To address the event-driven, rapidly changing needs of 21st- century society, water science must be open and data-driven, use the best available scientific methods, and rapidly communicate findings to the public using multiple platforms, including print, web, and social media. To support these needs, the USGS Office of Water Information (OWI) Data Science team has developed a foundation for rigorous and efficient data-driven scientific analysis, quick assembly of thematic teams and dissemination of contemporary science, and advancement of scientific computing best practices across the USGS and partners.
The OWI Data Science team, which is a cross-functional team of earth scientists, data analysts, and computer scientists, has four objective functions that are designed to increase the efficiency of USGS science and more effectively communicate its value to the Nation:
We build tools and software that enable reproducible data & modeling pipelines. The development of these tools is aligned with the architectural recommendations of the software development lifecycle best practices. We work with scientists to design, implement, and communicate reproducible workflows for earth science (primarily water science) that will satisfy USGS requirements for data access. Many of these tools take advantage of High-Throughput Computing (HTC) resources, and we often publish papers that describe novel analytical techniques, methods, and research applications.
To promote and enable more scientists to conduct efficient, data-driven analyses, we offer training in scientific computing workshops. We currently have three different workshops: Introduction to R, USGS OWI R Tools, and Advising in Scientific Computing. The Introduction to R workshops are typically 2-3 days long, are hands-on, and cover basic skills for using R for a reproducible, scalable, and transparent scientific workflow. The curriculum takes students through common data analysis workflow steps:
The USGS OWI R Tools workshop focuses on specific applications of R for earth science analyses, and teaches the basics of using OWI R packages. This course expects participants to have an intermediate level of R experience. Advising in Scientific Computing is an informal workshop where a group interested in developing their own R package can work with some of our experienced R package developers. The workshop covers version control (Git/GitHub), and package development best practices. By the end of the week, participants should have functioning package that they can continue to develop and maintain without the OWI team.
Please visit our R Community website for more information on Introduction to R training workshops. To learn more about USGS OWI R Tools or Advising in Scientific Computing contact <firstname.lastname@example.org>
We rapidly develop and deploy visualizations on current water issues as part of the USGS Visualization Lab (VIZLAB). These visualizations involve working with USGS scientists and partners to highlight timely science in accessible and appealing visual stories.
We collaborate with water science practitioners to tackle water resources questions that are analytically complex and computationally challenging, broad or highly resolved in space and time, and diverse in the type or structure of supporting data. This research portfolio demonstrates the value of a data-driven scientific approach through application of data science best practices, and is accomplished using transparent, scalable, and efficient data science approaches. Our group continues to identify, expand on, and share skills in the application of modeling approaches, machine learning, algorithm development, and remote sensing image processing to water science.
Snortheim CA, PC Hanson, KD McMahon, JS Read, CC Carey, HA Dugan. 2017. Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake. Ecological Modelling. 343: 39-53. doi: 10.1016/j.ecolmodel.2016.10.014
Read, E., L Carr, LA De Cicco, HA Dugan, PC Hanson, JA Hart, J Kreft, JS Read. and LA Winslow, 2017. Water quality data for national‐scale aquatic research: The Water Quality Portal. Water Resources Research. doi: 10.1002/2016WR019993
Baldwin AK, SR Corsi, LA De Cicco, PL Lenaker, MA Lutz, DJ Sullivan, KD Richards. 2016. Organic contaminants in Great Lakes tributaries: Prevalence and potential aquatic toxicity. Science of the Total Environment 554-555: 10.1016/j.scitotenv.2016.02.137.
Read EK, M O'Rourke,GS Hong, PC Hanson , LA Winslow, S Crowley, CA Brewer, KC Weathers. 2016. Building the team for team science. Ecosphere 7(3):e01291.10.1002/ecs2.1291.
Thieler ER, SL Zeigler, LA Winslow, MK Hines, JS Read, JI Walker. 2016. Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability. PLoS ONE. 11: e0164979. doi:10.1371/journal.pone.0164979
Hansen GJ, JS Read, JF Hansen, LA Winslow. 2016. Projected shifts in fish species dominance in Wisconsin lakes under climate change. Global Change Biology. doi:10.1111/gcb.13462
Winslow LA, S Chamberlain, AP Appling, JS Read. 2016. sbtools: A package connecting R to cloud-based data for collaborative online research. The R Journal. 8(1):387-98.
Dugan HA, RI Woolway, AB Santoso, JR Corman, A Jaimes, ER Nodine, VP Patil, JA Zwart, JA Brentrup, AL Hetherington, SK Oliver, JS Read, KM Winters, PC Hanson, EK Read, LA Winslow, KC Weathers. 2016. Consequences of gas flux model choice on the interpretation of metabolic balance across 15 lakes. Inland Waters. 6: 581-592. doi:10.5268/IW-6.4.836
Read JS, C Gries, EK Read, J Klug, PC Hanson, MR Hipsey, E Jennings, CM O'Reilly, LA Winslow, D Pierson, C McBride, DP Hamilton. 2016. Generating community-built tools for data sharing and analysis in environmental networks. Inland Waters. 6: 637-644. doi:10.5268/IW-6.4.889
Brentrup JA, CE Williamson, W Colom-Montero, W Eckert, E de Eyto, H-P Grossart, Y Huot, P Isles, LB Knoll, TH Leach, CG McBride, D Pierson, F Pomati, JS Read, KC Rose, NR Samal, PA Stæhr, LA Winslow. 2016. The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model. Inland Waters. 6: 565-580. doi:10.5268/IW-6.4.890
Winslow LA, JA Zwart, RD Batt, HA Dugan, RI Woolway, JR Corman, PC Hanson, JS Read. 2016. LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models. Inland Waters. 6: 622-636. doi:10.5268/IW-6.4.883
KC Rose, LA Winslow, JS Read, GJA Hansen. 2016. Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity. Limnology and Oceanography Letters. 1: 44-53. doi:10.1002/lol2.10027
Blodgett D, E Read, J Lucido, T Slawecki, D Young. 2016. An analysis of water data systems to inform the Open Water Data Initiative. Journal of the American Water Resources Association. DOI: 10.1111/1752-1688.12417.
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Winner, 2016 US Geological Survey Shoemaker Awards for External Communications Excellence. Awarded to Read E, J Walker, A Adams, D Bunk, C Touton, N Booth, A Appling, A Butler, D Button, A Danner, M Hines, D Pearson, JR Read, I Suftin, F Thompson, J Van Den Hoek, J Vrabel, M Wernimont, L Winslow for Drought in the Colorado River Basin: Insights Using Open Data in the category of Internet Communications.
Finalist, 2015 National Science Foundation and Popular Science The Vizzies Visualization Challenge. Awarded to Read E, M Bucknell, M Hines, J Kreft, J Lucido, JR Read, C Schroedl, D Sibley, S Stephan, I Suftin, P Thongsavanh, J Walker, M Wernimont, L Winslow, A Yan for California Drought, Visualized with Open Data in the category of Games & Apps.