How Do Researchers Achieve Societal Impact? Results of an Empirical Survey Among Researchers in Germany

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

A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing

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

DDI — more than just an XML-metadata-standard

The DDI standard (Codebook / Lifecycle) is designed as an XML standard. In the process of “moving forward” the community is working on a model-based representation of the concepts and structures included in the standard. But what is this good for? XML is only one possible solution for the technical representation of the metadata — and there many other possibilities. The presentation gives an overview of technologies that are actively used by members of the community, like storing metadata in relational databases, developing APIs to link software systems, representing the standard as classes in object-oriented-languages and others. A particular focus lays on the JSON format, which has become increasingly important recently in the field of web-development. A second aspect of this presentation is that DDI could be used for more than just metadata — it might also be a good starting point for the storage and exchange of research data, providing an alternative to the common formats of proprietary statistical software packages. The presentation is intended for both a technical and a non-technical audience

A Metadata-Driven Approach to Panel Data Management and its Application in DDI on Rails

This dissertation designs a metadata-driven infrastructure for panel data that aims to increase both the quality and the usability of the resulting research data. Data quality determines whether the data appropriately represent a particular aspect of our reality. Usability originates notably from a conceivable documentation, accessibility of the data, and interoperability with tools and other data sources. In a metadata-driven infrastructure, metadata are prepared before the digital objects and process steps that they describe. This enables data providers to utilize metadata for many purposes, including process control and data validation. Furthermore, a metadata-driven design reduces the overall costs of data production and facilitates the reuse of both data and metadata. The main use case is the German Socio-Economic Panel (SOEP), but the results claim to be re-usable for other panel studies. The introduction of the Generic Longitudinal Business Process Model (GLBPM) and a general discussion of digital objects managed by panel studies provide a generic framework for the development of a metadata-driven infrastructure for panel studies. A first theoretical application presents two designs for variable linkage to support record linkage and statistical matching with structured metadata: concepts for omnidirectional relations and process models for unidirectional relations. Furthermore, a reference architecture for a metadata-driven infrastructure is designed and implemented. This provides a proof of concept for the previous discussion and an environment for the development of DDI on Rails. DDI on Rails is a data portal, optimized for the documentation and dissemination of panel data. The design considers the process model of the GLBPM, the generic discussion of digital objects, the design of a metadata-driven infrastructure, and the proposed solutions for variable linkage

Reputation instead of obligation: forging new policies to motivate academic data sharing.

Despite strong support from funding agencies and policy makers academic data sharing sees hardly any adoption among researchers. Current policies that try to foster academic data sharing fail, as they try to either motivate researchers to share for the common good or force researchers to publish their data. Instead, Dr Sascha Friesike, Benedikt Fecher, Marcel Hebing, and Stephanie Linek argue that in order to tap into the vast potential that is attributed to academic data sharing we need to forge new policies that follow the guiding principle reputation instead of obligation

Replikationen, Reputation und gute wissenschaftliche Praxis

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

What Drives Academic Data Sharing?

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

What Drives Academic Data Sharing?

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 as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress

Documenting Panel Data

<p>The key characteristics of panel studies include repeated measures for a more or less stable sample over time. The core challenge in documenting panel studies is the documentation of these repeated measures (usually questions) and the resulting variables because various reasons can require modifications of measures over time - resulting in comparable but not identical data structures.</p> <p>The DDI standard provides not one but multiple options for the documentation of panel data. In this workshop we like to present various options and discuss their feasibility for common use cases. The German Socio-Economic Panel (SOEP) will provide the primary use case, but participants are also invited to introduce and discuss their own use cases.</p> <p>The workshop starts with a short introduction of both panel studies and the DDI standard. Therefore, no previous knowledge of the DDI standard is required to participate in the workshop. The goal for the workshop is to gain a deeper understanding of possible documentation strategies for panel studies.</p

A reputation economy: how individual reward considerations trump systemic arguments for open access to data

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