What drives academic data sharing? Fecher, Benedikt; Friesike, Sascha; Hebing, Marcel
PloS one,
02/2015, Letnik:
10, Številka:
2
Journal Article
Recenzirano
Odprti dostop
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.
Setting up crowd science projects Scheliga, Kaja; Friesike, Sascha; Puschmann, Cornelius ...
Public understanding of science,
07/2018, Letnik:
27, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Crowd science is scientific research that is conducted with the participation of volunteers who are not professional scientists. Thanks to the Internet and online platforms, project initiators can ...draw on a potentially large number of volunteers. This crowd can be involved to support data-rich or labour-intensive projects that would otherwise be unfeasible. So far, research on crowd science has mainly focused on analysing individual crowd science projects. In our research, we focus on the perspective of project initiators and explore how crowd science projects are set up. Based on multiple case study research, we discuss the objectives of crowd science projects and the strategies of their initiators for accessing volunteers. We also categorise the tasks allocated to volunteers and reflect on the issue of quality assurance as well as feedback mechanisms. With this article, we contribute to a better understanding of how crowd science projects are set up and how volunteers can contribute to science. We suggest that our findings are of practical relevance for initiators of crowd science projects, for science communication as well as for informed science policy making.
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.
Digital technologies carry the promise of transforming science and opening up the research process. We interviewed researchers from a variety of backgrounds about their attitudes towards and ...experiences with openness in their research practices. We observe a considerable discrepancy between the concept of open science and scholarly reality. While many researchers support open science in theory, the individual researcher is confronted with various difficulties when putting open science into practice. We analyse the major obstacles to open science and group them into two main categories: individual obstacles and systemic obstacles. We argue that the phenomenon of open science can be seen through the prism of a social dilemma: what is in the collective best interest of the scientific community is not necessarily in the best interest of the individual scientist. We discuss the possibilities of transferring theoretical solutions to social dilemma problems to the realm of open science.
The present study explores the phenomenon of remixing in product design for additive manufacturing (AM). In contrast to other manufacturing techniques, AM offers unprecedented flexibility in adapting ...existing product designs to changing requirements. However, in order to benefit from this potential, structured design procedures and tools are indispensable. As a possible solution, online platforms for collaborative 3D model creation are increasingly implementing features for remixing, a concept describing the creation of new models on the foundation of existing design elements. Against this backdrop, the objective of this research is to provide evidence for the value of remixing as an organizational intervention for improving product design processes. To this end, we present a mixed methods approach using data from Thingiverse, the world's largest AM‐related online community. In a first step, we investigate qualitative data from 81 individual remix‐based designs to identify the underlying mechanisms of remixing. We identify six such mechanisms that can further be grouped by the intended outcome of the respective process (creativity‐oriented: inspiration, play, learning; productivity‐oriented: speed, improvement, empowerment). In a second step, we turn to a quantitative analysis of platform data, which indicates that remixing may lead to better design process outcomes in terms of quantity and diversity of designs. Furthermore, we find that designs created by remixing designers are significantly more often printed by community members suggesting that remixing helps ensure manufacturing compatibility akin to continuous process improvement. Our research has several implications for individual designers and organizations engaging with product design for AM.
The shift towards open innovation has substantially changed the academic and practical understanding of corporate innovation. While academic studies on open innovation are burgeoning, most research ...on the topic focuses on the later phases of the innovation process. So far, the impact and implications of the general tendency towards more openness in academic and industrial science at the very front-end of the innovation process have been mostly neglected. Our paper presents a conceptualization of this
open science
as a new research paradigm. Based on empirical data and current literature, we analyze the phenomenon and propose four perspectives of open science. Furthermore, we outline current trends and propose directions for future developments.
Job Careers in Science and Engineering; Computers and Society; Communication Studies; Web 2.0 and interoperability; Scientific micro blogging; Social networking platforms; Creative commons; Dynamic ...publication formats; Scientific intellectual property; Collaborative work; Scientific wiki; Open source science; Open data
The reuse of existing knowledge is an indispensable part of the creation of novel ideas.
In the creative domain knowledge reuse is a common practice known as “remixing”. With the
emergence of open ...internet-based platforms in recent years, remixing has found its way
from the world of music and art to the design of arbitrary physical goods. However,
despite its obvious relevance for the number and quality of innovations on such platforms,
little is known about the process of remixing and its contextual factors. This paper
considers the example of Thingiverse, a platform for the 3D printing community that allows
its users to create, share, and access a broad range of printable digital models. We
present an explorative study of remixing activities that took place on the platform over
the course of six years by using an extensive set of data on models and users. On the
foundation of these empirically observed phenomena, we formulate a set of theoretical
propositions and managerial implications regarding (1) the role of remixes in design
communities, (2) the different patterns of remixing processes, (3) the platform features
that facilitate remixes, and (4) the profile of the remixing platform's users.
Over the last few decades, two domains have undergone seemingly similar transformations: Closed innovation turned into open innovation, closed science into open science. In this essay we engage ...critically with recent calls for a close coupling of the two domains based on their apparent commonality: openness. Comparing the historically-specific ways in which openness has been defined and mobilised, we find substantial differences between open innovation and open science. While openness in innovation was developed as an analytic concept and redefined quite flexibly over time, openness in science was created as a programmatic concept and its initial definition has been preserved rather rigidly. Contrasting openness in innovation and science helps anticipate some of the unintended consequences that a close coupling of these domains might yield. A close coupling might alienate advocates for change within the academic community, marginalise maintenance-oriented collaborations between science and practice, and increase the dependence of science on profit-oriented platforms. Reflecting upon these unintended consequences can help policy-makers and researchers to fine-tune their concepts for new forms of engagement across the science-practice divide.
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