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Based on a communication-centered approach, this article examines how researchers approach societal impact, that is, what they think about societal impact in research governance, what their societal goals are, and how they use communication formats. Hence, this study offers empirical evidence on a group that has received remarkably little attention in the scholarly discourse on the societal impact of research—academic researchers. Our analysis is based on an empirical survey among 499 researchers in Germany conducted from April to June 2020. We show that most researchers regard societal engagement as part of their job and are generally in favor of impact evaluation. However, few think that societal impact is a priority at their institution, and even fewer think that institutional communication departments reach relevant stakeholders in society. Moreover, we show that researchers’ societal goals and use of communication formats differ greatly between their disciplines and the types of organization that they work at. Our results add to the ongoing metascientific discourse on the relationship between science and society and offer empirical support for the hypothesis that assessment needs to be sensitive to disciplinary and organizational context factors.

It is widely acknowledged that data sharing has great potential for scientific progress. However, so far making data available has little impact on a researcher’s reputation. Thus, data sharing can be conceptualized as a social dilemma. In the presented study we investigated the influence of the researcher's personality within the social dilemma of data sharing. The theoretical background was the appropriateness framework. We conducted a survey among 1564 researchers about data sharing, which also included standardized questions on selected personality factors, namely the so-called Big Five, Machiavellianism and social desirability. Using regression analysis, we investigated how these personality domains relate to four groups of dependent variables: attitudes towards data sharing, the importance of factors that might foster or hinder data sharing, the willingness to share data, and actual data sharing. Our analyses showed the predictive value of personality for all four groups of dependent variables. However, there was not a global consistent pattern of influence, but rather different compositions of effects. Our results indicate that the implications of data sharing are dependent on age, gender, and personality. In order to foster data sharing, it seems advantageous to provide more personal incentives and to address the researchers’ individual responsibility.

Open access to research data has been described as a driver of innovation and a potential cure for the reproducibility crisis in many academic fields. Against this backdrop, policy makers are increasingly advocating for making research data and supporting material openly available online. Despite its potential to further scientific progress, widespread data sharing in small science is still an ideal practised in moderation. In this article, we explore the question of what drives open access to research data using a survey among 1564 mainly German researchers across all disciplines. We show that, regardless of their disciplinary background, researchers recognize the benefits of open access to research data for both their own research and scientific progress as a whole. Nonetheless, most researchers share their data only selectively. We show that individual reward considerations conflict with widespread data sharing. Based on our results, we present policy implications that are in line with both individual reward considerations and scientific progress.

The acceptance and adoption of a standard like DDI highly depends on the availability of software tools to use it. The DDI Developers Community is a part of the DDI Alliance where software developers from around the world can meet and swap ideas on working with DDI in various programming environments and languages. In this session we like to give you an introduction to our work and present you a selection of available tools. This Session will give you an overview of tools available from the community. Most of the presenters will be available during the subsequent poster session for detailed questions or further demonstrations of their tools.

Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with “old” data. It is therefore not surprising that data sharing is advocated by funding agencies, journals, and researchers alike. We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole — still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation — expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others — namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.

As presented at IASSIST 2014 conference

This paper describes key characteristics of SOEP users, measures their satisfaction with SOEP service, and studies their habits in the use of the SOEP data. The analysis is based primarily on data from the SOEP user surveys conducted in 2004, 2011, 2012, and 2013. Older user surveys (in the 1980s and 1990s) are documented briefly. The description of users focuses on their research habits and preferred analytical methods. In the area of user satisfaction, the focus is on data access, the data themselves, and data documentation. As a special feature of the 2012 survey, questions from the main SOEP survey were included to allow for a comparison between SOEP users and SOEP respondents. The results give insight into the research habits of empirical social, behavioral, and economic researchers. The results point to significant changes, particularly in the statistical packages used and the preferred data formats. Dieses Papier charakterisiert die SOEP-Nutzer, beschreibt ihre Zufriedenheit mit dem SOEP-Service und untersucht ihre Nutzungsgewohnheiten bei der Arbeit mit den Daten. Die Untersuchungen bauen primär auf den Daten der SOEP-Nutzerbefragungen 2004, 2011, 2012 und 2013 auf. Ältere Nutzerbefragungen (in den 1980er und 1990er Jahren) werden kurz dokumentiert. Die Beschreibung der Nutzer konzentriert sich auf deren Forschungsgewohnheiten und bevorzugten Analysemethoden. Im Bereich der Zufriedenheit geht es primär um die Bereiche des Datenzugangs, der Daten selbst und ihrer Dokumentation. Als Besonderheit der 2012er Erhebung wurden Fragen aus der SOEP-Haupterhebung eingebaut, welche einen Vergleich von Nutzern und SOEP-Befragten ermöglichen. Die Ergebnisse geben dabei Einblicke in die Forschungsgewohnheiten von empirischen Sozial-, Verhaltens- und Wirtschaftsforscher. Insbesondere hinsichtlich der verwendeten Statistikpakete und der bevorzugten Datenformate zeichnen sich deutliche Veränderungen ab.

Up to now in the social sciences, what is known as citizen science—the involvement of interested citizens in scientific surveys—has been used relatively little as a method of empirical social research. While the “citizens’ dialogues” that are becoming more widespread in politics can be considered a kind of social scientific citizen science, the participants in these dialogues are not selected randomly from the population (as is the case in surveys) but volunteer to participate because of their interest in actively shaping the public sphere. However, the socio-structural characteristics of participants in citizens’ dialogues are usually unknown and therefore not statistically comparable with the characteristics of the population at large or of specific population groups. In the present paper, we report on a pretest conducted with visitors to the Long Night of the Sciences 2014 in Berlin (http://www.langenachtderwissenschaften.de/). At the event, visitors to the event—who are a kind of citizen scientists—were surveyed on socio-structural characteristics and were also asked whether they would be willing to take part in later focus group discussions. The survey was conducted with 31 participants (out of a total of around 150 visitors to DIW Berlin on the evening of the event), who answered questions on their socio-economic characteristics. Of these, eight individuals agreed to take part in later, more in-depth (focus group) discussions. The technology developed for this paper introduces the survey to respondents, describes the recording and analysis the statistical results, and extends to a statement on data privacy and the computer-based survey itself.

German Abstract: Dieses Papier charakterisiert die SOEP-Nutzer, beschreibt ihre Zufriedenheit mit dem SOEP-Service und untersucht ihre Nutzungsgewohnheiten bei der Arbeit mit den Daten. Die Untersuchungen bauen primar auf den Daten der SOEP-Nutzerbefragungen 2004, 2011, 2012 und 2013 auf. Altere Nutzerbefragungen (in den 1980er und 1990er Jahren) werden kurz dokumentiert. Die Beschreibung der Nutzer konzentriert sich auf deren Forschungsgewohnheiten und bevorzugten Analysemethoden. Im Bereich der Zufriedenheit geht es primar um die Bereiche des Datenzugangs, der Daten selbst und ihrer Dokumentation. Als Besonderheit der 2012er Erhebung wurden Fragen aus der SOEP-Haupterhebung eingebaut, welche einen Vergleich von Nutzern und SOEP-Befragten ermoglichen. Die Ergebnisse geben dabei Einblicke in die Forschungsgewohnheiten von empirischen Sozial-, Verhaltens- und Wirtschaftsforscher. Insbesondere hinsichtlich der verwendeten Statistikpakete und der bevorzugten Datenformate zeichnen sich deutliche Veranderungen ab. English Abstract: This paper describes key characteristics of SOEP users, measures their satisfaction with SOEP service, and studies their habits in the use of the SOEP data. The analysis is based primarily on data from the SOEP user surveys conducted in 2004, 2011, 2012, and 2013. Older user surveys (in the 1980s and 1990s) are documented briefly. The description of users focuses on their research habits and preferred analytical methods. In the area of user satisfaction, the focus is on data access, the data themselves, and data documentation. As a special feature of the 2012 survey, questions from the main SOEP survey were included to allow for a comparison between SOEP users and SOEP respondents. The results give insight into the research habits of empirical social, behavioral, and economic researchers. The results point to significant changes, particularly in the statistical packages used and the preferred data formats.

We propose different behaviour and interaction related indicators of artificial actors (bots) and show how they can be separated from natural users in a virtual dating market. A finite mixture classification model is applied on the different behavioural and interactional information to classify users into bot vs. non-bot-categories. Finally the validity of the classification model and the impact of bots on sociodemographic distributions and scientific analysis is discussed.