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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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The kingdom Archaeplastida encompasses red and green algae, along with land plants. Unlike other protists with chloroplasts that arose through secondary endosymbiosis, only red and green algae originated from primary endosymbiotic events. This diverse group of eukaryotic organisms contains chlorophyll and performs oxygenic photosynthesis.Algae exist in various forms, from large brown kelp in coastal waters to green scum in puddles and stains on rocks or soil. Some species are responsible for...
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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression
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Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

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Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton.

Patricia S Thibodeau1, Jongsun Kim2

  • 1Graduate School of Oceanography, University of Rhode Island; pthibodeau1@une.edu.

Journal of Visualized Experiments : Jove
|August 14, 2023
PubMed
Summary
This summary is machine-generated.

This study visualizes long-term phytoplankton changes in Narragansett Bay using art. It transforms microscopic images into repeating patterns, revealing shifts in biomass and temperature trends over decades.

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Area of Science:

  • Marine biology
  • Ecosystem dynamics
  • Data visualization

Background:

  • Oceanographic time series are crucial for understanding ecosystem changes.
  • The Narragansett Bay Long-Term Plankton Time Series (NBPTS) offers a unique dataset (1959-present) for studying aquatic ecosystems.
  • Phytoplankton, the base of marine food webs, are vital to coastal populations.

Purpose of the Study:

  • To develop a novel protocol for visualizing phytoplankton diversity and magnitude.
  • To communicate the importance of phytoplankton to coastal communities.
  • To illustrate long-term changes in phytoplankton biomass and ecosystem temperature.

Main Methods:

  • Converting microscopic phytoplankton images from NBPTS into vector graphics using Adobe Illustrator.
  • Creating repeating visual patterns based on the relative abundance of numerically abundant or harmful taxa (e.g., Pseudo-nitzschia spp.).
  • Designing large-scale printed panels (96x34 inches) with phytoplankton patterns, incorporating decadal biomass data and a blue-to-red color gradient representing temperature increase.

Main Results:

  • Generated visual patterns effectively represent decadal phytoplankton biomass shifts.
  • The color gradient in the artwork illustrates a long-term warming trend in Narragansett Bay.
  • The project successfully visualizes typically invisible changes in phytoplankton biomass and abundance over time.

Conclusions:

  • This artistic data visualization approach makes long-term plankton time series data accessible and understandable.
  • The method can be applied to other plankton time series for enhanced data communication, education, and outreach.
  • The project highlights the value of integrating scientific data with artistic expression for broader public engagement.