Dataset Containing 173 College Common Data Sets

Contains Common Data Sets for the Following Schools:

• Alabama State University
• Angelo State University
• Arapahoe Community College
• Arkansas Tech University
• Aurora University
• Baldwin Wallace University
• Beloit College
• Bemidji State University
• Berea College
• Binghamton University
• Boston University
• Bucknell University
• Cabrini University
• California Baptist University
• California State University, Bakersfield
• California State University, Long Beach
• California State University, Los Angeles
• California State University, Sacramento
• Carnegie Mellon University
• Case Western Reserve University
• Christopher Newport University
• Clark University
• Colby College
• College of Charleston
• Collin College
• Colorado College
• Colorado School of Mines
• Colorado State University-Pueblo
• Columbia College
• Concordia University Texas
• Cornell University
• Davidson College
• Delaware Technical Community College
• DeSales University
• Dickinson College
• Drake University
• Drew University
• Duquesne University
• East Central University
• Eastern Washington University
• Embry Riddle Aeronautical University-Daytona Beach
• Fairfield University
• Florida Gulf Coast
• Florida International University
• Fort Hays State University
• Georgia Institute Of Technology
• Gettysburg College
• Hamilton College
• Hollins University
• Humboldt State University
• Iowa State University
• Jackson State University
• John Jay College of Criminal Justice
• Kennesaw State University
• Lafayette College
• Lane College
• Lee University
• Le Moyne College
• Lenoir Rhyne University
• Life University
• Loyola University Maryland
• Lubbock Christian University
• Lycoming College
• Lynn University Common Data Set
• Malone University
• Marlboro College
• Maryville University
• Massachusetts Maritime Academy
• Metropolitan State University of Denver
• Michigan Technological University
• Middlebury College
• Millersville University
• Mississippi State University
• Mott Community College
• Neumann University
• Northeastern State University
• Northern Arizona University
• Northern Kentucky University
• Nyack College
• Oklahoma Christian University
• Oklahoma State University
• Old Dominion University
• Oral Roberts University
• Pepperdine University
• Pomona College
• Prescott College
• Providence College
• Reed College
• Regis University
• Rensselaer Polytechnic Institute
• Rice University
• Rochester College
• Rutgers University
• Saint Vincent College
• San Francisco State University
• Santa Clara University
• Scripps College
• Seton Hill University
• Sewanee
• Shippensburg University
• Simpson University
• Slippery Rock University
• Smith College
• Sonoma State University
• Southeastern Community College
• Southeastern Oklahoma State University
• Southwestern Oklahoma State University
• Springfield College
• St. Ambrose University
• Stanford University
• Stephen F. Austin State University
• SUNY Oneonta
• SUNY Potsdam
• Sweet Briar College
• Taylor University
• Temple University
• Tennessee Wesleyan University
• Texas A&M University - Kingsville
• Texas A&M University
• Texas Wesleyan University
• The College at Brockport
• The College of New Jersey
• The University of Scranton
• The University of Southern Mississippi
• The University of Tennessee
• Trinity University
• Tufts University
• Tulane University
• Tulsa Community College
• University at Buffalo
• University Enrollment
• University of California - Davis
• University of California, Riverside
• University of Colorado Boulder, 2015
• University of Delaware
• University of Kentucky
• University of Louisville
• University of Maine
• University of Missouri
• University of Montana
• University of Mount Olive: 2016
• University of Nebraska Kearney
• University of Nebraska-Lincoln
• University of Nevada, Reno
• University of New Hampshire
• University of New Mexico
• University of North Alabama
• University of North Carolina at Charlotte
• University of Pennsylvania
• University of Pikeville
• University of Puget Sound
• University of Science and Arts
• University of Texas Rio
• University of the Sciences in Philadelphia
• University of Wisconsin
• University Wide Common Data Set 2015
• Villanova University
• Virginia Commonwealth University
• Washburn University
• Washington and Lee University
• Washington College
• Weber State University
• Wellesley College
• Wesleyan University
• Westfield State University
• Westminster College
• Wheaton College
• Whitman College
• Widener University
• Worcester Polytechnic Institute
• Xavier University of Louisiana
• Xavier University
• Yale University

Though we dream of the day when humans will first walk on Mars, these dreams remain in the distance. For now, we explore vicariously by sending robotic agents like the Curiosity rover in our stead. Though our current robotic systems are extremely capable, they lack perceptual common sense. This characteristic will be increasingly needed as we create robotic extensions of humanity to reach across the stars, for several reasons. First, robots can go places that humans cannot. If we manage to get a human on Mars by 2035, as predicted by the current NASA timeline, this will still represent a 60 year lag from the time of the first robotic lander. Second, while it is possible to replace common sense in robots with human teleoperated control to some extent, this becomes infeasible as the distance to the base planet and the associated radio signal delay increase. Finally, as we pack more and more sensors onboard, the fraction of data that can be sent back to earth decreases. Data triage (finding the few frames containing a curious object on a planet's surface out of terabytes of data) becomes more important.

In the last few years, research into a class of scalable unsupervised algorithms, also called deep learning algorithms, has blossomed, in part due to state of the art performance in a number of areas. A common thread among many recent deep learning algorithms is that they tend to represent the world in ways similar to how our brains represent the world. For example, thanks to decades of work by neuroscientists, we now know that in the V1 area of the visual cortex, the first region that visual information passes through after the retina, neurons tune themselves to respond to oriented edges and do so in a way that groups them together based on similarity. With this behavior as a goal, researchers set out to devise simple algorithms that reproduce this effect. It turns out that there are several. One, known as Topographic Independent Component Analysis, has each neuron start with random connections and then look for patterns that are statistically out of the ordinary. When it finds one, it locks onto this pattern, discouraging other neurons from duplicating its findings but simultaneously trying to group itself with other neurons that have learned patterns which are similar, but not identical.

My proposed research plan is to develop existing and new unsupervised learning algorithms of this type and apply them to a robotic system. Specifically, I will demonstrate a prototype system capable of (1) learning about itself and its environment and of (2) actively carrying out experiments to learn more about itself and its environment. Research will be kept focused by developing a system aimed at eventual deployment on an unmanned space mission. Key components of the project will include synthetic data experiments, experiments on data recorded from a real robot, and finally experiments with learning in the loop as the robot explores its environment and learns actively.

The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. Thus, advances are likely to have far-reaching implications for many areas of autonomous space exploration. Tantalizing though this is, it is equally exciting that unsupervised learning is already finding application with surprisingly impressive performance right now, indicating great promise for near-term application to unmanned space exploration.

This data collection contains information from the first wave of High School and Beyond (HSB), a longitudinal study of American youth conducted by the National Opinion Research Center on behalf of the National Center for Education Statistics (NCES). Data were collected from 58,270 high school students (28,240 seniors and 30,030 sophomores) and 1,015 secondary schools in the spring of 1980. Many items overlap with the NCES's NATIONAL LONGITUDINAL STUDY OF THE CLASS OF 1972 (ICPSR 8085). The HSB study's data are contained in eight files. Part 1 (School Data) contains data from questionnaires completed by high school principals about various school attributes and programs. Part 2 (Student Data) contains data from surveys administered to students. Included are questionnaire responses on family and religious background, perceptions of self and others, personal values, extracurricular activities, type of high school program, and educational expectations and aspirations. Also supplied are scores on a battery of cognitive tests including vocabulary, reading, mathematics, science, writing, civics, spatial orientation, and visualization. To gather the data in Part 3 (Parent Data), a subsample of the seniors and sophomores surveyed in HSB was drawn, and questionnaires were administered to one parent of each of 3,367 sophomores and of 3,197 seniors. The questionnaires contain a number of items in common with the student questionnaires, and there are a number of items in common between the parent-of-sophomore and the parent-of-senior questionnaires. This is a revised file from the one originally released in Autumn 1981, and it includes 22 new analytically constructed variables imputed by NCES from the original survey data gathered from parents. The new data are concerned primarily with the areas of family income, liabilities, and assets. Other data in the file concentrate on financing of post-secondary education, including numerous parent opinions and projections concerning the educational future of the student, anticipated financial aid, student's plans after high school, expected ages for student's marriage and childbearing, estimated costs of post-secondary education, and government financial aid policies. Also supplied are data on family size, value of property and other assets, home financing, family income and debts, and the age, sex, marital, and employment status of parents, plus current income and expenses for the student. Part 4 (Language Data) provides information on each student who reported some non-English language experience, with data on past and current exposure to and use of languages. In Parts 5-6, there are responses from 14,103 teachers about 18,291 senior and sophomore students from 616 schools. Students were evaluated by an average of four different teachers who had the opportunity to express knowledge or opinions of HSB students whom they had taught during the 1979-1980 school year. Part 5 (Teacher Comment Data: Seniors) contains 67,053 records, and Part 6 (Teacher Comment Data: Sophomores) contains 76,560 records. Questions were asked regarding the teacher's opinions of their student's likelihood of attending college, popularity, and physical or emotional handicaps affecting school work. The sophomore file also contains questions on teacher characteristics, e.g., sex, ethnic origin, subjects taught, and time devoted to maintaining order. The data in Part 7 (Twins and Siblings Data) are from students in the HSB sample identified as twins, triplets, or other siblings. Of the 1,348 families included, 524 had twins or triplets only, 810 contained non-twin siblings only, and the remaining 14 contained both types of siblings. Finally, Part 8 (Friends Data) contained the first-, second-, and third-choice friends listed by each of the students in Part 2, along with identifying information allowing links between friendship pairs. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR as four separate datasets here:

1980: https://doi.org/10.3886/ICPSR07896.v2

1982: https://doi.org/10.3886/ICPSR08297.v3

1984: https://doi.org/10.3886/ICPSR08443.v1

1986: https://doi.org/10.3886/ICPSR08896.v3

We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.