CoPe EAGER: Collaborative Research: COMET: the Coastlines and people Open data and MachinE learning sprinT

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Timely release of research results is important to advance understanding of the impacts of climate change and sea level rise on coastlines and the communities that live there. The growing library of open, freely accessible data and analysis tools (i.e., code) enables scientists to investigate a range of societally relevant questions at the intersection of Coastlines and People. This project pilots an incubator approach to catalyzing data-driven research and creating networks of researchers ready to tackle the complex problems of the coast. The program is modeled on other 'science sprints', where teams of researchers assemble to transform an idea into open, freely accessible research products within a short, fixed time window - thereby accelerating scientific advances. This project will advance science in three ways: 1) By creating cohorts of scientists using data-driven approaches to address the interdisciplinary problems along the coast; 2) Scientists at each event will create open tools, code, deliverables and data products, creating freely available methods and knowledge; 3) Multiple events and iteration between events will enable evaluation of the sprint approach, and its success in producing science at the intersection of Coastlines and People.The three sprint events are focused on quick turn-around research using open data and machine learning, and will take advantage of the vast data volumes available through data.gov and other FAIR (Findable, Accessible, Interoperable, Re-usable) sources. The objective is to produce results and deliverables rapidly. Applications from the scientific community will be solicited for each of the three planned events, and selection of cohorts will prioritize having representation of a diverse set of fields and perspectives. Each event will adhere to a Code of Conduct that will additionally include an 'open by default' statement for code, data, and reports generated at the sprint. At each event, participants will break into small groups to spend 72 hours working on selected projects. Groups will produce oral and written reports, as well as associated open source code at the end of each event. Outcomes from each event will be measured using surveys (pre- and post- event), and by following the use of digital object identifiers associated with the open deliverables from each event. Datasets used by the participants will also be collected and curated on a publicly available website as a crowd-sourced list of relevant open data. The three sprint events will take place in North Carolina and Colorado. A range of external collaborators will interact and network with participants.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.