Organismal Biology - Graph Instructions
7 Pages

Organismal Biology - Graph Instructions

Course Number: BIO 1130, Fall 2009

College/University: University of Ottawa

Word Count: 1809

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APPENDIX Graphing Data Use a graph when you wish to: see overall trends, patterns or relationships in the data, compare two or more factors in a general or quantitative fashion, present large data sets in a comprehensible way and analyse data. General Principles of Graph Construction Towards a clear comprehension 1. 2. 3. Transform observations into graphical form. Proofread your graphs (at least three times)....

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Data Use APPENDIX Graphing a graph when you wish to: see overall trends, patterns or relationships in the data, compare two or more factors in a general or quantitative fashion, present large data sets in a comprehensible way and analyse data. General Principles of Graph Construction Towards a clear comprehension 1. 2. 3. Transform observations into graphical form. Proofread your graphs (at least three times). Work towards clarity. Evaluation Criteria Evaluation criteria for graphs (include but are not limited to the following): Presentation: Graph type, Data : ink ratio, Layout Data: Symbols, Axes units, Axes labels Caption: Description, Analysis (If applies), nomenclature All graphs must be done by hand and on millimetre paper. Presentation I- Plot type When choosing the plot type one should consider whether the graph is appropriate for scientific presentation and the way that the data were collected. The simplicity of computers in plotting will raise questions that one should address: the appropriateness of histograms vs. pie charts, vs. the data points themselves. Which is appropriate, when and under what circumstances? Here are some examples of different graph or plot types: Bar graphs (horizontal or vertical) These are very simple graphs that consist of a number of proportional bars of equal width and variable length. Variations in quantity are scaled along one axis only (values may be scaled on the x-axis for horizontal bar graphs and on the y-axis for vertical bar graphs). Bars can represent a wide range of variables, i.e. places, areas, items of different types and time periods. They may be used on maps as alternatives to pie charts. Their bulk and clumsiness restrict their visual impact. They allow quantitative information to be scaled against time, distance or some other variable. Bar graphs should not be confused with histograms, which have quantitative scale along both axes. Histograms Histograms are similar to bar graphs in that they consist of a number of proportional bars of equal width and variable length. They differ in that histograms are used to analyse and study distributions. Also, histograms have a quantitative scale on both axes while bar graphs have a quantitative scale on only one axis. Typically, before data are plotted on a histogram, the data range must first be divided into a number of intervals and the number of observations falling into each interval recorded. The percent of observations in each interval can be calculated by dividing the number of observations in the interval by the total number of observations and multiplying the resultant term by 100. These results are then plotted with the intervals on the x-axis and the percent values on the y-axis, resulting in a series of vertical bars representing the intervals of the distribution. Pie charts A pie chart shows the relationship or proportion of parts to a whole. It is useful if one element makes up a significant portion of the whole. Its form is that of a circle divided up into sectors (pie slices). Since generally no scale is provided one must judge the sizes of the angles to infer percentages or proportions represented by a given slice. This type of chart is poor for determining exact values and according to Tufte (1983), "should never be used". Straight-line graphs They are generally used when many data points (n>30) are available at constant intervals, in order to see trends or changes in a variable through time. Points are connected by straight lines to indicate the fluctuation in values through time. One may also use running means to determine trends through time (this is simply the summing of several data points over several intervals and then dividing by the number of intervals). Transect graphs When using the transect method to collect data one can plot the information in the form of a graph with distance scaled along the horizontal axis. The important feature of any transect graph is that it represents variation in value or quantity along a line, which may or may not be straight. Scatterplots Scatterplots are used to investigate the relationship between two different sets of data. In these graphs variable quantities are scaled along both axes. Each item must have two values, one from each set of data. These are used as x and y co-ordinates, allowing the item to be plotted as a point on the graph. There should be some logical reason for expecting a relationship between the two variables concerned. Otherwise, any relationship found may be purely coincidental. Usually, there is a cause and effect association between the two data sets plotted. Where a causal factor or independent variable can be clearly defined, its customary to scale it on the horizontal axis (x axis), while the resultant factor or dependent variable is placed on the vertical axis (y axis). Scatter diagrams allow a subjective evaluation of the degree of relationship between the two variables. Trends or tendencies may be clear from the graph. At times one variable may increase while the other decreases or vice versa (an indirect relationship). If both variables increase or decrease in conjunction with each other, a direct relationship is present. The closer the points on a scatter diagram are to being on a straight or curved line, the greater the degree of relationship (correlation) between the two variables. A line of best fit (by eye) or a regression line may also be plotted to further illustrate the relationship between the two variables. Two panel bar or graph histogram (vertical layout): Y axis common for 2 panels Y axis scale and value range is the same for both panels X axis label is only written on lower panel axis (ticks marks on both axis). Only one symbol key is presented Data to ink ratio The data to ink ratio tries to emphasise the importance of the data itself relative to the other elements of the graph. Data-ink ratio = amount of ink used for the data / total amount of ink used to print the graph. Maximise the data to ink ratio by reducing the amount of non-data ink (grids, borders and 3D effects). Layout Layout should consider the size, placement and orientation of the graph. Typically the graph uses 2/3 of the page, the last 1/3 being used for the caption. II- Data Symbols 1. Make the data stand out. Avoid unnecessary or superfluous ink. 2. Use visually prominent plotting symbols to show the data. The size and appearance of the symbols should be considered. Open or filled circles, squares and triangles are, for example, appropriate. Data symbols should be more prominent than any line linking them or drawn through them. 3. Use a reference line when you wish to show an important value across the entire graph but it should be less prominent than the data. 4. Lessen the visual impact of data labels so they do not interfere with the data or disorder the graph. 5. Symbols that overlap must be distinguishable. 6. If data sets superpose, they should be visually separable. 7. If more than one series of data is plotted, a key for the symbols should be used. Place the symbol key within the rectangle of the graph in a space devoid of data. If this is not possible, place it either immediately above or to the right of the x-y axis area. 8. Don't fill the figure with too many details. 9. After reduction or reproduction, visual clarity must be maintained. Axis units and scale 1. Choose an interval for the x- and y- axis that includes the entire data range. Zero does not have to be in this interval. 2. Choose an interval so that the data occupy most of your figure. 3. If data are to be compared between different graphs, choose similar scales. 4. Don't put too many divisions (tick marks) on the axes. Three to five tick marks (excluding zero) are enough to give the measurement scale and allow accurate determination of data points. 5. Tick marks should point outwards so they do not obscure data points. 6. Only when necessary use a scale break (a break in the x- or y-axis). Do not connect the data points on each side of the break. Axis labels 1. Axes labels must be appropriate for the variables plotted and indicate the units that are involved. 2. Appropriate SI abbreviations for units must be used (see appendix) 3. If you plot logarithms of a variable, the axis label should correspond to the tick mark labels. Error Bars 1. If means are plotted, error bars must be indicated. Error bars may be either the sample standard deviation, the standard error of the mean or the 95% confidence interval. 2. The error bars should be slightly less prominent than the data points. 3. If a bar graph is used, only present the means + the error bars. III- Caption Under almost all circumstances, a graph presented for publication must have a caption. It must be placed immediately below the graph. It should begin with a figure no. (e.g. Figure 2). What should be included in the caption? 1. The first sentence in the caption should be a specific and informative title. If the graph presents a comparison, the first sentence should mention it. 2. The body of the caption should be sufficient enough to explain briefly the mechanism by which the data were collected, analysed and the number of samples involved (e.g. n=30 or n=10 to 15). 3. If only two symbols are used they may be explained here or in a key. For more than 2 symbol types use a key (see Symbols). Binomial nomenclature of Organisms In addition to common name(s) given to organisms, each of them possesses a unique name based on phylogenetic relationships it shares with all other organisms. The most common way to name an organism is to use the binomial nomenclature initiated by the 18th century biologist Linnaeus. This nomenclature indicates the genus and species, the two most specific characteristics in the hierarchy of designations. Specific typography rules apply: only the first letter of the genus is capitalized (not the species name) and both genus and species are printed in italics (or underlined when hand written). Ex: dog (common name) is Canis domesticus or Canis domesticus. Binomial nomenclature must be used in captions. Analysis 1. If means are presented this should be stated. 2. If data are transformed (if you did calculation), the type of transformation should be indicated. 3. Where statistical significance has been determined, the result and the level of probability should be included. 4. The method of curve fitting should also be mentioned here. 5. Clearly explain error bars. One of the following three statements should unambiguously explain your error bars: Means one sample standard deviation. Means one standard error. Means the 95% confidence interval. A Sample Line Graph 5000 Abundance (no./10 ml) 4000 3000 2000 1000 0 0 DO NOT PRINT THIS GRAPH 3 6 9 TSeconds (s) ime (days) 12 Figure 1: Population growth of Paramecium aurelia at 20oC in a sterile solution of 1% (w/v) proteose peptone. Mean totals ( one standard error) of 20 counts are presented. A least squares polynomial is fitted to the data (y=25.88x2 + 51.76x + 5, R2=0.98)

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