The conceit around which I hung the presentation was the suggestion that the scientific paper is the modern equivalent of a palimpsest.
A palimpsest, as Wikipedia will tell you is a scroll or manuscript that has been written on, had its text scraped off, and then reused. The practice was common in Medieval times when the costs of publishing were very high and it was cheaper to destroy a copy of another work than manufacture new parchment.
A great deal of success has been made in extracting the original texts from these works. Probably the most famous example is the Archimedes Palimpsest (some nice photos here).
The underlying text is known as the scriptio inferior, and may actually be more valuable than the more visible content.
I likened the process of authoring a scientific paper to that of the creation of a palimpsest. Starting from original research results and working through the synthesis of a cogent explanation of the results or discovery, at each step the content becomes more abstracted from the original results, the previous work being “lost” to the reader.
Data is presented in pre-analysed forms and is not amenable to reuse. Like the palimpsest the raw data has not really been lost, its just not (easily) accessible to the reader.
If the scriptio inferior, the underlying data, were made available to the reader, then there a lot of interesting possibilities arise.
This idea isn’t new of course. Many scientists have been pushing for this for many years. However with the general trends towards open data sharing and “Web 2.0″, the time is perhaps ripe for the web development and scientific communities to engage with one another to try and make more of these ideas a reality.
In my presentation I tried to stick to a pragmatic and practical line and demonstrate the possibilities by referring to actual examples. I ended up pointing to three:
Firstly I demonstrated how I could re-plot the some results of a geological study using Google Maps. This served to highlight the interactivity in modern web applications, and illustrate how more compelling and dynamic interfaces can be made from existing data sets. The very trivial demo is available online. It wouldn’t be hard to envisage an application that amassed data from a range of related but independent studies in order to provide an alternative way of navigating through the corpus of documents.
iSpecies is a nice example of a science “mashup” that illustrates an alternative search interface for finding related content. I used the false results that can appear when performing simple keyword searches to reinforce the need for standard identifiers. (The need for a common, scoped identifier for authors, is a particular hobby horse of mine)
I also showed the excellent HubMed as an example of how both an alternative user interface can be better than the original, and also how content can be “enriched” by mixing in other sources. The “terms” feature which dynamically links keywords in an abstract through to a number of data sources, demonstrates this very well. I used the fact that material can be sourced from user contributed sources such as Wikipedia, to promote the idea that content needn’t be fixed at the point of publication but can be annotated after the fact.
The general theme of the forum was “reaching new markets” and I closed the presentation by suggesting that making more data open and the content more engaging might help promote the role of the amateur in science.
There seems to be a lot of current interest in exploring possibilities available in “eScience”, webbyscience and “Science 2.0“; although I dislike that last term! The recent Nature articles on these topics are also definitely worth reading.

So some thoughts on this:

• encouraging small world social graphs in a company is beneficial to the flow of information. This is “small world for spreading memes” in the microcosm. However it can also be detrimental as this similar social structure encourages spreading of other kinds of information: gossip and rumours. We might take from this that even if morale is low in a company, at least the lines of communication are still open
• that networking is important is not really news to anyone, but small world studies prove it’s effective, and support all those fluffy corporate events.
• that the optimum corporate structure isn’t hierarchical, neither is it completely decentralised, its somewhere in between. Networks that lie in the middle of these two should gain the most benefits (stability, but still good sharing of knowledge)
• that inter-team communication is as important as bonding in a team, and that the manager alone shouldn’t be the gateway between the team and the rest of the company. If the manage leaves, or is on leave, then you’ve lost the all important weak links and while the team may still stay cohesive they can become isolated.
• that as an individual, your role in a company can be secured by networking with others. However the detrimental side of this is that as you quickly become a “hub” (people come to you for information because you can find it quicker, or know who to refer them to) the many communication channels can distract you from your key role, and also lift you away from the work that you’re interested in. A company would do wise to acknowledge it’s hubs, but immediately route around them so that they have backups and that those weaks links don’t cripple the company during leave/job changes.
• it’s the small odd little tasks that people do, those that have them interact with a slightly wider social circle, that can be the most important: they build weak links between teams. It’s too easy to re-organize and draw lines, saying “this isn’t something that Team X should do”, but then you’re further isolating Team X.

Filippo Menczer at the University of Indiana is carrying out research in this area. His list of papers is online, and a quick surf through them makes interesting reading.
For example in Topical Web Crawlers: Evaluating Adaptive Algorithms (PDF) Menczer et al describe “topical crawlers” (emphasis mine):

Topical crawlers (also known as focused crawlers) respond to the particular information
needs expressed by topical queries or interest profiles. These could be the
needs of an individual user (query time or online crawlers) or those of a community
with shared interests (topical or vertical search engines and portals). Topical
crawlers support decentralizing the crawling process, which is a more scalable approach…An additional benefit is that such crawlers can be driven by a rich context (topics, queries, user profiles) within which to interpret pages and select the links to be visited.

In other words, the crawler can get away with indexing less pages as it’s guided to the most relevant material by other cues. The paper Search Engine-Crawler Symbiosis: Adapting to Community Interests describes how a community search engine can improve web crawler performance and vice versa through learning the communities interests.
I seems to me that FOAF could play a role here: rather than solely rely on machine learning to discover information about a document and community interests, one could explicitly gather than data from the aggregated FOAF descriptions of that community.
E.g. Using a FOAF description one can not only determine the interests of a person linking to a given document, but one can also determine the interests of the author of that document, assuming there are appropriate links from the HTML to the FOAF description (cf: autodiscovery, and I Made This). There’s even a grouping mechanism that can further help search agents to adapt their paths, using the same technique as Milgram’s test subjects. Again the algorithm seems natural: if you want to learn more about a particular topic or event, you’d start looking at the websites, documents and blogs of people you know are interested in that area.
Cool stuff. Just wish I had the maths to understand it all properly!