Posts
Following the principles of open science, it is typical for computer science researchers to share source code, tools or datasets accompanying a research paper. Being one of the most popular platforms, GitHub is often used to fulfill this task. While finding the repository associated with a paper usually is not difficult (researchers share the link in the camera-ready version of their paper), the reverse task - the discovery of the associated paper - is a more complicated task. To facilitate this process, researchers often add the citation to the paper to the main README file. However, for quite a while, GitHub facilitates this task allowing one to create a special citation file. The GitHub platform checks the presence of this file in each repository and adds a dropdown button with citation options if it finds it there. In this article, I explain how to add such citation files to your repository.
Although many researchers now prefer to write their papers using various SaaS solutions, such as Overleaf, I still promote an old-style way when everything is installed on your computer. Of course, this approach has some pros and cons. However, in this article, I am not going to discuss them and will concentrate instead on the topic of how to configure forward and inverse search. In particular, I will show how to do this for my setup with LaTeX Workshop, a VS Code extension facilitating text writing in TeX, and Okular, a PDF viewer available for Linux and Windows platforms.
In the previous article, I shared my setup for producing the graphs for research papers. However, recently when I was working on figures for a new paper, I discovered that my setup must be updated. The reason is that the new matplotlib version (since 3.6) produces a warning that the embedded seaborn styles are now deprecated. In this article, I provide the updates to the setup described in the previous article.
When you write a scientific paper, one of the most common tasks is to analyze the obtained results and design beautiful graphs explaining them. Currently, in the research community, Python’s ecosystem is the most popular for achieving these goals. It provides web-based interactive computational environments (e.g., Jupyter Notebook/Lab) to write code and describe the results, and pandas and matplotlib libraries to analyze data and produce graphs correspondingly. Unfortunately, due to the rich functionality, it is hard to start using them effectively in your everyday research activities when you initiate your path as a researcher. In this article, I would like to share some tips and tricks on how to employ the matplotlib library to produce nice graphs for research papers.
In the article, I show the differences in the website analytic metrics collected on the server- and client-side. It contains several dynamic values (e.g., pageviews or visits number, the date range, etc.) scattered throughout the text. To update them, I need to pass through the content and adjust them manually. So as I plan to bring the data in this post up to date regularly (I get new input every day), this task cumulatively could consume a lot of my time. Therefore, I have decided that the data in the post should be updated automatically. In this article, I describe how I have achieved this goal.
Some time ago, I wrote an article showing how much data is missed if you rely on only client-side web analytics numbers. To maintain this previous blog post in an actual state, I planned to update it from time to time. However, as you might expect, pretty soon, I got bored collecting manually Cloudflare and Google Analytics data and inserting it into a spreadsheet. As a normal human, I have decided to automate the process. Luckily, both systems provide the APIs that you can use to query and download the data. However, during the development of the data collection script, I discovered several limitations that inclined me to write a new blog post instead of updating the old one.