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Data 101

In this LibGuide, we introduce you to the wide world of data, including data types (qualitative, quantitative, ethnographic, geospatial, etc.), finding data, visualizing data, and managing data.

Data 101

Data is everywhere, and comes in a wide variety of formats. From fine arts, to robotics, to mathematics, to sociology, everyone engages with data. The important thing to remember is that the data may visually look different across these domains, but all are valid forms of research data. 

Examples of research data include: 

  • Photographs 
  • Hand-written notes from field observation
  • Machine learning training data sets
  • Ethnographic interview transcripts
  • Sheet music
  • Scripts for plays and musicals 
  • Observations from laboratory experiments

Types of Data

Quantitative data

Quantitative data can be broken down into discrete and continuous data. 

Discrete data is data that can be counted, such as the number of heads in 100 coin flips and the number of books in a library. If you can count it, it is discrete!

Continuous data is data that cannot be counted, but can be measured, such as a person's height, a dog's weight, and the time of a marathon run. Continuous data can be further broken down into interval and ratio data. Interval data is comprised of ordered units with the same difference between units, such as in the following list of values: -15, -10, -5, 0, 5, 10, 15. Further examples include credit scores (300-850), pH, and temperature in Fahrenheit. Here, we can add and subject in meaningful ways, and there is no "true" zero. In interval data, the "zero" is arbitrary.

Ratio data as a natural zero quantity of a thing being measured, such as weight, length, and temperature in Kelvin. When the variable equals 0, there is none of that variable.

Qualitative data

Qualitative data can be categorized as nominal or ordinal. 

Nominal data refers to data whose labels have no quantitative value, and can be in any order, such as a list of languages spoken, a list of country names, or a list of eye colors. It cannot be ordered, and it cannot be measured.

Ordinal data refers to ordered units of data, where numbers may be used to delineate the order in which they appear. For example, the following list is an example of ordinal data: 

1 = Elementary

2 = Middle School

3 = High School

Ethnographic Data

Ethnographers immerse themselves within an environment, and collect data about a certain environment using observation, conversational, and textual techniques. Ethnographic data can be quantitative or qualitative in nature, and can include the following: 

  • Interviews conducted with a population of nurses in Finland
  • Audio recordings of folk songs from a local Appalachian community
  • Photographs of backcountry areas in National Parks in New Zealand 

Geospatial Data

Geospatial data involves objects, events, and phenomena that have a location on Earth's surface. Therefore, the data have locational aspects tied to them, such as coordinates, addresses, city, and/or ZIP code. These data can exist at a variety of scales, from geospatial data of your neighborhood, for the state of Pennsylvania, and even for the entire globe. They can represent a static point in time, or several points in time. Some examples include: 

  • A collection of points signifying the location of park benches in a city 
  • A route demonstrating the shortest distance between two points at opposite ends of a town 
  • Locations of car accidents within 200 feet of a bar

 See CMU's Spatial Data guide for more information and resources.  


For help with data or data-related tools, or for any other questions, please contact the University Libraries Data and Publishing Services team.