Futrelle Joe

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  • Working Paper
    Standards and practices for reporting plankton and other particle observations from images
    (Woods Hole Oceanographic Institution, 2021-07-26) Neeley, Aimee ; Beaulieu, Stace E. ; Proctor, Chris ; Cetinić, Ivona ; Futrelle, Joe ; Soto Ramos, Inia ; Sosik, Heidi M. ; Devred, Emmanuel ; Karp-Boss, Lee ; Picheral, Marc ; Poulton, Nicole ; Roesler, Collin S. ; Shepherd, Adam
    This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genus) and scientificNameID, the unique identifier from a reference database such as the World Register of Marine Species or AlgaeBase. The data table described here includes the field names associatedMedia, scientificName/ scientificNameID for both automated and manual identification, biovolume, area_cross_section, length_representation and width_representation. Additional steps that instruct the user on how to format their data for a submission to the Ocean Biodiversity Information System (OBIS) are also included. Examples of documentation and data files are provided for the user to follow. The documentation requirements and data table format are approved by both NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and the National Science Foundation’s Biological and Chemical Oceanography Data Management Office (BCO-DMO).
  • Presentation
    Data Science Training Camp at Woods Hole Oceanographic Institution: Syllabus and slide presentations in 2020
    (Woods Hole Oceanographic Institution, 2020-08-21) Beaulieu, Stace E. ; Raymond, Lisa ; Mickle, Audrey ; Futrelle, Joe ; Symmonds, Nick ; Mazzoli, Roberta ; Brey, Rich ; Kinkade, Danie ; Rauch, Shannon
    With data and software increasingly recognized as scholarly research products, and aiming towards open science and reproducibility, it is imperative for today's oceanographers to learn foundational practices and skills for data management and research computing, as well as practices specific to the ocean sciences. This educational package was developed as a data science training camp for graduate students and professionals in the ocean sciences and implemented at the Woods Hole Oceanographic Institution (WHOI) in 2019 and 2020. Here we provide materials for the 2020 camp which was delivered in-person during two afternoons (total of 8 hours), with two modules per afternoon. We aimed for ~40 participants per camp, with disciplines spanning Earth and life sciences and engineering. Disciplines at each table were mixed on the first afternoon but similar on the second afternoon. Contents of this package include the syllabus and slide presentations for each of the four modules: 1 "Good enough practices in scientific computing," 2 Data management, 3 Software development and research computing, and 4 Best practices in the ocean sciences. The 3rd module is split into two parts. We also include a poster presented at the 2020 Ocean Science Meeting, which has some results from pre- and post-surveys. Funding: The camp was funded by WHOI Academic Programs Office through a Doherty Chair in Education Award, with additional support from WHOI Ocean Informatics Working Group, WHOI Information Services, MBLWHOI Library, the NSF-funded Biological and Chemical Oceanography Data Management Office (BCO-DMO), and an NSF-funded XSEDE Jetstream Education Allocation TG-OCE190011. We also utilized resources from the NSF-funded Pangeo project.
  • Article
    Environmental metabolomics : databases and tools for data analysis
    (Elsevier, 2015-06-19) Longnecker, Krista ; Futrelle, Joe ; Coburn, Elizabeth ; Kido Soule, Melissa C. ; Kujawinski, Elizabeth B.
    Metabolomics is the study of small molecules, or ‘metabolites’, that are the end products of biological processes. While -omics technologies such as genomics, transcriptomics, and proteomics measure the metabolic potential of organisms, metabolomics provides detailed information on the organic compounds produced during metabolism and found within cells and in the environment. Improvements in analytical techniques have expanded our understanding of metabolomics and developments in computational tools have made metabolomics data accessible to a broad segment of the scientific community. Yet, metabolomics methods have only been applied to a limited number of projects in the marine environment. Here, we review analysis techniques for mass spectrometry data and summarize the current state of metabolomics databases. We then describe a boutique database developed in our laboratory for efficient data analysis and selection of mass spectral targets for metabolite identification. The code to implement the database is freely available on GitHub (https://github.com/joefutrelle/domdb). Data organization and analysis are critical, but often under-appreciated, components of metabolomics research. Future advances in environmental metabolomics will take advantage of continued development of new tools that facilitate analysis of large metabolomics datasets.
  • Presentation
    Informatics solutions for large ocean optics datasets
    ( 2012-10) Sosik, Heidi M. ; Futrelle, Joe
    Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multi-stage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers (~1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.
  • Article
    Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification
    (National Academy of Sciences, 2023-07-03) Catlett, Dylan ; Peacock, Emily E. ; Crockford, E. Taylor ; Futrelle, Joe ; Batchelder, Sidney ; Stevens, Bethany L. F. ; Gast, Rebecca J. ; Zhang, Weifeng Gordon ; Sosik, Heidi M.
    Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic “epidemics” within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of Cryothecomonas aestivalis (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), Guinardia delicatula, with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to >1 billion images from a nearshore time series and >20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of G. delicatula abundance and infection dynamics. Suppression of parasitoid infection at temperatures <4 °C drives annual cycles in both G. delicatula infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for ~2 mo following cold periods, possibly due to temperature-induced local extinctions of the C. aestivalis strain(s) that infect G. delicatula. These findings have implications for predicting impacts of a warming NES surface ocean on G. delicatula abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.