RNS SEO: How 52 research networking sites perform on Google, and what that tells us

Research networking systems (RNS) like Vivo, Profiles, SciVal, and Pure are meant to be used — but often fail to be discoverable by real users because of poor search engine optimization (SEO).

That’s why we’re releasing RNS SEO 2015, the first-ever report describing how RNS performs in terms of real-world discoverability on Google.

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SEO for Research Networking: How to boost Profiles/VIVO traffic by an order of magnitude

"Redwoods" by Michael Balint (cc-by)

The UCSF Profiles team has increased site usage by over an order of magnitude since the site’s big campus-wide launch in 2010. This “growth hacking” cheat sheet distills the key lessons learned during that period, and can be applied to almost any research networking platform, including VIVO, Profiles, and home-grown solutions. Continue reading

Healthy Communities Data Summit

The Healthy Communities Data Summit held at UC San Francisco’s Mission Bay campus, organized by Health 2.0, the Foundation for Healthcare Innovation and sponsored by the California HealthCare Foundation, attracted a mix of civic leaders, medical/health professionals, academics, hackers and communicators – all eager to gain a better understanding and share the innovative uses of open health data.

Key topics from the event included cooperation and trust building between government, community and enterprise players, along with high impact applications by making health data accessible to the public (See tweets below).

As a communicator, I took a particular interest in the “A Better Pie Chart & Beyond: The Evolution of Visualization & Analysis” panel. Wess Grubbs, founder of Pitch Interactive, emphasized the human element behind all this data, and reminded users to prioritize the narrative when communicating health data. Although data is lifted from its silos and becomes easily accessible through visualizations, it should tell a complete story – not just grab for views, or “instant gratification.”

Here’s an event summary from the California HealthCare Foundation’s California Healthline.

Using Research Networking Effectively in Academia: UCSF-CTSI Team Presents On National AMIA Panel

Three of us from the Virtual Home team at CTSI went to this year’s AMIA (American Medical Informatics Assoc) meeting in DC and presented on a panel with Griffin Weber of Harvard University. The panel was called “Four Steps to Using Research Networking Effectively at Your Institution”

Griffin spoke on cutting edge features of research networking tools, such as linked open data and social network analysis.

Eric Meeks of UCSF spoke on standard APIs, such as OpenSocial, to leverage a community of developers, I spoke about incentivize usage and understand your audience, and to round it out, Brian Turner spoke about using data, tools and strangers to improve user interfaces.

The panel presentation was a 90 minute break out session and we were happy to have a good turnout and an engaged audience. I think that the work that UCSF has put into the ‘social engineering’ of the tool has really paid off. Our usage and engagement numbers are on the rise and comparatively speaking, Griffin mentioned that our traffic is about 5-times that of what Harvard Profiles is currently getting.

In addition, Eric also had a poster session at the meeting!

The UCSF presentations will be up on Slideshare, available on the CTSI channel and via our individual UCSF profiles:

http://profiles.ucsf.edu/ProfileDetails.aspx?From=SE&Person=5333232
http://profiles.ucsf.edu/ProfileDetails.aspx?From=SE&Person=4621800
http://profiles.ucsf.edu/ProfileDetails.aspx?From=SE&Person=5333232

Real-Time Stats from Google Analytics: Could we integrate the data with our UCSF Profiles activity stream and future dashboards?

I’m wondering what our tech team thinks about that…  

The “New Version” link is in the top right of Google Analytics. Real-Time reports are in the Dashboards tab (though they will move to the Home tab in the updated interface next week) .

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How to analyze internal site search stats


Web analytics guru Avinash Kaushik outlines a five-step process to understand data about internal search engine usage on A List Apart.

Why is this important?

“Now when people show up at a website, many of them ignore our lovingly crafted navigational elements and jump to the site search box.…All the search and clickstream data you have (from Google Analytics, Omniture, WebTrends, etc.) is missing one key ingredient: Customer intent. You have all the clicks, the pages people viewed, and where they bailed, but not why people came to the site, except where your referral logs contain information from search engines. For example, you can look at the “top ten pages viewed” report in your web analytics tool and know what people saw, but how do you know what they wanted to see? Your internal site-search data contains that missing ingredient: intent. Internal search queries contain, in your customers’ own words, what they want and why they’re there. Once you understand intent, you can easily figure out whether your website has the content your users need, and, if it does, where they can actually find it.”

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Mining internal search engine data

We do some limited of search terms on CTSI web properties, but this is a big gap, per user experience author Lou Rosenfeld in his new book Search Analytics for Your Site. Rosenfeld’s the author of the seminal Information Architecture for the World Wide Web, so when he speaks, I tend to pay attention. An interview in O’Reilly Radar digs into the details of what analyzing search data in internal search engines and systems:

“[Site search isn’t] necessarily overlooked by users, but definitely by site owners who assume it’s a simple application that gets set up and left alone. But the search engine is only one piece of a much larger puzzle that includes the design of the search interface and the results themselves, as well as content and tagging. So search requires ongoing testing and tuning to ensure that it will actually work.

“[Site search analytics Does SSA reveal user intent better than other forms of analytics?
I think so, as the data is far more semantically rich. While you might learn something about users’ information needs by analyzing their navigational paths, you’d be guessing far less if you studied what they’d actually searched for. Again, site search data is the best example of users telling us what they want in their own words. Site search analytics is a great tool for closing this feedback loop. Without it, the dialog between our users and ourselves — via our sites — is broken.”

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