Knowledge repositories. In digital knowledge we trust
- PDF / 489,287 Bytes
- 5 Pages / 595.276 x 790.866 pts Page_size
- 48 Downloads / 278 Views
EDITORIAL
Knowledge repositories. In digital knowledge we trust Tsjalling Swierstra1,2 · Sophia Efstathiou2 Published online: 17 September 2020 © Springer Nature B.V. 2020
It is self-evident that the practice of healthcare makes extensive use of scientific knowledge, while also contributing to it. However, the scientific work processes and the people that generate scientific knowledge often remain somewhat in the background of healthcare delivery. It is therefore less visible that when biomedical scientific practice changes, particularly in its methods, devices, and stakeholders, those changes also deeply affect how healthcare is organized and performed. This special section explores one such pervasive shift in biomedical science-making, and its effects on healthcare stakeholders: the rapidly increasing use of computational tools and resulting data. Somewhat unusually, the special section is spread over two issues: the contributions by Nydal et al. and by Gabrielsen have already been published in issue 23(3), the remaining three contributions constituting the special section you find in the current issue 23(4), right after this introduction. Though the idea of big data biology arguably dates back to post-WWII Big Science initiatives in natural science and ecology (Aronova et al 2010), the introduction of computers into the experimental and observational practices of biology crucially enabled the genomics revolution. Current bioscience is increasingly digital and—in the terms of philosopher of science Sabina Leonelli– ‘data-centric’: it aims to produce and handle data in new collective ways, while preserving enough detail to make data useful in particular and individual contexts (2016). Computational tools have raised important questions about the methods of ‘big data biomedicine’: is bioscience transforming into an engineering discipline “changing the living world without trying to understand it”, as microbiologist Carl Woese asks (2004,
* Tsjalling Swierstra [email protected] 1
Department of Philosophy, Maastricht University, Maastricht, The Netherlands
Department of Philosophy, Norwegian University of Science and Technology, Trondheim, Norway
2
173)? Is this work introducing new ways of thinking and doing science, e.g. by considering knowledge as a computable thing (Efstathiou et al 2019), or is this type of datacentric work a scaling up of past attempts at (perspectival and limited) scientific understanding (Callebaut 2012)? And what are the aims of this work? Are the extensive population data collected as part of ‘personalising’ medicine no more than “promissory data” to be used to achieve good results in some unidentified future (Hoeyer 2019)? Or can we imagine a future for personalised medicine that considers questions of justice and access alongside improved scientific understanding (Prainsack 2017)? Expectations for ‘decoding’ the book of life have been undercut by more messy realities of -omic complexities, but understanding and addressing health and illness on the molecular le
Data Loading...
