The METS RCN commenced in 2021, funded by the the National Science Foundation (NSF). Read the full proposal.
This project will support coordination efforts that bring together participants in large- and small group formats to foster the necessary dialog to develop Findable, Accessible, Interoperable, and Reusable (FAIR) data solutions and practices. The project will include a Consensus Building Workshop and METS Data Working Group to develop reference implementations of a data model for adoption by the METS community; formation of regional METS user networks and a Broadening Users Workshop to identify the needs of a broader range of data end users and associated data interfaces and tools to meet those needs; and a Data Hackathon to build capacity to ingest, analyze, and integrate METS data with other disciplinary and cross-disciplinary data to accelerate scientific discovery.
The objectives are to:
- Bring together members of the oceanographic, data science, and informatics communities to build consensus on key components of a FAIR data model for METS, including common vocabularies, metadata reporting standards, and data citation practices
- Engage broader METS data users, including modelers, educators, and decision makers, to facilitate broader applications of METS data and foster collaborations and regional networks to address climate and environmental challenges
- Identify use cases to develop reference implementation of data workflows that can be adopted by a range of METS data users
- Build community capacity for METS data analysis, statistical methods and data-model integration
This project will develop community consensus for a FAIR METS data model. The METS RCN will leverage the wealth of oceanographic coordination and community building experience and staff capacity of the Ocean Carbon and Biogeochemistry (OCB) Project Office and the infrastructure, expertise, and extensive METS data handling experience of the Biological and Chemical Oceanography Data Management Office (BCO-DMO), along with an RCN Steering Committee that comprises expertise in the fields of oceanography, data science, earth system models, statistics, and data synthesis.
FAIR data solutions - May 9
FAIR data solutions to support a global observing system of marine ecological time series
May 9, 2022 2:00-3:30 pm EDT
Watch the recording: https://youtu.be/4iFM7IAxZHU
This community meeting (a repeat of a recent Ocean Sciences Town Hall Meeting to enable broader participation) to learn more about a new NSF EarthCube-funded Research Coordination Network for Marine Ecological Time Series (METS-RCN) tasked with bringing together members of the oceanographic, data science, and informatics communities to build consensus on key components of a FAIR (Findable, Accessible, Interoperable, Reusable) data model for METS, including common vocabularies, metadata reporting standards, and data citation practices; engage broader METS data users (e.g., modelers, educators, decision makers) to facilitate broader applications of METS data; and build community capacity for METS data analysis, statistical methods, and data-model integration. This town hall meeting will also highlight a concurrent EuroSea-funded project led by members of the RCN leadership team focused on developing a pilot biogeochemical time series data product to help visualize spatial patterns and trends across ocean basins.
Overview of METS RCN (Heather Benway, OCB/WHOI)
What is FAIR and why do we need it in ocean science? (Adam Shepherd, BCO-DMO)
Shipboard time series use cases
- Carbon-relevant biogeochemical EOVs in a time series data product (Nico Lange, GEOMAR)
- Hawai’i Ocean Time-series (HOT) parameter mapping to Climate & Forecast (CF) vocabulary (Fernando Carvalho-Pacheco, UH)
- ENVRI-FAIR and Intelligent query dissolved oxygen use case (Justin Buck, NOC)
Q&A and open discussion
About this RCN
The METS RCN will support coordination efforts that bring together different cross-sections of the METS community (data producers, users, scientists, and managers) in large and small group formats to foster the necessary dialogue to develop FAIR data solutions and practices.