Welcome to ReproNim!
The Center for Reproducible Neuroimaging Computation, seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of a FAIR (Findable, Accessible, Interoperable and Reusable, Wilkinson et al., 2016) technology stack that supports a comprehensive set of data management, analysis, and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published results that are due to mistakes or turn out to be false positive (contributed to by the lack of statistical power). More importantly, given the current publication system, it is exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate.
In this project, we will integrate existing successful community platforms, extend existing data and search services, and develop new search and discovery tools to create a sophisticated, comprehensive, and dynamic search environment for working with distributed neuroimaging data, tools, workflows, and execution environments. This work will support users in discovery and also assist the end user with a specific analytic goal in finding the appropriate available data and software that can subsequently be submitted to the specified workflows and local or cloud-based execution environments.
Data Modeling and Integration
The primary aims of this project will be to provide a consistent and extensible data model for communicating information in brain imaging, associated software tools, and to provide a set of commonly used reproducible workflows with integrated provenance tracking to facilitate such communication. These tools need to be easy to use, supported, and documented. The workflows need to be validated and ideally be executable on infrastructure available to researchers. The workflows must also generate queryable results using standardized data models that are essential to allow software and people to communicate and interpret data precisely.
The goal of this project is to enable reproducible computation through full automation and thus tracking of origins of the computing environments. The result will be a NeuroImaging Computation Environments MANager (NICEMAN). The aim of this project is to support the easy and reproducible execution of neuroimaging analysis workflows on a variety of computational platforms while efficiently reusing and integrating existing free and open source software products and data sharing initiatives. Execution of workflows will be achieved by automatically creating computation environments where necessary software and datasets are available for computation, executing the workflow(s), and returning results and detailed provenance information about the environment.
Training and DisseminationView our training modules (still under development)
Our objectives are to provide the brain imaging community with online training materials based on the concepts and software developed by the center. We want to conduct training workshops to teach the fundamentals of reproducible neuroimaging and to use center resources and tools effectively. In particular, we aim at providing researchers and clinicians with the k nowhow t o integrate a full cycle of research: hypothesis testing, data and software discovery, finding and adapting "playbooks" or pipelines, allowing provenance tracking and results discovery, and reproducible computations. This training will cultivate a clear understanding of the concepts, assumptions, and limitations underlying the reproducible research automation tools.
For more information, contact email@example.com.
ReproNim is supported by NIH-NIBIB P41 EB019936.