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A cross‐disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

Digital archives are the preferred means for open access to research data. They play essential roles in knowledge infrastructures -robust networks of people, artifacts, and institutions -but little is known about how they mediate information exchange between stakeholders. We open the "black box" of data archives by studying DANS, the Data Archiving and Networked Services institute of The Netherlands, which manages 50+ years of data from the social sciences, humanities, and other domains. Our interviews, weblogs, ethnography, and document analyses reveal that a few large contributors provide a steady flow of content, but most are academic researchers who submit datasets infrequently and often restrict access to their files. Consumers are a diverse group that overlaps minimally with contributors. Archivists devote about half their time to aiding contributors with curation processes and half to assisting consumers. Given the diversity and infrequency of usage, human assistance in curation and search remains essential. DANS' knowledge infrastructure encompasses public and private stakeholders who contribute, consume, harvest, and serve their data -many of whom did not exist at the time the DANS collections originated -reinforcing the need for continuous investment in digital data archives as their communities, technologies, and services evolve.

This paper introduces a new approach to detecting scientists' field mobility by focusing on an author's self-citation network, and the co-authorships and keywords in self-citing articles. Contrary to much previous literature on self-citations, we will show that author's self-citation patterns reveal important information on the development and emergence of new research topics over time. More specifically, we will discuss self-citations as a means to detect scientists' field mobility. We introduce a network based definition of field mobility, using the Optimal Percolation Method (LAMBIOTTE & AUSLOOS, 2005;2006). The results of the study can be extended to selfcitation networks of groups of authors and, generally also for other types of networks.

This special issue brings together eight papers from experts of communities which often have been perceived as different once: bibliometrics, scientometrics and informetrics on the one side and information retrieval on the other. The idea of this special issue started at the workshop ''Combining Bibliometrics and Information Retrieval'' held at the 14th International Conference of Scientometrics and Informetrics, Vienna, July 14-19, 2013. Our motivation as guest editors started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides.

Open research data are heralded as having the potential to increase effectiveness, productivity, and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behavior and ultimately help to improve the design of data discovery systems.

This is a preprint of an article accepted to be published in a special issue of Scientometrics: Gläser, J., Scharnhorst, A. and Glänzel, W. (eds), Same data -different results? Towards a comparative approach to the identification of thematic structures in science. This is the last paper in the Synthesis section of the special issue on 'Same Data, Different Results'. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.

In this paper newly established characteristics of the so-called Matthew Effect for Countries (MEC) are presented: field-dependency, time-stability, order of magnitude. We find that the MEC is observable in all main scientific fields that were investigated. Over fifteen years the MEC has been relatively stable. The MEC is a redistribution phenomenon at the macro-level of the sciences. Its magnitude is small; the MEC affects only about five percent of the world production of citations. The MEC, however, crucially impacts many nations when their "national loss of citations" amounts to a high percentage of their expected citations. The relationship between the MEC and Merton's Matthew Principle is discussed. It is our hypothesis that the MEC provides an additional approach for the assessment of the scientific performance of nations.