Richard Price

I just want to start off by saying what a pleasure it is to be here. I am glad that more of a spotlight is being shined on science. I think a lot of VC and engineering capital goes into photosharing apps, and I think there are huge opportunities, I'm not one of those guys who is down on photosharing apps, but I think there are enormous opportunities around science startups. I think now is the time to act. Why is right now an interesting transformation time?

When I think about what is going on in science right now and how to predict the future, the main lense that I view the future through is that I imagine that science has historically been for the desktop, saved as a PDF and upload to the web. Scientific content is going to be made for the web and it will inherit native web content. As that transformation plays out, that will be exciting.

The first characteristic is instant distribution. The importance of this is hard to over-state. There is abuot 12 months for submitting a paper and then seeing it coming it out. There's at least 12 months to see a response from the community. You don't see that time lag on web content on tweets or facebook updates or anything in general. I hope we can see this in science.

A second use trend is that historically in science there has been a single mode of publication problem- all content is shared as a paper. I think over time we will see different kinds of , increasing number of scientists sharing blog posts, status updates, I think the key thing to unleash is credit metrics. Ithink we're talking about that too.

So a lot of people this morning have bee talking about instant distribution. The natural question is filtering and discovery and discovering the wheat from the chaff. They think that science will fall apart. I think when you look at the web as a whole, as a content distribution system, it doesn't have a formal peer review process. You have discovery engiens like search engines asnd social entwroks. The search engines show the links and they show these as recommendations, then they apply algorithms to the structure of the web to develop rankings and to extracct the sentiment of the web for each web page.

With social networks it is much more explicit. What do my peers recommend? I think you see this in science. When you talk with scientists these days, and this has been for 5 or 6 years, scientists to use search engines. Nobody walks down to the library to read the papers- they use the browser. Search ranking is doing the best it can to do this. It should be on the first page.

The second thing is social networks. You go through the current system, you're looking at a paper and it's being recommended by two peer reviewers, well, what about all your peers or choose the peers for the judgements that you trust? I would name 20 philosophers that I would adore to know what they read on a daily basis. That would be my reading list basically. That doesn't exist yet, but we're working on it.

The main metrics historically has been, to get ahead in their career are things like where to publish, the impact factor, and the last ten years it's been citation counts, that sort of thing. I think the generally, that generally, as scientific content becomes web content, you will see metrics based on attraction to the content on the web. Some of these things will be follow counts, some of these will be stackoverflow scores, there will be certain niches where you will see recursive algorithms for like PageRank, right now we have just raw citation counts, we don't even take into account who cited the paper.. you will see that change. I think the opening up of metrics will be powerful, you will get credit for datasets where as right now you can't get credit for data sets or blog posts. I think that will chagne. is on a mission to accelerate the world's research. We have a few million users. The rough structure of the site and how it works. This is Steven Pinker's page. You can upload papers. You can have questions and answers. You can have blog posts. You can have document view counts. Here's a full text paper that he uploaded. This is the news feed. It's a follow model like twitter. You can follow Steven Pinker and get his personal news. You can see who you are following. Every paper that gets uploaded gets into my news feed. It gets into non-distribution, or something, or bioinformatis or something and it gets immediately sent out to 5000 people's news feeds and that's distribution.

A really cool part, and it goes into the metric revolution, people see a dashboard of how many profile views they got, how many doc views they got, and some facts about their visotrs, which countries they come from, it doesn't need to go very deep you can see the dates and what search keywords which ones come from Google, and people, on a site, really like the analytics, it's like crack. So here are a few quotes from users and their thoughts on the analytics and on their professional lives.

Tim Ritchie, a Lecturer in the Department of Psychology at the University of Limerick

You said this was for uploading and sharing research. In order to contribute my research, do I retain copyright, do I link to it where it was already published? How does it work? You don't give us copyright.. they think of as sort of like arxiv, they upload their papers, tehy upload working drafts, preprints, and increasingly many journals are uploading an author version, incorporation of peer reviewed comments, ... increasing journals are adding a published version with formatting, so when that happens they do that to..

If I was looking to browse, if I was just reading and following other publishers, I would have to have access to read thsoe journals? No, they upload the full text to our site.

I have a quick question about your incentivization of metrics. One of my super fun jobs is that I have to analyze my faculty's portfolios and get to do lots of scorings and go to committees and tell them that this person is worthy of attending.. which department? USC. My question is.. we have a whole bunch of fudge factors to make our guys look good. If you want new and better stats for academic community to be accepted, it's the unviersity level where they set what is the criteria for advancement, what are your thoughts for legitimizing your metrics among those old bodies?

It's a great question.. when it comes to new credit metrics.. it's a main catalysis for scientific transformation.. what we're seeing is that when we built our analytics dashboard we didn't know that people would take screensshots and use those in promotion packages. We used that for internal ego boosting, but we didn't know that people were including that in their grant applications. As we see that take off, increasingly we will see that these cocnepts will socialize.. how many page views do you have, and increasingly, maybe 10% of applicants this year will include metrics in their grant applications. It will start to reach a tipping point where 90% or 75% have metrics, and these 25% have no metrics and just rely on journal titles, I don't know, not enough data there. I think more data is good. The competition for funding is so unbelievably intense, even for jobs and products, academics are desperate to see any areas where they can get ahead. People are doing this more and more to see.. understand these concepts. It happened with citations, I think it's very much on the uptick, they probably won't fix... hiring committees and grant committees 10 years ago.. and I think it's a bottom up grass roots movement. I think that's how we're going to see.. these stats and.

We don't have an API yet and we are planning to build one. It's so obvious that Facebook and Twitter advantage from an API and we need more engineers. If there are scientists in the audience, shoot me an email at, so tap me on the shoulder.