Presentations are a great opportunity to explain your work to a new audience and receive valuable feedback. A vital aspect of a presentation is keeping the audience’s attention which is generally quite tricky I have found (from experience).
One thing that I have noticed other presenters using, which has helped maintain my focus, is an indication of the progression of the presentation. Including in your slides information that there are only a few slides remaining, encourages the listeners to keep their focus for a little longer.
Instead I will show you how to do it using Apps Script, Google’s cloud platform that allows you to write JavaScript code which can work with its online products such as Docs or Slides.
Disclaimer: I used ChatGPT to improve the writing style of this article, in combination with some personal curation before obtaining a final version.
You’ve probably heard it all already, from ChatGPT writing code and doing proofreading for you to a rap battle between OPIG’s Antibodies and Small Molecules groups, and more.
Whether you like it or not, ChaGPT has unleashed people’s creative side regarding applications and attempts to find shortcuts. Questionable? Absolutely!
In this BLOPIG post, I show how I used ChatGPT to easily write a post summarising some material of my own intellectual property, which I presented as part of my group meeting talk. Mainly, I list some personal thoughts on the ethical concerns around using ChatGPT to assist your writing.
To start off, I passed on content from my own publication draft to ChatGPT, asking to generate a blog post in plain English for BLOPIG. The outcome:
Not bad.
But, it made me realise a number of things:
With great power comes great responsibility [Uncle Ben – Spiderman]. You are responsible for the ethics that go into using ChatGPT. Are you faking expertise? Are you being actually lazy or just being efficient? Think twice (or many more times) if you’re doing the right thing.
It can significantly reduce the number of writing iterations but don’t take it at face value. Can you actually trust the plain output? No. Never take its output as the ground truth, as Large Language Models such as ChatGPT often produce biased writing outputs. Keep in mind that whatever you produce as a scientist will be picked up by others, and prone to drive misinformation, if incorrect. It is OK to reduce mechanical iterations, but it’s NOT OK to skip quality control.
Be open about it. You don’t want to set the wrong example for your colleagues. So, mention if you use it, how you used it, and it is fine to encourage efficiency, but not incentivising a culture of scientific misconduct and plagiarism. Don’t skip the step of producing quality ideas on your own. This is such a concern that publishers like Elsevier have already reacted by publishing guidelines contemplating this possibility. While Nature Springer is working on ways to spot AI-generated outputs.
The bottom line
What are the dos and don’ts of using ChatGPT?
Yes, use it to have fun. Yes, use it to proofread or polish your writing. Yes, use it to summarise your own ideas. No, don’t use it to do the analysis and interpretation of your results. No, don’t copy and paste its direct output into your publication. No, don’t hide that you used it. Finally, NO, you can’t add ChatGPT as a contributing author!
Language models have token the world by storm recently and, given the already explored analogies between protein primary sequence and text, there’s been a lot of interest in applying these models to protein sequences. Interest is not only coming from academia and the pharmaceutical industry, but also some very unlikely suspects such as ByteDance – yes the same ByteDance of TikTok fame. So if you also fancy trying your hand at building a protein language model then read on, it’s surprisingly easy.
Training your own protein language model from scratch is made remarkably easy by the HuggingFace Transformers library, which allows you to specify a model architecture, tokenise your training data, and train a model in only a few lines of code. Under the hood, the Transformers library uses PyTorch (or optionally Tensorflow) models, allowing you to dig deeper into customising training or model architecture, or simply leave it to the highly abstracted Transformers library to handle it all for you.
For this article, I’ll assume you already understand how language models work, and are now looking to implement one yourself, trained from scratch.
It’s not always easy to live in an Anglophone scientific world when English isn’t your first language. When careers are built upon the ability to communicate ideas clearly and eloquently, struggling to find the right words can be a real hindrance to explain your science in a way that is taken seriously. Contrary to popular belief, it’s not something you can simply “work” on. Often, it doesn’t matter how many books you’ve read, how many years of education you have, or how articulate you are in your original language — your brain will refuse to summon the right expression, or get stuck in a construction that a native speaker would never use. Struggling with a second language is very much a biological phenomenon.
The standard recommendation for ESL (English as a Second Language) speakers has long been to ask a native colleague to read through any text that needs to be published or submitted somewhere (such as an article or a grant application). Well-intentioned as this advice may be, there are multiple problems with it. Lingua franca or not, only 15% of the world population speaks English, of which only 5% are native speakers — meaning that for most scientists not working in Anglophone countries, the option is rarely available. Even when available, it is unreasonable to expect these colleagues to add charitable proof-reading to their workload simply because they happened to be born speaking a different language. But, most importantly, I have always felt — and I want to emphasize that I truly believe most people who issue this kind of advice to be well-intentioned — that the underliying message sounds too much like “you need vetting by a member of our select linguistic club if you want your ideas to be taken seriously“.
Inspired by this blog post by the lovely Kate, I’ve been doing some BRICS decomposing of molecules myself. Like the structure-based goblin that I am, though, I’ve been applying it to 3D structures of molecules, rather than using the smiles approach she detailed. I thought it may be helpful to share the code snippets I’ve been using for this: unsurprisingly, it can also be done with RDKit!
I’ll use the same example as in the original blog post, propranolol.
Environment modules is a great tool for high-performance computing as it is a modular system to quickly and painlessly enable preset configurations of environment variables, for example a user may be provided with modulefile for an antiquated version of a tool and a bleeding-edge alpha version of that same tool and they can easily load whichever they wish. In many clusters the modules are created with a tool called EasyBuild, which delivered an out-of-the-box installation. This works for things like a single binary, but for conda this severely falls short as there are many many configuration changes needed.
Alternative Title: The tragic story of how I got trapped making slides with latex.
Typically after giving a presentation at least one person will approach me and ask if they could have access to my custom latex template to make slides with beamer that don’t look rubbish.
Personal websites are a great and increasingly important way to build your online presence. Along with professional social media pages, such as on LinkedIn and Twitter, a website can provide a boost to your career and/or job search.
This blog post is based on my recent experience creating a personal website, following guidelines from Lewis’ talk at the OPIG Retreat last year (thank you Lewis!). The method I used and will cover here, based on an HTML5 UP! template and GitHub pages, is free and fast.
Why have a personal website?
Improves your online presence and brand
Boost for your career, including by allowing potential future employers to find you
Share things you have accomplished or are interested in
Have you ever had an annoying dataset that looks something like this?
or even worse, just several of them
In this blog post, I will introduce basic techniques you can use and implement with Python to identify and clean outliers. The objective will be to get something more eye-pleasing (and mostly less troublesome for further data analysis) like this
A good note system should act like a second brain:
Accessible in seconds
Adding information should be frictionless
Searching should be exhaustive – if it’s there, you must find it
The benefits of such a note system are immense – never forget anything again! Search, perform the magic ritual of Copy Paste, and rejoice in the wisdom of your tried and tested past.
But how? Through the unreasonable effectiveness of interactive fuzzy search. This is how I have used Fuz, a terminal-based file fuzzy finder, for about 4 years.
Briefly, Fuz extracts all text within a directory using ripgrep, enables interactive fuzzy search with FZF, and returns you the selected item. As you type, the search results get narrowed down to a few matches. Files are opened at the exact line you found. And it’s FAST – 100,000 lines in half a second fast.
Using Fuz to quickly add a code-snippet in our note directory – then retrieving it with fuzzy-search. Here, on how to read FASTA files with Biopython, conveniently added to a file called biopython.py.Continue reading →
