Library Guides: Data Management: Introduction to Data Management

Introduction to Data Management

This guide will help you get started with the basics of data management as a researcher. It includes important terms, evaluating your data needs, creating a data management plan, and storing and preserving your data.

It is becoming the norm for publishers and funding agencies to require researchers to share their data in a clear and concise manner. Learning how to develop good data management practices early in your career will make it easier to keep your data organized, meet funder requirements, and prepare sustainable data for sharing with others!

Staaks, J. (2013). Research Data Management. CC BY-NC 2.0 Retrieved from https://www.flickr.com/photos/jannekestaaks/14391226325

Definition of Data

"Data is the output from any systematic investigation involving a process of observation, experiment or the testing of a hypothesis which when assembled in context and interpreted expertly will produce new knowledge." Pryor, G. (2012). Why manage research data? In G. Pryor (Ed.), Managing research data (pp. 1-16). London, England: Facet.

Glossary of Data Terms
Glossary of Data Management terms from Cornell University to help you get started.

Examples of Data

Research data is data that is collected, observed, or created, for purposes of analysis to produce original research results. The word “data” is used throughout this site to refer to research data. Research data can be generated for different purposes and through different processes, and can be divided into different categories, as shown below.

Data Types:

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.