Throughout his studies, Nelson has dedicated most of his free time to building software that can simplify life for students, researchers, and anyone interested in software. Listed below is a selection of projects he has worked on.

Python

The goal of cytounet is to provide an easy-to-use Keras based deep learning pipeline for image segmentation with particular focus on biological data. Nelson was inspired by and built upon ZhiXuHao’s unet implementation.

urlfix is a python package that aims to take a document that contains URLs, checks if they are broken/outdated, and automatically updates them. Nelson was inspired by urlchecker, an R package that does something similar and that was key in helping him solve issues with broken links in mde.

Given a file containing links to scientific articles, pycite generates citations in the Harvard referencing style (at the time of writing). This project is mainly aimed at researchers and students looking to save time in writing reference lists for their articles. This is also in line with Nelson’s belief in reproducible science.

The goal of pyautocv is to (semi) automate common image processing tasks such as thresholding, segmentation, and edge detection. The overall aim is to simplify this process and make it more accessible even to non-programmers.

A data scientist should ideally be able to pull data from several sources. One way to do that is through access to APIs. The goal of pyfdc is to pull information about different food(s) from the USDA nutrient database, now known as Food Data Central. This was inspired by and is related to rfdc and the now archived usdar

R

manymodelr is an R package focused on reducing the time spent in performing common machine learning tasks. Nelson started developing it after observing that most of what he was doing was repetitive. This was also his first project and was therefore key in developing his passion for software development and making users’ life easier. It is far from perfect but has certainly improved his understanding of machine learning and package development.

This is admittedly his favorite package of all the packages he has worked on. The adage that machine learning is 90%[citation needed] data cleaning is true. For this reason, mde aims to reduce the time spent in performing missing data exploration and analysis. It is aimed at missing data exploration, nothing more nothing less. It is hoped that this package will help researchers, students, and anyone interested in data analysis to minimise the time spent in data cleaning.

For some users, using a graphical user interface (GUI) is a more convenient way to explore their data. The goal of shinymde is to provide a shiny GUI to mde, the missing data explorer. This allows users to explore missingness without writing any line of code! This project can be tested at https://nelson-gon.shinyapps.io/shinymde.

shinymisc aims to make it possible for users less proficient at the shiny framework to understand key shiny concepts in an interactive environment. This app is hosted at https://nelson-gon.shinyapps.io/shinymisc.

Bash

The goal of linit is to provide a simple bash script that allows one to initialise a new Linux installation. The idea is that one simply downloads this script, runs it, and has some commonly used Linux applications and software installed.

The goal of officetools was to enable Nelson to quickly make beamer slides for his presentations at school. However, he later learned of other simpler ways(that do almost the same thing if not better) than this script. It was a nice way to improve his Terminal knowledge all the same.

Thank you very much for reading this site. If you have any feedback, please contact him and/or raise issues at any of the above repositories. Nelson is very open to on-job training to further improve his programming skills and contribute to humanity’s progress.