Computational Infrastructure for Brain Research: EAGER: BrainLab CI: Collaborative, Community Experiments with Data-Quality Controls through Continuous Integration

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The brain research community needs to increase the practice of sharing and combining data sets to increase the power of statistical analyses and to gain the most knowledge from collected data. This project aims to build a prototype system called BrainLab CI that will facilitate meaningful integration of thousands of publicly available Magnetic Resonance Imaging (MRI) and neurophysiology data sets, and allow researchers to define and conduct new large-scale community-level experiments on these data. BrainLab CI has the potential to transform research practice in neuroscience by overcoming major obstacles to data sharing: Scientists will be able to share data without losing control over data quality, and will maintain full visibility into how all subsequent experiments use their data and algorithms. This project may consequently drive a change in scientific culture by encouraging data sharing and the development of common analysis tools, and resulting accelerated discovery from connecting ideas, tools, data, and people. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare. The BrainLab CI prototype system will provide new paradigms for combining different analytic methods, meta-analysis with raw data, comparing the results of different laboratories and even synthesizing new experiments by combining different studies. An experimental-management software system will be deployed that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data. Example of controls include: requiring specific metadata, that data are registered to a given atlas, or that data are collected using specific experimentation protocols. BrainLab CI will initially focus on two different experimental patterns: (1) An incremental experiment defines an experiment against an existing data set which then opens to additional community contributions of data; and (2) a derived experiment forks/branches an existing experiment, allowing a researcher to change properties, such as an acceptance criteria or analysis algorithm, but otherwise run the same pipeline against the same inputs. The system will allow each experiment to maintain online dashboards showing how additional data changes results with complete provenance. To develop and validate the BrainLab CI prototype, several community experiments will be developed for MRI and for neurophysiology (including both optical and electrical physiology) data. These research domains were chosen because of the great potential gains for increased sharing of laboratory data in these domains. This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the SBE Division of Behavioral and Cognitive Sciences, with funds associated with the NSF Understanding the Brain activity including for developing national research infrastructure for neuroscience, and alignment with NSF objectives under the National Strategic Computing Initiative.