Partnerships and Collaborations
These seven groups are working with different implementations of the NeuroSys Data Management System. They provide valuable user feedback, and are essential for our testing, extension and refinement of the software.
Giorgio Ascoli, George Mason University
Jim Brinkley, University of Washington
Gully Burns, University of Southern California
Chris Fastnow, Montana State University
Bridget Garnsey, Bridger Informatics
Thom Hughes, Montana State University
Maryann Martone, University of California at San Diego
Giorgio Ascoli is leading an effort, in conjunction with the Neuroscience Information Framework (NIF), to build a community database of reconstructed neurons. This database contains digital reconstructions of neurons, related literature and a suite of software tools for acquiring, analyzing, visualizing and modeling the data. The database, Neuromorpho.org, has been implemented in a relational database using MySQL. Dr. Ascoli is interested in testing NeuroSys as a middleware application to annotate the reconstructions and subsequently load them and the associated metadata into their relational database. In addition, Dr. Ascoli and his colleagues will serve as beta testers of the MIEN software suite.
Jim Brinkley is Director of the Structural Informatics Group (SIG) at the University of Washington. Their Xbrain project is designed to allow researchers to query distributed databases and ontologies, where each database or ontology is "wrapped" as a web service that returns an XML document in response to a query. For native XML documents, the query language is XQuery. Although XBrain does allow XQueries to be saved for later reuse, it currently has no GUI-based system for creating the queries. One aspect of our collaboration will therefore be to investigate how we can incorporate the NeuroSys graphical query generator into the XBrain front-end, thereby allowing XBrain queries to be generated without requiring the user to understand the XQuery language.
Gully Burns and his colleagues at USC developed NeuroScholar, which provides a unique environment for creating a "knowledge representation" by superimposing structured ontologies over information fragments gleaned from the scientific literature. Recently this group has added an electronic laboratory notebook function to NeuroScholar. This application imports images, data files and scanned pages from a traditional lab notebook and provides the functionality to divide this information into concepts that can be part of a knowledge representation, along with information from the literature, thereby building a more comprehensive representation. NeuroSys could be used to preprocess the information prior to entering it into NeuroScholar. The query capabilities of NeuroSys would allow the investigator quick access to experimental metadata and images, streamlining the process of selecting fragments for inclusion in a knowledge representation.
Chris Fastnow, Assistant Director of Montana State University's Office of Planning and Analysis, has been working with our team to create an implementation of NeuroSys that will support a campus-wide survey of faculty activity. The faculty activity database provides a central repository of information that will be used in the annual review process, the development of annual reports and other publications, and for accountability, accreditation, and assessment efforts. You can explore a demo version of this software by logging in with username "demo" and password "demo" -- try it here!
Bridget Garnsey, CEO of Bridger Informatics, is engaged with us in a collaborative effort, funded through a grant from the Montana Research and Commercialization Board, to explore the possibility of developing a commercial version of NeuroSys for use as a database system for small start-up companies.
Thom Hughes and his collaborators have embarked on a team-based approach to improve fluorescent protein-based voltage sensors for fast, high resolution recording from many individual mammalian neurons. This project will create large libraries of membrane protein/fluorescent protein fusion constructs. This project therefore requires coordination of a distributed collaboration, tracking of thousands of clones and the experimental data associated with them and the establishment of a database that can house the metadata in a central repository with links to datasets stored across independent laboratories. In addition, this project is designed as an NIH contract (U24), so NIH personnel require a way to monitor the progress, milestones and outcomes of this project. We have created a version of NeuroSys that meets these requirements and functions as a distributed electronic lab notebook, which investigators at each site use to annotate data generated from their experiments. Dr. Hughes and his collaborators are currently beta-testing this system. You can check it out here-- log in with username "demo" and password "demo".
Maryann Martone has been leading several large informatics efforts aimed at promoting distributed collaboration and data sharing within the Neuroscience community. She leads the Cell Centered Database Project (CCDB), which makes 3D microscopic imaging data available to the neuroscience and structural biology and communities. She is also the Scientific Coordinator for the Mouse BIRN project, which uses multi-modal and multi-scale imaging data from mouse models of neurological disorders to better understand diseases of the brain. Each data file in the CCDB or any of the distributed BIRN sites must be annotated with metadata that conforms to both the ontology and the controlled vocabularies in use. Dr. Martone has been a leader in the creation of ontologies and controlled vocabularies for describing these data collections. She will act as the primary beta tester of the NeuroSys vocabulary suggestion engine, and we will work closely with her group to develop strategies for loading data annotated with NeuroSys into their Oracle database. Dr. Martone will also use NeuroSys as an in-lab electronic laboratory notebook for day-to-day management of data and experimental results.