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Monitoring Ideation and Data Generation in the Digital Realm

Tracing the reuse and replication of this data appears to be highly relevant also in order to assess how effective public research funding is.
Speedcurve Performance Analytics
© This work by Clemens Blümel is licensed under CC BY 4.0.

Particularly important for knowledge production are the processes of data generation and data processing, all of which are not represented by publication statistics.

There are new resources, such as data repositories or other large databases for research and development data that allow for much more direct access to these processes. Many resources and research data are not mentioned by publications, decreasing their visibility and further lowering the chance that they are re-used or reproduced at a later time (Mayernik, Hart, Maull, & Weber, 2016).

According to the Open Data movement, however, it is precisely the lack of reuse and replication that facilitated problems in research quality (Kitchin, 2014). Large databases for research data are, for instance, DataCite, a community run service that is currently the world’s largest provider of research data (Robinson-Garcia, Mongeon, Jeng, & Costas, 2017). There are other Open Data repositories for specific scientific fields, such as Pangaea for the Earth and geological sciences.

Tracing the reuse and replication of this data appears to be highly relevant also in order to assess how effective public research funding is. Moreover, analysing the reusage of research data in Open Data repositories that are curated and managed may allow us to trace innovative and inventive activity that could not be traced by using existing data souces, such as publications. Analysing the platforms of publicly funded Open Data repositories in the genomic sciences have provided some insights into the dynamics of that process (Williams, 2013).

© This work by Clemens Blümel is licensed under CC BY 4.0.
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Openness in Science and Innovation

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