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Browse titles from CUP. Keep up-to-date with NHBS products, news and offers. Book Description New Book. Shipped from UK in 4 to 14 days. Established seller since Seller Inventory BB Bookseller Inventory ST Seller Inventory ST Paul Griffiths. Publisher: Cambridge University Press , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title In the past century, nearly all of the biological sciences have been directly affected by discoveries and developments in genetics, a fast-evolving subject with important theoretical dimensions.

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Customers who bought this item also bought. Seller Image. New Paperback or Softback Quantity Available: Seller Rating:. Stock Image. This profound fragmentation, which philosophers call pluralism Kellert et al.

Cambridge Introductions to Philosophy and Biology: Genetics and Philosophy: An Introduction

Finding ways to tackle pluralism is a key challenge for big data biology. It is easy to dismiss these difficulties as purely technical matters that can be overcome by, for example, using interoperable databases and file formats to integrate data from difference sources so that they can be used and re-used across a variety of research contexts.

However, there are deeper conceptual and philosophical difficulties. The considerable labour involved in devising credible retrieval systems for biological databases speak to the difficulty of this task: this difficulty is illustrated by the lively debates over the definitions of terms such as 'pathogen' and 'metabolism' on the Gene Ontology database The Gene Ontology Consortium, The implications for big data biology are substantive. The choice and definition of keywords used to classify and retrieve data matters enormously to their subsequent interpretation.

Linking diverse datasets means making decisions about the concepts through which nature is best represented and investigated. In other words, the networks of concepts associated with data in big data infrastructures should be viewed as theories: ways of seeing the biological world that guide scientific reasoning and the direction of research, which are often revised to take into account new discoveries Leonelli et al.

The quest for large-scale data integration makes it necessary for all biological disciplines to identify such theories and debate their implications for the modelling and analysis of big data Leonelli, We need to acknowledge that no data are 'raw' in the sense of being independent from human interpretation. These studies demonstrated that biological concepts — no matter how loosely defined — are always embedded in broader theoretical perspectives on how nature works Callebaut, This is not to say that big data biology is fully determined by pre-existing hypotheses.

Rather, it draws on current theories and hypotheses but does not let them predetermine research outcomes Waters, It is also important to note that, no matter which method is used to generate them, observations and measurements are always situated in a specific framework Bogen, Irrespective of how standardised they are, the instruments used to generate those data are built to satisfy specific research agendas Rheinberger, This means that we need to acknowledge that no data are 'raw' in the sense of being independent from human interpretation.

Moreover, data can be processed differently. It is thus important to understand the conceptual choices that shaped the production and classification of data. Researchers using big data need to recognise that the theoretical structures that informed the production and processing of the data will influence their future use. One might ask if pluralism is an obstacle to the integration of data from different sources and to the extraction of reliable and accurate knowledge from these data.

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Philosophers of science have argued that pluralism may actually be beneficial when attempting to extract knowledge about the highly complex and variable processes encountered in the life sciences Dupre, ; Mitchell, Fragmented research traditions arise from centuries of fine-tuning research tools in order to study a given process or species in as much detail as possible. While this makes it more challenging to generalise these tools and the resulting knowledge Levins, ; Wimsatt, , it also ensures that the data collected are robust and inferences are accurate Longino, ; Wylie, It is crucial for big data biology to build on this legacy by creating ways to work with data from diverse sources without misinterpreting their provenance or losing the insights they provide into the complexity of life.

Many databases are not peer reviewed or curated, and even when they are, assessments of quality and reliability are often specific to certain fields of research and cannot easily be transferred to other research fields or other kinds of studies in the same research field Floridi and Illari, ; Leonelli, The potential for loss of data quality grows the more databases become interoperable, since extensive data linkage makes it possible for unreliable data sources to pollute the overall reliability of online data collections.

This is another realm where pluralism seems to be a problem for big data biology. Does a lack of consensus on how to assess the quality of data signal a distinctive weakness of how biology can and should engage in big data research? One way to answer is to challenge the very understanding of the data on which this question is grounded. Thinking of data as being intrinsically good or bad — independent of context and goals of inquiry — means thinking of them as being static representations of nature that are useful because they accurately and objectively document a feature of the world at a particular time and place.

This view certainly motivates the search for definitive, universal and context-independent ways of assessing which data are reliable and which are not. But it does not take into account that data are often extensively processed artefacts resulting from highly planned interactions with the world; nor does it do justice to the observation that biologists have different views of what counts as reliable data, or what counts as data in the first place Borgman, ; Leonelli, Building on these insights, I have argued that data are 'relational': in other words, the objects that best serve as data can change depending on the standards, goals and methods used to generate, process and interpret those objects as evidence Leonelli, This explains why assessments of data quality always relate to a specific investigation.

It also accounts for the reluctance of researchers to trust data sources whose history is not clearly documented, and the related drive to collect metadata about data provenance.