Week 2

Another week in the books. My main progress this week was assigning specific ages to the literature data I compiled. Some of the data tables I found had ages already assigned to the samples that were taken, but many only had a stratigraphic figure showing a plot of δ13C with both a height axis, a section of the geomagnetic polarity time scale, and — in rare instances — an absolute age axis. Luckily, I was able to use the paleomagnetic information in most cases in conjunction with the 2012 Geologic Time Scale to assign ages to the various sections. I would pinpoint a certain height in the section to the moment when a polarity reversal occurred, which in turn allowed me to match this stratigraphic height to exact age, and by using R, I was able to add these data points to each section and assign ages to the rest of the samples by assuming a constant sedimentation rate. Luckily, we do not care about assigning ages too specifically, since we are looking for trends in the data that occur over longer time scales, but it is still a good skill to learn and one that is of utmost importance in any geologic research.

Week 1

Compile, compile, compile is the theme for this week. Most of my time has been spent working on the literature compilation, and it’s been a bit tedious to say the least. I’ve been copying data from data table PDFs into Excel spreadsheets, and then into separate files for each location, and while it’s good to be organized, it has been a bit boring.

The good news is I’ve started coding! I knew coming into this project that using R would be an important part of my work, and until a couple weeks ago I had absolutely no idea how to code. Luckily, I found a free introductory course to R programming on Coursera that covered a lot of the basics and I was able to complete most of it before starting. I did a couple of the assignments, feeling very unsure of how much I had actually learned, but it looks like it paid off! I wrote a script that takes all of the separate data sheets and puts all the data into one table and saves it, and I also wrote a script that plots the locations of all of the literature data on a map of the Western United States. It can also filter out the data that is not in our time period of interest and can color code the points on the map by epoch: red points are from the Eocene, blue are from the Oligocene, and white are from the early Miocene. Note that some of these points overlap each other, so we have data from multiple epochs at the same locations.

LitCompMap2LitCompPlot3

So far, so good! I’ve been skyping and emailing with Jeremy, since he’s in Switzerland for the summer, and working with Dan and the rest of the group has been great. Looking forward to next week!