Supplemental madness: on the hunt for 'Figure S1'

I've just been looking at this new paper by Vanesste et al.  in Genome Research:

Analysis of 41 plant genomes supports a wave of successful genome duplications in association with the Cretaceous–Paleogene boundary

I was curious as to where their 41 plant genomes came from, so I jumped to the Methods section to see: 

No surprise there, this is exactly the sort of thing you expect to find in the supplementary material of a paper. So I followed the link to the supplementary material only to see this:

So the 'Supplemental Material' contains 'Supplemental Information' and the — recursively named — 'Supplemental Material'. So where do you think Supplemental Table S1 is? Well it turns out that this table is in the Supplemental Material PDF. But when looking at both of these files, I noticed something odd. Here is Figure S1 from the Supplemental Information:

And here is part of another Figure S1 from the Supplemental Material file:

You will notice that the former figure S1 (in the Supplemental Information) is actually called a Supporting Figure. I guess this helps distinguish it from the completely-different-and-in-no-way-to-be-confused Supplementary Figure S1.

This would possibly make some sort of sense if the main body of the paper distinguished between the two different types of Figure S1. Except the paper mentions 'Supplemental Figure S1' twice (not even 'Supplementary Figure S1) and doesn't mention Supporting Figure S1 at all (or any supporting figures for that matter)!

What does all of this mean? It means that Supplementary Material is a bit like the glove compartment in your car: a great place to stick all sorts of stuff that will possibly never be seen again. Maybe we need better reviewer guidelines to stop this sort of confusion happening? 

 

The Assemblathon Gives Back (a bit like The Empire Strikes Back, but with fewer lightsabers)

So we won an award for Open Data. Aside from a nice-looking slab of glass that is weighty enough to hold down all of the papers that someone with a low K-index has published, the award also comes with a cash prize.

Naturally, my first instinct was to find the nearest sculptor and request that they chisel a 20 foot recreation of my brain out of Swedish green marble. However, this prize has been — somewhat annoyingly — awarded to all of the Assemblathon 2 co-authors.

While we could split the cash prize 92 ways, this would probably only leave us with enough money to buy a packet of pork scratchings each (which is not such a bad thing if you are fan of salty, fatty, porcine goodness).

Instead we decided — and by 'we', I'm really talking about 'me' — to give that money back to the community. Not literally of course…though the idea of throwing a wad of cash into the air at an ISMB meeting is appealing.

Rather, we have worked with the fine folks at BioMed Central (that's BMC to those of us in the know), to pay for two waivers that will cover the cost of Article Processing Charges (that's APCs to those of us in the know). We decided that these will be awarded to papers in a few select categories relating to 'omics' assembly, Assemblathon-like contests, and things to do with 'Open Data' (sadly, papers that relate to 'pork scratchings' are not eligible).

We are calling this event the Assemblathon 'Publish For Free' Contest (that's APFFC to those of us in the know), and you can read all of the boring details and contest rules on the Assemblathon website.

The Tesla index: a measure of social isolation for scientists

Abstract

In the era of social media there are now many different ways that a scientist can build their public profile; the publication of high-quality scientific papers being just one. While publishing journal and book articles is a valuable tool for the dissemination of knowledge, there is a danger that scientists become isolated, and remain disconnected from reality, sitting alone in their ivory towers. Such reclusiveness has been long been all too common among academic scientists and we are losing sight of other key outreach efforts such as the use of social media as a tool for communicating science. To help quantify this problem of social isolation, I propose the ‘Tesla Index’, a measure of the discrepancy between the somewhat stuffy, outdated practice of generating peer-reviewed publications and the growing trend of vibrant, dynamic engagement with other scientists and the general public through use of social media.

Introduction

There are many scientists who actively take the time to pursue their science in as much of a public manner as possible. They work hard to ensure that their peers, and the public at large, are kept informed of their latest research. Consider Titus Brown, a genomics and evolution professor at Michigan State University[1]. Although he has contributed to a meagre number of — largely uninteresting — publications[2], he has instead embraced social media[3] to excite and stimulate others with news of his past, current, and future work.

Now consider Nikola Tesla[4]; although he may have forever changed the world through his many scientific inventions[5], he was a famous recluse[6] and surprisingly did not contribute to any blog, nor did he even bother to set up an account on twitter. I am concerned that the anti-social and secretive behavior of Nikola Tesla is something that is all too common in many other scientists, particularly in those who continue their obsession with publishing work that will forever live behind pay-walls, invisible to all but the priviledged few.

I therefore think it’s time that we develop a metric that will clearly indicate if a scientist is a reclusive introvert with no interest in sharing their work with others or engaging with the wider community. This will allow others to adjust our expectations of them accordingly. In order to quantify the problem and to devise a solution, I have compared the numbers of followers that research scientists have on twitter with the number of citations they have for their peer-reviewed work. This analysis has identified clear outliers, or ‘Teslas’, within the scientific community. I propose a new metric, which I call the ‘Tesla Index’, which allows a simple quantification as to the degree of social isolation of any particular scientist.

Results and Discussion

I took the number of Twitter followers as a measure of ‘social outreach and engagement’ while the number of citations was taken as a measure of ‘boring scientific output’. The data gathered are shown in Figure 1.

Figure 1: Twitter followers versus number of scientific citations for a sort-of-random sample of researcher tweeters

I propose that the Tesla Index (T-index) can be calculated as simply the number of Twitter followers a user has, divided by their total number of citations. A low T-index is a warning to the community that researcher 'X' may be forsaking all methods of publicly sharing their work at the expense of soley publishing manuscripts. In contrast, a very high T-index suggests that a scientist is being active in the community, informing and educating their peers, colleagues, and the wider public. They are thus playing a positive role in society. Here, I propose that those people whose T-index is lower than 0.5 can be considered ‘Science Teslas’; these individuals are highlighted in Figure 1.

References

  1. http://ged.msu.edu  ↩

  2. http://scholar.google.com/citations?user=O4rYanMAAAAJ&hl=en  ↩

  3. https://twitter.com/ctitusbrown  ↩

  4. http://en.wikipedia.org/wiki/Nikola_Tesla#Literary_works  ↩

  5. http://theoatmeal.com/comics/tesla  ↩

  6. http://www.viewzone.com/tesla.html  ↩

Acknowledgments

This research was inspired by a piece of completely unrelated work by Neil Hall. 

A CEGMA Virtual Machine (VM) is now available!

Last week I blogged about the ever growing popularity of CEGMA and also the problems of maintaining this difficult-to-install piece of software. In response to that post, people helpfully pointed out that you can more easily install/run CEGMA by using package managers such as Homebrew and/or even run CEGMA on a dedicated Amazon Machine Instance.

These responses led me to update the CEGMA FAQ to list all of the alternative methods of getting CEGMA to run (including running it as an iPlant application). I’m happy that I can today announce a new addition to this list: CEGMA is now available through virtualization:

Our CEGMA VM runs the Ubuntu operating system and is pre-configured to have everything installed that CEGMA needs. I’ve tested the VM using the free VirtualBox software and it seems to work just fine [1].

This also means that I will no longer be offering a service to run CEGMA on behalf of others. I had previously offered to run CEGMA for people who had trouble installing the software (or more commonly, the pieces of software that CEGMA requires). I’ve run CEGMA over 100 times for others and this has been a bit of a drain on my time to say the least. Hopefully, our CEGMA VM is a viable alternative. Many thanks are due to Richard Feltstykket at the UC Davis Genome Center’s Bioinformatics Core for setting this up.

  1. Words that will come back to haunt me I expect!  ↩