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Resources

Resources

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

Displaying 51 - 60 of 101
Factsheet |

This factsheet describes the process of environmental DNA (eDNA) water sampling in estuarine systems, and provides specific methdology recommendations to facilitate detection of invasive species.

Data |

This data resource includes eDNA sequences, fish species summary tables, and DNA extractions from Wells, Great Bay, Hudson, Apalachicola, South Slough, and Heʻeia National Estuarine Research Reserves.

Case Study |

These four case studies give examples of four best practices for conflict management in collaborative science. They were developed as part of the Resilience Dialogues project to share lessons learned about effective collaboration from within the National Estuarine Research Reserve System.

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.

News |

Webinar Summary |

This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the July 2020 webinar Innovative Approaches to Integrating Research and K-12 Education to Advance Estuary Stewardship.

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.

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.