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Abstract

A new, thoroughly updated edition of a comprehensive overview of knowledge management (KM), covering theoretical foundations, the KM process, tools, and professions. The ability to manage knowledge has become increasingly important in today's knowledge economy. Knowledge is considered a valuable commodity, embedded in products and in the tacit knowledge of highly mobile individual employees. Knowledge management (KM) represents a deliberate and systematic approach to cultivating and sharing an organization's knowledge base. This textbook and professional reference offers a comprehensive overview of the field. Drawing on ideas, tools, and techniques from such disciplines as sociology, cognitive science, organizational behavior, and information science, it describes KM theory and practice at the individual, community, and organizational levels. Chapters cover such topics as tacit and explicit knowledge, theoretical modeling of KM, the KM cycle from knowledge capture to knowledge use, KM tools, KM assessment, and KM professionals.

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Abstract

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey (N = 258) at a medium-sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

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Date

20. 1.2015 18:30:22