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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.

 

 

 

 

 

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, 1-16. London: Facet.

Glossary of Data Terms

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

Examples of 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!

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.

Data Management Training

Data Management Training Clearinghouse
This site houses the Data ONE Education Modules, the Earth Science Information Partners (ESIP) Data Management for Scientists Short Course, and the USGS Science Support Framework.

USGS Data Management Training Modules
Includes six interactive modules to assist in learning best practices in data management.

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