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Separation of Data, Interpreters and Likelihood

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March 2007 Technical Report to the LHNCBC Board of Scientific Counselors.
Abstract: 

One of the most important fallacies in information processing is the assumption that a particular interpretation of any given data is truth. Layers of complex information structures and belief systems are constructed on this foundation without questioning this assumption at each new layer. For example, only a small portion of information in the entire scientific literature is backed up with complete, currently available data from which the information has been deduced. For the rest, the data are no longer available; hence, there is no way to reinterpret the data and validate their conclusions. A sustainable model of biomedical data, information and knowledge requires a repository of objective data and metadata, associated with a set of software interpreters or interpretation protocols (algorithms). Outputs of interpreters become new sets of data to be interpreted by other interpreters. This model preserves not only abstractions and other underlying assumptions explicitly but also enables coexistence of differing interpretations, which may yield different conclusions on the same data.

Kayaalp M. Separation of Data, Interpreters and Likelihood March 2007 Technical Report to the LHNCBC Board of Scientific Counselors.