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The Evolving Data Lifecycle

on Sat, 10/19/2013 - 01:16

This essay is based on my presentation at eResearch Conference, Brisbane Australia 10/21/2013

The spotlight is on Data

Data within the research process has now taken center stage. The amount of data ranges the enormous quantities produced by large planned science missions to the smaller amounts produced by individual researchers, the so-called long tail of science. While the current focus in on data, it is important to look at data in context to the research process it self -- the data life cycle.

Looking at the Data Life Cycle

A scientific research process can be represented as a data lifecycle consisting of a series of stages through which data passes during its lifetime.  These stages include data processing, archiving, discovery, and finally use. Use by itself encompasses several sub-stages of access, integration, visualization, analysis, and sharing. These stages may have slight variations within different science domains and applications but in general remain consistent across many domains. The goal of informatics researchers is to make this process efficient for researchers, address existing gaps/hurdles, seamlessly integrate new evolving technology, and enable new types of research capabilities.

Factors Impacting Data Life Cycle

The data life cycle is dynamic, constantly evolving driven by several factors. The factors drive changes to the life cycle at both micro and macro level. At micro level, the changes are to the individual steps within the cycle where as at the macro level, the steps that constitute the cycle may get modified.  While these factors may overlap, they can be categorized based on four different perspectives. These are:

1. Data Perspective

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Science Informatics – What is in a name?

on Fri, 07/05/2013 - 21:10
What's in a name? that which we call a rose / By any other name would smell as sweet.” –Shakespeare, Romeo and Juliet
Informatics has become a commonly used term in a wide variety of science domains, yet what is really meant when we use this term? Does informatics mean the same thing for all domains, or are there nuanced differences in scope and meaning associated with the term? 
In order to investigate this, the definition and scope of different science informatics terms were reviewed and then compared along the dimensions of their defined objectives and the data life cycle components which they encompass. These different terms and their definitions are presented below in chronological order:
Bioinformatics:  Coined in 1970, the initial definition for this term was “the study of informatics processes in biotic systems” [6]. As evolutions in the field led to exponential increases in sequence data the definition evolved as well, eventually coming to mean the development and use of computational methods for data management and analysis of sequence data.
The current objective of Bioinformatics is to provide solutions for data management and analysis of bio-medical datasets. Bioinformatics focuses on the data life cycle components of data management and analysis.