When submitting a scientific paper written using LaTeX, to a journal, which high-quality image format do you usually use?
For scientific papers in matter modeling, I always use vector graphics (e.g.
.svg, though the journals will not typically accept
I stopped publishing in ACS journals (e.g. JCTC and JACS) after they turned one of my vector graphics into a pixelated raster, before publishing it. Even though it was 2015, and I'd been publishing papers with vector graphics in journals like JCP for many years already, they said they don't accept EPS or PDF files, when I was publishing this paper in JCTC. In Figure 2, you can see the difference between the published version (top) zoomed in as much as possible (and yet still pixelated) and the version on arXiv (bottom) which can be zoomed in much more, and still doesn't get pixelated (click the image to see it larger):
As Camps explained in his comment, they only accepted TIFF files at the time:
"Date: Tue, 9 Jun 2015 at 10:01
Subject: RE: FW: ct-2014-01066k Why Quantum Coherence Is Not Important in the FennaMatthewsOlsen Complex
Thank you for your message concerning this manuscript. We are currently exploring the use of vector graphics, but at this time we are unable to grant your request. If you wish to see if tif files submitted as explained below would improve the resolution of your figures, you may send new files directly to me by e-mail. Please advise.
American Chemical Society"
That was in response to an email in which I'd complained that the label for picoseconds in my original vector graphic (bottom of image below) had been pixelated by the journal when they converted it to a raster (top of image below), here is a screenshot of that email I sent:
Buttonwood made some very good points this comment, with which I completely agree. Vector images have searchable text whereas raster images typically do not. Furthermore the size of the image will be much smaller if you use a vector graphic instead of a raster: arXiv has a limit on the size of the files submitted, and I once had a submission that was at least 10x too big in file size to get through arXiv's submission portal, but when I converted the figures to PDF format, the entire paper's size was about 53kB.
Complementary to the answer given by Nike Dattani with focus on file format, I would like to draw your attention to how you present the content visually in photos, schemes and diagrams known as artwork.
Regardless of the file format eventually elected (where suitable, vector based like .(e)ps or .pdf), determine in advance the dimension of the illustration: Are you invited to design the outer/inner title of the issue of the journal, may you use color(s), will the illustration span over two columns, or are you constrained to the width of a single narrow column. This may be a guide to balance the content and detail it contains.
The blueprints by architects, datavisualizations in the newspapers, or work e.g., by Edward Tufte may be an inspiration to remove clutter and reorganization e.g., if the illustration is a photography. Beside inspiration by illustrations by your colleagues, well maintained scientific journals provide advice, too (example IUCr).
If the illustration isn't a photography, design it like a flag -- easy recognizable from distance (e.g., Chicago):
- Keep It Simple.
- Use Meaningful Symbolism.
- Use 2 or 3 Basic Colors.
- Be Distinctive or Be Related.
So if you plot data in a diagram, consider markers clearly distinctive by color, yet still distinctive by shape if the publication isn't transmitted in color passing a Xerox:
Note: If you start with a too large dimensions, the journal's request to scale the illustration to fit into a single column may render these markers difficult/impossible to discern (especially if a publisher converts them into pixel bitmaps).
The more discrete colors in the illustration, the more the potential lost of information when this is reproduced in grey scale:
Use freely accessible tools like ColorBrewer2 to probe color schemes safe enough to convey sequential/diverging, or qualitative data. You may toggle on/off options for greyscale, photocopy, color blindness.
Consider color blindness. There are much more men (and to lesser extent, women) prone to this, than you might think (see examples). Check your illustration in advance with tools like visicheck, or your image editor (example GIMP).
If your illustration is a 2D projection of 3D map where e.g., intensity of a property is color encoded, substitute default color schemes like «jet» by one like «viridis» (continous data):
or «blue-white-red» about divergent data by «bent-cool-warm»:
It is worth to invest some time and effort into this topic. The work by Kenneth Moreland (a presentation), or Kristen Thyng may guide you further. Often, such palettes are freely available for e.g., Python's mathplotlib, or gnuplot; so, once set up well, it will work in the background for you.
It isn't easy to follow all these points at once equally well. Like any profession, it asks for training, curiosity, and testing.