| dc.description.abstract | The amount of publicly available brain-related data has significantly increased over the past decade. Neuroscience data is spread across a variety of sources, typically provisioned in ad-hoc manners and non-standard formats, and often with no connections between the various sources. This makes it difficult for researchers to understand, integrate, and reuse brain-related data. There is a clear need to find effective mechanisms to manage data in this field, especially since brain-related data is highly interconnected, evolving over time, and often needed in combination. At the same time, the field of data management has recently seen a shift from representing data in the relational model towards alternative data models. Especially graph databases have seen an increase in use due to their ability to manage highly-interconnected, continuously evolving data. This thesis presents an approach for organizing brain-related data in a graph model, investigates how the graph representation affects the understanding of the data, how it facilitates the integration of data from various sources, and how it enhances the usability of the data. The thesis exemplifies the approach in the context of a unique data set of quantitative neuroanatomical data about the murine basal ganglia — a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modeled as a graph, integrated with relevant data from third-party repositories (Brain Architecture Management System, InterLex, and NeuroMorpho.Org), and analyzed this data using popular graph algorithms to extract new insights. Access to the data is provisioned via a web-based user interface and API. A thorough evaluation of the graph model and the results of the graph data analysis and usability study of the user interface indicate the potential of graph-based data management in the neuroscience domain. The thesis contributes with a practical and generic approach for representing, integrating, analyzing, and provisioning brain-related data, and a set of software tools to support the proposed approach. | eng |