I wrote a piece for Genome Biology, you may have read it, about open science.  I said a lot of things in there, but one thing I want to focus on is how journals could “add value”.  As brief background: I think if you’re going to make money from academic publishing (and I have no problem if that’s what you want to do), then I think you should “add value”.  Open science and open access is coming: open access journals are increasingly popular (and cheap!), preprint servers are more popular, green and gold open access policies are being implemented etc etc. Essentially, people are going to stop paying to access research articles pretty soon – think 5-10 year time frame.

So what can journals do to “add value”?  What can they do that will make us want to pay to access them?  Here are a few ideas, most of which focus on going beyond the PDF:

  1. Live figures: This is something F1000Research are already doing, and there’s no reason other journals couldn’t do the same.  Wherever there is a figure, let readers interact with it – change the colours, axes, chart type etc etc
  2. Re-run analyses in a browser: I think this is something Gigascience have attempted, and would be an incredible training opportunity.  Let readers repeat the entire data analysis, in a browser, using e.g. Galaxy or iPython Notebook
  3. Re-run analyses from the command-line: as above, but provide a VM and a list of commands that readers can run to repeat the analysis
  4. Re-run analyses as an R package: imagine it – every paper has a downloadable R package, with the data included, that allows readers to explore the data within the wonderful statistical and visualisation environment that is R.
  5. Add/remove data – “what happens to graphs, tables and statistics if I remove these data points?”
  6. Show discarded data – so the data had outliers that were cleaned/removed?  Show them to me.  Where are they in the graphs?  Where are they in the tables?  Why were they discarded?
  7. What would my data look like? – basically, if the reader has a dataset which is similar to that analysed in the paper, the reader can upload that dataset (in agreed format) and see how the graphs, tables and stats change
  8. Enforce conventions – when you’re parsing that word document and converting to PDF, pick up gene names and automatically suggest community standards (e.g. HGNC)
  9. Enforce data submission (or do it for the authors).  Yes, do not publish unless the data are submitted to a public archive.  In fact, help the authors do it!
  10. Find similar data in…. – help readers find similar (public) datasets in public databases
  11. Actually check the statistics.  Yes you, the journal.  Most biologists aren’t qualified to check the statistics and experimental design, or do power analysis, so you do it.  Employ some statisticians!

OK, so I’m not pretending any of the above is easy, but I unsure why none of the above is happening – some publishers make HUGE PROFITS, why on earth have they not updated their product?  Imagine if Apple were still trying to sell version 1 of the iPod – no-one would buy it.  Most products get updated on a frequent basis to keep customers happy, yet journals have stuck with the PDF/Print version for decades.  Time for an update, especially if you want to keep the money flowing in.