Michael Seadle

Keynote Speaker

Professor Michael Seadle
Director of the Berlin School for Library and Information Science
Dean of the Faculty of Arts I, Humboldt University
Chair of the ISchools Caucus

Professor Michael Seadle has agreed to deliver a keynote address at IMCW2014.

Professor Seadle holds a PhD degree in History from the University of Chicago and an MS degree in Information Science from the University of Michigan. He is currently Dean of the Faculty of Arts I as well as the Director of the Berlin School of Library and Information Science (Institut für Bibliotheks- und Informationswissenschaft) at Humboldt-Universität zu Berlin and serves as the chair of ISchools Caucus comprising more than 50 ISchools around the world. Prior to his current position, Professor Seadle served in various administrative capacities at the University of Chicago and Cornell University, among others, and carried out sponsored projects (LC, NSF, IMLS, DFG). He is the co-editor of the peer-reviewed journal “Library Hi Tech” published by Emerald. He has written more than 100 papers and authored books on long term digital archiving, computing management and copyright.

Title: “Managing and Mining Historical Research Data over Time.”

Abstract. This keynote addresses three interrelated issues in information management: 1) how to structure humanities research data for content mining, 2) how humanities data mining functions now and may function in the future, and 3) how to ensure that the data are there in 100 or more years. These three issues have an obvious curricular component as well, which is necessary in order to train students to prepare and to exploit the data. At present the management of research data takes place separately from the creation and use of that data, for example in libraries and computer centres, but that may be less true in a future where research teams include data scientists as standard and even as equal members. Concrete examples in this talk will use historical research data, but the principles apply generally to humanities research data. Research data from the natural sciences often has a different structure and is processed differently. For that reason those data will not be discussed in this keynote.