D. C. Soule

Queens College, CUNY

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William S. D. Wilcock

University of Washington

Javier Almendros

Universidad de Granada

Catherine M. O’Reilly

Illinois State University

T. Meixner

University of Arizona

Carlos Jose Araque Perez

Universidad de Granada

Rebekka Darner

Illinois State University

Luis Vizcaino

Nicholas E. Bader

Whitman College

D. R. Toomey

University of Oregon

E. E. Hooft

University of Oregon

Cooperative Institutions

University of Washington

Universidad de Granada

Woods Hole Oceanographic Institution

Oregon State University

Rutgers, The State University of New Jersey

Virginia Tech

Oceanography Society

Instituto Andaluz de Ciencias de la Tierra

Illinois State University

Massachusetts Institute of Technology

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OOICloud: Building a Pangeo System for the OOI CamHD Video Data using the Azure Cloud

AGU Fall Meeting Abstracts (2019)

T. J. Crone D. C. Soule Friedrich Knuth

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EDDIE modules are effective learning tools for developing quantitative literacy and seismological understanding

Journal of Geoscience Education (2018)

D. C. Soule Rebekka Darner Catherine M. O’Reilly Nicholas E. Bader T. Meixner

Environmental Data-Driven Inquiry and Exploration (EDDIE) modules engage students in analysis of data collected by networks of environmental sensors, which are used to study various natural phenomena, such as nutrient loading, climate change, and stream discharge. We compared two approaches to EDDIE module implementation in an undergraduate time-series analysis course. Course goals were to use high-frequency and long-term environmental datasets to improve quantitative literacy, develop data manipulation and analysis skills, construct scientific knowledge about natural phenomena, highlight the inherent variability in real data, and develop informed views about the nature of science (NOS). In both instructional treatments, students explored data and developed skills through a scaffolded in-class analysis and then solved more complex problems in homework assignments. In Treatment 1, engage and explore lesson phases involved discussion of instructor-prepared plots using the think–pair–share method. Conversely, in Treatment 2's engage and explore lesson phases, students prepared graphs and completed activities in a computer lab, which required more guidance in data manipulation and thus contained less structured discussion of data analysis and interpretation. We administered a pre/postquestionnaire to compare learning gains between the two treatments in quantitative literacy, statistical reasoning, nature-of-science (NOS) understanding, and understanding of seismological concepts. Results indicate that EDDIE modules are sufficiently flexible to be effective in both learning environments. Our results indicate that students reacted similarly to both instructional treatments, suggesting that EDDIE modules are flexible enough platforms to achieve measurable learning gains in a variety of pedagogical environments.

Scientific Literacy

10.1080/10899995.2018.1411708

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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

Abstracts with programs - Geological Society of America (2020)

Christopher J. Russoniello Sophie Clayton Kristen E. Fogaren Robert M. Levine Johna E. Rudzin

Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean.High-quality observatory 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 or access to laboratory and field equipment.The concurrent collection of distinct data types with relevance for oceanographic disciplines including physics, chemistry, biology, and geology yields a unique incubator for cutting-edge, timely, interdisciplinary research.These data are both an opportunity and an incentive for ECS to develop the computational skills and collaborative relationships necessary to interpret large data sets.Here, we use observatory data to demonstrate the potential for these interdisciplinary approaches by presenting a case study on the water-column response to anomalous atmospheric events (i.e., major storms) on the shelf of the Mid-Atlantic Bight southwest of Cape Cod, United States.Using data from the Ocean Observatories Initiative (OOI) Pioneer Array, we applied a simple data mining method to identify anomalous atmospheric events over a four-year period.Two closely occurring storm events in late 2018 were then selected to explore the dynamics of watercolumn response using mooring data from across the array.The comprehensive ECS knowledge base and computational skill sets allowed identification of data issues in the OOI data streams and technologically sound characterization of data from multiple

Leverage (statistics)

Community of practice

10.1130/abs/2020se-344158

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Citations (1)

Raw seismic reflection data from Orca Volcano, Bransfield Strait, Antarctica

Figshare (2023)

Luis Vizcaino Javier Almendros William S. D. Wilcock D. C. Soule Carlos Jose Araque Perez

Raw seismic data from Orca Volcano, Bransfield Strait, acquired onboard the Spanish vessel Sarmiento de Gamboa in the framework of the BRAVOSEIS PROJECT.

Reflection

10.6084/m9.figshare.24085110.v3

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Raw seismic reflection data from Orca Volcano, Bransfield Strait, Antarctica

Figshare (2023)

Luis Vizcaino Javier Almendros William S. D. Wilcock D. C. Soule Carlos Jose Araque Perez

Raw seismic data from Orca Volcano, Bransfield Strait, acquired onboard the Spanish vessel Sarmiento de Gamboa in the framework of the BRAVOSEIS PROJECT.

Reflection

10.6084/m9.figshare.24085110.v5

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BRAVOSEIS: Geophysical investigation of rifting and volcanism in the Bransfield strait, Antarctica

Journal of South American Earth Sciences (2020)

Javier Almendros William S. D. Wilcock D. C. Soule Teresa Teixidó Luis Vizcaino

Caldera

Seafloor Spreading

Seamount

10.1016/j.jsames.2020.102834

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Citations (23)

Late-Stage Rift Evolution at Back Arc Basins: Insights from a Tomography Experiment at Orca Volcano, Bransfield Basin

Authorea (Authorea) (2025)

Maleen Kidiwela William S. D. Wilcock D. C. Soule Javier Almendros

10.22541/essoar.173939638.88887432/v1

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OOI Data Labs Workshops: Equipping professors with the tools to tap into a fire hose of ocean data for undergraduate education

Ocean Sciences Meeting 2020 (2020)

Anna Pfeiffer-Herbert D. C. Soule Brooke A. Love E. A. R. Iverson Ellen Rydell Altermatt

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Tracking fin and blue whales above the Juan de Fuca Ridge with a local seafloor seismic network.

The Journal of the Acoustical Society of America (2009)

William S. D. Wilcock D. C. Soule Richard E. Thomson

The Endeavour segment of the Juan de Fuca mid-ocean ridge hosts several high-temperature hydrothermal fields. Previous analysis of bio-acoustical data shows that zooplankton are enhanced at all depths above the hydrothermal vent fields compared with sites ⩾10 km away. From 2003–2006, a seafloor seismic network was deployed around the hydrothermal vent fields to monitor earthquakes and it also recorded an extensive data set of fin and blue whale calls. As part of an investigation of a potential correlation between whale tracks, enhanced zooplankton concentrations, and hydrothermal vents above the Juan de Fuca Ridge, an automatic algorithm is being developed to track vocalizing whales that swim near the network. Events are detected by triggering with the ratio of short-term to long-term running RMS averages and whale calls are distinguished from earthquakes based on their spectra. For fin whales each 1-s arrival is identified based on its instantaneous amplitude and frequency and a pick is made at the mid-energy point. A grid search method is used to localize calls using direct and multipath arrivals. The algorithm and preliminary results will be presented. [The Keck Foundation supported the seismic network and the Office of Naval Research is supporting this study.]

Seafloor Spreading

Fin

10.1121/1.4783834

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Statistical analysis of fin whale vocalizations recorded by a seismic network at the Endeavour Segment of Juan de Fuca Ridge, N. E. Pacific Ocean.

The Journal of the Acoustical Society of America (2011)

D. C. Soule William S. D. Wilcock Richard E. Thompson

From 2003–2006, an eight-station seafloor seismic network was deployed along the Endeavour Segment of the Juan de Fuca ridge that recorded an extensive data set of 20-Hz fin whale calls. Algorithms have been developed to detect and track vocalizing whales that swim near the seismic network. During the first year of operation, more than 100000 fin calls that include ∼100 whale tracks were identified. Tracks comprise both single whales distinguished by a stereotyped ∼25 s interpulse interval and inferred multiwhale tracks characterized by more complex interpulse intervals. Whale tracks vary from individuals or groups that cross the network in a few hours to those that meander for up to 24 h. The call rates vary seasonally with the highest rates in winter and exhibit an apparent weak diurnal variation. The center frequencies range from 17–34 Hz, with the primary population centered at 20 Hz and a secondary population centered at 25 Hz. Statistical analysis of observed bandwidths and center frequencies, interpulse intervals, seasonality, and diurnal patterns will be presented. Additionally, the ∼100 whale tracks will be used for migration analysis and to quantify the swimming patterns in the network. [Funding from the ONR.]

Seafloor Spreading

Meander (mathematics)

10.1121/1.3588784

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Citations (3)