Brief Description of the contents of our collections – Collections may be viewed athttp://digital.library.unt.edu/browse/?browseby=collection.  The collections are heterogeneous in nature including books, posters, photographs, born digital reports, musical scores, newspapers, letters, maps, etc.  The Portal to Texas History is the largest collection listed.

While we currently are not able to supply active API interfaces to our system, here are some of the services that will be supported in our initial release of our new infrastructure. 

The digital library infrastructure in development at UNT consists of a METS based delivery system. We use a locally developed METS profile for storing file and structural metadata, which is used in delivering content to our users.  Descriptive metadata is stored in a locally qualified Dublin Core metadata scheme which has been developed over the past five years and is documented on our libraries website <http://www.library.unt.edu/digitalprojects/metadata>. 

Services built on our digital objects revolve around existing protocols for sharing and reusing data in libraries.  These include support for OAI-PMH to provide access to both Dublin Core and MODS representations of each metadata record in the system.  The system supports queries in both SRU and OpenSearch. 

Each digital object has descriptive metadata available in a variety of  simple formats for reuse.  Examples include XML, JSON and TXT.  Automatically generated citations are also available in a variety of formats.  We are using the ARK identifier scheme and providing ERC records containing simple metadata for each object in an easy to use format. Additionally it is planned to support COinS and unAPI as well as other Microformats where appropriate. 

Additional planned services include the use of Open Text Mining Interfaces for supplying other organizations the full-text of our digital objects without compromising the terms of our agreements with content providers.   In addition to the full text, this will provide word count vectors for use in visualization tools such as word clouds and other data graphs.