This dataset contains physical, chemical, and biological data collected from the Monterey Bay Aquarium Research Institute, MBARI, Central California Carbon PH and Oxygen (C3PO) leg 1 Western Flyer cruise (EXPOCODE 31WF20190511) in the Central California Current system between Point Conception and Point Arena from 2019-05-11 to 2019-05-16. Twenty stations were occupied along three across shore transects, extending to ~250 km offshore. Casts were conducted to 2000 m or water column depth, whichever was shallower.

This dataset contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA Ship Rainier in the North Pacific Ocean from 2020-08-03 to 2020-08-12. Office of Marine and Aviation Operations (OMAO) personnel aboard the ship submitted these data and associated metadata to NCEI. In addition, this dataset contains supplementary cruise-level metadata, which OMAO personnel logged in the Ship Daily Activity Log (SDAL).

This dataset contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA Ship Rainier in the Coastal Waters of Southeast Alaska and British Columbia from 2020-09-01 to 2020-09-17. Office of Marine and Aviation Operations (OMAO) personnel aboard the ship submitted these data and associated metadata to NCEI. In addition, this dataset contains supplementary cruise-level metadata, which OMAO personnel logged in the Ship Daily Activity Log (SDAL).

This dataset contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA Ship Rainier in the North Pacific Ocean from 2019-08-25 to 2019-09-10. Office of Marine and Aviation Operations (OMAO) personnel aboard the ship submitted these data and associated metadata to NCEI. In addition, this dataset contains supplementary cruise-level metadata, which OMAO personnel logged in the Ship Daily Activity Log (SDAL).

This dataset contains raw underway meteorological, navigational, physical and time series data logged by the Scientific Computer System (SCS) aboard NOAA Ship Rainier in the North Pacific Ocean from 2019-09-16 to 2019-09-23. Office of Marine and Aviation Operations (OMAO) personnel aboard the ship submitted these data and associated metadata to NCEI. In addition, this dataset contains supplementary cruise-level metadata, which OMAO personnel logged in the Ship Daily Activity Log (SDAL).

Data were collected in association with the removal of PCB contaminated sediment during the Manistique River Area of Concern Operable Unit 2 remediation construction efforts. Data collected included the following:
(i) Water column turbidity readings acquired using a YSI PRO DSS - handheld multi-parameter instrument for documenting potential impacts to water quality during construction activities;

This dataset includes lakebed maps and associated geospatial data in Lake Michigan within the proposed Wisconsin - Lake Michigan National Marine Sanctuary. Associated data include spatial lakebed mapping priorities, remotely sensed lakebed imagery (LiDAR bathymetry, multibeam bathymetry, backscatter, and sidescan sonar), and underwater photos and videos. The dataset includes original data collections, data assessments, and modeled data. Original data collection spans the years 2017 to 2018, and assessments and modeled data were undertaken from 2017 to 2020.

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This dataset contains global monthly climatology of oceanic total dissolved inorganic carbon (DIC). (DIC) monthly climatology was created from a neural network approach (Broullón et al., 2020). The neural network was trained with GLODAPv2.2019 (Olsen et al., 2019) and LDEOv2016 (Takahashi et al., 2017) data, using as predictor variables position (latitude, longitude and depth), year, temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. pCO2 from LDEOv2016 and AT from Broullón et al.

This dataset contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen.