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Metadata Analysis of Scholarly Events of Computer Science, Physics, Engineering, and Mathematics

  • Said FathallaEmail author
  • Sahar Vahdati
  • Sören Auer
  • Christoph Lange
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11057)

Abstract

Although digitization has significantly eased publishing, finding a relevant and a suitable channel of publishing remains challenging. Scientific events such as conferences, workshops or symposia are among the most popular channels, especially in computer science, natural sciences, and technology. To obtain a better understanding of scholarly communication in different fields and the role of scientific events, metadata of scientific events of four research communities have analyzed: Computer Science, Physics, Engineering, and Mathematics. Our transferable analysis methodology is based on descriptive statistics as well as exploratory data analysis. Metadata used in this work have been collected from the OpenResearch.org community platform and SCImago as the main resources containing metadata of scientific events in a semantically structured way. There is no comprehensive information about submission numbers and acceptance rates in fields other than Computer Science. The evaluation uses metrics such as continuity, geographical and time-wise distribution, field popularity and productivity as well as event progress ratio and rankings based on the SJR indicator and h5-indices. Recommendations for different stakeholders involved in the life cycle of events, such as chairs, potential authors, and sponsors, are given.

Keywords

Scientific events Metadata analysis Scholarly communication Citation count OpenResearch.org 

Notes

Acknowledgments

Said Fathalla would like to acknowledge the Ministry of Higher Education (MoHE) of Egypt for providing a scholarship to conduct this study. I would like to offer my special thanks to Heba Mohamed for her support in data gathering process.

References

  1. 1.
    Aumüller, D., Rahm, E.: Affiliation analysis of database publications. SIGMOD Rec. 40(1), 26–31 (2011)CrossRefGoogle Scholar
  2. 2.
    Barbosa, S., Silveira, M., Gasparini, I.: What publications metadata tell us about the evolution of a scientific community: the case of the Brazilian human-computer interaction conference series. Scientometrics 110(1), 275–300 (2017)CrossRefGoogle Scholar
  3. 3.
    Biryukov, M., Dong, C.: Analysis of computer science communities based on DBLP. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 228–235. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15464-5_24CrossRefGoogle Scholar
  4. 4.
    El-Din, H., Eldin, A., Hanora, A.: Bibliometric analysis of Egyptian publications on Hepatitis C virus from PubMed using data mining of an in-house developed database. Scientometrics 108(2), 895–915 (2016)CrossRefGoogle Scholar
  5. 5.
    Fathalla, S., Lange, C.: EVENTS: a dataset on the history of top-prestigious events in five computer science communities. In: Workshop on Semantics, Analytics, Visualization: Enhancing Scholarly Dissemination. Springer, Heidelberg (2018, in press)Google Scholar
  6. 6.
    Fathalla, S., Lange, C.: EVENTSKG: a knowledge graph representation for top- prestigious computer science events metadata. In: Conference on Computational Collective Intelligence Technologies and Applications. Springer, Heidelberg (2018, in press)Google Scholar
  7. 7.
    Fathalla, S., Vahdati, S., Lange, C., Auer, S.: Analysing scholarly communication metadata of computer science events. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 342–354. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_27CrossRefGoogle Scholar
  8. 8.
    González-Pereira, B., Guerrero-Bote, V.P., Moya-Anegón, F.: A new approach to the metric of journals’ scientific prestige: the SJR indicator. J. Inf. 4(3), 379–391 (2010)Google Scholar
  9. 9.
    Guilera, G., Barrios, M., Gómez-Benito, J.: Meta-analysis in psychology: a bibliometric study. Scientometrics 94(3), 943–954 (2013)CrossRefGoogle Scholar
  10. 10.
    Hiemstra, D., Hauff, C., De Jong, F., Kraaij, W.: SIGIR’s 30th anniversary: an analysis of trends in IR research and the topology of its community. In: ACM SIGIR Forum, vol. 41, no. 2, pp. 18–24. ACM (2007)Google Scholar
  11. 11.
    Martinez, W., Martinez, A., Martinez, A., Solka, J.: Exploratory Data Analysis with MATLAB. CRC Press, Boca Raton (2010)CrossRefGoogle Scholar
  12. 12.
    Nascimento, M.A., Sander, J., Pound, J.: Analysis of SIGMOD’s co-authorship graph. ACM SIGMOD Rec. 32(3), 8–10 (2003)CrossRefGoogle Scholar
  13. 13.
    Vahdati, S., Arndt, N., Auer, S., Lange, C.: OpenResearch: collaborative management of scholarly communication metadata. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 778–793. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49004-5_50CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Smart Data Analytics (SDA)University of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany
  3. 3.Faculty of ScienceAlexandria UniversityAlexandriaEgypt
  4. 4.Computer ScienceLeibniz University of HannoverHannoverGermany
  5. 5.TIB Leibniz Information Center for Science and TechnologyHannoverGermany

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