Grand challenges in data integration—state of the art and future perspectives: an introduction

Scientometrics

Volumn 108, Issue 1, 2016, Pages 391-400

  Daraio, Cinzia ^a   Glänzel, Wolfgang ^b,c  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84961195886     PISSN: 01389130     EISSN: 15882861     Source Type: Journal    
DOI: 10.1007/s11192-016-1914-5     Document Type: Editorial

References (18)

1. Biesenbender, S., & Hornbostel, S. (2016). The Research Core Dataset for the German science system: Developing standards for an integrated management of research information. Scientometrics. doi:10.1007/s11192-016-1909-2.
2. Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. Cambridge: MIT Press.
3. Daejeon Declaration. (2015). Daejeon Declaration on science, technology, and innovation policies for the global and digital age. http://www.oecd.org/sti/daejeon-declaration-2015.htm.
4. Daraio, C. (Ed.). (2015). Efficiency, effectiveness and impact of research and innovation. In Proceedings of the Workshop of the 20 February 2015 DIAG, Sapienza University of Rome. Efesto Edizioni, Rome. ISBN 9788899104306.
5. Daraio, C., & Bonaccorsi, A. (2016). Beyond university rankings? Generating new indicators on universities by linking data in open platforms. Journal of the American Society for Information Science and Technology (forthcoming).
6. Daraio, C., Lenzerini, M., Leporelli, C., Naggar, P., Bonaccorsi, A., & Bartolucci, A. (2016). The advantages of an Ontology-Based Data Management approach: Openness, interoperability and data quality. Scientometrics. doi:10.1007/s11192-016-1913-6.
7. Glänzel, W. (1996). The need for standards in bibliometric research and technology. Scientometrics, 35(2), 167–176.
8. Glänzel, W., Beck, R., Milzow, K., Slipersæter, S., Tóth, G., Kołodziejski, M., et al. (2016). Data collection and use in research funding and performing organisations. General outlines and first results of a project launched by Science Europe. Scientometrics, 106(2), 825–835.
9. Glänzel, W., & Willems, H. (2016). Towards standardisation, harmonisation and integration of data from heterogeneous sources for funding and evaluation purposes. Scientometrics, 106(2), 821–823.
10. Haustein, S. (2016). Grand challenges in altmetrics: Heterogeneity, data quality and dependencies. Scientometrics. doi:10.1007/s11192-016-1910-9.
11. Kosten, J. (2016). A classification of the use of research indicators. Scientometrics. doi:10.1007/s11192-016-1904-7.
12. Luwel, M. (2015). Heterogeneity of data in research assessment. In Daraio, C. (Ed.). Efficiency, effectiveness and impact of research and innovation. Proceedings of the Workshop of the 20 February 2015 DIAG, Sapienza University of Rome (pp. 157–163). Efesto Edizioni, Rome. ISBN 9788899104306.
13. Moed, H. F., & Halevi, G. (2015). The multidimensional assessment of scholarly research impact. Journal of the American Society for Information Science and Technology, 66(10), 1988–2002.
14. OECD. (2011). Quality framework and guidelines for OECD statistical activities. Paris: OECD Publishing.
15. OECD. (2014). Data-driven innovation for growth and well-being. Paris: OECD Publishing.
16. Vancauwenbergh, S., De Leenheer, P., & Van Grootel, G. (2016). On research information and classification governance in an inter-organizational context: The Flanders Research Information Space. Scientometrics. doi:10.1007/s11192-016-1912-7.
17. Wilsdon, J., et al. (2015). The metric tide: Report of the independent review of the role of metrics in research assessment and management. doi:10.13140/RG.2.1.4929.1363.
18. Zuccala, A., & Cornacchia, R. (2016). Data matching, integration, and interoperability for a metric assessment of monographs. Scientometrics. doi:10.1007/s11192-016-1911-8.