1. A TEDMED Data Reveal: Big and Little Dr. Brand Niemann Director and Senior Data Scientist Semantic Community http://semanticommunity.info/ AOL Government Blogger http://gov.aol.com/bloggers/brand-niemann/ April 22, 2013 http://semanticommunity.info/A_TEDMED_Data_Reveal 1

2. Background I did a story about TEDMED 2012 for the 2012 Health Datapalooza III and was invited to go to TEDMED 2013 as a Journalist! Session 2: “How Can Big Data Become Real Wisdom?” and Session 6: “Going Farther while Staying Closer” were the most interesting and motivating to me. See next slide. I heard about Big and Little Data and saw an opportunity to help TEDMED with a taxonomy that is a semantic Index to a knowledge base for improved search and to help TEDMED with examples of big and little data science. And the best data source for my work was Professor Christopher Murray’s (IHME/GBD) presentation and demonstration on “What does a $100 million public health data revolution look like?” funded by the Bill and Melinda Gates Foundation to prioritize global health research and help. It made me think of the Monica Rogati’s Tweet @ Strata 2012: More data beats clever algorithms but better data beats more data. I Tweeted: @TEDMED @Storify Yes, and working on IHME/GDB (Global Burden of Disease) Visualizations like: http://semanticommunity.info/Census_Data_Visualizationhttp://semanticommunity.info/Census_Data_Visualization But I want to volunteer to help TEDMED 2013 and 2014 as a data scientist/data journalist and saw on their Web site: If you are a talented designer and/or illustrator with experience in bringing presentations to life, you could help with our speaker presentation materials. I attended the First Great Challenges Day, participated in the Inventing Wellness Programs Breakout Session, and learned the importance of scientists storifying with “and, but, and therefore”. Therefore my story is a TEDMED Data Reveal: Big (IHME/GBD) and Little (TEDMED Web Site) with “and, but, and therefore.” 2

3. My TEDMED 2013 Highlights SESSION 2: How Can Big Data Become Real Wisdom? – Jay Walker: Introduction. Need a macro-scope to gather, network, store, and access data and to go from data to wisdom by finding patterns in the data. – Larry Smarr: Can you coordinate the dance of your body's 100 trillion microorganisms? How to quantify self movement with medical detail in real time by an astrophysicist turned computer scientist. SESSION 6: Going Farther while Staying Closer – Christopher Murray: What does a $100 million public health data revolution look like? Talk and live demo of Global Burden of Disease Treemap, Map, Time Plot, Age Plot, and Stacked Bar Chart by Age and Sex. 3 See: http://blog.tedmed.com/http://blog.tedmed.com/

4. TEDMED 2013 http://www.tedmed.com/ 4 My Note: I decided to make this a Searchable Knowledge Base.

5. TEDMED Knowledge Base 5 http://semanticommunity.info/A_TEDMED_Data_Reveal Google Chrome: Find

6. TEDMED Speakers 6 http://www.tedmed.com/speakers My Note: I decided to make this a little data set for faceted search.

7. TEDMED Speakers Spreadsheet 7 http://semanticommunity.info/@api/deki/files/23881/TEDMED.xlsx My Note: The facets are Year, Keywords, and Tags.

8. Institutions hosting TEDMEDLive 2013 8 http://www.tedmed.com/event/tedmedlive?ref=participating My Note: I decided to make this a little data set for mapping, but it was difficult to get the geo-referenced data set.

9. TEDMEDLive 2013 Institutions Spreadsheet 9 http://semanticommunity.info/@api/deki/files/23881/TEDMED.xlsx My Note: Simple Geo-referencing of Institutions.

10. TEDMED 2013: Spotfire 10 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

11. TEDMED 2009-2012: Spotfire 11 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

12. Institute for Health Metrics and Evaluation (IHME) 12 http://www.healthmetricsandevaluation.org/ My Note: I heard this talk and decide to work with this big data. My Note: There are three Web site

13. Press Release The Global Burden of Disease (GBD) is a first-of-its-kind study of health around the world. The GBD findings present a new way to look at health, allowing countries to track progress against diseases ranging from malaria to cancer to diabetes, identify risks including smoking and poor diet, see how people in 187 countries are faring in terms of health and gauge emerging health challenges. The GBD is a collaboration of nearly 500 researchers in 50 countries, and is led by IHME, part of the University of Washington. Some of the countries included in the GBD, such as the UK and Indonesia, already have started to produce their own policy recommendations as a result of the study. Australia and China are also planning to produce studies that use GBD to drill down and develop local-level health data. IHME is working with three localities in the US to produce GBD-type data at the community level as well. Efforts are underway to provide continuous updates to the GBD and expand the range of health issues included in the study. The GBD measures health issues around the world through more than 1 billion pieces of data that can also be explored through interactive visualization tools online. 13 http://semanticommunity.info/@api/deki/files/23885/TEDMED-media-advisory-Chris-Murray.docx

14. GHDx Catalog of Demographic and Health Data by IHME 14 http://ghdx.healthmetricsandevaluation.org/ My Note: Download Data

15. Global Burden of Disease Study 2010 Data Downloads 15 http://ghdx.healthmetricsandevaluation.org/global-burden-disease-study-2010-gbd-2010-data-downloads My Note: I downloaded 17 files totaling 1.13 GB. Two Codebook files were damaged and I repaired them.

16. GBD Compare 16 http://viz.healthmetricsandevaluation.org/gbd-compare/ My Note: Treemap and Map.

17. GBD Cause Patterns 17 http://www.healthmetricsandevaluation.org/gbd/visualizations/gbd-cause-patterns My Note: Stacked Bar Chart.

18. GBD Cause Patterns: Reports 18 http://www.healthmetricsandevaluation.org/gbd/visualizations/gbd-cause-patterns#/publications-presentations/reports

19. IHME-GBD Causes of Death: Spotfire 19 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

20. IHME-GBD Life Expectancy: Spotfire 20 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

21. IHME-GBD Mortality: Spotfire 21 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

22. IHME-GBD Risk Factors: Spotfire 22 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

23. IHME-GBD Breast and Cervical Cancer: Spotfire 23 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire Navigation and Metadata Data Set World Map Bar Chart My Note: Data Visualizations are Linked.

24. Data Ecosystem: Spotfire 24 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?TEDMED2013-Spotfire

25. IHME-GBD Life Expectancy by Country: Spotfire 25 Navigation and Metadata Code Book Filters Details-on-Demand My Note: The Visualizations Are Linked to One Another. Data Set My Note: 19 files totaling 1.13 GB of data in a Spotfire file of only 0.5 GB! Life Expectancy by Region Life Expectancy (LE) Versus Health Adjusted Life Expectancy (HALE) https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD-Spotfire https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD1-Spotfire

26. Conclusions and Recommendations My story is a TEDMED Data Reveal: Big (IHME/GBD) and Little (TEDMED) with “and, but, and therefore.” – I have done as Jay Walker suggested: We need a macro-scope to gather, network, store, and access data and to go from data to wisdom by finding patterns in the data. But to do that, TEDMED needs a taxonomy that is a semantic index to a knowledge base for improved search and help with examples of big and little data science. – I found the best big data source for my work was Professor Christopher Murray’s IHME/GBD funded by the Bill and Melinda Gates Foundation to prioritize global health research and help. But I found I could improved the access and simplify the visualizations of the IHME/GBD data. – Therefore, I did both of the above and volunteered to help TEDMED 2013 and 2014 as a data scientist/data journalist. 26

27. Data Visualizations 27 http://www.healthmetricsandevaluation.org/tools/data-visualizations?page=3

28. GBD Data Visualizations Spreadsheet 28 http://semanticommunity.info/@api/deki/files/23881/TEDMED.xlsx My Note: See All 13 Tabs.

29. GBD Data Visualizations Inventory 29 My Note: Download 36 flies totaling 19 MB and selected a few for visualizations.

30. Diabetes Prevalence by County (US) Maps 30 http://www.healthmetricsandevaluation.org/tools/data-visualization/diabetes-prevalence-county-us-maps#/overview/explore My Note: I used this in my 2013 Health Datapalooza IV Submission and the The Sanofi US 2013 Data Design Diabetes Innovation Challenge – Prove It!

31. Research Articles 31 http://www.healthmetricsandevaluation.org/tools/data-visualization/diabetes-prevalence-county-us-maps#/publications-presentations/publications My Note: Research Article.

32. Research Articles 32 http://www.pophealthmetrics.com/content/8/1/26 My Note: Included this in the Knowledge Base.

33. Datasets 33 http://www.healthmetricsandevaluation.org/publications/summaries/novel-framework-validating-and-applying-standardized-small-area-measurement-s#/data-methods My Note: Downloaded this dataset.

34. Diabetes prevalence rates by age, sex, and county, 2008 (21KB* xls) 34 http://www.healthmetricsandevaluation.org/sites/default/files/datasets/diabetes_prevalence_by_county_rank_age_and_sex_2008_US_IHME_1010.xls http://ghdx.healthmetricsandevaluation.org/sites/ghdx/files/record-attached-files/IHME_USA_DIABETES_BY_COUNTY_2008.xls *My Note: Actual size is 556KB. My Note: Needed to be separated into county and state.

35. Metadata 35 http://ghdx.healthmetricsandevaluation.org/record/united-states-diabetes-prevalence-county-2008 My Note: Another Excel file name, but same file.

36. IHME Diabetes County 2009: Spotfire 36 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?Diabetes-Spotfire Navigation and Metadata Data Set Map Top 10 Counties With High Prevalence of Diabetes Higher Female Than Male Diabetes Prevalence

37. IHME-GBD Mortality by Country: Spotfire 37 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD1-Spotfire

38. IHME-GBD Disability Factors by Health State: Spotfire 38 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD1-Spotfire

39. IHME-GBD Risk Factors by Region: Spotfire 39 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD-Spotfire

40. IHME-GBD Cause of Death by Region: Spotfire 40 https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?IHME-GBD1-Spotfire