Computer Science in The Netherlands Survey

This special blog post relates to a survey sent out to Dutch Computer Scientists as part of my PhD research in cooperation with the Rathenau Institute.

In my PhD research I investigate the dynamics of social networks. I specifically study one social system of social beings – scientists, to understand dynamics of knowledge systems. Scientists were selected as a population of study are there are rich publicly accessible data on scientists, whether that be from publication databases, to web profiles, and meta data from multiple Web sources. These data sources provide a rich set of information for studying dynamics of a specific system.

My work up to now has solely used publication (bibliometric data) and available meta data to study dynamics of social systems [1]. In an effort to provide more detailed insight into dynamics a survey was recently prepared asking a set of scientists, from multiple universities, positions, and experience, to provide additional data for this study. In this project one field and one national setting is investigated- Computer Science in The Netherlands. It is this set of scientists that have received an invitation to partake in a survey to collect further data on the field.
Why Dutch computer scientists? The Dutch context was selected for it is a typical European research environment with funding on multiple levels for different types of research. Notably the Dutch research environment also has a diversity of universities, from research to vocation, at which to examine different processes in a relatively small geographical space.

Why research Computer Science? The field of Computer Science was selected for three reasons: the traditions of the field with multiple sub-fields within the discipline; and the known tendency for collaboration through co-authorship; as well as the validity and reliability of online sources documenting publications.

Computer Science is a field based on both information and computation studies, coming together in the use of computers as systems and or tools for solving research problems. The discipline of Computer Science is a mature, intellectually unified field with a number of mature sub-fields existing as self-sustaining practices. Computer Science subjects range from bioinformatics, artificial intelligence/cognitive science, cybernetics, quantum computing and business applications. Consequently the field not only works on internal questions but has a tendency to work with other fields. Within the Netherlands the discipline of Computer Science is a field of high research quality, (Nationale Informaticakamer 2010).

The field of Computer Science has a number of publication databases that are internally managed. These databases allow us to make a valid selection of publications from our sample population, compared to the use of Web of Science which has acknowledged biases for this field [2]. In this study we use DataBase systems and Logic Programming (DBLP, see [3]) to acquire publication data. DBLP is server which provides bibliographic information on major Computer Science journals and conference proceedings.

Why this survey? As mentioned in the introduction, my work up to now has solely used publicly available data about scientists, I aim to combine this with information about a specific set of scientists to provide greater insights into network dynamics. This survey is in part traditional- asking for information about position and gender to explain different dynamics. But also has a social network component- asking scientists to reflect on their co-authors during a period of five years. Social network surveys require the identification of specific individuals in order to collect information on the relationships between actors [4]. The data will in no way be used for any kind of evaluation. The answers are confidential and the data are treated anonymously. Scientists are asked about individual co-authors as compiled from DBLP, but these data will be aggregated for analysis and will never be connected to individual names. Neither will the presentation of the results relate to your name or your co-authors names. Access to the collected data is only available to the PhD researcher.

What insight can be drawn from such a study? The combination of richer data, not available on the Web, allows more concrete insights into dynamics that contributes not only to scientific knowledge but also practical knowledge for the scientists themselves, of any field, but also policymakers. It allows the further extension of models that I have been using in my PhD research to contribute to knowledge on under what conditions social networks evolve.

[1] Birkholz, J.M., Bakhshi, R., Harige, R., van Steen, M., & Groenewegen, P. (2012). Scalable Analysis for Large Social Networks: the data-aware mean-field approach. Social Informatics, 406-419, see:
[2] Bar-Ilan, J. (2010). Web of Science with the Conference Proceedings Citation Indexes: the case of computer science. Scientometrics, 83(3), 809-824.

[3] DBLP-

[4] Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.


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