Trip Database Blog

Trip has now integrated LibKey in to Trip. LibKey is an amazing tool and it makes Trip even better at supporting our users to obtain full-text articles.

One of the traditional methods of obtaining full-text has been to use something called a link resolver. This aims to link a user with their institutions full-text holdings. For instance if a University subscribes to a journal, the idea is that we send the user to the full-text journal article instead of the PubMed abstract. This is great, to a point. The downside is that it’s dumb! By that it doesn’t know if the institution actually subscribes to the journal – we simply insert the link out (using the link resolver) for every journal article! So, if the University doesn’t subscribe you get a disappointed user.

LibKey, on the other hand, is smart! It knows which institutions have which subscriptions and over which time-period. So, now no more misleading linkouts.

For this to work we need to link your institutions LibKey details with Trip and away we go. Fingers crossed your institution uses LibKey!

Our original SmartSearch worked very well. A user selected a couple of documents and we’d suggested closely related articles, based on clickstream data. This worked really well, when we had clickstream data, often we didn’t.  So, that was a problem.

Our new system will work on a similar principle but will include citation data and semantic similarity data – which articles cite which other articles and which articles are semantically similar to other articles? This will allow for a very rich network of connected articles; in turn allowing users to easily find other articles of interest. Think about this, instead of scrolling through loads of articles the system should overcome much of that. Find 1 or 2 articles that interest you – our system finds the rest.

Testing has been really positive.

Initially, this will be just for Covid-19 data but when the new site is released (?September) we’ll look to incorporate it in to the main Trip.

Much of this work was inspired by speeding up evidence reviews. An important second component of this work is to allow rapid quantitative synthesis. This is being planned and we’re currently exploring funding opportunities to get this off the ground. The intention with this is to create SR quality reviews all done within a handful of hours. Longer than our fully-automated system but much nuanced and could be an important step to full automation.