In previous articles I've looked at how various apps display scientific articles. The apps I looked at were:So, where next? As Ian Mulvany noted in a comment on an earlier post, I haven't attempted to summarise the best user interface metaphors for navigation. Rather than try and do that in the abstract, I'd like to create some prototypes to play with various ideas. The Sencha Touch framework looks a good place to start. It's web-based, so things can be prototyped rapidly (I'm not going to learn Objective C anytime soon). There's a moderately steep learning curve, unless you've written a lot of Javascript (I've dond some, but not a lot), but it seems to offer a lot of functionality. Another advantage of developing a web app is that it keeps the focus on making the content accessible across devices, and using the web as the means to display and interact with content.
Then there is also the issue (in addition to displaying an individual article) of how to browse and find articles to view. Here are some possibilities.
Publisher's stream
Apps such as the Nature app and the PLos Reader provide you with a stream of articles from a single publisher. This is obviously a bit limiting for the reader, but might have some advantages if the publisher has specifically enhanced their content for devices such as the iPad.
Personal library
Apps such as Mendeley and Papers provide articles from your personal library. These are papers you care about, and one you may make active use of.
Social
Social readers such as Flipboard show the power of bringing together in one place content derived from social streams, such as Twitter and Facebook, as well as curated sources and publisher streams. Mendeley and other social bookmarking services (e.g., CiteULike, Connotea) could be used to provide social similar streams of papers for an article viewer. Here the goal is probably to find out what papers people you know find interesting.
Spatial
In an earlier post I used a map to explore papers in my BioStor archive. This would be an obvious thing to add to an iPad app, especially as the iPad knows where you are. Hence, you could imagine browsing papers about areas that are near you, or perhaps by authors near you. This would be useful if, say, you wanted to know about ecological or health studies of the area you live in. If the geographic search was for people rather than papers, you could easily discovering what kind of research is published by universities or other research bodies that are near your current location.
Of course, Earth is not the only thing we can explore spatially. Google maps can display other bodies in the solar system, (e.g., Mars), as well as the night sky. Imagine being interested in astronomy and being able to browse papers about specific planetary or stellar objects. Likewise, genomes can be browsed using Google maps-inspired browsers (e.g., jBrowse), so we could have an app where you could easily retrieve articles about a particular gene or other region of a genome.
Categories
Another way to browse content is by topic. Classifying knowledge into categories is somewhat fraught, but there are some obvious wasy this could be useful. A biologist might want to navigate content by taxonomic group, particularly if they want to browse through the 1000's of articles published in a journal such as Zootaxa (hence my experiments on browsing EOL). Of course, a tree is not the only way to navigate hierarchical content. Treemaps are another example, and I've played with various versions in the past (see here and here).
I have a love-hate relationship with treemaps, but some of the most interesting work I've seen on treemaps has been motivated by displaying information on small screens, e.g. "Using treemaps to visualize threaded discussion forums on PDAs" (doi:10.1145/1056808.1056915).
Summary
These notes list some of the more obvious ways to browse a collection of articles. It would be fun to explore these (and other approaches) in parallel with thinking about how to display the actual articles. These two issues are related, in the sense that the more metadata we can extract from the articles (such as keywords, taxonomic names and other named entities, geographic localities, etc.) the richer the possibilities for finding our way through those articles.