Data Presentation: How to Effectively Communicate

Your Findings

Mary Purugganan, Ph.D.

maryp@rice.edu

Leadership & Professional Development Workshop

November 20, 2004

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

Graphical Excellence

“The well-designed presentation of interesting data--a matter of substance, of statistics, and of design” (Tufte, 1983)

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

Example: Small Table

Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press

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

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)

Line Graphs

Day, R.A. (1998) How to Write and Publish a Scientific Paper. Phoenix: Oryx Press

Line Graphs

Kaufmann(2003) J of Hydrology 276:53-70

Scatterplots• Function

Good for identifying non-linear relationships

Good for identifying clusters and outliers (out-of-range points)

• Design As for line graphs

Example: Scatterplot

Sanchez et al. (2004) Chem Eng J. 104:1-6

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

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.

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

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

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

Example: Frequency Polygon

http://www.olemiss.edu/courses/psy214/Lectures/Lecture2/lex_2.htm

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

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

Photographs

Shahbazian et al., Neuron (2002)

C.R. Twidale (2004) Earth Sci Rev 67:159-218

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.

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.

Blots and Gels

Author must “transform” raw data

• Select lanes and/or create montage

• Crop image

• Label lanes, bands

Clumsy labeling of lanes

CH

OK

1; 0

g

CH

OK

1 ; 0

.3

g

CH

OK

1 ; 1

.5

g

xrs-

6 ; 0

g

xrs-

6 ; 0

.3

g

xrs-

6 ; 1

.5

g

Cell type;

DNA transfected

User-friendly labeling of lanes

Purugganan et al., Nucleic Acids Research (2001) 29:1638-46.

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

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.

Remember your context

Written documents

Theses

Manuscripts

Reports

Visual presentations

Seminars/ oral presentation

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 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

Replace titles and captions with message headings

Visual Explanations

• Tag image with explanations

• Interpret (don’t just show) data (esp. on posters!)

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)

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)

Ethics in Data Representation

Photographic data: Particularly vulnerable to trimming field of view selection

cropping

software (Photoshop) manipulation of contrast, brightness, etc.

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

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/

Small Group Exercise

Look at Visuals: The good, the bad, and the ugly;

Present and discuss