Accelerating Science Discovery - Join the Discussion

Published by Sharon Jordan

It is said that a picture is worth a thousand words.  Such is the justification and hope for visualizations.  Examples of enlightening visualizations are structural models of molecules like the carbon-60 Bucky Ball used in OSTI’s recent YouTube video.  The model shows a carbon atom at each intersection of molecular bonds. 

Published by Kristin Bingham

“World Wide Science is the world’s most important scientific resource, where the global science community can share knowledge.”  This remarkable encomium did not come from just any casual observer, but from a leader of one of the world’s top information organizations.  While interviewing with Information World Review, Richard Boulderstone, director of e-strategy and information systems at the British Library, shared this perspective. 

Published by Dr. Walt Warnick

A term of art now catching on is “e-Science.”  According to Wikipedia, “The term e-Science (or eScience) is used to describe computationally intensive science that is carried out in highly distributed network environments, or science that uses immense data sets that require grid computing; the term sometimes includes technologies that enable distributed collaboration, such as the Access Grid. The term was created by John Taylor, the Director General of the United Kingdom's Office of Science and Technology in 1999 and was used to describe a large funding initiative starting in November 2000. Examples of the kind of science include social simulations, particle physics, earth sciences and bio-informatics.”

Published by Linda McBrearty

There was good news coming from the University of Tennessee (UT) and the State of Tennessee in 2009!  A $1.8 million grant was announced that will help put more math and science teachers into Tennessee schools!  This program, called VolsTeach, is designed to meet the increasing need for more math and science te

Published by Dr. William Watson

In December 2008, Luis Bettencourt and David Kaiser reported their findings[1] from studies of research collaboration networks, which included their discovery that, as coauthorship networks in a particular field reach the point of forming a single giant component of interconnected authors that dwarfs all other coauthor groups in that field, the growth near that point depends in a universal way on the average number <k> of coauthors per author.  In particular, the fraction of coauthor links that belong to the giant component appears to be proportional to (<k> - kc)0.35, where kc, which marks the critical point, depends on the research field.[2]  The remarkable fact is that the exponent, 0.35, fits the data for networks in several quite distinct fields.  This value apparently isn’t common to networks in general, though.  I had wondered what features of a network do determine the exponent’s value.