Video Link: http://www.youtube.com/watch?v=NPlBejxhLJg&feature=youtu.be
The interdisciplinary nature of neuroscience research leads to an explosion of different informatics tools, data structures, platforms and terminologies. A central difficulty faced by developers is that knowledge representations for any neuroscience subdomain must serve the domain-specific needs of that specified sub-community. Related representations overlap, they contradict each other, they have competing standards. The process of standardization is itself difficult to organize within the community and even harder to enforce in practice. This involves complex issues involving ease of use, computability, data availability as well as scientific correctness and philosophical purity.
In this talk, I present a novel, relatively simple conceptual design that makes a clear distinction between interpretive and observation knowledge to build a general framework for scientific data. Our methodology (called 'Knowledge Engineering from Experimental Design' or KEfED) uses an experiment's protocol's to define the dependencies between its independent and dependent variables. These dependencies support the construction of a data structure that can capture (a) data points, (b) mean values, (c) statistical significance relations and (d) correlations. We will describe the underlying formalism of the KEfED approach, the tools we provide to help researchers build their own models, our approach to unify and standardize the definition of variables, the application of KEfED to complex neuroscience knowledge and possible research directions for this technology in the future.
The interdisciplinary nature of neuroscience research leads to an explosion of different informatics tools, data structures, platforms and terminologies. A central difficulty faced by developers is that knowledge representations for any neuroscience subdomain must serve the domain-specific needs of that specified sub-community. Related representations overlap, they contradict each other, they have competing standards. The process of standardization is itself difficult to organize within the community and even harder to enforce in practice. This involves complex issues involving ease of use, computability, data availability as well as scientific correctness and philosophical purity.
In this talk, I present a novel, relatively simple conceptual design that makes a clear distinction between interpretive and observation knowledge to build a general framework for scientific data. Our methodology (called 'Knowledge Engineering from Experimental Design' or KEfED) uses an experiment's protocol's to define the dependencies between its independent and dependent variables. These dependencies support the construction of a data structure that can capture (a) data points, (b) mean values, (c) statistical significance relations and (d) correlations. We will describe the underlying formalism of the KEfED approach, the tools we provide to help researchers build their own models, our approach to unify and standardize the definition of variables, the application of KEfED to complex neuroscience knowledge and possible research directions for this technology in the future.
