We spend a lot of time talking about problems with gender inclusivity at scientific conferences, and rightly so – things are unlikely to change if we don’t call attention to the problems. But it’s also important to recognize conferences that are doing a good job! So, I’d like to take this opportunity to do that. For the sake of this series of posts, I’m just going to focus on gender inclusivity, although obviously inclusivity towards all groups is also important. There are two easily measurable elements of gender inclusivity: anti-harassment policies and speaker balance (if people have other ideas, I’d love to hear them!). Part one of this blog post series will deal with anti-harassment policies, while part two will deal with speaker balance. A third part may be forthcoming if I get enough ideas for alternative ways of judging […]
Recently, I and two other members of the Devolab had the opportunity to participate in an instructor training workshop for Software Carpentry and Data Carpentry. Software Carpentry is an organization that runs workshops aimed at helping scientists who can program a little improve their programming, while Data Carpentry is aimed at getting scientists with little to no programming experience started with data analysis beyond a spreadsheet. These workshops (including the instructor training) use an evidence-based, open-source teaching style that really appeals to me. Every time a pedagogical approach was mentioned, citations were provided to back it up. All of the lessons are maintained in github repositories, which everyone is encouraged to submit pull requests to (doing so is even a requirement for becoming an instructor!).
While Avida is used most often in experimental mode where digital organisms are allowed to evolve and a whole lot of data is produced and saved, there is another way you can use Avida after the experiment is over: analyze mode.
One of my stepping stones towards becoming an evolutionary biologist was playing with engaging programs that combine evolution and artificial life. Although these “games” are often neither intended for educational or research purposes, I found them instrumental in developing an appreciation for the power and creativity of evolution by natural selection. Here is a collection of my favorites in no particular order: Bitozoa http://www.bpp.com.pl/bitozoa2/bitozoa2.html
I flip-flop between Python, R, and D3 for my data visualizations depending on what exactly I’m doing. My default, though, is definitely Python. One of the most well-established data visualization libraries in Python is Matplotlib. If you dig deep enough in it, you can find a wide variety of features beyond standard graphs. One of the less well-documented of these features is the animation library. The FuncAnimation class in particular is quite powerful, allowing you to programmatically generate the frames for your animation and compile them together. Jake VanderPlas has a great tutorial on using FuncAnimation which I’m not going to try to duplicate. Here, I’m just going to focus on a small but critical aspect of using FuncAnimation that is glossed over elsewhere: blitting. Here’s how critical blitting is: My first attempted Matplotlib animation took […]