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Pixel Font Maker Software S' title='Pixel Font Maker Software S' />How to Create Publication Quality Figures. Introduction. So, after months years of toil in the lab, youre finally ready to share your ground breaking discovery with the world. Youve collected enough data to impress even the harshest reviewers. Youve tied it all together in a story so brilliant, its sure to be one of the most cited papers of all time. Congratulations But before you can submit your magnum opus to Your Favorite Journal, you have one more hurdle to cross. You have to build the figures. Vocaloids singing synthesis technology is generally categorized into the concatenative synthesis in the frequency domain, which splices and processes the vocal. And they have to be publication quality. Those Power. Point slides youve been showing at lab meetings Not going to cut it. So, what exactly do you need to do for publication quality figures The journal probably has a long and incomprehensible set of rules. They may suggest software called Photoshop or Illustrator. You may have heard of them. If you are developing on a 2. Storage offers a superior solution for persisting application data. It is important to note that this API will only. You may be terrified by their price tags. But heres the good news It is entirely possible to build publication quality figures that will satisfy the requirements of most if not all journals using only software that is free and open source. This guide describes how to do it. Not only will you save money on software licenses, youll also be able to set up a workflow that is transparent, maintains the integrity of your data, and is guaranteed to wring every possible picogram of image quality out of the journals publication format. Tools. Here are the software packages that will make up the core of the figure building workflow R Charts, graphs, and statistics. A steep learning curve, but absolutely worth the effort. If youre lazy though, the graph making program that you already use is probably fine. Image. J Prepare your images. Yes, the user interface is a but rough, but this is a much more appropriate tool than Photoshop. For Image. J bundled with a large collection of useful analysis tools, try the Fiji distribution. Inkscape Arrange, crop, and annotate your images bring in graphs and charts draw diagrams and export the final figure in whatever format the journal wants. Illustrator is the non free alternative. Trying to do this with Photoshop is begging for trouble. Embed and Crop Images extension for Inkscape and The PDF Shrinker Control image compression in your final figure files. The focus on free software is facultative rather than ideological. All of these programs are available for Windows, Mac, and Linux, which is not always the case for commercial software. Furthermore, the fact that they are non commercial avoids both monetary and bureaucratic hassles, so you can build your figures with the same computer you use to store and analyze your data, rather than relying on shared workstations keep backups. Most importantly, these tools are often better than their commercial alternatives for building figures. Goals. First of all, this guide is not intended to be a commentary on figure design. Its an introduction to the technical issues involved in turning your experimental data into something that can be displayed on a computer monitor, smart phone, or dead tree while preserving as much information as possible. You will still be able to produce ugly and uninformative figures, even if they are technically perfect. So, before we dive into the details of the figure building workflow, lets take a moment to consider what we want to accomplish. Generally speaking, we have four goals accurately present the data, conform to the journals formatting requirements, preserve image quality, and maintain transparency. Data dont lie. And neither should your figures, even unintentionally. So its important that you understand every step that stands between your raw data and the final figure. One way to think of this is that your data undergoes a series of transformations to get from what you measure to what ends up in the journal. For example, you might start with a set of mouse weight measurements. These numbers get transformed into the figure as the vertical position of points on a chart, arranged in such a way that 5. Or, a raw immunofluorescence image a grid of photon counts gets transformed by the application of a lookup table into a grayscale image. Either way, exactly what each transformation entails should be clear and reproducible. Nothing in the workflow should be a magic black box. Follow the formatting rules. Following one set of formatting rules shouldnt be too hard, at least when the journal is clear about what it expects, which isnt always the case. But the trick is developing a workflow that is sufficiently flexible to handle a wide variety of formatting rules 3. Tiff or Post. Script, margins or no margins. The general approach should be to push decisions affecting the final figure format as far back in the workflow as possible so that switching does not require rebuilding the entire figure from scratch. Quality. Unfortunately, making sure your figures look just the way you like is one of the most difficult goals of the figure building process. Why Because what you give the journal is not the same thing that will end up on the website or in the PDF. Or in print, but who reads print journals these days The final figure files you hand over to the editor will be further processed generally through some of those magic black boxes. Though you cant control journal induced figure quality loss, you can make sure the files you give them are as high quality as possible going in. Transparency. If Reviewer 3 or some guy in a bad mood who reads your paper five years after it gets published doesnt like what he sees, you are going to have to prove that you prepared the figure appropriately. That means the figure building workflow must be transparent. Every intermediate step from the raw data to the final figure should be saved, and it must be clear how each step is linked. Another reason to avoid black boxes. This workflow should accomplish each of these goals. That being said, its not really a matter of follow the checklist and get perfect figures. Rather, its about understanding exactly what youre doing to get your data from its raw form to the electronic journal page. A computers view of the journal page. In order to understand how to get data into a presentable form, we need to consider a few details of how visual information gets represented on a computer. Raster data vs. vector data. There are two fundamentally different ways that visual information can be described digitally. The first is by dividing an image into a grid, and representing the color of each cell in the grid called a pixel with a numeric value. This is raster data, and youre probably already familiar with it. Nearly all digital pictures, from artsy landscapes captured with high end cameras to snapshots taken by cell phones, are represented as raster data. Raster data is also called bitmap data. The second way computers can represent images is with a set of instructions. Kind of like draw a thin dashed red line from point A to point B, then draw a blue circle with radius r centered at point C, but with more computer readable syntax. 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