How and under what conditions can academic research contribute to solving societal challenges? So far, research on this topic has focused on questions of impact measurability and the public perception of research, and far less on the question of how researchers themselves assess their societal impact. In the same way that it is important to understand how the public receives research, it is important to better understand how researchers anticipate the public and achieve societal impact in order to draft effective policies. In this article we report the results of an empirical survey among 499 researchers in Germany on their pathways to societal impact, i.e. their attitudes towards impact policies, their societal goals and use of engagement formats. We are able to 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 fewer think that institutional communication departments reach relevant stakeholders in society. Moreover, we are able to show that impact goals differ greatly between disciplines and organizational types. Based on our results, we give recommendations for a governance of impact that is responsive to epistemic cultures and point towards avenues for further research.
In Zeiten wachsender Publikationszahlen und zunehmend datenintensiver Forschung stoßen die klassischen Qualitätssicherungsmaßnahmen, wie die Peer-Review, an ihre Grenzen. Vor diesem Hintergrund werden Replikationsstudien verstärkt als gute wissenschaftliche Praxis und Lösungsansatz diskutiert, um dem Problem methodisch unzureichender und oftmals fehlerbehafteter Analysen zu begegnen. Denn schlechte Analysen untergraben nicht zuletzt das Vertrauen der Öffentlichkeit in die Wissenschaft. Dennoch werden in allen Disziplinen bisher nur wenige Replikationsstudien durchgeführt. In diesem Aufsatz zeigen wir die zentralen Probleme bei der Replizierbarkeit wissenschaftlicher Ergebnisse auf und schlagen Maßnahmen vor, die auf den impliziten Reputationsmechanismen der akademischen Wissenschaft beruhen.
Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher's point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients . Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.
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.
In den Sozialwissenschaften spielt die sogenannte Citizen Science, also der Einbezug von an Wissenschaft interessierte Bürgerinnen und Bürgern in wissenschaftliche Erhebungen als Methode empirischer Sozialforschung bislang keine Rolle. Freilich sind die in der Politik zunehmend häufiger anzutreffenden Bürgerdialoge eine Art sozialwissenschaftlicher Citizen Science, jedoch werden die Teilnehmer nicht zufällig ausgewählt (wie bei Survey-Erhebungen). Sondern Bürgerinnen und Bürger, die an der aktiven Gestaltung des Gemeinwesens interessiert sind, melden sich selbst. Deren sozial-strukturellen Merkmale sind aber in der Regel nicht bekannt und nicht mit den Merkmalen aller Mitbürgerinnen und Mitbürgern bzw. ausgewählter Gruppen statistisch vergleichbar. Im vorliegenden Papier wird über einen Pretest berichtet, mit dem bei Besucherinnen und Besucher der Langen Nacht der Wissenschaft in Berlin (http://www.langenachtderwissenschaften.de/) - also eine Art von Citizen Scientists - sozial-strukturelle Merkmale erhoben und zudem die Bereitschaft für spätere Fokus- Gruppen-Gespräche erfragt wurde. Für 31 Interessierte (von insgesamt etwa 150 Besuchern beim Deutschen Institut für Wirtschaftsforschung - DIW Berlin) werden sozio-ökonomische Merkmale erhoben. Darunter haben acht Personen die Bereitschaft für vertiefende (Fokus-Gruppen)-Gespräche angegeben. Die für dieses Papier entwickelte Technologie beginnt bei der Aufklärung der Befragten zur Erhebung, Speicherung sowie Analyse ihrer statistischen Angaben und reicht über die Datenschutzerklärung hin zur eigentlichen computergestützten Befragung. ; 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.
