Insights into proteomics in 2024 from blog traffic numbers!

The dashboard of this blog gives me interesting insights at times. I get easy metrics for what posts people (completely anonymous) engage with. And where (geographically) traffic is coming from as well as some insights into what links people are coming from, to some degree. Like when Twitter had a lot of functional features I could tell that people were coming from Twitter links that were posted. None of that works since...well....you know....

While I've got 2 hours of sleep because one of those adorable little germ factories gave my adorable little germ factory the worst virus of his entire life, I thought I'd summarize some of what I see when I log in because I don't have enough sleep to remember what I was actually planning to do. 

Insight #1: If I want to decrease traffic, write about single cell proteomics (SCP)

While the outside world really seems to care about SCP, while still being largely confused about what it is, who is doing it, and why the scSeq core can't help them with it, my small reader base is not interested in today's newest advance. Since I have been personally interested, this has directly hurt blog viewership numbers. For real, we're talking crushing readership numbers. The only thing worse is to write about Metabolomics. 

Insight #2: If I want to attention post a tutorial or talk about a "next gen" proteomics technology

Here are a couple examples of posts with recent traffic metrics. These are between 4 and 10x more traffic than any recent SCP post. Highlighting SomaScan's ....trepidation....for actual quantitative comparisons and how MS1 libraries work. 

Same here, recent post where that group forced one protein through a NanoPore and just a simple tutorial (thanks to Ed Emmott's lab making an XML) for how to process data that was not -at the time -natively compatible with MaxQuant (I'm sure it is now). Big engagement numbers relative to other posts.

Insight #3: - The big one - People really need very basic help with proteomics. Check out the recent traffic to some of these permanent pages!

The "terminology translator" has always been a major draw to the blog. "What is this weird acronym the guy in the basement used that I don't know?" But "what is proteomics" still gets a lot of hits even though I made a long drawn out analogy about a car I was obsessed with when I was a kid. And both "resources for newbies" and "now I have a protein list" get slammed with new traffic, which makes me realize how important it is that I update these once in a while. I'm sure I'll physically cringe when I see how old some of the links in those posts/pages are. 

We see this exact same thing with r/proteomics, though most of the stuff appears to go to r/massspectrometry or r/bioinformatics first. A lot of the traffic is "how do I process these data in DIA-NN". "What does this q-value thing mean?" 

This is all good stuff, I think. It means that new people are engaging with proteomics technology. And -- since you still can't get a degree (though many European schools have formal coursework in mass spectrometry and proteomics) people need help with these things. The bad news is that they might just be Googling it and ending up at a site maintained by someone who has less time to put into it every year. 

If you are generating high quality freely/openly available content to help people new to proteomics please reach out so I can direct people your way!