Data Presentation: How to Effectively Communicate Your Findings

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Data Presentation How to Effectively
CommunicateYour Findings

• Mary Purugganan, Ph.D.
• maryp_at_rice.edu
• Leadership Professional Development Workshop
• November 20, 2004

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Todays Plan

• Function design of common graphics for data in
  SE
• 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/wr
iting/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/wr
iting/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 27653-70
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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. 1041-6
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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
Figure 1. Ras12V37G transforms human cells.
Anchorage-independent growth of NIH 3T3 (black
bars) or human HEKHT (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
HEKHT cells yielded 380 50 or 289 47
colonies in soft agar, respectively.
Hamad, N.M.et al., Distinct requirements for Ras
oncogenesis in human versus mouse cells, Genes
Development, 16(16)
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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 323105-109
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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/Lec
ture2/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
C.R. Twidale (2004) Earth Sci Rev 67159-218
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Micrographs
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 ac) or
together with SHA (squares di) 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.
Lambert et al. (2004) Virology 330158-67
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Micrographs
Fig. 3. STM micrographs of Ag (100). (a) 0.1
Å0.1 area. (b) Edge enhanced image of (a), (c)
500 ÅÅ500 Å and (d) 100 ÅÅ100 Å areas,
respectively.
Ali et al. (1998) Thin Solid Films 323105-109
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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
CHOK1 1.5 mg
CHOK1 0.3 mg
CHOK1 0 mg
xrs-6 0.3 mg
xrs-6 1.5 mg
xrs-6 0 mg
Cell type DNA transfected
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User-friendly labeling of lanes
Purugganan et al., Nucleic Acids Research (2001)
291638-46.
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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
35253-54. Supplemental movie S2 online
at http//www.neuron.org/cgi/content/full/35/2/24
3/DC1/
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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 (dont 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 gt 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 62271-73.
• Goodstein, David. Conduct and Misconduct in
  Science. Accessed 11/19/04. http//www.physics.oh
  io-state.edu/wilkins/onepage/conduct.html/

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Small Group ExerciseLook at Visuals The good,
the bad, and the uglyPresent and discuss