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Nonspecific Use of Large: Never just use the words 'large' or 'huge' to de-
scribe a dataset or the scalability of a technique without giving numbers to clarify
the order of magnitude under discussion: hundreds, tens of thousands, millions?
Every author has a different idea of what these words mean, ranging from 128
to billions, so be specific. Also, you should provide the size of all datasets used
in results figures, so that readers don't have to count dots in an image to guess
the numbers.
5.2 Submission Pitfalls
Finally, I caution against pitfalls at the very end of the project, when submitting
your paper.
Slimy Simultaneous Submission: Simultaneous submission of the same work
at multiple venues who clearly request original work is highly unethical. More-
over, simultaneous submission is stupid, because it is often detected when the
same reviewer is independently selected by different conference chairs. The num-
ber of experts in any particular subfield can be quite a small set. The standard
penalty upon detection is instant dual rejection, and multi-conference blacklists
are beginning to be compiled. Finally, even if you do succeed in getting the same
work published twice, any gains you make by having a higher publication count
will be offset when you lose credibility within your field from those who actually
read the work and are annoyed to wade through multiple papers that say the
same thing.
Resubmit Unchanged: If your paper is rejected, don't completely ignore the
reviews and resubmit to another venue without making any changes. As above,
there's a reasonable chance that you'll get the one of the same reviewers again.
That reviewer will be highly irritated.
6 Pitfalls By Generality
A cross-cutting way to categorize these pitfalls is by generality. Many hold true
for any scientific research paper, rather than being specific to visualization. Of
the latter, many hold true for both scientific visualization (scivis) and informa-
tion visualization (infovis). As many have lamented, the names of these subfields
are unfortunate and confusing for outsiders. The definition I use is that it's in-
fovis when the spatial representation is chosen, and it's scivis when the spatial
representation is given. Operationally, InfoVis split off as a sister conference from
IEEE Visualization (Vis) in 1995. At Vis, the focus is now on scivis.
The choice of paper types is specific to the InfoVis author guide, because
this categorization is not explicitly discussed in the Vis call for papers. The
first-stage type pitfalls are thus quite specific to infovis. The middle pitfalls on
visual encoding are specific to visualization. Color Cacophony and Rainbows
Just Like In The Sky certainly pertain to both infovis and scivis. Unjustified
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