The NeuroScholar System is a neuroscientific knowledge management system for the published literature.
The NeuroScholar Project is the flagship project for the Biomedical Knowledge Engineering Research Group at the Information Sciences Institute in Marina Del Rey. We are a group specializing in computational approaches (based on Natural Language Processing and Knowledge Engineering) to computing with information drawn from the scientific literature.
The literature is the natural repository for scientific knowledge, our mission is therefore to find ways to make that information readily available to non-computational biomedical scientists and help them organize their understanding of their systems of interest.
The subject of neuroscience is complex, broad and deep. It uses data from many disciplines: anatomy, physiology, chemistry, physics, molecular biology, cognitive science and ethology to name a few. It traverses many temporal and spatial scales; from milliseconds to generations, and angstroms to meters. The brain itself has been called 'the most complex object in the known universe' (by Nobel-Prize winner James Watson) and the number of individual cells, and connections between cells is (literally) astronomical. The biggest challenge to understanding the large-scale organization of the brain across systems, modalities and scales is therefore complexity of our own data. We contend that knowledge management systems could be built that address this challenge.
The NeuroScholar project provides knowledge engineering software for use by the neuroscience community. The basic framework of our approach is illustrated in the Figure shown below. Shown here is a typical scenario facing neuroscientists: the information required is scattered throughout a number of knowledge sources (in the literature, on the web, in local data files in the lab). NeuroScholar permits users to capture 'fragments' from the knowledge sources, which then can be used to define knowledge representation items within the primary system.The NeuroScholar system permits users to isolate fragments of data from those sources and then bring them together to form facts which may then be incorporated with interpretations and relations to build representations of knowledge within NeuroScholar that may then be used.
Rather than having to remember or keep physical notes about the thousands of individual facts, assumptions and interpretations that underlie a theoretical perspective, scientists can use NeuroScholar to store, retrieve, evaluate and communicate what they think and the reasoning that defines why they think it.
The NeuroScholar system is the flagship application of this project but we have developed other tools to be used in conjunction with the system. These deliver specialized functionality to a neuroscience knowledge user such as the 'Electronic Laboratory Notebook' (ELN), support for schematic diagrams (Diagrammar) and neuroanatomical mapping functions (NeuARt II). The NAWS system is a method for using NeuroScholar to be able to run analyses on it's contents as a remote webservice, and the Sangam project is concerned with intergrating information between different web services (of which NeuroScholar could be one). We have also built software engineering tools to assist with the construction of NeuroScholar-like knowledge bases. This subsystem is called the 'View-Primitive Data Model framework' (VPDMf').