In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data “impacts” the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics “foot-prints” is even lower (4–9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.

How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.

In this paper, we make the case for an open science in technology enhanced learning (TEL). Open science means opening up the research process by making all of its outcomes, and the way in which these outcomes were achieved, publicly available on the World Wide Web. In our vision, the adoption of open science instruments provides a set of solid and sustainable ways to connect the disjoint communities in TEL. Furthermore, we envision that researchers in TEL would be able to reproduce the results from any paper using the instruments of open science. Therefore, we introduce the concept of open methodology, which stands for sharing the methodological details of the evaluation provided, and the tools used for data collection and analysis. We discuss the potential benefits, but also the issues of an open science, and conclude with a set of recommendations for implementing open science in TEL.

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Zur Zeit gibt es starke Bemühungen, die offensichtlichen Defizite des wissenschaftlichen Kommunikationssystems zu beheben. Open Science hat das Potenzial, die Produktion und Verbreitung von wissenschaftlichem Wissen positiv zu verändern; es existiert aber keine gemeinsam geteilte Vision, die das System wissenschaftlicher Kommunikation beschreibt, welches wir erschaffen wollen. Zwischen April 2015 und Juni 2016 trafen sich in Wien die Mitglieder der Open Access Network Austria (OANA) Arbeitsgruppe "Open Access and Scholarly Communication", um diese Angelegenheit zu diskutieren. Das Hauptergebnis unserer Überlegungen sind zwölf Prinzipien, die die Eckpfeiler eines künftigen wissenschaftlichen Kommunikationssystems dedarstellen. Diese Prinzipien sollen einen kohärenten Bezugsrahmen für die Debatte zur Verbesserung des derzeitigen Systems liefern. Mit diesem Dokument hoffen wir, eine breite Diskussion über eine gemeinsame Vision für die wissenschaftliche Kommunikation im 21. Jahrhundert anzustoßen.

In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. First, we investigate the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we use co-readership patterns to map the field of educational technology. The resulting visualization prototype, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.

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