Finishing the code! (week 5)

This week I took up the task of completely restructuring the chunk of code that I was assigned to work on. In the process of getting everything nice and organized, I managed to completely finish writing the code for the new problem I was meant to solve -- that is, the elusive multiple channel non... Continue Reading →

Jane’s Week 6: Gene Annotation and R

It's week 6! I'm excited for this coming long weekend! I feel like I learned a lot this week! After looking at the aggregated abundances, I've been using tools such as Prokka and BlastKOALA to annotate the gene. That essentially means these programs tell you what your bins are composed of; when I ran my... Continue Reading →

Assembly Assessments – Susanna wk 6

This past week, I have been doing preliminary assessments with the data that I have gotten back from Xander.¬†For every gene, I used R to examine the kmer abundance distribution, calculate the length (in basepairs) statistics, the percent identity to the gene of interest, and number at 99% identity. I also use R to calculate... Continue Reading →

Week 6 Update

Attempting to apply time discretize Galerkin method to the (1-1) dimensional space-fractional diffusion equation.

Alex’s Week 6

Over the past week, I have been beginning improving my newly working code in various ways. My advisor said he likes how the data structure and search algorithm are implemented, and gave me basically two tasks. The first was that my implementation did not leave much room for parallelism. While very quick serially, for large... Continue Reading →

Week 6 – Sean

Hey y'all, It's been a bit of slow week in my project, as I'm just running the same code over and over for different values of three variables (about 40 times in total). However, once this is done, I will be expanding my model of the 1-D wire into one a 2-D "slice" of brain... Continue Reading →

Regression and Modeling on Data–Week #6 (Jay)

This past week, I finished cleaning up the plant/tree dataset that I will be using for my project. I wrote R scripts to run several imputations using predictive mean matching (pmm). The pmm linear regression method worked the best on the data. I also started using the R package Rphylopars to reconstruct the phylogenetic tree of the species to predict the missing traits.

Sweet, Sweet Data

During the course of this past week my time was spent modeling many aspects of our simulated galaxy. In particular, I focused on plotting vorticity and mass as a function of time, as well as the angular momentum for all three axes as a radial profile. We've also now begun to realize that our simulation... Continue Reading →

Blog at

Up ↑