List of free and open source software packages From Wikipedia, the free encyclopedia This is a list of free and open-source software packages, computer software licensed under free software licenses and open-source licenses. Software that fits the Free Software Definition may be more appropriately called free software; the GNU project in particular objects to their works being referred to as open-source.[1] For more information about the philosophical background for open-source software, see free software movement and Open Source Initiative. Artificial intelligence[edit] General AI[edit] OpenCog – A project that aims to build an artificial general intelligence (AGI) framework. Computer vision[edit] Machine learning[edit] Planning[edit] TREX – Reactive planning Robotics[edit] Robot Operating System (ROS)Webots – Robot SimulatorYARP – Yet Another Robot Platform Assistive technology[edit] Speech (synthesis and recognition)[edit] Other assistive technology[edit] CAD[edit] Finite Element Analysis (FEA)[edit] Electronic design automation (EDA)[edit]
Free and Open Source for Geospatial Conference Top 5 Vizzes of 2013 Winners 2013 was a great year for Tableau Public; countless amazing vizzes were published. Thanks to you, we were able to narrow it down to the Top 5 of the year! 5.Petco Park is Changing by Ryan Robitaille Ryan's creative, infographic-style viz was your fifth favorite viz of 2013. 4.Every Recorded Meteorite Impact on Earth by Ramon Martinez Ramon showed us that his analytical skills go far beyond public health data with this stunning viz on meteor impacts. 3.Top Movie Directors by Film by Adam McCann Adam's self-proclaimed "stupid" viz was smart enough to grab the third spot in your top 5. 2.Volunteerism in America by Jim Wahl Jim won our Civic Data Viz Contest and a spot in the 2013 Iron Viz with this highly informational viz on volunteering in America. 1.It's a bird, it's a plane... by Kelly Martin Kelly amazed us all with her beautiful remake of a dashboard on bird strikes.
Protege Wiki Spreading the gospel... ...one data viz nerd at a time. {The following blog post is the latest in the Data Blogging Month series, and is a guest post by Tableau Zen Master and Facebook data viz guru Andy Kriebel. Andy also runs VizWiz, a popular data visualization blog. Andy originally published this blog post on his site here.} Fact: If I didn’t start my blog, I wouldn’t be working at Facebook. When Facebook was looking for people to build out their new Tableau team, naturally they started scouring the web to find people that could make an impact. Back on September 3, 2007, in no less than one hour, I: Ran a Google search for dashboard softwareDownloaded and installed Tableau 3 (people new to Tableau have no idea how good they have it now)Watched the first two intro training videosCreated my first dashboard That one hour changed my life. Almost another two years passed. There was a void, so I created vizwiz.blogspot.com. Naturally, once your blog gains some followers you’ll begin getting feedback.
My Data Blog Story {The following blog post is the latest in the Data Blogging Month series, and is a guest post by Tableau Public author and Team Leader of Supervisory Reporting and Analysis at the Office of the Comptroller of the Currency, Emily Kund. Emily recently launched a data blog section on her website Wannabe Awesome Me, where she also blogs about other topics like fashion and family. Opinions expressed below are the author's own and do not reflect the opinion or viewpoints of her employer.} I had attempted to blog a few years ago, but it just didn't take...I did maybe two posts, a year apart. That's not so great. My First Post As I read over my first post, A Look Back at TCC13, it describes how I feel about the customer conferences. I was resolved this year to not let that happen, at least on a personal level. Some Technical Details On little side note. Inspired by Design Then in October, I was inspired again by Tableau Design Month. Best, Emily
Why I Blog and You Should, Too It was the first night of TCC (Tableau Customer Conference) 2013. I had just grabbed a large glass of red wine and set out to find someone I could talk viz with. Across the room I locked eyes with a face I recognized, despite only ever seeing a 50 by 50 pixel Twitter profile picture of it. I cautiously approached… “Excuse me… are you Peter?” And that’s how Peter Gilks and I (and Carl Allchin) became TCC BFFs: a mutual admiration for each other’s blogs. Blogging has always been something that I’ve wanted to do, but I had a hard time carving out the right corner of the internet for myself. I started my blog just shy of a year ago. When I started my blog, I didn’t do a whole lot of promotion for it. That’s a really emotional account on why I love data blogging. Having a blog with viz examples is the fastest way to explain to my family and friends what I do for work. But enough about me, how about you? First, you need to determine what kind of blog you want to have. Topical.
Setting Up a Data Blog: An A-Z Miniguide Last week Jewel shared several reasons why she started her blog and why you should too, hopefully you read it and now you're convinced your should start your very own blog. So you’re now ready and excited to get start, but wait, before you can start writing your first post you'll need to decide how complex and customizable you want your blog to be. Ready, Set, Blog The easiest way to get going is using a mainstream blogging tool such as Tumblr or Blogger. These tools provide limited customizability but what they lack in complexity they make up for in easy of setup and use. In fact it took me just a couple minutes to start a blog and post a sample visualization on Tumblr, while Andy Cotgreave was able to do the same on Blogger in just under 3 minutes, check out his #3minwin video walkthrough. If you found that these tools cover everything you want in a blog, feel free to stop reading here. Baking a blog from scratch How I got started Choosing a Platform Selecting a hosting service
Hazy There is an arms race to perform increasingly sophisticated data analysis on ever more varied types of data (text, audio, video, OCR, sensor data, etc.). Current data processing systems typically assume that the data have rigid, precise semantics, which these new data sources do not possess. On the other hand, many of the state-of-the-art approaches to both cope with variations in the structure of data and to deeply anlayze data are statistical. The Hazy project is exploring integrating statistical processing techniques with data processing systems with the goal of making such systems easier to build, to deploy, and to maintain. The key technical hypothesis behind Hazy is that a large fraction of the processing performed by applications that use and analyze these new sources of data can be captured using a small handful of primitives. Identifying this small handful of primitives is one of Hazy's chief goals. Support Media