[Page said] “Science has a real marketing problem. If all the growth in world is due to
science and technology and no one pays attention to you, then you have a serious
marketing problem.”

To that end,…[he] said that scientists should get in the habit of investing
part of their scientific grant money to marketing budgets, in order to get the
word out to the media about their research….

Finally, he called on the scientists to make more of their research available
digitally. Even though Google Scholar tries to open access to scientific work,
it still falls short.

“Most of the works you guys have done are not represented in those searches.
We have to unlock the wealth of scientific knowledge and get it to everyone. I
don’t care what we do, but we need to do something,” he
said….

It is great to see awareness at the top level within Google of the information barriers faced by scientists, and the shortcomings of the current channels through which scientific results are communicated. It’s not simply that what is published is typically hidden behind subscription barriers – it’s also that many useful results are not published at all, and even those results that are published are generally not available in a form that will facilitate future reuse.

Google can make a difference on this issue. Below is a list of  three practical things that Google could do to improve the communication of scientific research:

1. Highlight universally accessible articles on Google Scholar
   Users of Google Scholar keep asking for this, but right now there is no way to tell which articles on Google Scholar are available to all. PubMed, for example, handles this well, showing a green icon for articles which have universally accessible full text, and a gold icon if that article is the publisher has made the article available for redistribution (via PubMed Central). Google’s main web search index does at least offer the ability to filter searches to Creative Commons licensed content only (example), all of which is free and reusable, but so far Google Scholar lacks this functionality. By highlighting universally accessible research, Google can both help readers to find accessible research, while providing additional motivation for scientists to share their work openly.
2. Generate alternative citation metrics for the scientific literature
   Journal citation metrics from a single company currently dominate the research evaluation process. This is unhealthy in all sorts of ways. When (and whether) a journal gets tracked by Thomson Scientific is something of a lottery, and even once a journal is tracked, it typically takes 3 more years before an official impact factor is made available. Google Scholar could generate citation metrics more rapidly and more comprehensively than this, and in doing so would help level the playing field between established journals and innovative new journals which, while highly cited, do not yet have impact factors..
3. Build search tools that take advantage of the semantic web
   Many scientists are well aware of the need to move beyond simply churning out the equivalent of printed articles in online form. Seringhaus and Gerstein’s recent article in BMC Bioinformatics identifies interesting possibilities, and computational linguist Mark Liberman outlined related ideas in a blog post. BioMed Central is engaged in various projects, such as Neurocommons, and the Journal of Medical Case Reports, which aim to use semantic web technologies to make scientific knowledge available in a form that is rich with computer-readable semantic structure. But there is a problem – unless good tools are available to work with this structured data,  researchers lack the motivation to publish results in semantically-enriched form. By providing retrieval tools that take advantage of structured semantic data where it exists in standard form, Google could encourage such developments. Google Base is an interesting step in the right direction, towards making inherently structured information retrievable. How about a project to extend Google Base to scientific datasets?