ALBERT

Description: The Surface Ocean – Lower Atmosphere Study (SOLAS) (http://www.solas-int.org/) is an international research initiative focused on understanding the key biogeochemical-physical interactions and feedbacks between the ocean and atmosphere that are critical elements of climate and global biogeochemical cycles. Following the release of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016), the Ocean-Atmosphere Interaction Committee (OAIC) was formed as a subcommittee of the Ocean Carbon and Biogeochemistry (OCB) Scientific Steering Committee to coordinate US SOLAS efforts and activities, facilitate interactions among atmospheric and ocean scientists, and strengthen US contributions to international SOLAS. In October 2019, with support from OCB, the OAIC convened an open community workshop, Ocean-Atmosphere Interactions: Scoping directions for new research with the goal of fostering new collaborations and identifying knowledge gaps and high-priority science questions to formulate a US SOLAS Science Plan. Based on presentations and discussions at the workshop, the OAIC and workshop participants have developed this US SOLAS Science Plan. The first part of the workshop and this Science Plan were purposefully designed around the five themes of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016) to provide a common set of research priorities and ensure a more cohesive US contribution to international SOLAS.

Description: This report was developed with federal support of NSF (OCE-1558412) and NASA (NNX17AB17G).

Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Brandt, P. D., Sturzenegger Varvayanis, S., Baas, T., Bolgioni, A. F., Alder, J., Petrie, K. A., Dominguez, I., Brown, A. M., Stayart, C. A., Singh, H., Van Wart, A., Chow, C. S., Mathur, A., Schreiber, B. M., Fruman, D. A., Bowden, B., Wiesen, C. A., Golightly, Y. M., Holmquist, C. E., Arneman, D., Hall, J. D., Hyman, L. E., Gould, K. L., Chalkley, R., Brennwald, P. J., Layton, R. L. A cross-institutional analysis of the effects of broadening trainee professional development on research productivity. Plos Biology, 19(7), (2021): e3000956, https://doi.org/10.1371/journal.pbio.3000956.

Description: PhD-trained scientists are essential contributors to the workforce in diverse employment sectors that include academia, industry, government, and nonprofit organizations. Hence, best practices for training the future biomedical workforce are of national concern. Complementing coursework and laboratory research training, many institutions now offer professional training that enables career exploration and develops a broad set of skills critical to various career paths. The National Institutes of Health (NIH) funded academic institutions to design innovative programming to enable this professional development through a mechanism known as Broadening Experiences in Scientific Training (BEST). Programming at the NIH BEST awardee institutions included career panels, skill-building workshops, job search workshops, site visits, and internships. Because doctoral training is lengthy and requires focused attention on dissertation research, an initial concern was that students participating in additional complementary training activities might exhibit an increased time to degree or diminished research productivity. Metrics were analyzed from 10 NIH BEST awardee institutions to address this concern, using time to degree and publication records as measures of efficiency and productivity. Comparing doctoral students who participated to those who did not, results revealed that across these diverse academic institutions, there were no differences in time to degree or manuscript output. Our findings support the policy that doctoral students should participate in career and professional development opportunities that are intended to prepare them for a variety of diverse and important careers in the workforce.

Description: Funding sources included the Common Fund NIH Director’s Biomedical Research Workforce Innovation Broadening Experiences in Scientific Training (BEST) Award. The following institutional NIH BEST awards (alphabetical by institution) included: DP7OD020322 (Boston University; AFB, ID, BMS, LEH); DP7OD020316 (University of Chicago; CAS); DP7OD018425 (Cornell University; SSV); DP7OD018428 (Virginia Polytechnic Institute; AVW, BB); DP7OD020314 (Rutgers University; JA); DP7OD020315 (University of Rochester; TB); DP7OD018423 (Vanderbilt University; KAP, AMB, KLG, RC); DP7OD020321 (University of California, Irvine; HS, DAF); DP7OD020317 (University of North Carolina, Chapel Hill; PDB, PJB, RLL); DP7 OD018427 (Wayne State University; CSC, AM). National Institutes of Health (NIH) General Medical Sciences - Science of Science Policy Approach to Analyzing and Innovating the Biomedical Research Enterprise (SCISIPBIO) Award (GM-19-011) - 1R01GM140282-01 (University of North Carolina at Chapel Hill; RLL, PDB, PJB).

Description: Dataset: GP16 Soluble and Dissolved Fe and Fe Isotopes

Description: Concentrations of soluble and dissolved iron (Fe) and iron isotope ratios from the U.S. GEOTRACES EPZT cruise (GP16, TN303) on R/V Thomas G. Thompson in the tropical Pacific during November & December 2013. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/818707

Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1234827, NSF Division of Ocean Sciences (NSF OCE) OCE-1713677, NSF Division of Ocean Sciences (NSF OCE) OCE-1434493

Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hopkinson, B. M., King, A. C., Owen, D. P., Johnson-Roberson, M., Long, M. H., & Bhandarkar, S. M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS One, 15(3), (2020): e0230671, doi: 10.1371/journal.pone.0230671.

Description: Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming manual analysis, either during a dive survey or on images collected during a survey. Reef structural complexity is essential for ecological function but is challenging to measure and often relegated to simple metrics such as rugosity. Recent advances in computer vision and machine learning offer the potential to alleviate some of these limitations. We developed an approach to automatically classify 3D reconstructions of reef sections and assessed the accuracy of this approach. 3D reconstructions of reef sections were generated using commercial Structure-from-Motion software with images extracted from video surveys. To generate a 3D classified map, locations on the 3D reconstruction were mapped back into the original images to extract multiple views of the location. Several approaches were tested to merge information from multiple views of a point into a single classification, all of which used convolutional neural networks to classify or extract features from the images, but differ in the strategy employed for merging information. Approaches to merging information entailed voting, probability averaging, and a learned neural-network layer. All approaches performed similarly achieving overall classification accuracies of ~96% and 〉90% accuracy on most classes. With this high classification accuracy, these approaches are suitable for many ecological applications.

Description: This study was funded by grants from the Alfred P. Sloan Foundation (BMH, BR2014-049; https://sloan.org), and the National Science Foundation (MHL, OCE-1657727; https://www.nsf.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Description: Dataset: OlyGrowth_Expt_2018_Mortality

Description: Olympia oyster mortality samples from cultures in 50 unique combinations of temperature, salinity, pCO2 at Shannon Point Marine Center in May 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/776293

Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1538626

Description: Dataset: OlyGrowth_Expt_2018_Growth

Description: Olympia oyster growth samples cultured in 50 unique combinations of temperature, salinity, pCO2 at Shannon Point Marine Center in May 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/776281

Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1538626

Description: In January 2020, the US Ocean Carbon & Biogeochemistry (OCB) Project Office funded the Ocean Nucleic Acids 'omics Intercalibration and Standardization workshop held at the University of North Carolina in Chapel Hill. Thirty-two participants from across the US, along with guests from Canada and France, met to develop a framework for standardization and intercalibration (S&I) of ocean nucleic acid ‘omics (na’omics) approaches (i.e., amplicon sequencing, metagenomics and metatranscriptomics). During the three-day workshop, participants discussed numerous topics, including: a) sample biomass collection and nucleic acid preservation for downstream analysis, b) extraction protocols for nucleic acids, c) addition of standard reference material to nucleic acid isolation protocols, d) isolation methods unique to RNA, e) sequence library construction, and f ) integration of bioinformatic considerations. This report provides a summary of these and other topics covered during the workshop and a series of recommendations for future S&I activities for na’omics approaches.

Description: The Ocean Nucleic Acids ‘Omics Intercalibration and Standardization Workshop was supported by grants from the Ocean Carbon & Biogeochemistry Program (OCB) – funding provided by the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) – and the Simons Foundation. This report was developed with federal support of NSF (OCE-1558412) and NASA (NNX17AB17G).

Description: Dataset: Coral Surface Area and Zooxanthellae Counts

Description: This dataset presents coral fragment surface area calculations utilizing two methods (tin foil and Image J) and corresponding zooxanthellae count data. Three coral species were utilized in this experiment: the octocoral, Eunicea flexuosa, and two hard coral species, Acropora cervicornis and Orbicella faveolata. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/880711

Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1924540, NSF Division of Ocean Sciences (NSF OCE) OCE-1923962

Description: The goal of this cruise was to deploy 51 ocean bottom seismometers (OBS) and conduct rock dredges at the Quebrada/Discovery/Gofar (QDG) transform fault systems, with a focus on the Gofar system. QDG is located in the equatorial east Pacific on the East Pacific Rise (Figure 1). Sections of the Gofar and Discovery systems rupture with large (Mw 〉 5.3) earthquakes every 5-6 years, while only one event of this size has occurred on Quebrada in the last 35 years. Variations in along-strike earthquake behavior during these seismic cycles are further constrained by results from a 2008 OBS experiment on QDG. The 2008 experiment revealed the presence of ‘rupture zones’, which fail quasi-periodically in M6 earthquakes, and ‘rupture barriers’, which repeatedly stop large ruptures, yet undergo intense foreshock sequences. The current OBS deployment on Gofar is thus aimed at recording variations in stress build up, stress release, and fault strength within the context of a well-known seismic cycle.

Description: 2022-03-03

Description: Dataset: WAP model SSH and tracer

Description: NetCDF model output of the simulated sea surface height, surface passive tracer concentration. and model grid data from a West Antarctic Peninsula ROMS model. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/831045

Description: NSF Office of Polar Programs (formerly NSF PLR) (NSF OPP) OPP-1643468, NSF Office of Polar Programs (formerly NSF PLR) (NSF OPP) OPP-1643386