Final presentation from the A2 Data Dive. Feb. 10- 12, 2012. visit the wiki for more information: http://wiki.datawithoutborders.cc/index.php?title=Project:Current_events:A2_DD

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A2 DataDive 2012
Project: African Health OER Network
group: content-focus (AHON)
all content in this presentation is licensed under a Creative
Commons Attribution CC:BY license.

2. +
original sources:
 RESEARCH QUESTIONS:  DATA
 How often are the resources  Google Analytics data
being accessed? From where?
When?  Youtube analytics/stats –
geographic, text
 Can we see geographically how fields, engagement
the resources are being
disbursed?  CiviCRM data on material
creators, events
 How actively engaged are our
content creators?  Detailed word doc describing
data
 Etc.

3. + two groups:
INDIVIDUALS
&
(Gin Corden, Lettie Malan, Rodger
Devine, Mandar Gokhale, Kathleen CONTENT
Omollo [client])
(Jude Yew, Brian Vickers, Derrick
TOOLS USED: Lin, Whitney K, Lidia, Tawfig, Jackie
Cohen, Kathleen Omollo [client])
-R TOOLS USED:
- Convert from CVS to Pajek - R
- Pajek - SAS
- GUESS - SPSS
- GEFI - Excel
- Google Fusion - Python

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several projects overall
 word frequency
 word frequency by video, content by video
 top
10 Youtube Videos – engagement by
country
 site traffic trends
 viewers’ gender and age trends

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tools used
 R, and various R libraries
 GraphViz
 .csv files and text input
 SAS
 SPSS
 Excel
 Python
 Wordle
 …and various knowledge/ideas/energy

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specific output
 Visualizations of word frequency in Youtube comments
 Plots and boxplots of engagement types by country and
continent
 Charts of site traffic trends
 KPI (Key Performance Indicator) charts
 Beginnings of R and Python scripts to produce data that may
be used for new visualizations and statistical analyses

9. visualization of greatest word frequency in AHON Youtube video comments – from wordle.com

10. video comment word frequency – stacked histogram (R, ggplot)

11. engagement in top 10 youtube videos

14. Video Legend
Episiotomy Repair: Infiltration anaesthesia at the time of
Video 1
crowning
Video 2 Real-Time Polymerase Chain Reaction
Video 3 Enzyme-Linked Immunosorbent Assay
Video 4 Intro to Polymerase Chain Reaction
Examination of the Pregnant Woman: Examination of the
Video 5
chest
Examination of the Pregnant Woman: Examination of the
Video 6
pregnant abdomen
Video 7 Enzyme immunoassay to detect antigens
Examination of the Pregnant Woman: Reporting the
Video 8
Obstetric Abdominal Examination
Video 9 Caesarean Section: Closure of the abdomen
Video 10 Episiotomy Repair: Episiotomy and delivery of the baby

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some take-away points
 There may be different values attached to different forms of
engagement in different areas of the world – meaning different
takeaways from content analysis
 AHON can look at trends of language in comments (for
example) by video  Access to answers to questions like: what
videos are people most outwardly grateful for? In what videos
is the content being most discussed, and which content?
 With access to scripts like these, AHON in turn has access to
data which can more easily be displayed and analyzed

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questions for further research
 What does the variety of engagement with video content by
geography suggest?
 Can site traffic and time depth information be measured more
accurately or should it be measured differently?
 Is there surprising data regarding gender, age, demographic
information with respect to engagement with content?
 How can AHON best use increased knowledge about network
connections in combination with content engagement and
views?