"I Go Into a Lot of Different Places to Get my Research": Graduate Students' Mental Models of Research Tools and Services

Students have access to a wealth of digital information resources from a wide range of publishers. This paper introduces a preliminary analysis of focus group data that asked graduate students in the U.K. how they used a range of library and non-library tools and services to seek information for class projects. The findings suggest a mental model of graduate student information practice is built on a ‘whatever works’ approach rather than any deep knowledge of technical information seeking practices. This model does not differentiate significantly between different services and tools, and has as its goal getting (retrieving) and using (deploying) information in support of various aspects of student life.ye

"I Go Into a Lot of Different Places to Get my Research": Graduate Students' Mental Models of Research Tools and Services

Students have access to a wealth of digital information resources from a wide range of publishers. This paper introduces a preliminary analysis of focus group data that asked graduate students in the U.K. how they used a range of library and non-library tools and services to seek information for class projects. The findings suggest a mental model of graduate student information practice is built on a ‘whatever works’ approach rather than any deep knowledge of technical information seeking practices. This model does not differentiate significantly between different services and tools, and has as its goal getting (retrieving) and using (deploying) information in support of various aspects of student life.ye

Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

Purpose – The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query. Design/methodology/approach – The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records. Findings – The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies. Research limitations/implications – The metadata harvest required manual intervention and the evaluation was resource intensive. Future research will look at evaluation methodologies that take account of issues of consistency and ecological validity. Practical implications – The method does not require training data and is easily scalable. The pipeline can be customized for individual use cases, for example, recall or precision enhancing. Social implications – The approach can provide centralized access to information from multiple domains currently provided by individual digital libraries. Originality/value – The approach addresses metadata normalization in the context of web resources. The automatic classification approach accounts for matches within hierarchies, aggregating lower level matches to broader parents and thus approximates the practices of a human cataloger. </jats:sec

Digging into Metadata: Final Report White Paper

The Digging into Metadata project team, which won funding as part of the 2nd Digging into Data Challenge, came together through a synergy of past collaborations, mutual contacts and debates about data. We had shared experience of the problems and pitfalls of bringing together large, disparate datasets with widely varying standards of metadata. Initial discussions about how legacy Intute data might be of use to the Internet Public Library, led on to wider debates about the problems of cross searching such datasets and how the metadata associated with them can be linked up or standardised to facilitate resource discovery. As we debated how issues of interoperability could be addressed, the Digging into Data Challenge gave us the impetus to turn our previous work, existing connections and discussions into a winning proposal. Our two year project aimed to closely examine the metadata associated with our chosen datasets and enhance that metadata through a variety of automatic, scalable techniques which built on previous collaborative work. Through this enhanced metadata our intention was to enable improved search capability over disparate digital libraries which had hugely varying levels and standards of subject metadata and which would previously have been difficult to search in a consistent way. Through this work we aimed to show firstly that our techniques could enhance poor or inconsistent metadata in a meaningful and consistent way and secondly that this enhanced metadata could lead to improved search functionality which would add value for end users