NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF.

Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.

Enabling the sharing of neuroimaging data through well-defined intermediate levels of visibility.

The sharing of neuroimagery data offers great benefits to science, however, data owners sharing their data face substantial custodial responsibilities, such as ensuring data sets are correctly interpreted in their new shared context, protecting the identity and privacy of human research participants, and safeguarding the understood order of use. Given choices of sharing widely or not at all, the result will often be no sharing, due to the inability of data owners to control their exposure to the risks associated with data sharing. In this context, data sharing is enabled by providing data owners with well-defined intermediate levels of data visibility, progressing incrementally toward public visibility. In this paper, we define a novel and general data sharing model, Structured Sharing Communities (SSC), meeting this requirement. Arbitrary visibility levels representing collaborative agreements, consortium memberships, research organizations, and other affiliations are structured into a policy space through explicit paths of permissible information flow. Operations enable users and applications to manage the visibility of data and enforce access permissions and restrictions. We show how a policy space can be implemented in realistic neuroinformatic architectures with acceptable assurance of correctness, and briefly describe an open source implementation effort.