Software Infrastructure for Sustained Innovation (SI 2 ) is a bold and long-term investment that maintains a sustained focus on realizing the Cyberinfrastructure Framework for 21st Century Science and Engineering , which envisions a highly reusable and interoperable cyberinfrastructure architecture that integrates large-scale computing, high-speed networks, massive data archives, instruments and major facilities, observatories, experiments, and embedded sensors and actuators, across the nation and the world, to help make great strides towards revolutionizing virtually every science and engineering discipline. 

The SI2 program focuses on supporting robust, reliable and sustainable software that will support and advance sustained scientific innovation and discovery. The goal of this program is to catalyze and nurture the interdisciplinary processes required to support the entire software lifecycle, and result in the development of sustainable community software elements and reusable components at all levels of the software stack. The SI2 program aspires to support vibrant partnerships among academia, government, and industry researchers, including international entities, for the development and stewardship of a sustainable software infrastructure.

SI2 currently invites applications in the following classification:

The purpose of this FOA is to invite applications proposing the innovative analysis of existing social science, behavioral, administrative, and neuroimaging data to study the etiology and epidemiology of drug using behaviors (defined as alcohol, tobacco, prescription and other drug) and related disorders, prevention of drug use and HIV, and health service utilization. This FOA encourages the analyses of public use and other extant community-based or clinical datasets to their full potential in order to increase our knowledge of etiology, trajectories of drug using behaviors and their consequences including morbidity and mortality, risk and resilience in the development of psychopathology, strategies to guide the development, testing, implementation, and delivery of high quality, effective and efficient services for the prevention and treatment of drug abuse and HIV.   

This funding opportunity announcement seeks to leverage data from existing basic, clinical, and intervention research on suicide risk and behaviors as well as social media and healthcare records data, by encouraging the integration of existing data sets for novel secondary analyses aimed at identifying potential biological, experiential, and other predictors and moderators of suicide risk. The use of dimensional variables and inclusion of multiple levels of analyses is particularly encouraged. A secondary goal of this FOA is to support innovative projects that will generate foundational work for research studies on suicide-related behaviors that inform a Research Domain Criteria (RDoC) approach in this area. Projects supported by this FOA will help address gaps identified in the  2014 Prioritized Research Agenda for Suicide Prevention.

A Technical Assistance teleconference will be held for potential applicants on August 16, 2017 from 1:00pm - 2:00 pm EDT. The dial in number is 866-692-3158 and participant code is 5800728#. 

The primary objective of this FOA is to stimulate innovative Convergent Neuroscience (CN) approaches to establish causal and/or probabilistic linkages across contiguous levels of analysis (e.g., gene, molecule, cell, circuit, system, behavior) in an explanatory model of psychopathology. In particular, applicants should focus on how specific constituent biological processes at one level of analysis contribute to quantifiable properties at other levels, either directly or as emergent phenomena. Although not required, it is preferable that applications link at least three levels of analysis and include an emphasis on genetics. The projects under this FOA will develop novel methods, theories, and approaches through a CN team framework, bringing together highly synergistic inter/transdisciplinary teams from neuroscience and "orthogonal" fields (e.g., data/computational science, physics, engineering, mathematics, and environmental sciences). Successful teams will combine, expand upon, or develop conceptual frameworks and theoretical approaches, and build explanatory computational models that connect contiguous levels of analysis. Such frameworks, theories, and computational explanatory models should be validated through experimental approaches to elucidate biological underpinnings of complex behavioral (including cognitive and affective) outcomes in psychopathology. Additionally, a goal of this program is to advance research in CN by creating a shared community framework of resources which may be used by the broader research community to further research, as such, successful team will have robust plan for sharing data and other resources.

Applications requiring two or more collaborating sites to complete the proposed research should apply as a linked set of collaborative U01 applications to the companion collaborative U01 FOA ( PAR-17-176).  All awards supported under this FOA and the companion collaborative U01 FOA  will be governed by the Convergent Neuroscience Network for Psychiatry (CNN-Psych) Steering Committee.