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With digital curation’s increasingly important role in the fast-paced and data-intensive information environment, there is a need to identify a set of competencies for professionals in this growing field. As part of a curriculum development project funded by the U.S. Institute of Museum and Library Services, a total of 173 job advertisements posted between October 2011 and April 2012 were collected from various sources to take into account varying types of professionals in the field of digital curation across North America. Position title, institution types and location, educational background, experience, knowledge and skills, and duties were examined and analyzed. The results of the analysis show that digital curation jobs are characterized by a complex interplay of various skills and knowledge. The findings of this study present emerging requirements for a qualified workforce in the field of digital curation.

A collaborative study was carried out to test the use of the Lovibond 2000 honey color comparator. Fourteen collaborators classified 6 honeys in the test. Results were generally favorable and the method has been adopted official first action.

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This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.