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Knowledge sharing and discovery across heterogeneous research infrastructures.
Farshidi, Siamak; Liao, Xiaofeng; Li, Na; Goldfarb, Doron; Magagna, Barbara; Stocker, Markus; Jeffery, Keith; Thijsse, Peter; Pichot, Christian; Petzold, Andreas; Zhao, Zhiming.
Afiliação
  • Farshidi S; Department of Information and Computer Science, Utrecht University, Utrecht, The Netherlands.
  • Liao X; MultiScale Networked Systems (MNS), University of Amsterdam, Amsterdam, Netherlands, 1098 XK, The Netherlands.
  • Li N; MultiScale Networked Systems (MNS), University of Amsterdam, Amsterdam, Netherlands, 1098 XK, The Netherlands.
  • Goldfarb D; Environment Agency Austria, Vienna, Austria.
  • Magagna B; Environment Agency Austria, Vienna, Austria.
  • Stocker M; TIB - Leibniz Information Centre for Science and Technology, Hannover, Germany.
  • Jeffery K; British Geological Survey, London, UK.
  • Thijsse P; MARiene Informatie Service, Nootdorp, The Netherlands.
  • Pichot C; French National Institute for Agriculture, Food, and Environment, Paris, France.
  • Petzold A; Forschungszentrum Juelich GmbH, Jülich, Germany.
  • Zhao Z; MultiScale Networked Systems (MNS), University of Amsterdam, Amsterdam, Netherlands, 1098 XK, The Netherlands.
Open Res Eur ; 1: 68, 2021.
Article em En | MEDLINE | ID: mdl-37645187
ABSTRACT
Research infrastructures play an increasingly essential role in scientific research. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. However, such research infrastructures are typically domain-specific and often not connected. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts' ability to formalize and capture tacit and explicit knowledge efficiently. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Open Res Eur Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Open Res Eur Ano de publicação: 2021 Tipo de documento: Article