Every year a set of memes is circulated widely on social media, the 2022 version of which goes along the lines of:
My goal for 2022 is to accomplish the goals of 2021 which I should have done in 2020 because I made a promise in 2019 and planned in 2018.
Over the years, this remained my most favorite ‘new year’ meme for no real reason other than that it was hilarious, funny, and relatable, but in 2021, the actuality of these lines hit me hard as it tested my mettle out and out by bestowing a series of unfinished businesses and frequent hiccups throughout the year!
The last day of 2020 is finally here. To bring this decade to a close, I thought I would take a moment to share some of the highlights from this year in the form of pictures and anecdotes.
As much as I would love to remember this year as a year of setbacks and bottlenecks, in retrospect, it has been packed with some very surprising yet noteworthy moments.
On a personal level, 2020 has been a year of many firsts, much as has been the case for the society in general where we have together achieved incredible things as a community despite challenging circumstances.
As tough as this year has been, it goes without saying that 2020 is a particularly good year to be thankful for science, which happens to be one of the many things that are often taken for granted. While similar thankfulness posts have appeared elsewhere, I want to share a different take as a computational scientist.
As the title suggests, I am shamelessly borrowing idea from the so-called most influential series that are widespread in the popular culture.
This post is motivated by the growing list of awesome public repositories that curate a list of resources dedicated to a specific topic (such as this, this, and this, among others).
As a graduate student, I often struggled to find such a centralized resource, especially when it comes to student paper awards and travel grants, geared towards data science graduate students and postdocs (broadly defined). On the surface, these awards help you save a few hundred dollars of registration and other out-of-pocket travel expenses but they are also worth the effort to go the extra mile in getting a nice gold star for a CV.
As scientists, we rarely celebrate milestones be it a certain number of citations we have received or a certain number of impactful papers/patents we have co-authored. This is in sharp contrast to other professions where it is customary to celebrate even the tiniest bit of success in numbers. Be it how many runs a Cricketer has scored (yes I am an ardent Cricket fan!) or the number of box office successes a filmmaker has delivered.
This 15th August marked 7 years since I submitted my first paper (first-authored research article). Coincidentally, it also completed five calendar years last December, which gave me a chance to measure it’s 5-year impact (erring on the conservative side of course).
I am very happy to see that it has been well-received as shown in the figure above (the R code to generate the plot is publicly available). To interpret it more accurately in plain English, it has performed significantly better than an average paper in the same journal or same discipline (Statistics).
Following my previous post (Part I), I received a few requests from my fellow mathematical and physical scientist colleagues who prepare their CVs and resumes in the popular typesetting system LaTeX. In this post, I will go over the steps required to automatically import Google Scholar citation metrics in a LaTeX document without any hassle or compromise.
Motivation Similar to Part I, here are just a few things I would like to maintain:
The inspiration behind this post comes from my non-computational scientist colleagues who simply wanted to import Google Scholar citation metrics in their CVs and resumes with as little overhead as possible. After a few quick searches, several solutions popped up that used either Markdown or LaTeX. While I appreciate these more sophisticated maneuvers, they did not serve any useful purpose for my specific motivation as described below.
Motivation There are just a few things my scientist colleagues wanted to achieve: