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University Library, University of Illinois at Urbana-Champaign

Introduction to Data Management for Undergraduate Students: Home

This guide covers the basics and best practices for data management for individuals who are new to the research and data-collecting process.


Glossary of Data Terms

The U.S. Government's open data website,, has compiled a list of important terms related to data and data management.

More Data Terminology

This list of terms was compiled by the Scholarly Commons. Here, you will find information about university owned works. 

DataOne Best Practices

The DataONE Best Practices database provides you with guidelines and recommendations on how to effectively work with their data through all stages of the data lifecycle. Check out the left-hand column for some important tools.

Why Do You Need to Manage Data?

Why do you need to manage data?


Have you ever tried to find better ways of keeping track of your photos, emails, application materials, etc.?  The data you work with as part of your research will need the same attention. The data provides direct evidence for your conclusions and demonstrates the quality of your work. This guide will give a quick introduction to data management.


There are many kinds of data, some of which include:


  • Research Notes: Any notes that you might gather to document your primary resources. Examples: lab notebook entries, or notebook from trip to the archives

  • Observational: Data captured in real-time that is usually irreplaceable. This data can be in raw form as well as processed or reduced forms. Examples: sensor data, telemetry, survey data, or field notes

  • Experimental: Data from lab equipment, often reproducible but may be expensive to recreate. This data can be in raw form as well as processed or reduced forms. Examples: gene sequences, chromatograms, toroid magnetic field data, or magnetic field readings

  • Simulation: Data generated from test models where model and metadata (inputs) are more important than output data. Reproducibility varies as does expense. Examples: climate models, economic models, or visualizations.

  • Derived or Compiled: Data that is gathered from public documents and analyzed. It is reproducible (but often very expensive). Examples: text and data mining, compiled databases, 3D models, or data gathered from public documents.

  • Reference/Analyzed: Data that is published in charts and figures. Examples: charts, tables, or figures in published materials.

Data Life Cycle

Below is a diagram of the “Data Life Cycle.”  This graphic helps to give you a visual sense of one evolution pathway of data from discovery to preservation.