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Bioinformatics as an adaptable model for HPS informatics

The life sciences have experienced unprecedented growth and developed a new explanatory paradigm of systems biology that has involved a shift away from simple causal explanations based on clearly identifiable factors (such as individual genes) towards an emphasis on interactions among multiple factors.

Technological and conceptual advances in bioinformatics have played a central transformative role in this transition. Consequently, the life sciences are now to a large degree information-based with the relevant information stored in both centralized and distributed databases. Sophisticated search algorithms and queries based on conceptual models with standardized vocabularies and shared ontologies, standardized annotation practices and new generations of relational databases connecting different kinds of data provide the foundation.  All these developments have contributed to robust cyber-infrastructure, which has changed the ways biologists go about their work.

Other sciences, such as the physical and earth sciences as well as parts of the social sciences have adopted similar approaches. As a consequence, all these fields have become increasingly collaborative and interdisciplinary in the sense that cutting-edge research requires a whole range of different methodological approaches. And all these advances have been built on an advanced cyberinfrastructure that enables collaboration through shared data and methods.   

Science studies scholars, including history and philosophy of science (HPS) scholars, have also always emphasized complex explanations of historical events.  Yet in contrast, these are mostly presented as richly contextual narratives by individual scholars after years of (isoloated) scholarship.

The science studies community has not yet embraced the enormous benefits of the informatics revolution with respect to organization of multiple forms of complex data, shared access to data, searches in distributed relational databases that are organized around standardized practices of database management, and possibilities for digital workbenches for collaborative and distributed research.  

Without such new ways to conduct and organize research and to store, access, distribute and analyze data, the science studies community cannot yet carry out transformative scholarship to understand the complexities and inter-connections among the sciences.  Insofar as one goal is to better understand and explain to others science at large in its various contexts (technological, theoretical, historical, social or political), informatics approaches can help individual scholars move beyond the particular and discover general patterns that are not visible without such tools.

Adopting new methods, scholars can carry out their individual studies and also contribute to a collaborative enterprise that goes far beyond each one.  An effective science studies research collaboration needs to develop shared informatics tools and infrastructure for different kinds of data and knowledge. Small pilot projects suggest that such approaches transform the ways researchers work and the knowledge products they generate.

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