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Ontario Scholars Portal – Yours to a Discovery Layer

(This is a little something I wrote to support this work)

What is a “Discovery Layer”?

To me, a Discovery Layer allows a user to search across a library catalogue (or several), an ebook platform (or several), and a source of articles (or several). 

A Discovery Layer could make use of one or more combinations of the following:

• Federated Searching
• a query is distributed to multiple sources, and responses are compiled, de-duped, and returned
• e.g. Sirsi Single Search
• Metasearching
• a single, regularly complied index is created from the collection of metadata from multiple sources
• e.g. Endeca, Google
• Single host environment
• metadata and content are placed on a common server
• e.g. WorldCat, Ontario Scholars Portal

Why a Discovery Layer?

The pursuit of a Discovery Layer seem to be driven by the need to present one, strong and stable user interface over many disparate sources of information. Some benefits of a discovery layer include:

• users only have to learn one interface, instead of many
• users don’t have to choose from lists of dozens of indexes
• users don’t have to repeat searches depending on format (one search for books, then one for dissertations, then one for articles…)
• users expect simple, effective search tools like Google

What’s the problem?
The challenges that face the construction of a discovery layer include:

• many of our research tools are very difficult to extract data from as they make use of a multitude of non-standard formats and protocols
• most of our research tools (especially the library catalogue) generate search results with poor relevance ranking
• some sources will be rich in text and metadata (articles, ebooks) while other sources will only be represented by metadata (print books)

How much can an improved interface improve things?

At the present time, I would say that there are 3 archetypes of Discovery Layer Interfaces.

• The first uses tabs, like PubMed and Illumina
• The second uses facets, like Endeca, WorldCat, and VuFind
• The third uses page partitions, like Google Experimental and UBC’s OneSearch.

How much can an improved interface, improve relevant results?
Coming up with what a user might deem relevant from 2 or 3 keywords is challenging in a regular search environment. Producing consistently relevant results in a federated or metasearch environment is extremely difficult.

Relevance might be improved through one or more of the following:

• by taking into account the user’s previous searching behaviour
• by weighing results by the number of times an item has been bookmarked, printed, or saved
• by using citation information to determine ‘likeness’ (e.g. based on a percentage of shared citations in item’s bibliography)
• by using user-created lists articles to generate similar items of possible interest
• by knowing what courses a users is currently taking/teaching and emphasizing relevant resources accordingly
  

What is a Good Enough Discovery Layer?
Is it realistic to expect a Discovery Layer to serve both the novice researcher and the expert to access a variety of formats in a multitude of disciplines? Can one size fit all? Should we develop several Discovery Layers with one for each discipline? (Arts, Social Sciences, Medicine). Should we develop one interface for undergraduates and one for faculty and graduate students?

How will we know we have reached the Promised Land?
Most discovery layers are still in the earliest stages of their development and by appearances, they seem more alike than unalike. How should we choose what is an acceptable product? One suggestion is to measure the success of a Discovery Layer by comparing its search results to Google.