I figured some goal setting for this month would be a good way to start off my lab notebook. Here are some things I would like to complete (or at least get underway).
Gathering genomic resources for coral species in Moorea
Before completing any DNA methylation work for the E5 project, I’ll need to compile genome data. I would like to have data for Porites lutea, Pocillopora verrucosa, Pocillopora acuta, and Montipora capitata. Data for P. lutea and P. verrucosa are available on ReefGenomics. P. acuta and M. capitata will be slightly more challenging to put together. Once all data is compiled, I will display it in IGV. I also need to set up a gitnet directory so that the session is sharable.
Exploring remotely-sensed data options for environmental parameter assessment
Given my background in remote sensing, I’m interested to see if any Earth observations data might be useful in the modelling efforts of the E5 project. I am familiar with datasets and algorithms used to estimate various water quality parameters, like chlorophyll-a, turbidity, suspended sediment concentration, CDOM (colored dissolved organic matter), and SST (sea surface temperature). I am particularly interested in harnessing the higher resolution of the Landsat Series and the Sentinel Missions to provide data products at 10m-30m resolution.
I’m currently in the process of assessing the remote sensing resources compiled by the Moorea Coral Reef LTER. I’m also in communication with colleagues from my previous place of work, NASA DEVELOP, about a tool created during their Fall 2019 term. The ORCAA tool is a good base for assessing water quality parameters via remote sensing in Google Earth Engine. The tool needs some updating and could be spruced up with more complex analysis, so I am hoping to stay involved with development to improve access to relevant data products.
Compiling statistics resources in R
I’m currently taking Q SCI 482, and since ths is my first time in years back in the classroom learning R-based stats, I want to keep a library of common statistical methods in R that I can use for analysis and testing later in my research. Hopefully this will be a good foundation as I build skills in statistical programming. It also doesn’t hurt that this is a great opportunity for me to bruch up on R.