data presentation: how to effectively communicate your findings mary purugganan, ph.d....
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Data Presentation: How to Effectively Communicate
Your Findings
Mary Purugganan, Ph.D.
Leadership & Professional Development Workshop
November 20, 2004
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Today’s Plan
• Function & design of common graphics for data in S&E Tables
Line & bar charts
Scatter plots
Histograms
Frequency polygons
Photographs, micrographs
Video clips
• Designing for context
• Ethical issues in data presentation
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Graphical Excellence
“The well-designed presentation of interesting data--a matter of substance, of statistics, and of design” (Tufte, 1983)
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Tables• Function
Organize verbal and numeric data
Good for showing specific results
Not good for showing overview / trends
Not good for quick communication of ideas
• Design Place title and caption above table
Place units in column headings
Avoid rules (gridlines) in small tables
Use rules cautiously in large tables
• Choose narrow and/or gray lines
• Use blocks of light color instead of rules
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Example: Small Table
Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press
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Example: Rules in Large Table
Rules should be narrow, faint, and unobtrusive
J. Donnell, Georgia Tech; http://www.me.vt.edu/writing/handbook
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Example: Color Bars in Large Table
Color bars aid readers who may have to, for example, look up and compare values often
J. Donnell, Georgia Tech; http://www.me.vt.edu/writing/handbook
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Line Graphs• Function
Good for showing trends / relationships
Not good for showing precise data values
• Design Place title and caption below graph
Place units in axes labels
Avoid legends (keys) off to side in box• Label lines (best for projected work), or
• Place key in caption or within graph (written documents)
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Line Graphs
Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press
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Line Graphs
Kaufmann(2003) J of Hydrology 276:53-70
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Scatterplots• Function
Good for identifying non-linear relationships
Good for identifying clusters and outliers (out-of-range points)
• Design As for line graphs
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Example: Scatterplot
Sanchez et al. (2004) Chem Eng J. 104:1-6
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Bar Graphs• Function
Good for comparing proportions, amounts, values
Good for displaying data sets that are close together in value (would overlap in line graphs)
Not good for showing precise data values
• Design Place title and caption below graph
Place units in axes labels
Spacing between bars should be half the size of bars
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Example: Bar Graph
Hamad, N.M.et al., Distinct requirements for Ras oncogenesis in human versus mouse cells, Genes & Development, 16(16)
Figure 1. Ras12V37G transforms human cells. Anchorage-independent growth of NIH 3T3 (black bars) or human HEK–HT (white bars) cells expressing the described constructs, calculated from the average number of colonies observed from three plates and expressed as the percent of colonies observed in Ras12V-transformed cells. A total of 50,000 Ras12V-transformed NIH 3T3 or HEK–
HT cells yielded 380 ± 50 or 289 ± 47 colonies in soft agar, respectively.
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Histograms• Function
Constructed from frequency tables
Good for seeing shape of the distribution
Good for screening of outliers or checking normality
Not good for seeing exact values (usually data is grouped into categories)
• Design Place title and caption below graph
Place midpoints of intervals on horizontal axis
Place frequencies on vertical axis
Bars should touch one another (unlike bar graphs)
Use only with continuous data
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Example: Histograms
Fig. 4. Height histograms a, b, c and d corresponding to micrographs of Fig. 3b,c,d and Fig. 2, respectively.
Ali et al. (1998) Thin Solid Films 323:105-109
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Frequency Polygons
• Function Constructed from frequency tables
Visually appealing way of showing counts/ frequency
Better than histogram for two sets of data because the graph appears less cluttered
• Design Place title and caption below graph
Use a point (instead of histogram bar) and connect the points with straight lines
May shade area underneath the line
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Example: Frequency Polygon
http://www.olemiss.edu/courses/psy214/Lectures/Lecture2/lex_2.htm
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No chartjunk!
• Graphical simplicity: keep “data-ink” to “non-data-ink” ratio high
• Gridlines Rarely necessary
Better when thin, gray
• Fill patterns Avoid moiré effects / vibrations
Gray shading is preferable to hatching
• Avoid 3-dimensional bars
Tufte, 1983
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Example: Moiré effects
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Photographs• Function
Illustration
Good for documenting physical observations
Usually qualitative but supported by quantitative data
• Design Place title and caption below photograph(s)
Crop and arrange several photographs to facilitate understanding
Insert scale bars when necessary
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Photographs
Shahbazian et al., Neuron (2002)
C.R. Twidale (2004) Earth Sci Rev 67:159-218
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Micrographs
Lambert et al. (2004) Virology 330:158-67
Fig. 2. GFP.S co-localizes with wild-type S at the ER. Shown is the intracellular distribution of GFP.S expressed either alone (squares a–c) or together with SHA (squares d–i) in COS-7 cells. Cells were fixed, permeabilized, and examined by fluorescence microscopy. (a, d, and g) GFP fluorescence (green); (b and e) immunostaining with a mouse antibody to PDI followed by AlexaFluor 494-conjugated goat anti-mouse IgG (red); (h) immunostaining with a mouse anti-HA antibody followed by AlexaFluor 494-conjugated goat anti-mouse IgG (red) to visualize SHA. Squares c, f, and i are the corresponding merged images so that overlapping red and green signals appear yellow.
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Micrographs
Ali et al. (1998) Thin Solid Films 323:105-109
Fig. 3. STM micrographs of Ag (100). (a) 0.1 Å~0.1 area. (b) Edge enhanced image of (a), (c) 500 ÅÅ~500 Å and (d) 100 ÅÅ~100 Å areas, respectively.
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Blots and Gels
Author must “transform” raw data
• Select lanes and/or create montage
• Crop image
• Label lanes, bands
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Clumsy labeling of lanes
CH
OK
1; 0
g
CH
OK
1 ; 0
.3
g
CH
OK
1 ; 1
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g
xrs-
6 ; 0
g
xrs-
6 ; 0
.3
g
xrs-
6 ; 1
.5
g
Cell type;
DNA transfected
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User-friendly labeling of lanes
Purugganan et al., Nucleic Acids Research (2001) 29:1638-46.
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Video clips
• Function Utilize web technology for innovative ways
to share data
Show processes in real-time
May be qualitative but supported by quantitatve data
• Design No conventions yet observed/published
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Video clips
Shahbazian et al., (2002) Neuron 35:253-54.
Supplemental movie S2 online at:
http://www.neuron.org/cgi/content/full/35/2/243/DC1/
QuickTime™ and aH.263 decompressor
are needed to see this picture.
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Remember your context
Written documents
Theses
Manuscripts
Reports
Visual presentations
Seminars/ oral presentation
Posters
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Conventions for Written Documents
• Number and title (caption) each graphic Table 1. Xxxxxxx…
Figure 3. Xxxxxxx…
• Identify graphics correctly Tables are “tables”
Everything else (graph, illustration, photo, etc.) is a “figure”
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Conventions for Written Documents
• Refer to graphics in the text “Table 5 shows…”
“… as shown in Figure 1.”
“… (Table 2).”
• Incorporate graphics correctly Place graphics close to text reference
Caption correctly• Above tables
• Below figures
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Tips for Written Documents
• Design graphics for black-and-white printers and photocopies
• Figure and table captions can be long and informative (follow individual discipline and journal conventions)
• Remember audience when designing Journals: learn as much as possible about
audience to identify needs, areas of expertise
Thesis: design for “outside” committee member
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Tips for Visual Presentations
Uniqueness of posters and oral presentations
• User is not a reader Can assimilate less detail May not have time to process confusing data
• Oral communication accompanies what is printed / projected
• “Free” and “guaranteed” color Use color purposefully Avoid overuse of decorative color Avoid too much color (e.g., background fill) Avoid layering two colors of similar intensity (e.g., red on blue) Be sensitive to red/green color blindness
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Replace titles and captions with message headings
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Visual Explanations
• Tag image with explanations
• Interpret (don’t just show) data (esp. on posters!)
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Ethics in Data Representation
Intent to deceive = scientific fraud
Distortion: when visual representation is not consistent with numerical representation
Visual representation = perceived visual effect
• e.g., readers do not compare areas in circles correctly (larger circle does not appear to have the increased area it actually does)
• 3-dimensional graphs may fool the eye
Context is crucial (show enough data)
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Distortion in 3-D bar chart
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Ethics in Data Representation• Data distortion in graphing
Scale of graph (limits; log)
Placement of origin
Shape (length of axes)
Omission of data range in a continuum (implied continuum)
• Cooking and trimming Charles Babbage (1830) “Reflections on the Decline of Science in
England and on Some of Its Causes”
Cooking: making multiple observations, selecting from those that agree with theory/preconceptions (Mendel?)
Trimming: smoothing irregularities to make data appear accurate and precise; excluding extreme values in a data set (lots of researcher excuses)
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Ethics in Data Representation
Photographic data: Particularly vulnerable to trimming field of view selection
cropping
software (Photoshop) manipulation of contrast, brightness, etc.
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Ethics in Data Representation
Number one discipline to be guilty of fraud (historically):
Biomedical science
• Welfare of patients > Scientific integrity
• M.D.s less rigorously trained in research than Ph.D.s
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Resources• Tufte, Edward R. (1983) The Visual Display of Quantitative
Information. Cheshire, CT: Graphics Press.
• Burnett, Rebecca (2001) Technical Communication. Fort Worth: Harcourt College Publishers.
• Technical Writing: Resources for Teaching (esp. Illustration section written by J. Donnell, Georgia Tech). Accessed 11/18/04. http://www.me.vt.edu/writing/handbook/
• Klotz, Irving M. (1992) Cooking and trimming by scientific giants. FASEB J 6:2271-73.
• Goodstein, David. Conduct and Misconduct in Science. Accessed 11/19/04. http://www.physics.ohio-state.edu/~wilkins/onepage/conduct.html/
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Small Group Exercise
Look at Visuals: The good, the bad, and the ugly;
Present and discuss