Resources
During my time as a PhD student, I came across some useful resources online. Most of these relate to things you don’t necessary learn at school, such as how to organise your research or visualise your data, which is why I decided to collate a list of helpful sites and tools that would help other young researchers. These resources are mostly (if not all) free and open source. Credit goes to the respective content creators for the links below.
Research management
Handy tools for managing literature:
- Zotero: open source and free citation manager with a built-in pdf reader
- Obsidian: closed-source but arguably one of the most customisable note-taking apps that’s free
- NotebookLM: Google’s free AI research assistant that allows you to add up to 50 attachments/source to get a swift overview of a collection of papers
- ResearchRabbit: another AI-based tool which lets you map articles forwards and backwards to see how papers are linked, and to stay updated with the state-of-the-art in the literature
The beauty of some these tools is that they often have plug-ins that allow them to interact with each other. For instance, you could easily import citations from Zotero into Obsidian and into ResearchRabbit.
I am in the middle of writing a guide on using Obsidian for research alongside integrations with Zotero, so stay tuned! :)
Formatting papers and slides (mostly LaTeX):
- Overleaf’s guide to LaTeX
- Overleaf is a web-based and beginner-friendly version of LaTeX (an alternative formatter to Word)
- There are a plethora of templates available, which are the best way to learn LaTeX, ranging from working papers, journal articles to CV templates
- Another pro is the ability to collaborate online with others (for more than two collaborators, the premium version is required, however)
- TeXstudio: the LaTeX editor I started with…it’s a straightforward basic editor for a local installation of LaTeX
- Download a LaTeX distribution (e.g., MiKTeX) and then TeXstudio
- But nowadays I just use VS Code alongside the LaTeX Workshop extension
- Benefits: easy Git integration, formatting extensions, GitHub copilot (although Microsoft has recently pulled the plug regarding usage limits due to rising costs of AI and RAM shortages…)
- An excellent guide by Vincent Grégoire (HEC Montreal) on using LaTeX through VS Code is available on YouTube
- Beamer (the equivalent of Power Point in LaTeX) resources:
- Guide from MIT
- For visual and hands-on learners: this great guide on YouTube by Trefor Bazett using Overleaf
- A GitHub repository with links to different Beamer templates
- List of LaTeX symbols
- Convenient LaTeX math tools:
- Online tool for copy-pasting or uploading images and translating them into LaTeX math
- LaTeX-OCR: if you want a more local way using Python for something similar to the above
- Mathpix: another similar tool but has its limits on the number of conversions you can do
- Online LaTeX table generator: copy-paste Excel tables to easily generate LaTeX tables (but with AI tools these days you can also easily and more efficiently do so)
Data and statistical software
Currently, I use R most of the time so these resources are mostly R-related!
R:
- Free online books
- R for Non-Programmers
- R for Data Science (2e)
- Happy Git and GitHub for the useR: an introduction to version control with Git in the context of R
- Best Coding Practices for R
- R Markdown: The Definitive Guide
- A Little Book of R for Time Series
- Introduction to Econometrics with R
- Free online courses and videos
- University of Washington’s CSSS508: this also has more additional links to other resources for R
- Data Wrangling with R and data.table: useful for big data!
- Data Wrangling with R and tidyverse
- Using Arrow to handle big data: [1] [2] [3]
Stata:
Miscellaneous:
- Coding for Economists: a free online book that focuses on programming as an economist; inludes useful “conversion” tables for those interested in using Python coming from Stata/R/Matlab
- The Missing Semester of Your CS Education: an online course/resource on using the command line, shell and Git among other things by a group of MIT students
- Using R, Python and Julia for Introductory Econometrics