ScienceBlogling Bora, in discussing the new release of journal impact factors--an estimation of how widely read journal articles are--writes:
One day, hopefully very soon, this will not be news. What I mean by it is that there soon will be better metrics - ways to evaluate individual articles and individual people in way that is transparent and useful and, hopefully, helps treat the "CNS Disease".
There is a better metric than the impact factor: the eigenfactor.
More like this
Topology usually starts with the idea of a *metric space*. A metric space is a set of values with some concept of *distance*. We need to define that first, before we can get into anything really interesting.
Metric Spaces and Distance
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After my [initial post about manifolds](http://scienceblogs.com/goodmath/2006/10/manifolds_and_glue.php), I wanted to say a bit more about gluing.
When we talk about topology, in general, the way we talk about it is in terms of *shapes*: geometric objects and spaces, surfaces, bodies that enclose things, etc. We talk about the topology of a *torus*, or a *coffee mug*, or a *sphere*.
We are now just 12 hours from the release of the National Research Council Data Based Assessment of Graduate Programs.
The tension is just overwhelming...
I'm on board with everything except cross-referencing to the social sciences. I would guess that it is responsible for this outcome:
Nature: EF 1.9917, AI 17.563
Science: EF 1.905, AI 18.287
Cell: EF 0.65975, AI 17.037
PNAS: EF 1.8301, 5.1534
I like PNAS, but its EF seems overly high. Nothing is perfect, of course, but this worries me.