Article
Promoting scientific impact by data sharing: choosing the right data platform in Life Sciences
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Authors
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Mareike Peters
- Kiel University, Institute of Business Administration and Innovation Management, Technology Management Research Group, Kiel, Germany
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Daniel Laufs
- Kiel University, Institute of Business Administration and Innovation Management, Technology Management Research Group, Kiel, Germany
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Carsten Schultz
- Kiel University, Institute of Business Administration and Innovation Management, Technology Management Research Group, Kiel, Germany
| Published: | October 7, 2020 |
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Outline
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Extensive interdisciplinary research projects profit from Open Science approaches, as they foster the collaboration far beyond existing research boundaries. Data platforms are a centerpiece in Open Science and repeatedly demanded by researchers, because the platforms can centralize and link data and go beyond pure data storage. As a result, data platforms are increasingly gaining a place in the scientific community as new intermediaries. Present literature focuses on commercial data platforms and underlying business models, yet there is a lack of understanding of scientific open data platforms. The aim of this empirical qualitative-oriented study is the systematic investigation of data platforms in life sciences through the investigation on how they can be classified and utilized. We further examine how platform governance is designed, how data exchange can be incentivized, and how quality can be ensured and trust be built.
For interdisciplinary research projects like FucoSan, it is especially important to carefully chose the right platform, based on the heterogeneity of the type, format, structure, and volume of the data. Structuring heterogenous data might be a challenge but is inevitable to allow outsiders to utilize complex data. A suitable platform enables knowledge conservation, as specific data may be shared, cited and discovered even after the formal end of the project, functioning as a door opener for future research.
To answer the research questions, a qualitative-oriented study was conducted. To get an overview of the platforms, we first examined 223 platforms and developed four different classes, based on the function of the platform: Data platforms, Distributor platforms, Aggregator platforms and Umbrella platforms. Focusing on the class of Data platforms, we developed the ‘Domain-specificity and Extent-of-Technology Intermediation Matrix‘ for further classification. We distinguish three categories, each providing different value creation: Generalist platform, Backbone platform and Enabler platform. We systematically investigated 22 data platforms by means of individual, semi-structured expert interviews, examining papers about investigated platforms, doing research on platform websites and collecting further data from database searches. We performed an evaluation of the transcribed interviews using content analysis with inductive category formation.
Our results show that several factors determine the success of data platforms in life sciences. Appropriate technology and platform governance determine the framework for the use of data and the platform. The strategic alignment in research communities and providing incentives is crucial. Furthermore, trust in data and platform as well as data quality determine usage. Depending on the platform category, the focus of the presented success factors differs.
Our findings suggest that a Generalist platform is most suitable for FucoSan. Generalist platforms offer a high degree of open platform governance and flexibility concerning the requirements of data. Yet, this flexibility comes to the expense of assuring quality standards. Trust can be built by platform disclosure. Therefore, FucoSan members must ensure a high quality of the data themselves and provide additional information on methods and metadata.
