Some images are worth 1000 words. Similarly, some movies, or animations, are worth 1000 images, even if they are made up of fewer. What I'm trying to say is that there are some concepts which are better illustrated using animations rather than just graphs or images. Hal Levison, for example, shows movies of orbits changing as a function of time that give a better impression of orbital evolution, and in less time, than a succession of plots. Strategic use of animations really increases the amount of understanding that you can transmit to your audience in a 7-minute talk. Generating simple animations is easy using the command-line-driven ImageMagick software.
Creating shaded relief maps or combination colorized/shaded relief maps with MOLA data is easy to do with the Generic Mapping Tools (GMT).
The MOLA gridded data set is a great resource. The Generic Mapping Tools (GMT) are a great tool set. This article shows you how to take the raw binary MOLA gridded data set and convert it into the netCDF format that GMT uses. Once you do that, a whole range of mapping options are open to you.
At the AGU 2005 Fall Meeting there was an under-publicized meeting on Thursday entitled the "AGU Publications Open Forum". It was held by the AGU Publications Committee, and one of the items on the agenda was "AGU plans for open access", so I decided to attend and see what they had to say on the issue.
I recently submitted a paper and was a little lost as to what to put in a cover letter. I found a nice guide by an editor of Nature. There is a lot of useful advice in there, but the cover letter suggestions included things that I hadn't thought of before.
Oftentimes when faced with a data-rich environment, a good way to begin the process of analyzing and organizing the data in order to get a look at the big picture is to use a classification scheme. Here I describe some ways to classify data, practical uses, an in-progress application of the data to Visual and Infrared Mapping Spectrometer (VIMS) spectra of Titan, and some links to other places to obtain further information.
Not too many authors think critically about their selection of color for their data graphics, I know I have been guilty of that. This article by Light and Bartlein (2004) is a good discussion of the issues, and they also have a nice color scheme resource.
If you use the Generic Mapping Tools (GMT), I've attached some color tables below that can be used by GMT's
makecpt program. If they aren't the ones that you want, they should serve as a guide for how to make them.
If you have not served on a review panel, you may find it handy to understand what happens to proposals after they've been sent in. While the details of who has served on which panels is confidential (at least officially), the process itself is pretty straightforward and can help illuminate common pitfalls...
Here then are thirteen points, in vague order.
I describe here principal components analysis, a method for condensing the information present in images with many colors into fewer channels. This section is heavy on linear algebra—just a warning.
I am presently using this to try to map Titan into spectral classification units using the Visual and Infrared Mapping Spectrometer (VIMS). VIMS takes simultaneous 64×64 images in 256 different infrared channels at wavelengths between 0.9 and 5.2 microns. However, VIMS can only see through Titan's atmosphere and down to the surface in a handful of spectral windows, totalling maybe 20–30 channels. The remaining channels probe different levels in the atmospheric haze, but most are redundant.
I am using principal components analysis (PCA) to bring out subtle variations by reprojecting the VIMS 256-color maps into a different set of orthonormal basis vectors that span the same space, but have most of the data's information in only 9 or 10 channels instead of 256. Woah.
When writing a scientific paper, how long should the paper be? The short answer: about 10 pages.
The long answer: Well, to some degree it does depend on the goal of the paper. Note that it does not depend on how complex the problem that you solved is. The main question is, at whom is the paper directed? Who is the paper intended to inform?