bookmarks  172

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    Free thinkers. Curious people collaborating across borders. Pioneers pushing back the boundaries of what is possible. Teams building upon the work of others. People trying things just to see what happens. Those are all phrases that could be applied to KDE - or to scientists. The scientific mindset shares a lot with that of free software and so it is no surprise that there are plenty of scientists within our community, nor that KDE has some strong applications in the world of science.
    14 years ago by @thorade
     
     
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    Git is an extremely fast, efficient, distributed version control system ideal for the collaborative development of software. GitHub is the best way to participate in that collaboration: fork projects, send pull requests, create issues, and monitor development with all of your public and private code. GitHub is a web-based hosting service for projects that use the Git revision control system.
    14 years ago by @thorade
     
     
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    Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab.
    14 years ago by @thorade
     
     
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    textext: Inkscape extension for adding LaTeX objects to SVG drawings
    14 years ago by @thorade
     
     
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    CAELinux: an open source LiveDVD Linux distribution dedicated to computer aided engineering / finite element analysis (CAE / FEA)
    14 years ago by @thorade
     
     
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    NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP)
    14 years ago by @thorade
     
     
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    GnuWin32 provides Win32 (MS Windows 95 / 98 / ME / NT / 2000 / XP / 2003 / Vista / 2008) ports of tools with a GNU or similar open source license.
    14 years ago by @thorade
     
     
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    SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!
    13 years ago by @thorade
     
     
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    Digitizing software for converting graphs and maps into numbers. Image files from scanners, digital cameras and screenshots are easily converted, and exported into spreadsheets. Introduction This open source, digitizing software converts an image file showing a graph or map, into numbers. The image file can come from a scanner, digital camera or screenshot. The numbers can be read on the screen, and written or copied to a spreadsheet. The process starts with an image file containing a graph or map. The final result is digitized data that can be used by other tools such as Microsoft Excel and Gnumeric. Engauge (from en "make" and gauge "to measure") verb meaning to convert an image file containing a graph or map, into numbers. The term "Engauge" in Engauge Digitizer was invented for this project, since there seems to be no similar term in common use. Why Would You Need This Tool? Here are some real-life examples: You are an engineer with some graphs in decades-old documents, but you really need the numbers represented in those graphs so you can do analyses that will determine if a space vehicle is safe to fly. You are a graduate student gathering historical data from charts for your thesis. You are a webmaster with visitor statistics charts and you want to do statistical analyses. You ride a bike or boat and want to know how much distance you covered in your last trip, but you do not have an odometer or GPS unit. However, you do have a map. Nice Features Automatic curve tracing of line plots Automatic point matching of point plots Automatic axes matching Automatic grid line removal for improved curve tracing Handles cartesian, polar, linear and logarithmic graphs Support for drag-and-drop and copy-and-paste makes data transfer fast and easy Image processing tools highlight data by removing grid lines and backgrounds Status bar suggestions guide beginners Context sensitive popup help windows reveal explain feature of the user interface Tutorials with pictures explain strategies for common operations Browser-based user manual is extensive yet easy to navigate Preview windows give immediate feedback while modifying settings Dates and times are imported with the Date/Time Converter Import support for common image file formats such as BMP, GIF, JPEG, PNG and XPM Export support for common software packages such as Microsoft Excel, OpenOffice CALC, gnuplot, gnumeric, MATLAB and Mathematica Engauge is available for a wide variety of platforms (Linux, Mac OSX, Windows) Engauge Digitizer is completely open source and free courtesy of Sourceforge, Trolltech and FFTW
    12 years ago by @thorade
     
     
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    The pack­age en­able the user to type­set pro­grams (pro­gram­ming code) within LaTeX. The source code is read di­rectly by TeX. Key­words, com­ments and strings can be type­set us­ing dif­fer­ent styles (de­fault is bold for key­words, italic for com­ments and no spe­cial style for strings). In­cludes sup­port for hy­per­ref. To use, sim­ply \usep­a­ck­age{list­ings}, iden­tify the lan­guage with \lst­set{lan­guage=Python}, then em­ploy the \be­gin{lstlist­ing} ... \end{lstlist­ing} en­vi­ron­ment or the \lstin­put­list­ing{file­name.py} com­mand. Short (in-line) list­ings are also avail­able, us­ing ei­ther \lstin­line|...| or | ... | (af­ter defin­ing the | to­ken with the \lstMakeShortIn­line com­mand).
    11 years ago by @thorade
     
     
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    Welcome to Learn You a Haskell for Great Good! If you're reading this, chances are you want to learn Haskell. Well, you've come to the right place, but let's talk about this tutorial a bit first. I decided to write this because I wanted to solidify my own knowledge of Haskell and because I thought I could help people new to Haskell learn it from my perspective. There are quite a few tutorials on Haskell floating around on the internet. When I was starting out in Haskell, I didn't learn from just one resource. The way I learned it was by reading several different tutorials and articles because each explained something in a different way than the other did. By going through several resources, I was able put together the pieces and it all just came falling into place. So this is an attempt at adding another useful resource for learning Haskell so you have a bigger chance of finding one you like. This tutorial is aimed at people who have experience in imperative programming languages (C, C++, Java, Python …) but haven't programmed in a functional language before (Haskell, ML, OCaml …). Although I bet that even if you don't have any significant programming experience, a smart person such as yourself will be able to follow along and learn Haskell. The channel #haskell on the freenode network is a great place to ask questions if you're feeling stuck. People there are extremely nice, patient and understanding to newbies. I failed to learn Haskell approximately 2 times before finally grasping it because it all just seemed too weird to me and I didn't get it. But then once it just "clicked" and after getting over that initial hurdle, it was pretty much smooth sailing. I guess what I'm trying to say is: Haskell is great and if you're interested in programming you should really learn it even if it seems weird at first. Learning Haskell is much like learning to program for the first time — it's fun! It forces you to think differently, which brings us to the next section …
    12 years ago by @thorade
     
     
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    RStudio is a free and open source integrated development environment for R. You can run it on your desktop (Windows, Mac, or Linux) or even over the web using RStudio Server.
    11 years ago by @thorade
     
     

publications  27