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Data-Intensive Research  

This guide covers data services and resources available to the WCMC community.
Last Updated: May 14, 2015 URL: Print Guide RSS Updates

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Intro to Data-Intensive Research

Data-Intensive Research is a phrase used to describe the data revolution currently being witnessed by the scientific community. Over time, research methodology has shifted from being empirical to being theoretical. However, when the theoretical models became too complicated to solve analytically, simulations and computational models were needed. These simulations and models are now producing an astonishing amount of data, so here enters the "fourth paradigm" known as data-intensive research. Data-intensive research requires researchers to work as a team to solve complicated problems using data extrapolation. 

In order to continually address the complex needs of researchers doing data-intensive work, this guide provides links to both physical and virtual data resources and services to assist in managing, analyzing, visualizing, and preserving data throughout the entire "data life cycle".


Data Life Cycle

Data Life Cycle

 Source: DataOne Best Practices

Though there are many versions of the data life cycle, the above figure illustrates the basic concept inherent in each model: data needs to be managed throughout every stage of research. DataOne, a National Science Foundation partner, defines each stage of the above cycle as follows:

  • Plan: description of the data is compiled along with and how the data will be managed and made accessible throughout its lifetime
  • Collect: observations are made and the data are placed into digital form
  • Assure: the quality of the data are assured through checks other forms of inspection
  • Describe: data are accurately and completely described using appropriate metadata standards
  • Preserve: data are submitted to an appropriate long-term archive
  • Discover: potentially useful data are located and obtained, in addition to the relevant information about the metadata
  • Integrate: data from disparate sources are combined to form one dataset that can be readily analyzed
  • Analyze: data analysis takes place

(DataOne Best Practices Primer 2012)

Additional data life cycle models:

MITDigital Curation CenterUS Geological Survey, UK Data Archive, University of Virginia


Data and Medicine

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TED Talks

Making Sense of Too Much Data Play list of ten presentations given at conferences for the organization, Technology, Entertainment, Design (TED). These talks explore practical, ethical, and visual ways to understand near-infinite data.


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