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Resources

Resources

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

Displaying 1 - 9 of 9
Data |

This resource includes links to five datasets generated by a collaborative research project that measured nitrogen removal from oyster aquaculture using complement biogeochemistry and genetic methods.

Data |

These marsh sustainability and hydrology datasets were collected as part of a 2017 collaborative research project.

Data |

This dataset comprises the data collected and produced as part of the 2016 research project Investigating the Interconnectedness of Climate Change, Nuisance Mosquitoes, and Resilience of Coastal Salt Marsh Systems.

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These five related carbon storage, greenhouse gas flux and environmental variable datasets were generated by the Bringing Wetlands to Market research team and used to develop a coastal wetland greenhouse gas model for New England.

Data |
About this Project

Thin-layer placement (TLP) is an emergent climate adaptation strategy that mimics natural deposition processes in tidal marshes by adding a small amount of sediment on top of marsh in order to maintain elevation relative to sea level rise.

Data |

This dataset compiles salt marsh monitoring from four New England NERRs from 2010 to 2018, as part of a catalyst project to sythesize and identify regional trends in salt marsh data in the reserve system.

Collections |
This collection features blue carbon work completed by project teams from 2010-2019. The collection includes a detailed management brief narrative, an infographic showing the progress of blue carbon work across the U.S., and a webinar recording from a panel discussion on March 17, 2020.
Collections |
This collection features climate resilience and adaptation work completed by project teams from 2015-2018. The collection includes a detailed management brief narrative, an infographic showing how the interconnected nature of the NERRS facilitates collective learning and accelerated action, and a webinar recording from a panel discussion on September 9, 2019.
Data |

This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.