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Resources

Resources

A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.

Displaying 41 - 50 of 136
Tool |

This curriculum was developed as part of a 2018 Science Transfer project to share knowledge and lessons learned about managing conflict in collaborative science.

Factsheet |

The Native Olympia Oyster Collaborative brochure Restoring Resilient Native Oysters from Baja California to British Columbia provides an introduction to Olympia oyster restoration for general audiences.

News |

Project Overview |

This project overview describes a 2018 Catalyst project that created the web-based toolkit Resilience Metrics to share lessons learned on successful climate adaptation planning within the National Estuarine Research Reserve System.

Project Overview |

This project overview describes a 2017 Science Transfer project in which the southeastern National Estuarine Research Reserves created a region-wide, student-driven program for teachers to further understanding of estuary restoration.

Project Overview |

This project overview describes a multi-year collaborative research project that analyzed a suite of living shoreline possibilities for South Carolina to help the state develop a living shoreline policy.

Project Overview |

This project overview describes a 2016 Science Transfer project where staff members from the North Carolina National Estuarine Research Reserve are being trained in the application of the CCVATCH tool to assess the vulnerabilities of local coastal habitats to climate change.

Project Overview |

This project overview describes a 2018 Catalyst project that created an Olympia oyster restoration network to enhance the success of West Coast restoration efforts.

Report |

This national synthesis report analyzes SET data from 15 National Estuarine Research Reserves across the continental United States, summarizing wetland water level trends over a 19-year period.

Tool |

A 2018 catalyst project developed tools for working with SET data including a series of computer codes - R scripts - for processing, quality checking, analyzing and visualizing these complex datasets. The statistical codes re available through GitHub and are explained in a Guide to the SETr Workflow.