Which repository: Learning Objects vs Open Access research

As previously referred to, there has been an amount of cross-project work with Streamline through which it has become apparent that the requirements of a Learning Object repository are potentially very different from those of a repository dedicated to Open Access to research (primarily due to the way in which the respective types of repository are searched) and that the software that has been reviewed (both Open Source and proprietary) tends to be specialised to one specific type of content (although the respective software developers themselves may disagree).

The issue (heavily informed by the work being undertaken by the Streamline team) has necessarily delayed the choice of software that will be used and the ideal is to choose the platform that is best suited to both projects as well as being extensible to an ever wider range of digital objects in the future.

I’m sure my colleagues from Streamline will correct me if necessary – bear with me, I’m still getting to grips with that project and am able to follow more of the discussion at each subsequent meeting (!) – but my understanding is that two of the aims of Streamline are to:

• develop a tool that will auto-generate standard metadata based on the documentation associated with multi-media LOs
• develop a repository search-interface that then uses that auto-generated metadata in a controlled way to provide targeted resource-discovery but that will also facilitate serendipitous discovery of resources in a way analogous to spotting an adjacent book on a library shelf.

The consensus from the Streamline team seems to be that the majority of the software we have looked at does not make adequate use of metadata (often painstakingly entered) using it only as a filter to refine a search after a brute-force Google type trawl through repository content and perhaps the metadata side of repositories dedicated to OA has been neglected as (as noted in a previous post) traffic tends to come in via the mighty Google (or similar) anyway; this is obviously not conducive to an LO repository that will be searched in a very different way – often from within an individual institution or small subset of institutions.

Which brings us to Google itself. I confess to not knowing a great deal about how Google actually works but it is my understanding that it does not pay much heed to metadata and just indexes everything – how this is then ranked is something of a mystery to me and I have heard it suggested that the success of the interface is actually based on it’s perceived efficacy at discovering pertinent resources in the top 10 or 20 hits rather than its actual ability to identify the most relevant resource all (or even some) of the time.

NB. I assume the OAI-PMH (Open Access Initiative on Metadata Harvesting) with which all OA archives are compliant has some beneficial effects on hit ranking than would be achieved through brute indexing of repository content in the normal course of things (note to self: must look again at the OAI-PMH tutorial.)

I am a very long way from being a metadata expert but I believe there are those who think that metadata as we traditionally understand it has had its day and that Web 2.0 technologies (social bookmarking, tagging et al) coupled with Google’s bulldozer approach will usher in a brave new world of resource discovery – there may indeed be some truth in that though the librarian in me, and a little historical perspective, suggest that they will rather complement the existing models.

And anyway, at this stage, the burning issue is STILL “Which repository?” – IR day (as I call it) is now scheduled for April 16th!