Did someone say that he/she wants to go to Mars? Look what I have: a ticket to board rocket Delta IV Heavy to Mars!!!!
Did someone say that he/she wants to go to Mars? Look what I have: a ticket to board rocket Delta IV Heavy to Mars!!!!
The 6th Argonne Soil Metagenomic Meeting/Workshop (#ASMM14) just happened earlier this week (Oct 1-3, 2014) in Pheasant Run Resort, St. Charles, Il. I have been attending this meeting when I was still studying bacteria-metal interactions. Although my main study focus was not soil at the time, I have always found this meeting useful and filled with positive scientific interactions. It was no exception this year. To better conclude on things I have learned, here is my brief summary on soil, science, and the community that studies communities.
The flight back to China is always long. To make the trip a bit more bearable, I always flew from Chicago to Hong Kong directly to avoid any unnecessary detours. While the sceneries are good from time to time along the route, I have really reached my limits of taking creative photos. Due to various reason, I had to detour to Dallas, TX to fly to Hong Kong. I was really dreading it (Come on! Chicago is only 400 miles away!). However, the views were absolutely stunning (did I mention that I became bored of the views on the Chicago-HongKong route?) and I may consider this detour again next time. Here are some of my Ansel Adams moments.
I taught my very first Software Carpentry (SWC) Workshop officially on July 10, 2014. The workshop was hosted by Federation of Eartch Science Information Partners (ESIP) and held at Copper Mountain resort, Frisco, CO, US. There were some nerve wracking moments but it was an absolutely wonderful experience. Although it was a little bit late, it’s better late than never, I have to share my virgin SWC teaching experience.
I am a newbie when it comes to lots of things, including python and R. I can use them but I cannot make them do magic in the most efficient way… I always use python to parse files (e.g., get rid of redundant lines/columns, insert tabs, matching ID’s, etc.) and get them into the desired format before I use R for downstream analysis (e.g., statistics, plotting, etc.). It worked well until recentrly when I was trying to connect ID’s in UniprotKB with my assembly ID’s. The UniprotKB ID list was simply too large for python dictionary to work efficiently (and it could also simiply be that I didn’t know other ways to use python more efficiently). Regardless, I decided to give it a go in R and surprisingly (to me), R memory handled it really well. Below is some of the codes and examples (mostly a note to self for next time).