Title:

Open data, collaborative working platforms, and interdisciplinary collaboration: building an Early Career Scientist community of practice to leverage Ocean Observatories Initiative data to address critical questions in marine science.

Authors:

Sophie Clayton sclayton@odu.edu

Kristen Fogaren kristen.fogaren@oregonstate.edu***

Robert Levine Leviner@uw.edu**

Johna Rudzin johna.rudzin.ctr@nrlmry.navy.mil

Chris Russoniello chris.russoniello@wvu.edu*

Dax Soule dax.soule@qc.cuny.edu

Justin Stopa stopa@hawaii.edu

Justine Whitaker justine.whitaker@nicholls.edu

project lead **task 1 lead **task 2 lead

Abstract:

Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean. The arrays of the NSF-funded Ocean Observatories Initiative (OOI) are among the most advanced of these platforms in the world and have the potential to enhance our capacity for addressing critical issues such as climate change, ecosystem variability and ocean acidification. The co-located sensors on OOI arrays measure key variables for describing forcing and exchanges at the air-sea and ocean-earth boundaries and throughout the water column off the US coasts and in the Irminger Sea. This high-quality data is freely available and accessible to all members of the global oceanographic community—a democratization of data that is particularly useful for early career scientists (ECS), enabling ECS to conduct research independent of traditional funding models. The concurrent collection of broad data types with relevance for oceanographic disciplines including physics, chemistry, biology and geology yields a unique incubator for cutting edge, timely, interdisciplinary research. This potential must be realized by bringing knowledge and methods from diverse disciplines that ECS are particularly qualified for—they possess the computational skills necessary to interpret large data sets and are eager for collaboration. However progress has been hindered by difficulties in accessing and analyzing the data. Here, we demonstrate how ECS are overcoming these challenges and driving new approaches in ocean science through close collaboration and interdisciplinary skill sharing. We showcase two different data interrogation approaches using OOI data to observe and characterize water physical-chemical-biological coupling.
First, we demonstrate an event-driven method to interrogate the wide range of data products available from the OOI during the passage of Post-Tropical Storm Michael over the Pioneer Array in October 2018. As the storm passes the wind speeds peak over 20 m s-1, wind-driven turbulence cools the sea surface, colored dissolved organic matter (CDOM) increases, dissolved oxygen (DO) decreases, and chlorophyll concentrations decrease in the upper ocean. This level of integration between concurrent measurements of so many physically, chemically and biologically relevant variables has generally been restricted to short-duration shipboard process studies. This example exploits the continuous concurrent diverse data measurements made by the OOI platforms before, during, and after potentially disruptive events to uncover surface to seafloor response and recovery. The broad ECS knowledge base and computational skill sets allowed identification of data issues in the OOI datastreams and technologically-sound characterization of data from multiple sensor packages to broadly characterize ocean-atmosphere interactions.
Second, we demonstrate the potential for predictive analysis with an anomaly-detection algorithm. Using a combination of data streams, we remove known signals and identify anomalous events within the OOI time series data. Following recognition of an event in a primary datastream, the tool interrogates and automatically marks signals in other datastreams to highlight potential connections for further investigation by subject experts. Here we present an example where this approach has been used to produce and explore an anomaly time series of sea level from the Endurance Array, and we demonstrate that this approach can be applied to a wide range of variables. Like the event-driven method, the anomaly-detection method increases the breadth of the enquiry by coupling analysis of concurrent OOI datastreams with the broad knowledge and skillsets of our cross-disciplinary ECS team.
While the above examples are specific, the lesson is broad – an ECS-driven approach that emphasizes collaborative and interdisciplinary working practices adds significant value to existing datasets and programs (like OOI) and has the potential to produce meaningful scientific advances. Future success in utilizing ocean observatory data requires continued investment in ECS education, collaboration, and research; in turn, the ECS community provides feedback, develops knowledge and builds new tools to enhance the value of the ocean observing systems of today and tomorrow. These findings present an argument for building a community of practice to augment ECS ocean scientist skills and foster ECS collaborations to broaden the context, reach, and societal utility of ocean science.