Cool Toys Pic of the Day – Scholarometer

Scholarometer:
http://scholarometer.indiana.edu/

Professionals from a wide variety of backgrounds have expressed
concerns about the flaws in the peer review process (quality, bias,
politics, inefficiency, time delays, lack of reviewers for narrow
topics, and much more), and many have also proposed solutions (many of
which use social media in some fashion). I rarely encounter scientists
or researchers who express a complete lack of concern about the
process unless they are fairly new to publishing or in grad school.
Here is just one of several recent articles on the topic.

Alberts B, Hanson B, Kelner KL. Reviewing Peer Review. SCIENCE 321
(2008):15. DOI 10.1126/science.1162115.
http://www.sciencemag.org/content/321/5885/15.summary

Scholarometer is one of the proposed solutions.

In their words:
“Scholarometer(beta) is a social tool to facilitate citation analysis
and help evaluate the impact of an author’s publications.”

I first mentioned this to people around a year ago, and noticed today
when observing some of the emerging buzz about it that I’d never
actually blogged it. Basically, the idea is to use social media to
both generate and gather data on the reactions to publications, the
crowdsource an assessment of the quality of research. The tool
functions via a browser plugin and works best with Firefox and Chrome.
It is integrated with Twitter and other social media platforms, and
pushes out activities to these. On the social side, it can engage both
discipline specific commentary as well as the general public. On the
tech side, it has an API, uses Linked Data, offers an embedded widget,
generates visualizations, and calculates impact factors and Hirsch
index (h-index).

Hoang, Diep Thi and Kaur, Jasleen and Menczer, Filippo (2010)
Crowdsourcing Scholarly Data. In: Proceedings of the WebSci10:
Extending the Frontiers of Society On-Line, April 26-27th, 2010,
Raleigh, NC: US.
http://journal.webscience.org/321/

Xiaoling Sun, Jasleen Kuar, Lino Possamai and Filippo Menczer (2011).
Detecting Ambiguous Author Names in Crowdsourced Scholarly Data. In:
Proceedings of 3rd IEEE Conference on Social Computing, Oct. 9-11th,
2011, MIT, Boston, USA.
http://cnets.indiana.edu/wp-content/uploads/Socialcom11_name_disambiguation.pdf

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