2017
Journal article
Open Access
Research data reusability: conceptual foundations, barriers and enabling technologies
Thanos C.
metadata data representation Library and Information Sciences Metadata data abstraction data reuse Data publishing explicit knowledge Computer Science Applications Data reuse data discoverability data publishing Data understandability relational thinking Communication Business and International Management Data abstraction Relational thinking tacit knowledge data understandability Data presentation Explicit knowledge Media Technology Data discoverability Tacit knowledge
High-throughput scientific instruments are generating massive amounts of data. Today, one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice. By data (re)usability, we mean the ease of using data for legitimate scientific research by one or more communities of research (consumer communities) that is produced by other communities of research (producer communities). Data (re)usability allows the reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. It has four main dimensions: policy, legal, economic and technological. The paper addresses the technological dimension of data reusability. The conceptual foundations of data reuse as well as the barriers that hamper data reuse are presented and discussed. The data publication process is proposed as a bridge between the data author and user and the relevant technologies enabling this process are presented.
Source: Publications 5 (2017). doi:10.3390/publications5010002
Publisher: MDPI AG, Basel, Switzerland
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26. Ikeda, R.; Widom, J. Panda: A System for Provenance and Data. IEEE Data Eng. Bull. 2010, 33, 42-49.
27. Moreau, L.; Freire, J.; Futrelle, J.; Mcgrath, R.E.; Myers, J.; Paulson, P. The Open Provenance Model: An Overview. In IPAW 2008: Provenance and Annotation of Data and Processes; Springer: Berlin, Germany, 2008; Volume 5272.
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34. Poggi, A.; Lembo, D.; Calvanese, D.; Giacomo, G.D.; Lenzerini, M.; Rosati, R. Linking Data to Ontologies. J. Data Semant. 2008, 10, 133-173.
35. Thanos, C. The Future of Digital Scholarship. Procedia Comput. Sci. 2014, 38, 22-28. [CrossRef]
36. Paskin, N. Digital object identifier for scientific data. In Presented at the 19th International CODATA Conference, Berlin, Germany, 7-10 November 2004.
37. Altman, M.; King, G. A Proposed Standard for the Scholarly Citation of Quantitative Data. D-Lib Mag. 2007, 13, 11-26.
38. Thanos, C. Mediation: The Technological Foundation of the Modern Science. Data Sci. J. 2014, 13, 88-105. [CrossRef]
39. Bizer, C.; Heath, T.; Berners-Lee, T. Linked Data-The Story So Far. Int. J. Semant. Web Inf. Syst. 2009, 5, 1-22. [CrossRef]
40. Bizer, C. Interlinking Scientific Data on a Global Scale. Data Sci. J. 2013, 12, GRDI6-GRDI12. [CrossRef]
41. Zimmerman, A.S. New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data. Sci. Technol. Hum. Values 2008, 33, 631-652. [CrossRef]
42. Thanos, C. Scientific Data (Re)Usability: Concepts, Impediments, and Enabling Technologies. In Proceedings of the International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage, Veliko Tarnovo, Bulgaria, 28-30 September 1015.
43. JISK, Data Centers: their use, value and impact. A research Information Network Report, September 2011. Available online: http://www.rin.ac.uk/system/files/attachments/Data_Centres_Report.pdf (accessed on 6 January 2017).
Thanos, C. Global Research Data Infrastructures: Towards a 10-Year Vision for Global Research Data Infrastructures-Final Report. 2011. Available online: http://www.grdi2020.eu/repository/filescaricati/ e2b03611-e58f-4242-946a-5b21f17d2947.pdf (accessed on 6 January 2017).
Zimmerman, A. Data Sharing and Secondary Use of Scientific Data: Experiences of Ecologists. Thesis, Degree of Doctor of Pholosophy Information and Library Studies, University of Michigan, Ann Arbor, MI, USA, 2003. Available online: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/39373/ann_ zimmerman_dissertation_2003.pdf?sequence=2 (accessed on 6 January 2017).
European Commission. Commission Recommendation on Access to and Preservation of Scientific Information. 2012. Available online: https://ec.europa.eu/research/science-society/document_ library/pdf_06/recommendation-access-and-preservation-scientific-information_en.pdf (accessed on 6 January 2017).
Amsterdam Call for Action on Open Science. 2016. Available online: https://english.eu2016.nl/documents/ reports/2016/04/04/amsterdam-call-for-action-on-open-science (accessed on 6 January 2017).
European Commission; Directorate-General for Research & Innovation. H2020 Programme, Guidelines on FAIR Data Management in Horizon 2020. 2016. Available online: http://ec.europa.eu/research/ participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf (accessed on 6 January 2017).
National Research Council. Bits of Power: Issues in Global Access to Scientific Data; National Academy Press: Washington, DC, USA, 1997.
8. National Research Council-Committee for a Study on Promoting Access to Scientific and Technical Data for the Public Interest. A Question of Balance: Private Rights and the Public Interest in Scientific and Technical Databases; National Academy Press: Washington, DC, USA, 1999.
9. National Science Board. Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century. 2005. Available online: https://www.nsf.gov/nsb/documents/2005/LLDDC_report.pdf (accessed on 6 January 2017).
10. Osterlund, C.; Carlile, P. Relations in practice: Sorting through practice theories on knowledge sharing in complex organizations. Inf. Soc. 2005, 21, 91-107. [CrossRef]
11. Musen, M. Dimensions of Knowledge Sharing and Reuse. Comput. Biomed. Res. 1992, 25, 435-467. [CrossRef]
12. Kanfer, A.G.; Bruce, B.C.; Haythornthwaite, C.; Burbules, N.; Wade, J.; Bowker, G.C.; Porac, J. Modeling distributed knowledge processes in next generation multidisciplinary alliances. Inf. Syst. Front. 2000, 2, 317-331. [CrossRef]
13. Star, S.; Griesemer, J. Institutional ecology, translations, and coherence: Amateurs and professionals in Berkeley's museum of vertebrate zoology. Soc. Stud. Sci. 1989, 19, 387-420. [CrossRef]
14. Floridi, L.; Sanders, J. Levellism and the Method of Abstraction; Research Report 22.11.04; Information Ethics Group (Oxford University and University of Bari): Oxford, UK, 2004.
15. Wickett, K.; Sacchi, S.; Dubin, D.; Renear, A. Identifying Content and Levels of Representation in Scientific Data. In Proceedings of the American Society for Information Science and Technology, Baltimore, MD, USA, 28-31 October 2012.
16. Stonebraker, M.; Becla, J.; Dewitt, D.J.; Lim, K.T.; Maier, D.; Ratzesberger, O.; Zdonik, S.B. Requirements for Science Data Bases and SciDB. In Proceedings of the CIDR 2009, Fourth Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, 4-7 January 2009.
17. Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing. AI Mag. 1991, 12, 36-56.
18. Lawrence, B.; Jones, C.; Matthews, B.; Pepler, S.; Callaghan, S. Citation and Peer Review of Data: Moving Towards Formal Data Publication. Int. J. Digit. Curation 2011, 6. [CrossRef]
19. The Royal Society Science Center. Science as an Open Enterprise; The Royal Society Science Center: London, UK, 2012.
20. Costello, M. Motivating online publication of data. Bioscience 2009, 59, 418-427. [CrossRef]
21. Parsons, M.A.; Fox, P.A. Is data Publication the Right Metaphor? Data Sci. J. 2013, 12, WDS32-WDS46. [CrossRef]
22. Willinsky, J. The Access Principle: The Case for Open Access to Research and Scholarship; MIT Press: Cambridge, MA, USA, 2006.
23. Cragin, M.; Heidorn, P.B.; Palmer, C.; Smith, L. An Educational Program on Data Curation. In Proceedings of the American Library Association Conference, Science and Technology Section, Washington, DC, USA, 25 June 2007.
24. Kobielus, J. Big Data and the Power of Positive Curation. 2014. Available online: http://www. ibmbigdatahub.com/blog/big-data-and-power-positive-curation (accessed on 6 January 2017).
25. Gray, J.; Szalay, A.S.; Thakar, A.R.; Stoughton, C.; Vandenberg, J. Online Scientific Data Curation, Publication and Archiving; Technical Report MSR-TR-2002-74; Microsoft Research: Redmond, WA, USA, 2002.
26. Ikeda, R.; Widom, J. Panda: A System for Provenance and Data. IEEE Data Eng. Bull. 2010, 33, 42-49.
27. Moreau, L.; Freire, J.; Futrelle, J.; Mcgrath, R.E.; Myers, J.; Paulson, P. The Open Provenance Model: An Overview. In IPAW 2008: Provenance and Annotation of Data and Processes; Springer: Berlin, Germany, 2008; Volume 5272.
28. Strang, T.; Linnhoff-Poppien, C. A Context Modeling Survey. In Proceedings of the First International Workshop on Advanced Context Modeling, Reasoning and Management Associated with the Sixth International Conference on Ubiquitous Computing, Nottingham, UK, 7 September 2004.
29. Batini, C.; Scannapieco, M. Data Quality: Concepts, Methodologies, and Techniques; Springer: New York, NY, USA, 2006.
30. Gray, J.; Liu, D.T.; Nieto-Santisteban, M.; Szalay, A.; Dewitt, D.J.; Heber, G. Scientific Data Management in the Coming Decade. SIGMOD Rec. 2005, 34, 34-41. [CrossRef]
31. Chavan, V.; Penev, L. The Data Paper: A Mechanism to Incentivize Data Publishing in Biodiversity Science. BMC Bioinform. 2011, 12, 2399-2405. [CrossRef] [PubMed]
32. Gruber, T. Towards Principles for the Design of Ontologies Used for Knowledge Sharing. In Formal Ontology in Conceptual Analysis and Knowledge Representation; Technical Report KSL 93-04; Knowledge Systems Laboratory, Stanford University: Palo Alto, CA, US, 1995.
33. Calvanese, D.; Giacomo, G.D.; Lembo, D.; Lenzerini, M.; Poggi, A.; Rosati, R. Ontology-based Database Access. In Proceedings of the Fifteenth Italian Symposium on Advanced Database Systems, SEBD 2007, Torre Canne, Fasano, Italy, 17-20 June 2007; pp. 324-331.
34. Poggi, A.; Lembo, D.; Calvanese, D.; Giacomo, G.D.; Lenzerini, M.; Rosati, R. Linking Data to Ontologies. J. Data Semant. 2008, 10, 133-173.
35. Thanos, C. The Future of Digital Scholarship. Procedia Comput. Sci. 2014, 38, 22-28. [CrossRef]
36. Paskin, N. Digital object identifier for scientific data. In Presented at the 19th International CODATA Conference, Berlin, Germany, 7-10 November 2004.
37. Altman, M.; King, G. A Proposed Standard for the Scholarly Citation of Quantitative Data. D-Lib Mag. 2007, 13, 11-26.
38. Thanos, C. Mediation: The Technological Foundation of the Modern Science. Data Sci. J. 2014, 13, 88-105. [CrossRef]
39. Bizer, C.; Heath, T.; Berners-Lee, T. Linked Data-The Story So Far. Int. J. Semant. Web Inf. Syst. 2009, 5, 1-22. [CrossRef]
40. Bizer, C. Interlinking Scientific Data on a Global Scale. Data Sci. J. 2013, 12, GRDI6-GRDI12. [CrossRef]
41. Zimmerman, A.S. New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data. Sci. Technol. Hum. Values 2008, 33, 631-652. [CrossRef]
42. Thanos, C. Scientific Data (Re)Usability: Concepts, Impediments, and Enabling Technologies. In Proceedings of the International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage, Veliko Tarnovo, Bulgaria, 28-30 September 1015.
43. JISK, Data Centers: their use, value and impact. A research Information Network Report, September 2011. Available online: http://www.rin.ac.uk/system/files/attachments/Data_Centres_Report.pdf (accessed on 6 January 2017).
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BibTeX entry
@article{oai:it.cnr:prodotti:364782,
title = {Research data reusability: conceptual foundations, barriers and enabling technologies},
author = {Thanos C.},
publisher = {MDPI AG, Basel, Switzerland},
doi = {10.3390/publications5010002},
journal = {Publications},
volume = {5},
year = {2017}
}CNR authorsLaboratories
CNR ExploRA
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
