Data Display
How to Effectively Communicate
Your Findings
Mary Purugganan, Ph.D.
maryp@rice.edu
http://www.owlnet.rice.edu/~cainproj/
Leadership & Professional Development Workshop
March 23, 2007
The population of the earth
0.0004%
0.05%
0.7%
Deevey, E. S., Jr. Scientific American (1960) 194–204.
Why improve your data presentation?
• To draw accurate conclusions
• To demonstrate professionalism
• To increase your credibility
• To better analyze, synthesize, and understand
your data
 To see hidden relationships
 To appreciate limitations, gaps
 To formulate new questions
Today’s plan
• Examine function and design






Tables
Scatter plots and line graphs
Bar charts, histograms, frequency polygons
Photographs, micrographs
Diagrams
Video clips
• Recognize differences in contexts
 Written documents
 Visual presentations (posters, oral presentations)
• Discuss ethical issues in data display
• Revisit your own work
Tables
Function

Organize complicated data

Show specific results
Known (units)
variable/
unknown (units)
Tables
Design
 Legend
• Place above table contents
• Must contain table number and title
• May contain a caption as well
 Avoid rules (gridlines) in small tables
 Use rules cautiously in large tables
• Choose narrow and/or gray lines
• Consider blocks of light color instead of
rules
Example: Small table
Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press
Decked
heading
Example: Rules in large table
Rules should be narrow, faint, and unobtrusive
J. Donnell, Georgia Tech; http://www.me.vt.edu/writing/handbook
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
Bivariate graphs
• X/Y axis: independent variable (what
you control or choose to observe) vs.
dependent variable
• Examples:
 Scatter plots/ line graphs
 Bar graphs/ histograms
Scatterplots and line graphs
• Function
 Plot two variables; x and y represent actual,
continuous space
 Good for showing trends / relationships
• Design
 Avoid legends (keys) off to side in box
• Label lines (best for projected work), or
• Place key in caption or within graph (written documents)
Scatterplot with key in graph
Sanchez et al. (2004) Chem Eng J. 104:1-6
Line graph with key in legend
Appropriate for
written work,
not projection
Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press
Revise: Distribution of
Extensions based on Wi
0.4
0<Wi<5
10<Wi<15
Frequency
0.3
0.2
0.1
0
0
0.2
0.4
0.6
Fractional Extension
0.8
1
Exercise: How would you
revise?
Balanya et al. Science (2006) 313:1773.
Packed graphs: use with caution
Chmiola et al. Science (2006) 313:1760.
Ways to represent data sets
Valiela (2001) Doing Science: Design, Analysis, and Communication of Scientific
Research. New York: Oxford University Press.
Ways to represent data sets
Max
Upper/lower quartiles
median
Min
Valiela (2001) Doing Science: Design, Analysis, and Communication of Scientific
Research. New York: Oxford University Press.
Bar Graphs
Allow comparisons in values when the
independent variable is a classification
or category
Dependent
variable
Classification or category
Choose the right graph
If your variables are categorical (distinct,
with no intermediates), you cannot plot
with a line graph
Nonpoint Source News-Notes 43:5 (1995)
Histograms
• Function




Plot frequency vs. intervals of values
Good for seeing shape of the distribution
Good for screening of outliers or checking normality
Not good for seeing exact values (data is grouped
into categories)
• Design
 Bars should touch one another (unlike bar graphs)-lower limit of one interval is also upper limit of
previous interval
 Use only with continuous data
Example: Histograms
Fig. 5. Frequency histograms of
ΔP2/μ values using different step
distances. At a step distance of 10
μ (a) the percent histogram is
symmetric, i.e. positive and
negative values have similar
frequencies. At larger step
distances the histograms become
broader (50 μ) and then
disintegrate (500 μ). Class size: 1
torr.
Baumgartl et al. (2002) Comparative
Biochemistry and Physiology 132:75-85.
a, For the coherent splitting, a BEC is produced in the single well, which is then deformed to a double well. We observe a
narrow phase distribution for many repetitions of an interference experiment between these two matter waves, showing that
there is a deterministic phase evolution during the splitting. b, To produce two independent BECs, the double well is formed
while the atomic sample is thermal. Condensation is then achieved by evaporative cooling in the dressed-state potential.
The observed relative phase between the two BECs is completely random, as expected for two independent matter waves.
S. Hofferberth et al. Radiofrequency-dressed-state potentials for neutral atoms
Nature Physics 2, - pp710 - 716 (2006)
Exercise: how would you revise
these histograms?
Fig. 2. (a) Histogram of total detected TPF
photons from single-molecule time traces
and an exponential fit to the distribution,
yielding an e-1 value of 6024 ± 730 photons.
A histogram of single-molecule TPF
lifetimes of DCDHF-6 in PMMA is shown
in (b). The lifetime distribution is fit to a
Gaussian; fit parameters are given in the
text.
Schuck, P.J. et al. (2005) Chemical
Physics. 318:7-11.
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
 Use a point (instead of
histogram bar) and connect the
points with straight lines
 May shade area underneath the
line
http://www.olemiss.edu/courses/psy214/L
ectures/Lecture2/lex_2.htm
Three-variable graphs
• Perspective graphs
• Contour plots
• See Doing Science: Design, Analysis, and
Communication of Scientific Data (Valiela, 2001)
Kazhdan, D. et al. (1995) Physics of Fluids 7:2679-2685
http://www.itl.nist.gov/div898/handb
ook/eda/section3/contour.htm
No chartjunk!
Graphical simplicity: keep “data-ink” to “non-dataink” ratio high
Rate of seedling growth at three different
temperatures
Mean seedling height (mm)
40
35
30
25
20 C
25 C
30 C
20
15
10
Mean seedling height (mm)
45
30oC
45
40
25oC
35
30
25
20oC
20
15
10
5
0
0
5
8
16
Days of growth
0
0
8
16
24
Days of growth
Too much non-data ink
Emphasis on data
24
No chartjunk!
• Gridlines
 Rarely necessary
 Better when thin, gray
10
9
8
7
6
5
• Fill patterns
4
3
2
1
 Avoid moiré effects / vibrations
 Gray shading is preferable to hatching
0
• Avoid 3-dimensional bars
Series1
Series2
Series3
Series4
Photographs
• Function
 Good for documenting physical
observations
 Usually qualitative but supported
by quantitative data
Shahbazian et al., Neuron (2002)
• Design
 Place title and caption below
photograph(s)
 Crop and arrange several
photographs to facilitate
understanding
 Insert scale bars when necessary
C.R. Twidale (2004) Earth
Sci Rev 67:159-218
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 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 antimouse 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 330:158-67
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.
Ali et al. (1998) Thin Solid Films 323:105-109
Diagrams & drawings
• Function
 Show parts and
relationships
 Focus audience attention
to what is essential
• Design
 Use color to show
relationships and draw
eye
 Avoid unintentional
changes in proportion
and scale
Leuptow, R.M. (June
2004) NASA Tech Briefs.
Video clips
• Function
 Show processes in real-time
 Supplement online journal articles
 May be qualitative but supported by
quantitative data
• Design
 No conventions yet observed / published
Video clips
QuickTime™ and a
H.263 decompressor
are needed to see this picture.
Shahbazian et al., (2002) Neuron 35:253-54.
Supplemental movie S2 online at:
http://www.neuron.org/cgi/content/full/35/2/243/DC1/
Design data display for your context
Written
documents
 Theses
Visual
presentations
 Manuscripts
 Seminars/ oral
presentation
 Reports
 Posters
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”
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
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
Tips for visual presentations
Uniqueness of posters and oral presentations
• User is not a reader
 Is not able to assimilate great 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
Replace titles and captions with
message headings
Visual explanations
• Tag image with explanations
• Interpret (don’t just show) data (esp. on posters!)
Exercise: How would you
revise for PPT?
Farchioni et al. Eur. Phys. J. C (2006) 47:461.
Ethics in data display
 Putting data in the best light vs. trying to deceive
through display
 Data can be
• Distorted (perceived visual effect different from
numerical representation)
• Misrepresented (particularly visual data)
• Cooked (selecting from among observations)
– Mendel?
• Trimmed (ignoring extreme values in a data set)
Distortion
 Readers do not
compare areas
in circles
correctly
Number of people on
Drug A
(larger circle does
not appear to
have the
increased area it
actually does)
Number of people on
Drug B
Distortion
3-dimensional graphs
may fool the eye
90
80
70
60
Se
50
40
30
20
10
0
A
B
C
Cleveland’s experiments (1985)
Accuracy in perceiving graphical cues:
most accurate
perception
Position along axis
Length
Angle / slope
Area
Volume
least accurate
perception
Color / shade
How to avoid distortion
• Show enough data
• Be aware of potential sources of distortion
 Scale of graph (limits; log)
 Placement of origin
 Shape (length of axes)
 Omission of data range in a continuum (implied
continuum)
Linear and logarithmic scales
Schulze and Mealy (2001) American Scientist 89: 209.
Taking a log spreads out small values and compresses
larger ones!
Ethics in display of visual data
Photographic data: Particularly
vulnerable to trimming
 field of view selection
 cropping
 software (e.g., Photoshop)
manipulation of contrast,
brightness, etc.
• Editorial in Nature (Feb 23, 2006)
 “In Nature’s view, beautification
is a form of misrepresentation”
 Concise guide to image handling
in Guides for Authors (Nature
family of journals)
http://www.nature.com
/nature/authors/infosh
eets.html
Accessed 10/12/06
Summary
•
•
•
•
•
Consider function when choosing visual
Follow design conventions
Adapt visual for context (written vs. visual)
Design for audience
Question your data selection and
representation; avoid cooking, trimming, and
distortion
Resources
•
Burnett, Rebecca (2001) Technical Communication. Fort Worth: Harcourt College
Publishers.
•
•
Cleveland, W.S. (1985) The Elements of Graphing Data. Wadsworth.
•
Goodstein, David. Conduct and Misconduct in Science. Accessed 11/19/04.
http://www.physics.ohio-state.edu/~wilkins/onepage/conduct.html/
•
Klotz, Irving M. (1992) Cooking and trimming by scientific giants. FASEB J
6:2271-73.
•
Not picture-perfect: Nature’s new guidelines for digital images encourage
openness about the way data are manipulated. Editorial. (2006) Nature 439:89192.
•
Tufte, Edward R. (1983) The Visual Display of Quantitative Information.
Cheshire, CT: Graphics Press.
•
Valiela, Ivan (2001) Doing Science: Design, Analysis, and Communication of
Scientific Research. New York: Oxford University Press.
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/
SAMPLES
Fig.1: Loading plot for the first three PCs vs. the assay index
Cytocompatibility: Direct contact assay
fraction of LIVE cells
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
20/0
I
18/5
% HEA/%AAm
I
10/13
I
15/15
Ave. Peak Force vs. Pulling Velocity for Various Spring Constants
300
280
260
Ave. Peak Force (pN)
240
Fernandez
220
k = 0.017 N/m
k = 0.068 N/m
k = 0.071 N/m
Log. (k = 0.017 N/m)
Log. (Fernandez)
Log. (k = 0.071 N/m)
Log. (k = 0.068 N/m)
200
180
160
140
120
100
1
10
100
1000
Pulling Velocity (nm /s)
10000
100000