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.
An assertive title can be used to identify a specific trend found in a graph or highlight the key message of a diagram. Assertive titles can help your audience to quickly identify the key message contained within your figure but you should ensure your title does not mislead your audience or overstate your results.
Example 1. Descriptive: Figure 1. Effects of dam construction on fish biodiversity. Assertive: Figure 1. Dam construction results in loss of fish biodiversity. Example 2. Descriptive: Figure 2. Height distribution of two Eucalyptus grandis plantations in Queensland. Assertive: Figure 2. Insect defoliation of Eucalyptus grandis reduces canopy height. Including any of the following optional extras will depend on what is displayed in the figure and what you feel your audience needs to understand the figure.
The optional extras you include will also depend on what information you have included in your methods and how you refer to the figure in your results section. View the examples above to see how the optional extras are used to describe a variety of figures. If you have used symbols, lines, colours or acronyms in your figure that have not been identified on the actual figure, you need to ensure they are referred to in the figure legend. If you have used colour in your figure, make sure your audience will be able to view it in colour, otherwise the figure will be difficult to interpret.
If you are plotting mean values and including error bars, you need to state this in the figure legend. Some figure legends will mention the type of statistical test used, the sample size, p-values, or other statistical information. Describing the graph: The x-axis shows the variable of time in units of years, and the y-axis shows the range of the variable of CO 2 concentration in units of parts per million ppm.
The dots are individual measurements of concentrations — the numbers shown in Table 1. Thus, the graph is showing us the change in atmospheric CO 2 concentrations over time.
Describing the data and trends: The line connects consecutive measurements, making it easier to see both the short- and long-term trends within the data. On the graph, it is easy to see that the concentration of atmospheric CO 2 steadily rose over time, from a low of about ppm in to a current level of about ppm. Within that long-term trend, it's also easy to see that there are short-term, annual cycles of about 5 ppm. Making interpretations: On the graph, scientists can derive additional information from the numerical data, such as how fast CO 2 concentration is rising.
This rate can be determined by calculating the slope of the long-term trend in the numerical data, and seeing this rate on a graph makes it easily apparent. While a keen observer may have been able to pick out of the table the increase in CO 2 concentrations over the five decades provided, it would be difficult for even a highly trained scientist to note the yearly cycling in atmospheric CO 2 in the numerical data — a feature elegantly demonstrated in the sawtooth pattern of the line.
Putting data into a visual format is one step in data analysis and interpretation , and well-designed graphs can help scientists interpret their data. Interpretation involves explaining why there is a long-term rise in atmospheric CO 2 concentrations on top of an annual fluctuation, thus moving beyond the graph itself to put the data into context.
Seeing the regular and repeating cycle of about 5 ppm, scientists realized that this fluctuation must be related to natural changes on the planet due to seasonal plant activity. Visual representation of these data also helped scientists to realize that the increase in CO 2 concentrations over the five decades shown occurs in parallel with the industrial revolution and thus are almost certainly related to the growing number of human activities that release CO 2 IPCC, It is important to note that neither one of these trends the long-term rise or the annual cycling nor the interpretation can be seen in a single measurement or data point.
That's one reason why you almost never hear scientists use the singular of the word data — datum. Imagine just one point on a graph. You could draw a trend line going through it in any direction. Rigorous scientific practice requires multiple data points to make a clear interpretation, and a graph can be critical not only in showing the data themselves, but in demonstrating on how much data a scientist is basing his or her interpretation.
We just followed a short, logical process to extract a lot of information from this graph. Although an infinite variety of data can appear in graphical form, this same procedure can apply when reading any kind of graph. To reiterate:. Describe the graph: What does the title say? What variable is represented on the x-axis? What is on the y-axis? What are the units of measurement? What do the symbols and colors mean? Describe the data: What is the numerical range of the data?
What kinds of patterns can you see in the distribution of the data as they are plotted? Interpret the data: How do the patterns you see in the graph relate to other things you know? The same questions apply whether you are looking at a graph of two variables or something more complex. Because creating graphs is a form of data analysis and interpretation , it is important to scrutinize a scientist's graphs as much as his or her written interpretation.
Graphs and other visual representations of scientific information also commonly contain another key element of scientific data analysis — a measure of the uncertainty or error within measurements see our Uncertainty, Error, and Confidence module.
For example, the graph in Figure 3 presents mean measurements of mercury emissions from soil at various times over the course of a single day. The error bars on each vertical bar provide the standard deviation of each measurement. These error bars are included to demonstrate that the change in emissions with time are greater than the inherent variability within each measurement see our Statistics in Science module for more information.
Graphical displays of data can also be used not just to display error, but to quantify error and uncertainty in a system. For example, Figure 4 shows a gas chromatograph of a fuel oil spill. Peaks in the chromatograph the blue line provide information about the chemicals identified in the spill, and the peak size can provide an estimate of the relative concentration of that specific chemical in the spill.
However, before this information can be extracted from the graph, instrument error and uncertainty must be calculated the red line and subtracted from the peak area. As you can see in Figure 4, instrument variability decreases as you move from left to right in the graph, and in this case, the graphical display of the error is therefore critical to accurate analysis of the data. Poor use of graphics can highlight trends that don't really exist, or can make real trends disappear.
Some have tried to point out errors with the now widely accepted notion of climate change by using misleading graphics. Figure 5, below, is one such graphic that has appeared in print. The point drawn by the creator of this is that the bottom graph, which shows relatively little change in temperature over the past 1, years, disputes the top graph used by the Intergovernmental Panel on Climate Change that shows a recent, rapid temperature increase. At first glance the bottom graph does seem to contradict the top graph.
However, looking more closely you realize:. The two graphs actually represent completely different datasets. The top graph is a representation of change in annual mean global temperature normalized to a year period, , whereas the bottom graph represents average temperatures in Europe compared to an average over the 20th-century.
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.
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