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SciTech Connect: Subject and Author Filters

by Tim Byrne 24 Jul, 2013 in Products and Content

One of the nice features of SciTech Connect is the ability to filter search results by subject and author.  On the Search Results page, these filters are midway down the left side.

The full SciTech Connect database contains over 2.5 million citations.  Filtering the full database by subject [23 MB AVI] shows the top subject in the database to be materials science with 184,200 citations.  Not too far down the top ten list you will also find materials with another 127,916 citations.  While this shows that SciTech Connect is quite strong in this area, the rest of the top subjects are good indicators of the diversity of scientific disciplines found in SciTech Connect.  Note especially environmental sciences as the ninth most frequent subject term.

  • materials science (184,198)
  • elements (174,970)
  • physics (174,180)
  • organic compounds (172,389)
  • design (157,517)
  • oxygen compounds (145,684)
  • chemical reactions (142,736)
  • materials (127,924)
  • environmental sciences (127,863)
  • fuels (123,001)

Selecting the Electronic Full-Text tab and filtering by subject [18 MB AVI] will give you a significantly different list of top ten subjects.  Materials science is still high on the list, along with design and environmental sciences, but the other seven subjects are new. 

A New Way To Find Reviewers

DOE program managers are routinely called upon to identify peer reviewer candidates for grant and field work proposals.  Each proposal requires a minimum of three reviewers and often more to cover separate aspects of the proposal.  To generate reviewer candidates, program managers draw upon their subject matter expertise and manually scour journal literature.  Although this process is facilitated by the availability of electronic journals, it is labor intensive and represents a major cost.

Information experts at OSTI have demonstrated the capability to identify high quality potential reviewers by analyzing the scientific content underlying its R&D information collections and other integrated sources. This analytical procedure combines sophisticated semantic algorithms with informed judgments. Not only is it efficient, it also raises the prospect of broadening the reviewer base by finding new qualified reviewers.

In non-technical language, the OSTI Reviewer Finder works as follows. First a semantic technique is used to find a core set of papers that are directly related to the proposal in question. This can also be done for groups of proposals or other topic specific needs.

Second, a sophisticated semantic algorithm, developed by OSTI, is used to find all those papers that are closely related to the core papers. In fact these papers are ranked according to the degree of closeness. This means the pool of related papers can be made larger or smaller as needed.

This two step process may be repeated when a proposal involves several distinct topics, methods or other aspects, which often happens. For example, in addition to subject matter experts one might want to have reviewers who are experts in the methods used.

All of the authors of the core and closely related papers are potential reviewers, so they are abstracted and listed. However, some authors must be excluded, for various reasons. For example, it is common practice...

Related Topics: authors, r&d, reviewers, semantic