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Meaningful Data Reuse in Research Communities

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1003)

Abstract

FAIR data principles declare data interoperability and reuse according to machine and human readable shared specifications. Adherence to this set of principles brings some implications for data infrastructures and research communities. Meaningful data exchange and reuse by humans and machines require formal specifications of research domains accompanying data and allowing automatic reasoning. Development of formal conceptual specifications in research communities can be stimulated by a necessity to reach semantic interoperability of data collections and components, and reuse of data resources. Usage of formal domain specifications reduces data heterogeneity costs. Formal reasoning allows meaningful search and verified reuse of data, methods, and processes from collections. These means can make research lifecycle in communities more efficient. A lifecycle includes collecting domain knowledge specifications, classifying all data, methods, and processes according to such specifications, reusing relevant data and methods, and collecting and sharing results for reuse.

Keywords

  • FAIR data principles
  • Research data infrastructure
  • Research community
  • Conceptual modeling of research domains

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Acknowledgments

The work was supported by the Russian Foundation for Basic Research (grants 18-07-01434, 18-29-22096, 19-07-01198).

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Correspondence to Nikolay A. Skvortsov .

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Skvortsov, N.A. (2019). Meaningful Data Reuse in Research Communities. In: Manolopoulos, Y., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003. Springer, Cham. https://doi.org/10.1007/978-3-030-23584-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-23584-0_3

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