What is your message? Graphs should clearly communicate a message to your audience. You should keep this message in mind when creating and formatting your graph. As a general rule, you should ensure that all of your figures for scientific articles or lab reports can be easily interpreted when printed in black and white. Colour can be used if your audience is likely to view the graph in colour i.
Pie charts are rarely used in scientific articles, but they can be useful when communicating with the public. You should check the requirements of your assignment with your lecturer for guidance on how to display your data.
Continuous variable: Continuous variables are numeric measurements or observations that can include any number of values within a certain range. Discrete variable: Discrete variables are measured as whole units. Categorical variable: Categorical variables describe a quality or a characteristic E. Colour, species, sex, blood type.
Independent variable: The independent variable is the variable which you control or manipulate in your experiment, or the variable that you think will affect the dependent variable. Independent variables are placed on the x-axis of a graph. Dependent or response variable: The dependent variable is the variable you think will be influenced by the independent variable. Changes in the dependent variable are observed or measured in relation to changes in the independent variable.
The independent variable is the amount of light exposure and the dependent variable is the rate of growth. Sometimes a table will be more appropriate for displaying your data. Tables are great for displaying multiple variables, specific values, and comparing categories. A table will often require an audience to look up specific information to understand the data.
Therefore, you should ensure your table is presented in a neat and logical manner. Similar to graphs, you need to consider the message in your data that you want to communicate to your audience. You may need to perform a statistical analysis on your data or summarise your results before adding the information to a table.
For large tables, you may need to shade alternate rows or highlight important details by using a bold font to allow your audience to read the table efficiently. All of the tables and graphs that you create for scientific articles and lab reports will require a legend.
The concise description in a table or figure legend should convey the key message of the table or graph to your audience without having to read the full article. This module focuses on graphs and tables for use in scientific articles and lab reports.
If you are designing a graph for a presentation or poster, you should refer to the relevant module for further design guidelines.
Figure number Figure 1 or Fig. The figure number is used to allow your audience to find the figure you have referred to in your text. A descriptive figure title briefly describes what the figure is displaying but lets the reader identify any trends or relationships, or is guided by the text you include in the results section.
Thank you! Published by Pauline Terry Modified over 5 years ago. Interpolating Data Displays data in a easy to read format. Relationships easy to recognize Can be used to predict future events Interpolating Data — finding the value in between 2 values on a drawn graph Extrapolating Data- finding the value of a future event base on a previous pattern and trends.
Temperature of the planet. Dependent on the Y axis. Graphing Section 1. Why use graphs? Graph- used to make data easier to read and understand- shows patterns and trends. Representing numerical information in a picture. Graph shows a picture of a relationship -how two processes relate -what happens when two events. Aim: What are graphs? Do Now: Answer the following questions in your notebook. In addition, the y-axes of the two graphs are displayed on differing scales — the bottom graph has more space between the 0.
Both of these techniques tend to exaggerate the variability in the lower graph. However, the primary reason for the difference in the graphs is not actually shown in the graphs. The author of the graphic created the image on the bottom using different calculations that did not take into account all of the variables that climate scientists used to create the top graph. In other words, the graphs simply do not show the same data.
These are common techniques used to distort visual forms of data — manipulating axes, changing one of the variables in a comparison, changing calculations without full explanation — that can obscure a true comparison. There are other kinds of visual data aside from graphs. You might think of a topographic map or a satellite image as a picture or a sketch of the surface of the earth, but both of these images are ways of visualizing spatial data.
A topographic map shows data collected on elevation and the location of geographic features like lakes or mountain peaks see Figure 6. These data may have been collected in the field by surveyors or by looking at aerial photographs, but nonetheless the map is not a picture of a region — it is a visual representation of data.
The topographic map in Figure 6 is actually accomplishing a second goal beyond simply visualizing data: It is taking three-dimensional data variations in land elevation and displaying them in two dimensions on a flat piece of paper. Likewise, satellite images are commonly misunderstood to be photographs of the Earth from space, but in reality they are much more complex than that. A satellite records numerical data for each pixel, and it does so at certain predefined wavelengths in the electromagnetic spectrum see our Light II: Electromagnetism module for more information.
In other words, the image itself is a visualization of data that has been processed from the raw data received from the satellite. For example, the Landsat satellites record data in seven different wavelengths: three in the visible spectrum and four in the infrared wavelengths. The composite image of four of those wavelengths is displayed in the image of a portion of the Colorado Rocky Mountains shown in Figure 7.
The large red region in the lower portion of the image is not red vegetation in the mountains; instead, it is a region with high values for emission of infrared or thermal wavelengths. In fact, this region was the site of a large forest fire, known as the Hayman Fire, a month prior to the acquisition of the satellite image in July The advent of satellite imagery vastly expanded one data collection method: extracting data from an image.
For example, from a series of satellite images of the Hayman Fire acquired while it was burning, scientists and forest managers were able to extract data about the extent of the fire which burned deep into National Forest land where it could not be monitored by people on the ground , the rate of spread, and the temperature at which it was burning.
By comparing two satellite images, they could find the area that had burned over the course of a day, a week, or a month. Thus, although the images themselves consist of numerical data, additional information can be extracted from these images as a form of data collection. Another example can be taken from the realm of atomic physics. In , Sir Isaac Newton discovered that when light from the sun is passed through a prism, it separates into a characteristic rainbow of light.
Almost years after Newton, John Herschel and W. Fox Talbot demonstrated that when substances are heated and the light they give off is passed through a prism, each element gives off a characteristic pattern of bright lines of color, but they did not understand why see Figure 8.
In , the Danish physicist Niels Bohr used these images to make a startling proposal: He suggested that the line spectra of elements were due to the movement of electrons between different orbitals, and thus these spectra could provide information regarding the electron configuration of the elements see our Atomic Theory II: Ions, Isotopes, and Electron Shells module for more information.
You can actually calculate the potential energy difference between electron orbitals in atoms by analyzing the color and thus wavelength of light emitted. Photographs and videos are also visual data. In , a group of scientists based in part at the Cornell Ornithology lab published their findings that a bird believed to be extinct in North America, the Ivory-billed Woodpecker, had been spotted in Arkansas Fitzpatrick et al.
Their primary evidence consisted of video footage and photographs of a bird in flight, which they included in their paper along with a detailed analysis of the features of the images and video that suggested that the bird was an Ivory-billed Woodpecker. You can read the article and see the photographs here.
Many areas of study within science have more specialized graphs used for specific kinds of data. Evolutionary biologists, for example, use evolutionary trees or cladograms to show how species are related to each other, what characteristics they share, and how they evolve over time.
Geologists use a type of graph called a stereonet that represents the inside of a hemisphere in order to depict the orientation of rock layers in three-dimensional space. Many fields now use three-dimensional graphs to represent three variables , though they may not actually represent three-dimensional space. Regardless of the exact type of graph, the creation of clear, understandable visualizations of data is of fundamental importance in all branches of science.
In recognition of the critical contribution of visuals to science, the National Science Foundation and the American Association for the Advancement of Science sponsor an annual Science and Engineering Visualization Challenge, in which submissions are judged based on their visual impact, effective communication, and originality NSF, Likewise, reading and interpreting graphs is a key skill at all levels, from the introductory student to the research scientist.
Graphs are a key component of scientific research papers, where new data are routinely presented. Presenting the data from which conclusions are drawn allows other scientists the opportunity to analyze the data for themselves, a process whose purpose is to keep scientific experiments and analysis as objective as possible. Although tables are necessary to record the data, graphs allow readers to visualize complex datasets in a simple, concise manner.
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