Developed in partnership with the University of California, San Diego (UCSD), and with funding from the Victorian state government, the OptiPortal is a large, multi-screen video conferencing facility. It uses an ultra-high bandwidth network connection to provide high-resolution imaging and almost instant transmission of sound and movement.

The OptiPortal is particularly suitable for teams of researchers who want to examine, manipulate and discuss large sets of data or images during a video-conferencing session.

The development grant is open to all research staff and postgraduate students at the University of Melbourne. Entries must be received by  Tuesday 23 December 2008 (more details).

From Eigenfactor’s home page you can also search for a specific journal and find two numbers that describe the journal:

• Article Influence (AI): a measure of a journal’s prestige based on per article citations and comparable to Impact Factor.
• Eigenfactor (EF): a measure of the overall value provided by all of the articles published in a given journal in a year.

Eigenfactor is a research project sponsored by the Bergstrom lab in the Department of Biology at the University of Washington.

The Information Aesthetics blog describes Citespace as “a powerful network data visualization technique that facilitates the detection of emerging trends & transient patterns in scientific literature. CiteSpace is based on 2 concepts: ‘research fronts’, defined as an emergent grouping of concepts & underlying research issues & ‘intellectual base’, the network of citations & co-citations of a research front in scientific literature. the size of a node is proportional to the normalized citation counts in the latest time interval.”

Created by Chaomei Chen at Drexel University, Citespace is Java-based and rather less user-friendly than Eigenfactor (by which I mean you have to download the software and feed it some data in order to produce a pretty picture), though it seems to have potential as a visual analysis tool.

In fact, the Information Futures Commission is interested in much more than text-based materials.

For example, let’s consider the image as a piece of scholarly information. In this post, I will describe two types of ’scholarly image’ — you can probably think of many more.

Image as illustration

Twenty years ago, a lecturer in literature might have used photographic slides, posters, prints or photocopies from a book — perhaps even a short section from a VHS video tape — to illustrate a lesson about the medieval Bayeux Tapestry. Our libraries, museums and archives still collect those types of materials, and our researchers and teachers still use them.

Today, though, the ease of digital access, manipulation and presentation is proving a winner in the classroom. A Melbourne Uni English Literary Studies lecturer, Stephanie Trigg, blogged recently about using digital sources in a lecture about the Bayeux Tapestry.

She described a process that’s familiar to thousands of academics in many different disciplines: search, find, evaluate, select, copy, manipulate, present and interpret. In Stephanie’s case, that process was entirely digital. I expect it was also faster and easier than it would have been 20 years ago — yet the quality of imagery and learning (for the students) in 2008 was probably equivalent to, or better than, in 1988.

Image as investigation

Another example of ‘image as information’ is in the burgeoning field of visualisation, the visual representation of a dataset.

As desktop computers have become more powerful in recent years, semi-automatic visualisation tools have also proliferated. Think, for example, of the Chart Wizard in Microsoft Excel — a basic visualisation tool that produces charts and graphs from a financial or statistical spreadsheet.

Real estate agents typically provide floor plans and 3D images on their web sites, allowing you to take a virtual tour of a property before you decide to inspect it in person. These are visualisations, as are the virtual models produced for by architects and landscape designers for their clients. Elevation drawings, floor plans and site maps convey design and construction details in a visual, graphical medium.

High-performance computing facilities have become incredibly powerful in recent years, due to advances in hardware technologies, bandwidth and programming methods. Pharmacology researchers can now create digital visual models of how novel drugs interact with certain types of molecule, pre-testing many different drug designs before they embark on traditional methods for drug development involving animal and human testing. Some universities teach specialist subjects in biomedical visualisation.

The University of California, Berkeley developed an open-source software platform, called BOINC, that runs distributed computing projects such as SETI@home and Einstein@home. These projects divide massive datasets into small chunks that can be downloaded, processed and returned to a central store automatically by your desktop computer — whilst you continue to work uninterrupted on other tasks. All these projects include an element of visualisation.

You may have heard of mashups — the practice of combining two or more data sets to produce new information, meaning or affordances. Again, these often include visualisation as an aid to understanding. Here are a few examples:

• John Snow’s cholera map, an early example of combining geographic information with medical records in order to understand a disease’s spread through 19th century London
• Google Maps, which combine GPS data, tagging, street maps, satellite photos, driving directions and other data sources (affordances galore!)
• the various projects produced in response to the Washington Post Remix initiative in 2006 — combining RSS feeds with data from Amazon.com, the US Congress Votes database and other sources; other US newspapers have produced mashups using geographic data, crime statistics and other newsfeeds
• flickrvision, which overlays Google Maps with geotagged photos from Flickr.com

At ReadWriteWeb.com, Sarah Perez offers an excellent annotated list of tools for visualisation — follow her links to see many inspiring examples of data turned into graphs, charts, images and animations.

So… why does the IFC care?

Most of the images I’ve described here can be, or become, scholarly information:

• when used for teaching
• or included in class notes or other ‘learning objects’
• and possibly created, modified or adapted by a student as part of a project;
• or used as input for research,
• created as part of a research activity
• and distributed to colleagues for comment;
• or published in a refereed journal article, textbook or monograph,
• analysed in an opinion piece for a newspaper or magazine;
• or acquired as part of an archive, library or museum collection
• and then digitised and published online…
• …and so on…

The scope of the Information Futures Commission goes beyond IT, and beyond libraries.

Questions for you

In the Consultation Paper we examined how changes in society, technology and scholarly practice are driving us towards new ways of understanding and managing our scholarly information.

• Did we identify all the major external changes that could influence our thinking about scholarly information and technologies? What did we miss?
• Have we made some assumptions that need to be challenged? Where did we stray?