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Administrative Data Usage Policy: Appendix A Definitions and Examples of Data Classification

Table of Contents

Not sensitive data—definition and examples

Data that are intentionally made public are classified as not sensitive. Any data that are published and broadly available are, of course, included in this classification. University policy holds that the volume of data classified as not sensitive should be as large as possible because widespread availability of such information will enable employees to make creative contributions in pursuit of the University's mission. Examples of digitally published data (a subset of not sensitive data) include:

Data available from University of Virginia Data Digest (categories from the 1996 edition)

Student information by semester/year

  • Admissions
    • Numbers of applications by school, status, gender, race, domicile, state/city/county, 1st-year vs. transfer, previous school
    • Admission test scores and high school rank in class (mean, median, middle 50% range) of matriculants by school
  • Enrollment
    • Headcount students enrolled by school, full/part-time status, gender, race, domicile, academic level, state/city/county, age, major, registration type, degree level, housing status
    • FTE students by school, academic level, domicile, degree level
  • Academic progress
    • University GPA by school, academic level
    • Undergraduate graduation rates (4 and 6-year rates) by school, gender, race, domicile
    • Numbers of degrees conferred by school, degree level, major, gender, race
    • Numbers of students on dean's list and the number suspended by school
  • Financial aid
    • Amount of aid awarded by category, school
    • Numbers of students receiving aid by category and school
  • Alumni
    • Numbers of alumni by state of residence, gender, race, school, degree, major

Employee information by month/year

  • Numbers of employees by organizational unit, gender, race, EEO category, class code, age
  • Numbers of faculty by function (instruction vs. administration etc.), organizational unit, gender, race, rank, age, tenure status, full/part-time status, endowed chair status
  • Mean instructional faculty salaries and compensation by rank and organizational unit

Financial information by fiscal year

  • Revenues and expenditures by organizational unit and major object code
  • Market value of endowment assets
  • Total dollar amount of gifts and bequests received
  • Dollar amount of sponsored program awards by school and grantor

Facilities information

  • Individual University buildings and renovations with their costs, completion dates, source of funds, square footage
  • Square footage of facilities by program classification and function
  • Total land holdings by location

Library information

  • Size of collections by library and type

Data Available from Financial Schedules (A Supplement to the President's Report—categories drawn from the 1995 edition)

Financial Report

  • Financial Highlights
  • Growth Over Time
  • Balance Sheet
  • Statement of Changes in Fund Balances
  • Statement of Current Funds Revenues, Expenditures, and Other Changes
  • Notes to the Financial Statements

Medical Center Financial Report

  • Balance Sheet
  • Statement of Revenues and Expenses
  • Statement of Changes in Fund Balances
  • Statement of Cash Flows—General Fund
  • Notes to Financial Statements

Divisional Financial Statements

  • Balance Sheet; Academic
  • Statement of Changes on Fund Balances; Academic
  • Statement of Current Funds Revenues, Expenditures, and Other Changes—Academic
  • Balance Sheet; Medical Center
  • Statement of Changes in Fund Balances— Medical Center
  • Statement of Current Funds Revenues, Expenditures, and Other Changes—Medical Center
  • Balance Sheet; Clinch Valley College
  • Statement of Changes in Fund Balances— Clinch Valley College
  • Statement of Current Funds Revenues, Expenditures, and Other Changes—Clinch Valley College
  • Analysis of Net Transfers Between Divisions
  • Statement of Current Funds Revenues, Expenditures, and Other Changes by Division

Data Available from Human Resources

  • Employee Name and Title


Highly sensitive data—definition and examples

Highly sensitive data include personal information that can lead to identity theft if exposed and health information that reveals an individual’s health condition and/or history of health services use. More specifically:

Personal information that, if exposed, can lead to identity theft

"Personal information” means the first name or first initial and last name in combination with and linked to any one or more of the following data elements about the individual:

  • Social security number
  • Driver’s license number or state identification card number issued in lieu of a driver’s license number
  • Passport number; or
  • Financial account number, or credit card or debit card number.

Health information that, if exposed, can reveal an individual’s health condition and/or history of health services use

“Health information,” also known as “protected health information (PHI),” includes health records combined in any way with one or more of the following data elements about the individual:

  • Names
  • All geographic subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code if, according to the current publicly available data from the Bureau of the Census the geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people, and the initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000;
  • All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older;
  • Telephone numbers;
  • Fax numbers;
  • Electronic mail addresses;
  • Social security numbers;
  • Medical record numbers;
  • Health plan beneficiary numbers;
  • Account numbers;
  • Certificate/license numbers;
  • Vehicle identifiers and serial numbers, including license plate numbers;
  • Device identifiers and serial numbers;
  • Web Universal Resource Locators (URLs);
  • Internet Protocol (IP) address numbers;
  • Biometric identifiers, including finger and voice prints;
  • Full face photographic images and any comparable images; and
  • Any other unique identifying number, characteristic, or code that is derived from or related to information about the individual.


Moderately sensitive data—definition and examples

This is the default classification: all data that is not explicitly defined as highly sensitive data or is not intended to be made publicly available. Examples include but are not limited to:

  • University ID numbers, i.e., those printed on University ID cards
  • FERPA-protected data not covered under the definition of “Highly Sensitive” data