Research data comes in many different formats and is gathered using a wide variety of methodologies. In this module, we will provide you with a basic definition and understanding of what research data is. We'll also explore how data fits into the scholarly research process.
Many people think of data-driven research as something that primarily happens in the sciences. It is often thought of as involving a spreadsheet filled with numbers. Both of these beliefs are incorrect. Research data is collected and used in scholarship across all academic disciplines and, while it can consist of numbers in a spreadsheet, it also takes many different formats, including videos, images, artifacts, and diaries. Whether a psychologist collecting survey data to better understand human behavior, an artist using data to generate images and sounds, or an anthropologist using audio files to document observations about different cultures, scholarly research across all academic fields is increasingly data-driven.
In our Data Literacy Modules, we will demonstrate the ways in which research data is gathered and used across various academic disciplines by discussing it in a very broad sense. We define research data as: any information collected, stored, and processed to produce and validate original research results. Data might be used to prove or disprove a theory, bolster claims made in research, or to further the knowledge around a specific topic or problem.
There are many different definitions of research data available. Here are just a few examples of other definitions. We share these examples to illustrate there is not universal consensus on a definition, although many similarities are apparent.
Research data takes many different forms. Data may be intangible as in measured numerical values found in a spreadsheet or an object as in physical research materials such samples of rocks, plants, or insects. Here are some examples of the formats that data can take: