have reduced the complexity of the dataset down to a single data file (v14). The CSV revolves around a fictitious company and the core data set contains names, DOBs, age, gender, marital status, date of hire, reasons for termination, department, whether they are active or terminated, position title, pay rate, manager name, and performance score.

Recent additions to the data include: - Absences - Most Recent Performance Review Date - Employee Engagement Score

Acknowledgements

Dr. Carla Patalano provided the baseline idea for creating this synthetic data set, which has been used now by over 200 Human Resource Management students at the college. Students in the course learn data visualization techniques with Tableau Desktop and use this data set to complete a series of assignments.

Inspiration

We've included some open-ended questions that you can explore and try to address through creating Tableau visualizations, or R or Python analyses. Good luck and enjoy the learning!

• Is there any relationship between who a person works for and their performance score?
• What is the overall diversity profile of the organization?
• What are our best recruiting sources if we want to ensure a diverse organization?
• Can we predict who is going to terminate and who isn't? What level of accuracy can we achieve on this?
• Are there areas of the company where pay is not equitable?

There are so many other interesting questions that could be addressed through this interesting data set. Dr. Patalano and I look forward to seeing what we can come up with.

If you have any questions or comments about the dataset, please do not hesitate to reach out to me on LinkedIn: http://www.linkedin.com/in/RichHuebner

You can also reach me via email at: Richard.Huebner@go.cambridgecollege.edu

DESCRIPTION

Johns Hopkins' county-level COVID-19 case and death data, paired with population and rates per 100,000

SUMMARY Updates April 9, 2020 The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County. April 20, 2020 Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well. April 29, 2020 The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

Overview The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

Filter cases by state here

Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

Pull the 100 counties with the highest per-capita confirmed cases here

Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive Embed Code

Caveats This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website. In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules. In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county" This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members. Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates. Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey. The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories --...

CollegeScorecard US College Graduation and Opportunity Data

Exploring Student Success and Outcomes

By Noah Rippner [source]

About this dataset

This dataset provides an in-depth look at the data elements for the US College CollegeScorecard Graduation and Opportunity Project Use Case. It contains information on the variables used to create a comprehensive report, including Year, dev-category, developer-friendly name, VARIABLE NAME, API data type, label, VALUE, LABEL , SCORECARD? Y/N , SOURCE and NOTES. The data is provided by the U.S Department of Education and allows parents, students and policymakers to take meaningful action to improve outcomes. This dataset contains more than enough information to allow people like Maria - a 25 year old recent US Army veteran who wants a degree in Management Systems and Information Technology -to distinguish between her school options; access services; find affordable housing near high-quality schools which are located in safe neighborhoods that have access to transport links as well as employment opportunities nearby. This highly useful dataset provides detailed analysis of all this criteria so that users can make an informed decision about which school is best for them!

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How to use the dataset

This dataset contains data related to college students, including their college graduation rates, access to opportunity indicators such as geographic mobility and career readiness, and other important indicators of the overall learning experience in the United States. This guide will show you how to use this dataset to make meaningful conclusions about high education in America.

First, you will need to be familiar with the different fields included in this CollegeScorecard’s US College Graduation and Opportunity Data set. Each record is comprised of several data elements which are defined by concise labels on the left side of each observation row. These include labels such as Name of Data Element, Year, dev-category (i.e., developmental category), Variable Name, API data type (i.e., type information for programmatic interface), Label (i.e., descriptive content labeling for visual reporting), Value , Label (i.e., descriptive value labeling for visual reporting). SCORECARD? Y/N indicates whether or not a field pertains to U.S Department of Education’s College Scorecard program and SOURCE indicates where the source of the variable can be found among other minor details about that variable are found within Notes column attributed beneath each row entry for further analysis or comparison between elements captured across observations

Now that you understand the components associated within each element or label related within Observation Rows identified beside each header label let’s go over some key steps you can take when working with this particular dataset:

• Utilize year specific filters on specified fields if needed — e.g.; Year = 2020 & API Data Type = Character

• Look up any ‘NCalPlaceHolder” values if applicable — these are placeholders often stating values have been absolved fromScorecards display versioning due conflicting formatting requirements across standard conditions being met or may state these details have still yet been updated recently so upon assessment wait patiently until returns minor changes via API interface incorporate latest returned results statements inventory configuration options relevant against budgetary cycle limits established positions

• Pivot data points into more custom tabular structured outputs tapering down complex unstructured RAW sources into more digestible Medium Level datasets consumed often via PowerBI / Tableau compatible Snapshots expanding upon Delimited text exports baseline formats provided formerly

• Explore correlations between education metrics our third parties documents generated frequently such values indicative educational adherence effects ROI growth potential looking beyond Campus Panoramic recognition metrics often supported outside Social Medial Primary

Research Ideas

• Creating an interactive dashboard to compare school performance in terms of safety, entrepreneurship and other criteria.
• Using the data to create a heat map visualization that shows which cities are most conducive to a successful educational experience for students like Maria.
• Gathering information about average course costs at different universities and mapping them relative to US unemployment rates indicates which states might offer the best value for money when it comes to higher education expenses

Ack...

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

• to provide annual data about the nation’s health;
• to estimate the proportion of people in England with specified health conditions;
• to estimate the prevalence of certain risk factors associated with these conditions;
• to examine differences between population subgroups in their likelihood of having specific conditions or risk factors;
• to assess the frequency with which particular combinations of risk factors are found, and which groups these combinations most commonly occur;
• to monitor progress towards selected health targets
• since 1995, to measure the height of children at different ages, replacing the National Study of Health and Growth;
• since 1995, monitor the prevalence of overweight and obesity in children.

Changes to the HSE from 2015:
Users should note that from 2015 survey onwards, only the individual data file is available. The household data file is no longer released for analysis. In addition, users may see other changes; for example only grouped age is now available instead of single year of age. NHS Digital have issued the following statement on changes to the HSE from 2015:

"NHS Digital has recently reviewed how we manage access to survey datasets. In doing this we have sought to strike a balance between protecting the privacy of individuals and enabling maximum use of these valuable, publicly funded data collections. We have thoroughly reviewed our disclosure control measures, including taking advice from experts at the Office of National Statistics. The result is that additional disclosure control measures have been applied to the 2015 survey [onwards] to enable a suitable dataset to be made available through the UK Data Service via end user licence. This involved providing less detail on some aspects, such as geographical classifications, ethnicity and household relationships. To provide greater protection of the answers of children and adults within households it is not possible to identify people within the same household on this dataset, however parent/guardian derived variables appended to their children (if they have any) have been added to enable some intra‐household analysis.”

It is hoped that a second dataset with more detail including family and household relationships will be made available via Special Licence. In the meantime, researchers who want to do analysis of health and behaviours within families or households, and the derived intra-household variables do not meet your needs, are advised to register their interest for a more detailed dataset with NatCen Social Research and provide information about their proposed research and which data they want.

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

Latest edition information
For the nineteenth edition (October 2025), data and documentation from the Next Steps 2019 Web Survey have been added to the study. The Longitudinal File has also been updated.

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

Hip fracture is a serious and costly injury affecting mainly older people. It usually results from the combination of weak bone structure (osteoporosis) and a fall. Around 76,000 hip fractures occur each year in the UK as a whole. Although there is good evidence on best practice in surgical, medical and rehabilitation care following hip fracture, such care and its outcomes – in terms of return home and also of mortality – continues to vary. The National Hip Fracture Database (NHFD), which was launched in 2007, aims to deliver improvements in the care of hip fracture patients. It documents case-mix, care and outcomes of hip fracture patients in England, Wales and Northern Ireland and is now, with over 1 million cases on record – by far the largest hip fracture audit in the world. It has demonstrated broad improvements at local and national level in patient care, and in England has supported the Department of Health’s highly successful Best Practice Tariff for hip fracture care. The work of the NHFD is now being replicated in Ireland, with the recent launch of Irish Hip Fracture Database, and similar developments are in hand in Australia and New Zealand, Canada and Hong Kong. The National Hip Fracture Database was founded as a collaboration between the British Orthopaedic Association and the British Geriatrics Society. It was developed between 2004 and 2007, and since 2009 it has received central funding as a national clinical audit via the Healthcare Quality Improvement Partnership (HQIP). Since April 2012 the NHFD has continued as part of the Falls and Fragility Fracture Audit Programme (FFFAP), managed on behalf of HQIP by the Royal College of Physicians (RCP). The audit covers England, Wales, Northern Ireland, Guernsey and the Isle of Man; however data files only refer to data for England and Wales.

This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The eighth cycle of the Ithaka S+R Faculty Survey queried a random sample of higher education faculty members in the United States to learn about their attitudes and practices related to their research and teaching. Respondents were asked about resource discovery and access; research topics and practices; research dissemination, including open access, data management, and preservation; instruction and perceptions of student research skills; the role and value of the academic library; and open-educational resources. Demographic variables include the respondent's age, gender, primary academic field, title or role, institution's Carnegie classification, how many years the respondent has worked at their current college or university, how many years the respondent has worked in their field, what format the courses they are currently teaching (if any) are in (synchronous, asynchronous, or a mix of both) and whether the respondent primarily identifies as a researcher, teacher, or somewhere in between.

DESCRIPTION US News Universities Rankings 2017 edition SUMMARY National Universities Rankings In-the-News From 9/13/16 press release:

U.S. News & World Report today announced the 2017 Best Colleges rankings to help students worldwide compare the academic quality of more than 1,800 U.S.-based schools. Princeton University remains No. 1 in the Best National Universities category. For the 14th consecutive year, Williams College takes the top spot on the Best National Liberal Arts Colleges list.

Source: U.S. News Best College Rankings

Schools in the National Universities category, such as Columbia University and the University of Pennsylvania, offer a full range of undergraduate majors, plus master's and doctoral programs. These colleges also are committed to producing groundbreaking research.

About the data Name - institution name

Location - City, State where located

Rank Read methodology here. This dataset does not include unranked schools or any data that requires purchase / special access (i.e., exclusive to the U.S. News College Compass product).

Description Snippet of text overview from U.S. News

Tuition and fees Combined tuition and fees. For public universities with different tuition structure for in-state vs. out-of-state students, this number reflects out-of-state tutition.

In-stateFor public universities with different tuition structure for in-state vs. out-of-state students, this number reflects in-state tuition.

Undergrad Enrollment Number of enrolled undergratuate students

Read the Best Colleges Methodology

More from press release (quoted verbatim):

The average six-year graduation rate is 95 percent for the top 10 National Universities and 93.9 percent for the top 10 National Liberal Arts Colleges. The average freshman retention rate is 98.1 percent for the top 10 National Universities and 96.6 percent for the top 10 National Liberal Arts Colleges. For comparison, the average six-year graduation rate among all numerically ranked schools on the National Universities list is 71.3 percent, and the average freshman retention rate is 86.9 percent. For comparison, the average six-year graduation rate among all numerically ranked schools on the National Liberal Arts Colleges list is 75.2 percent, and the average freshman retention rate is 85.6 percent.

All 174 trauma units in England, Wales and Northern Ireland regularly uploaded data describing the process, quality and outcome of the care they provided to the 67,302 people who presented with hip fracture in 2019. This report uses six NHFD key performance indicators (KPIs) to describe how the quality of care varies between hospitals and changes over time. The impact of COVID-19 on patient care and the organisation of trauma services will be examined in detail in next year’s NHFD report, but this year’s report helps units measure their readiness and prepare for the challenging time that we continue to face. Hip fracture is a serious and costly injury affecting mainly older people, and is more common in women. It usually results from the combination of weak bone structure (osteoporosis) and a fall. Around 76,000 hip fractures occur each year in the UK as a whole. Although there is good evidence on best practice in surgical, medical and rehabilitation care following hip fracture, such care and its outcomes – in terms of return home and also of mortality – continues to vary. The National Hip Fracture Database (NHFD), which was launched in 2007, aims to deliver improvements in the care of hip fracture patients. It documents case-mix, care and outcomes of hip fracture patients in England, Wales and Northern Ireland and is now, with more than 500,000 cases on record – by far the largest hip fracture audit in the world. It has demonstrated broad improvements at local and national level in patient care, and in England has supported the Department of Health’s highly successful Best Practice Tariff for hip fracture care. The work of the NHFD is now being replicated in Ireland, with the recent launch of Irish Hip Fracture Database, and similar developments are in hand in Australia and New Zealand, Canada and Hong Kong. The National Hip Fracture Database was founded as a collaboration between the British Orthopaedic Association and the British Geriatrics Society. It was developed between 2004 and 2007, and since 2009 it has received central funding as a national clinical audit via the Healthcare Quality Improvement Partnership (HQIP). Since April 2012 the NHFD has continued as part of the Falls and Fragility Fracture Audit Programme, managed on behalf of HQIP by the Royal College of Physicians (London). The audit covers England, Wales, Northern Ireland and the Isle of Man, however data files only refer to data for England and Wales.

Postsecondary enrolments, by detailed field of study, institution, institution type, registration status, program type, credential type, status of student in Canada and gender.

A curated dataset of Ivy League admissions metrics by class year and institution, including overall acceptance rate, Regular Decision acceptance rate, Early Decision/Action acceptance rate, % of class filled by early applicants, and application volumes.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

Hip fracture is a serious and costly injury affecting mainly older people, and is more common in women. It usually results from the combination of weak bone structure (osteoporosis) and a fall. Around 76,000 hip fractures occur each year in the UK as a whole. Although there is good evidence on best practice in surgical, medical and rehabilitation care following hip fracture, such care and its outcomes – in terms of return home and also of mortality – continues to vary. The National Hip Fracture Database (NHFD), which was launched in 2007, aims to deliver improvements in the care of hip fracture patients. It documents case-mix, care and outcomes of hip fracture patients in England, Wales and Northern Ireland and is now, with more than 250,000 cases on record – by far the largest hip fracture audit in the world. It has demonstrated broad improvements at local and national level in patient care, and in England has supported the Department of Health’s highly successful Best Practice Tariff for hip fracture care. The work of the NHFD is now being replicated in Ireland, with the recent launch of Irish Hip Fracture Database, and similar developments are in hand in Australia and New Zealand, Canada and Hong Kong. The National Hip Fracture Database was founded as a collaboration between the British Orthopaedic Association and the British Geriatrics Society. It was developed between 2004 and 2007, and since 2009 it has received central funding as a national clinical audit via the Healthcare Quality Improvement Partnership (HQIP). Since April 2012 the NHFD has continued as part of the Falls and Fragility Fracture Audit Programme, managed on behalf of HQIP by the Royal College of Physicians (London). This report serves as a companion to the NHFD 2014 annual report and the CCG outcome indicators sets, available from the HSCIC indicators portal, and describes a number of outcome indicators aggregated to CCG and area team level. The audit covers England, Wales, Northern Ireland and the Isle of Man and, however data files only refer to data for England.

Hip fracture is a serious and costly injury affecting mainly older people, and is more common in women. It usually results from the combination of weak bone structure (osteoporosis) and a fall. Around 76,000 hip fractures occur each year in the UK as a whole. Although there is good evidence on best practice in surgical, medical and rehabilitation care following hip fracture, such care and its outcomes – in terms of return home and also of mortality – continues to vary. The National Hip Fracture Database (NHFD), which was launched in 2007, aims to deliver improvements in the care of hip fracture patients. It documents case-mix, care and outcomes of hip fracture patients in England, Wales and Northern Ireland and is now, with more than 250,000 cases on record – by far the largest hip fracture audit in the world. It has demonstrated broad improvements at local and national level in patient care, and in England has supported the Department of Health’s highly successful Best Practice Tariff for hip fracture care. The work of the NHFD is now being replicated in Ireland, with the recent launch of Irish Hip Fracture Database, and similar developments are in hand in Australia and New Zealand, Canada and Hong Kong. The National Hip Fracture Database was founded as a collaboration between the British Orthopaedic Association and the British Geriatrics Society. It was developed between 2004 and 2007, and since 2009 it has received central funding as a national clinical audit via the Healthcare Quality Improvement Partnership (HQIP). Since April 2012 the NHFD has continued as part of the Falls and Fragility Fracture Audit Programme, managed on behalf of HQIP by the Royal College of Physicians (London). The audit covers England, Wales, Northern Ireland, the Isle of Man and the Channel Islands, however data files only refer to data for England.

Further information about the study may be found on the COVID Social Mobility and Opportunities Study (COSMO) webpage.

Further information about the study may be found on the COSMO website.

Context

It has been since COVID-19 hit my college's campus in the early spring of 2020 that I started to put up an image dataset, including the exteriors of the buildings around my campus. So I kept walking leisurely and scheduling time to visit the places across the campus, to use my phone to record the exterior end of buildings across the campus. I ever thought that this piece of work could be used in my thesis project, and I would want to build up a machine learning system that would allow the visitors to take photos, while recognizing the building that is right in front of them.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

It represents the exterior of buildings at Union College (Schenectady, NY), from March 20, 2020 to December 23, 2021, based on the official map provided by the Office of Residential Life. There are 68 labelled buildings on the map, and my dataset contains 8504 instances in total. However, since there are places that are away from campus when I haven't had time to visit, the following buildings have not yet had any instances: Abbe Hall Communications & Marketing Edwards House Hickok House Union College Academy for Lifelong Learning (UCALL)

The distribution right now is seriously non-Gaussian. However, I aim to get myself get ready for collecting and labelling more data in the upcoming Spring term to offset the non-normality.

Acknowledgements

I thank the inspiration brought by Prof. Xiang Cheng in Peking University, with whom I took a class with in the summer of 2019. I also am grateful to be able to call the toolboxes where I learned from Stanford University's CS229 Machine Learning, in the summer of 2020. The same to the committee members of Campus Operations and Facilities Minor Committee, who supported my idea mainly throughout my third academic year. Lastly, I am thankful that my general advisor Prof. Nicholas Webb and my thesis advisor Prof. Aaron Cass brought me the closest and state-of-the-art guidance.

Inspiration

I want to put up this machine learning system so that potentially it could be used by the Admissions Office at my home institution, Union College, in Schenectady, NY, to enhance the traveling experiences of visitors.