Skip to main content

Meaningful Data Reuse in Research Communities

  • 224 Accesses

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


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-23584-0_3
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-23584-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   74.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.


  1. ASTERICS: Astronomy ESFRI & Research Infrastructure Cluster. Accessed 01 Jan 2019

  2. EOSC Declaration. Accessed 01 Jan 2019

  3. FITS: Flexible Image Transport Specification.

  4. Guidelines on FAIR Data Management in Horizon 2020. Directorate-General for Research and Innovation European Commission (2016). Accessed 01 Jan 2019

  5. Improving Future Research Communication and e-Scholarship. Bournea, P., Clarkb, T., Dalec, R., de Waardd, A., Hermane, I., Hovyf, E., Shotton, D. (eds.) The Future of Research Communications and e-Scholarship (2011). Accessed 01 Jan 2019

  6. International Virtual Observatory Alliance.

  7. Strasbourg Astronomical Data Center (CDS).

  8. VOTable Format Definition. Version 1.3. IVOA Recommendation. IVOA (2013). Accessed 01 Jan 2019

  9. Abrial, J.-R.: The B-Book: Assigning Programs to Meanings. Cambridge University Press, Cambridge (1996)

    CrossRef  Google Scholar 

  10. Baader, F., Horrocks, I., Lutz, C., Sattler, U.: Introduction to Description Logic. Cambridge University Press, Cambridge (2017)

    CrossRef  Google Scholar 

  11. Belhajjame K., et al.: Workflow-centric research objects: a first class citizen in the scholarly discourse. In: ESWC2012 Workshop on the Future of Scholarly Communication in the Semantic Web (SePublica2012), Heraklion, pp. 1–12 (2012)

    Google Scholar 

  12. Doorn, P., Dillo, I.: FAIR Data in Trustworthy Data Repositories. DANS/ EUDAT/ OpenAIRE Webinar (2016). Accessed 01 Jan 2019

  13. Hodge, G.M.: Best practices for digital archiving: an information life cycle approach. D-Lib Mag. 6(1) (2000). ISSN 1082-9873. Accessed 01 Jan 2019

  14. Goble, C.A., De Roure, D.C.: myExperiment: social networking for workflow-using e-scientists. In: Workflows in Support of Large-Scale Science, pp. 1–2. ACM (2007)

    Google Scholar 

  15. Kalinichenko, L.A.: Compositional specification calculus for information systems development. In: Eder, J., Rozman, I., Welzer, T. (eds.) Advances in Databases and Information Systems. LNCS, vol. 1691, pp. 317–331. Springer, Heidelberg (1999).

    CrossRef  Google Scholar 

  16. Liskov, B., Wing, J.: A behavioral notion of subtyping. ACM Trans. Program. Lang. Syst. (TOPLAS) 16(6), 1811–1841 (1994)

    CrossRef  Google Scholar 

  17. Louys, M., et al.: Observation data model core components and its implementation in the table access protocol. Version 1.1. IVOA Recommendation, 09 May 2017. IVOA (2017). Accessed 01 Jan 2019

  18. Mons, B., et al.: Cloudy, increasingly FAIR; revisiting the FAIR data guiding principles for the European open science cloud. Inform. Serv. Use 37(1), 49–56 (2017).

    CrossRef  Google Scholar 

  19. Schentz, H., le Franc, Y.: Building a semantic repository using B2SHARE. In: EUDAT 3rd Conference (2014)

    Google Scholar 

  20. Skvortsov, N.A.: Meaningful data interoperability and reuse among heterogeneous scientific communities. In: Kalinichenko, L., Manolopoulos, Y., Stupnikov, S., Skvortsov, N., Sukhomlin, V. (eds.) Selected Papers of the XX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2018), vol. 2277, pp. 14–15. CEUR (2018). Accessed 01 Jan 2019

  21. Skvortsov, N.A., Avvakumova, E.A., Bryukhov, D.O., et al.: Conceptual approach to astronomical problems. Astrophys. Bull. 71(1), 114–124 (2016).

    CrossRef  Google Scholar 

  22. Skvortsov, N.A., Kalinichenko, L.A., Karchevsky, A.V., Kovaleva, D.A., Malkov, O.Y.: Matching and verification of multiple stellar systems in the identification list of binaries. In: Kalinichenko, L., Manolopoulos, Y., Malkov, O., Skvortsov, N., Stupnikov, S., Sukhomlin, V. (eds.) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2017. Communications in Computer and Information Science, vol. 822, pp. 102–112. Springer, Heiidelberg (2018).

    CrossRef  Google Scholar 

  23. Skvortsov, N.A., Vovchenko, A.E., Kalinichenko, L.A., Kovalev, D.A., Stupnikov S.A.: Metadata model for semantic search for rule-based workflow implementations. In: Systems and Means of Informatics. vol. 24, Iss. 4, pp. 4–28, IPI RAS, Moscow (2014). (In Russian)

    Google Scholar 

  24. Skvortsov, N.A., Kalinichenko, L.A., Kovalev, D.A.: Conceptualization of methods and experiments in data intensive research domains. In: Kalinichenko, L., Kuznetsov, S., Manolopoulos, Y. (eds.) Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2016). CCIS, vol. 706, pp. 3–17. Springer, Cham (2017).

    CrossRef  Google Scholar 

  25. Tolle, K.M., Tansley, D.S.W., Hey, A.J.G.: The Fourth paradigm: data-intensive scientific discovery [point of view]. Proc. IEEE. 99(8), 1334–1337 (2011).

    CrossRef  Google Scholar 

  26. Wilkinson, M., et al.: Interoperability and FAIRness through a novel combination of web technologies. PeerJ Preprints 5, e2522v2 (2017).

    CrossRef  Google Scholar 

  27. Wilkinson, M., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

  28. Wittenburg, P.: From persistent identifiers to digital objects to make data science more efficient. Data Intell. 1(1), 6–21 (2019).

    CrossRef  Google Scholar 

  29. Wittenburg, P., Strawn, G.: Common Patterns in Revolutionary Infrastructures and data. RDA (2018). Accessed 01 Jan 2019

Download references


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

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nikolay A. Skvortsov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23583-3

  • Online ISBN: 978-3-030-23584-0

  • eBook Packages: Computer ScienceComputer Science (R0)