NSF EPSCoR Track-2 2020: Big Data

NSF EPSCoR Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC). Funding Opportunity for Research in Harnessing Big Data to solve problems of national importance.
Program Solicitation: NSF 20-504 
Institutional Limits: A UM-led proposal must include at least one UM PI and at least one co-PI from an institution in another eligible jurisdiction: (AL, AK, AR, DE, Guam, HI, ID, IA, KS, KY, LA, ME, MS, MT, Nebraska, NE, NH, ND, OK, Puerto Rico, RI, SC, SD, VT, US Virgin Islands, WV, and WY).  UM (Oxford) can submit only 1 proposal as the lead institution.
PI Limits:  An investigator may serve as PI or Co-PI on only one Track-2 award at a time. 
Other Requirements: All proposals must promote collaborations among researchers in EPSCoR jurisdictions, emphasize STEM education and workforce development, and emphasize the recruitment/development of diverse early career faculty.
Budget: The maximum budget request allowed per four-year project/proposal is:
up to $1.0M/year for collaborations involving two RII-eligible jurisdictions; OR
up to $1.5M/year for collaborations involving 3 or more eligible jurisdictions.
Committed cost sharing is prohibited. Full F&A (on-campus research) is in effect.
Key Dates 
10/15/2019     NSF solicitation released
11/26/2019     Stage 1 Internal Notices of Intent due to ORSP
12/10/2019     Stage 2 Internal Pre-Proposals due to ORSP
12/18/2019 ORSP announces winning pre-proposal
12/20/2019     Required Letter of Intent due to NSF. Last day before Winter Break.
01/16/2019     Full proposal, transmittal, & subaward budgets due to ORSP for review
01/20/2019 MLK Holiday
01/24/2019     Full Proposal due to NSF, must comply PAPPG NSF 19-1 .
Limited Submission Selection Process: A two-stage internal selection process will be conducted consistent with ORSP’s standard process for Limited Submissions . For Stage 1, individuals proposing to lead a project must submit an internal Notice of Intent (NOI) to ORSP. Stage 1 submitters will be invited to submit an internal Pre-Proposal. ORSP will coordinate the selection of the winning pre-proposal, and invite the proposing PI/team to develop as a full proposal for submission to NSF. Both the Stage 1 NOI and the Stage 2 Pre-Proposal should be submitted via the Ole Miss InfoReady Review portal  by the dates below.  
The sole purpose of the Stage 1 Notice of Intent is to learn whether we will need to have an internal competition. Stage 1 Notices of Intent (NOI) should be no more than 1 page long, and should consist of an abbreviated Project Summary and a list of Prospective Collaborators. 
Abbreviated Project Summary: NSF-style Project Summary, abbreviated to ½ page. 
Prospective Collaborators: List of institutions and individuals that the UM PI/team is considering collaborating with. It is not expected that those proposed collaborators would have confirmed their intention to collaborate on a proposal at this stage, but please provide some idea of the state of discussions, what is decided, what is being considered, etc. No signatures are required at this point. ½ page limit.
Stage 2 Pre-Proposals are to see which of multiple competing ideas is the most developed so that we can choose which to move forward to a full proposal. Collaborators must be confirmed by the time of the Stage 2 Pre-Proposal, which should include these sections:
Project Summary: 1-page NSF-style Project Summary, including working title.
Confirmed Collaborators: Up to 1-page list of proposed collaborating institutions and individuals—the UM PI and up to four co-PIs, including at least one co-PI from another eligible EPSCoR jurisdiction. Summarize the specific role/contribution of each proposed collaborating investigator, and why each is critical to the project. Include the contact information and signatures of the proposed PI and each co-PI. E-mails from the collaborators stating their intention to collaborate may be included in lieu of signatures. 
Very Abbreviated Project Description: A short (up to 3 pages) version of the Project Description, containing (extremely abbreviated versions of) all (or as many as you can in the time available) of the required components, including: Status and Overview; Results from Relevant Prior Support; Research Plan; Inter-jurisdictional Collaborations and Partnerships; Workforce Development; Evaluation and Assessment Plan; & Sustainability Plan. Due to the limited space and time, it is not expected that any of these elements will be complete, but you should provide enough to convince internal reviewers that, if selected for UM’s institutional nomination, the ensuing full proposal will be complete and strong.
Finding Collaborators To request assistance finding potential collaborators in eligible jurisdictions, contact Jason Hale (jghale@olemiss.edu). 
Big Data Theme
Purpose: Solutions to many of the pressing problems facing society may require the integration of teams of scientists and engineers and the analysis of large and complex data sets arising from multi-disciplinary projects
Research Requirements: Proposals submitted for the FY19 RII Track-2 FEC competition should meet the following requirements:
strictly fall under NSF’s “Harnessing the Data Revolution,” one of NSF’s Ten Big Ideas; 
focus on harnessing Big Data to solve compelling problems of national importance
integrate multi-disciplinary teams of scientists and engineers; 
address analysis of large % complex data sets arising from multi-disciplinary projects;
identify and motivate the importance and relevance of the chosen topic area in the context of complex data sets and the current status of ongoing work in the area;
emphasize how new information can be obtained from better connections among data sources, utilization of data (including data from multiple facilities, techniques, and/or instruments), and how this will be used to address the specific problem of national importance;
Social and Educational Requirements: NSF and EPSCoR recognize that STEM talent must be cultivated in underrepresented populations of individuals and that the anticipated needs of the future workforce mandate that data science skills be incorporated broadly across education programs. Therefore, proposals should:
develop a strong commitment to building a diverse workforce, which may include but is not limited to the inclusion and involvement of diverse educational institutions (e.g., Primarily Undergraduate Institutions and Minority Serving Institutions) and under-represented minorities in STEM;
develop strong educational programs for analysis of complex data sets that can be implemented across institutions of higher learning in participating jurisdictions;
comprehend the involvement and mentoring of early-career faculty.
More information on NSF’s commitment to broadening participation can be found in the ‘Framework for Action Report.’”