Most of the job hunting season is behind us and for many astronomers, firm decisions about the direction of our careers and lives have been set in motion. For the uninitiated, every Fall (northern hemisphere) astronomers facing the end of their contracts (whether that be as a PhD student, postdoc or research scientist) head to the dreaded ADS Job Register to see what possible future awaits them (assuming they decide to stay in the field).
Today it is extremely easy to access music. The days of celebrating 4kb/s via Napster are long over. For a small fee or even for free, you can access large quantities of music, on demand. If you know of a band or song, it is easy to find it on Youtube or Spotify (or any of the other services). This is great if you already know the music you are looking for.
Today I wanted to get an idea of my home institute’s publication profile based on the staff list from its own website. I’m sure if you’re in academia you would have the same for your own. My list includes members which belong to various categories: faculty, affiliated faculty, postdoctoral scholar, student and technical staff.
To build the profile, we need to make use of ADS metrics. For example we can search for a paper of interest on ADS labs:
I took another look at ADS-python (a python tool for ADS) developed by Andy Casey. I modified his example script to email myself a digest of all of the papers published by my institute in the past month. I set it up as an automated cron job (10 0 1 * * python script.py) to be run on the 1st of each month so I don’t have to run the script anymore to get the digest.
The 27-club is a group of musicians who have died at the age of 27. Charles Cross, a Hendrix biographer put it as follows:
The number of musicians who died at 27 is truly remarkable by any standard. [Although] humans die regularly at all ages, there is a statistical spike for musicians who die at 27.
There are a large number of famous musicians who have all died at the age of 27 (e.
Create a simple GUI in under 10 minutes.
A while back I posted about a graph of the personalities on Wikipedia. This time I wanted to see which programming languages were linked to one another by user-entered “Influenced” and “Influenced-by” information. Take for instance the functional language Haskell:
In the infobox on the side we find a large list of languages Haskell is connected to in one way or another. Wikipedia devotes an entire section to how it is related to other programming languages for those interested.
Update! The work seems to have been picked up by Flowing Data, Business Insider, Future Journalism Project, Coppelia and a few other places.
The internet is big — very big. One such way to investigate all of this free online content is through graphs. The network visualisations by Simon Raper in his fantastic post about graphing the history of philosophy is one example of how to exploit such data. Let’s take this a step further and create a series of graphs using everyone on Wikipedia.
I’ve recently been digging around the topic of influences so I thought it would be interesting to examine a few subnetworks within the large network of everyone. This time I set my scopes on mathematicians. There is no primary reason why other than – I can. I’ve long been interested in the history of mathematics and so I wondered what a network of great mathematicians actually looked like? Could there be underlying structures between mathematicians who have influenced each other over history?