I jumped ship from Twitter following the Musk takeover in 2022. Seeking an alternative, I joined Mastodon and didn’t look back. Since then, I’ve enjoyed many wonderful interactions and conversations; and I feel a bit sad at what Twitter turned into.

Bands have been known to declare “No Synths!” on their albums. This statement was a badge of pride indicating that the artists hadn’t used any modern trickery in their recordings. Today, the growing use of artificial intelligence (AI) and large language models (LLMs) in science has created a similar scenario. Advocates argue that these tools improve every aspect of science, including the publication process.

Hopefully I will soon break out of this funk of posting about either Mastodon or Twitter. But not yet! This post is to say that: I made a static archive of tweets for @quantixed and for @clathrin. There, you can read all my posts, which ended in 2022. Edit: removed the archives in October 2024. How did I do it and why? I made the archive with this excellent tool written by Darius Kazemi.

Another post looking at Twitter data in R. It follows this one and this one. I wanted to look again at my tweeting frequency over the 12 years on Twitter, but this time do it in a calendar view. Something like a GitHub commit calendar would be perfect. I have used a library for this in the past.

Please consider this a “supplementary analysis” to my previous post looking at the frequency of tweets from my personal account over the last 12 years. I was curious about what times I was active on Twitter (measured by when I tweeted). Others might be interested in a solution to look at this in R. The code As in the previous post, we need to get the data into R and then make sure we have a date object to work with.

At the time of writing, I have essentially left Twitter. It was a fun ride and without going into what’s happening there now, this is a good opportunity to look at my 12 years on the platform. Early in November, I downloaded my data and locked my Twitter account. This gave me all the data I needed. Using R, a few nifty libraries and the tweets.js file that was part of the download, I could gain quite a lot of insight.

On Tuesday I was able to sit in on a conversation that is regularly held within the Computer Science department at University of Toronto that focuses broadly on what can computer science bring as a discipline and a skill set to the sciences more generally. The conversation is lead by Steve Easterbrook so there is a focus on climate science but we also roamed much more widely than that.

I am probably supposed to be writing up some weighty blog post on some issue of importance but this is much more fun. Last year’s International Conference on Intelligent Systems for Molecular Biology (ISMB) kicked off one of the first major live blogging exercises in a mainstream biology conference. It was so successful that the main instigators were invited to write up the exercise and the conference in a paper in PLoS Comp Biol.

So there’s been a lot of antagonistic and cynical commentary about Web2.0 tools particularly focused on Twitter, but also encompassing Friendfeed and the whole range of tools that are of interest to me. Some of this is ill informed and some of it more thoughtful but the overall tenor of the comments is that “this is all about chattering up the back, not paying attention, and making a disruption” or at the very least that it is all trivial

Coming from me that may sound a strange title, but while I am very positive about the potential for online tools to improve the way we communicate science, I sometimes despair about the irritating little barriers that constantly prevent us from starting to achieve what we might. Today I had a good example of that. Currently I am in Sydney, a city where many old, and some not so old friends live.