EAGER: DBfN: Data Bridge for Neuroscience: A novel way of discovery for Neuroscience Data

At a time when thousands of scientists are creating millions of datasets describing an increasingly diverse mix of neuroscience phenomena, the chances of an individual unaided scientist finding all of the data relevant to a particular line of investigation are shrinking every day. At the same time, this rapid increase in the amount and diversity of stored data implies a corresponding increase in the potential of these datasets to empower important new collaborative research and discovery. Meeting these challenges requires tools specifically created to assist scientists in their search for relevant datasets and collaborators. Massive number of relatively small datasets gathered and generated by individual scientists and groups, form a distinct class of Big Data called the 'long tail of science' data and harnessing their hidden power is crucial for advancing science. This project will engage in a set of planning activities for the application of a novel data discovery system for Neuroscience, based upon a platform called, DataBridge, which has been developed by the investigators and their collaborators under a grant from the NSF Big Data program. DataBridge applies 'signature' and 'similarity' algorithms to semantically bridge large numbers of diverse datasets into a 'sociometric' network. Prior work has focused on data from the Social Sciences. This work will study the extension of those techniques to the Neuroscience domain. If the techniques can be demonstrated to work with neuroscience data, the project would have broad impact not only in the neuroscience research community but, potentially, in other science communities as well.

Neuroscience is at an inflection point where more and more data are being aggregated and shared through common repositories. The main challenge facing the neuroscience community in the Big Data era is the difficulty of discovering relevant datasets across these repositories. The complication of effective discovery and identification of relevant data forms the last mile problem for long tail of science data. Solving this problem can increase the value of the data through reuse and repurposing and can immensely benefit the NSF Brain Initiative by providing increased access to data in its various thematic areas. This project will initiate studies on the application of a novel data discovery system for Neuroscience based upon a platform called DataBridge, which the project team has developed under a grant from the NSF Big Data program. DataBridge applies 'signature' and 'similarity' algorithms to semantically bridge large numbers of diverse datasets into a 'sociometric' network. The DataBridge for Neuroscience platform will attempt to harness complex analytics algorithms developed by neuroscience researchers in order to extract key signatures and find data associations from large volumes, and diverse collections, of so-called 'long-tail' neuroscience data. By providing a venue for defining complicated search criteria through pattern analysis, feature extraction and other relevance criteria, DataBridge provides a highly customizable search engine for scientific data. This project will conduct a preliminary, feasibility study on the applicability of DataBridge on Neuroscience data with two goals: 1. Implement a pilot DataBridge system for Neuroscience and demonstrate a proof of concept for semantically bridging a small collection of neuroscience datasets, and 2. Conduct a workshop to develop a coalition of users from the neuroscience community in order to build a sustainable DataBridge-based infrastructure for neuroscience. This community-based activity will leverage the community infrastructure created by the NSF Big Data Hubs and Spokes program.