APLIC 2014 - Impact? Intrigue? Value-add? The ins and outs of Data Visualization

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Erica Nybro of Measure DHS, and Amanda Makulec of JSI give Data Visualization tips for social scientists using DHS datasets

Science Education Technology

APRIL 29, 2014
DATA
VISUALIZATION
AMANDA MAKULEC | ERICA NYBRO
APLIC 2014
[IMPACT | INTRIGUE | VALUE ADD]

VISUALIZATION TO
TELL A STORY
AMPLIFY YOUR REACH
MAKE DECISIONS

THE VISUALIZATION PROCESS
IDENTIFY YOUR
AUDIENCE &
CONTEXT
FIND THE STORY
IN YOUR DATA
BUILD YOUR
VISUALIZATION
DISSEMINATE,
SHARE & USE

WHAT?
DATA
WHAT INFORMATION DO
YOU HAVE TO SHARE?

WHO?
AUDIENCE
WHO ARE YOU TRYING TO REACH
WITH YOUR INFORMATION?

WHO?
AUDIENCE
• LITERACY
• NUMERIC/DATA LITERACY
• EDUCATION

WHO?
AUDIENCE
JOB FUNCTION
• POLICY MAKERS
• PROGRAM MANAGERS
• RESEARCHERS/ACADEMICS
• FUNDERS
• JOURNALISTS
• GENERAL PUBLIC

WHY?
WHAT IS THEIR CONTEXT?
WHO ARE THE STAKEHOLDERS?
WHAT INFO DO THEY NEED?
HOW WILL THEY USE IT?
WHY DO THEY CARE? WHAT IS THE OBJECTIVE?
* a note on politics and perspective

WHO?
AUDIENCE EXAMPLE
Data available: population and
urbanization in all countries
International Donor working in Urban
Health
Objective: Effective use of funds for big
impact
Data need: Where is urban health a big
challenge? Where is there a service gap?

WHAT’S THE STORY?
LOOK FOR…
• Trends
• Ranges
• Patterns
• Comparisons
• Surprises

WHAT’S THE STORY?
AN EXAMPLE
• Audience
• Context
• Story

TIPS:
• Sort your
data different
ways
• Play with
graphs/charts
to find
patterns

WHAT’S THE STORY?
ONCE YOU HAVE YOUR
STORY CONCEPT
• Tweak for your audience
• Write a short headline to summarize your story
• What do you want your audience to take
away? Is there an action point?

An increase in child
malnutrition in
Egypt coincides
with the Avian Flu
outbreak of 2006.
We hope to see this
trend reverse in
2014.
WHAT’S THE STORY?

YOUR GOAL: SHARING A MESSAGE
THE USER EXPLORES
YOUR DATA AND CAN
DRAW THEIR OWN
CONCLUSIONS.
YOU DECIDE THE
STORY AND THE
MESSAGE, GUIDING
YOUR READER.
YOUR GOAL: PROMOTING EXPLORATION

THINK ABOUT HOW YOU WANT TO VISUALIZE THAT STORY
CHART OR GRAPH?
INFOGRAPHIC?
FLOW CHART?
WHAT’S YOUR “AH HA!”
MOMENT FOR YOUR
AUDIENCE?

START WITH A SKETCH
Sketch to start…
and you’ll work your way
to something beautiful.

A STARTING GUIDE TO CHOOSING A CHART TYPE

BEYOND THE BAR CHART: SOME IDEAS
tree map network map bubble chart

DRAW ATTENTION TO WHAT’S IMPORTANT: PREATTENTIVE ATTRIBUTES

DRAW ATTENTION TO WHAT’S IMPORTANT: GESTALT PRINCIPLES

NUMBERS | SHORT TEXT
TIMELINE
MOBILE
TRADITIONAL GRAPHS AND CHARTS

A GUT-CHECK FOR VIZ
JunkCharts Trifecta Checkup
What is the
practical
question?
What does
the data
say?
What does
the chart
say?

INSTRUCTIONS
1. Find a partner.
2. Review your data tables.
3. Sketch your viz for the
audience that you’ve been
assigned.

TAILORING YOUR
DATA VIZ
TO A SPECIFIC
AUDIENCE
AND CONTEXT
AN EXAMPLE
FROM THE
TANZANIA 2010
DEMOGRAPHIC
AND HEALTH
SURVEY
ACTIVITY

Example 1:
A condom
distributor in
Tanzania wants to
improve marketing
and asks: who are
the main users of
condoms in
Tanzania?
EXAMPLE 1

RESOURCES
WHERE TO FIND…
BOOKS & ARTICLES
EXAMPLES
TOOLS
COMMUNITIES

BOOKS
PEOPLE TO READ
Stephen Few
Show Me the
Numbers
Edward Tufte
The Visual Display
of Quantitative
Information
Alberto Cairo
The Functional Art:
An Introduction to
Information
Graphics
Cole
Nussbaumer
Storytelling
withdata.com

TOOLS
COLOR-HEX
COLOR ORACLE
COLOR BREWER Simple tool for picking palettes
Originally for maps
Simulates color blindness
Great for checking your viz
Technical info on color models
Generates CSS codes

BUT A WORD OF CAUTION…
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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.

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Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.

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Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.

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Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.

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Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.

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Description: Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics. From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise. Key topics covered include: Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties. Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties. Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications. Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism. Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena. With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.

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APLIC 2014 - Impact? Intrigue? Value-add? The ins and outs of Data Visualization

1. APRIL 29, 2014 DATA VISUALIZATION AMANDA MAKULEC | ERICA NYBRO APLIC 2014 [IMPACT | INTRIGUE | VALUE ADD]

2. DATA NEWBIES TO DATA NINJAS

3. VISUALIZATION TO TELL A STORY AMPLIFY YOUR REACH MAKE DECISIONS

4. THE VISUALIZATION PROCESS IDENTIFY YOUR AUDIENCE & CONTEXT FIND THE STORY IN YOUR DATA BUILD YOUR VISUALIZATION DISSEMINATE, SHARE & USE

5. WHAT? DATA WHAT INFORMATION DO YOU HAVE TO SHARE?

6. WHO? AUDIENCE WHO ARE YOU TRYING TO REACH WITH YOUR INFORMATION?

7. WHO? AUDIENCE • LITERACY • NUMERIC/DATA LITERACY • EDUCATION

8. WHO? AUDIENCE JOB FUNCTION • POLICY MAKERS • PROGRAM MANAGERS • RESEARCHERS/ACADEMICS • FUNDERS • JOURNALISTS • GENERAL PUBLIC

9. WHY? WHAT IS THEIR CONTEXT? WHO ARE THE STAKEHOLDERS? WHAT INFO DO THEY NEED? HOW WILL THEY USE IT? WHY DO THEY CARE? WHAT IS THE OBJECTIVE? * a note on politics and perspective

10. WHO? AUDIENCE EXAMPLE Data available: population and urbanization in all countries International Donor working in Urban Health Objective: Effective use of funds for big impact Data need: Where is urban health a big challenge? Where is there a service gap?

11. FIND YOUR DATA STORY

12. WHAT’S THE STORY? LOOK FOR… • Trends • Ranges • Patterns • Comparisons • Surprises

13. WHAT’S THE STORY? AN EXAMPLE • Audience • Context • Story

14. TIPS: • Sort your data different ways • Play with graphs/charts to find patterns

15.

16. WHAT’S THE STORY? ONCE YOU HAVE YOUR STORY CONCEPT • Tweak for your audience • Write a short headline to summarize your story • What do you want your audience to take away? Is there an action point?

17. An increase in child malnutrition in Egypt coincides with the Avian Flu outbreak of 2006. We hope to see this trend reverse in 2014. WHAT’S THE STORY?

18. BUILDING A VISUALIZATION THE BASICS

19. AN EXAMPLE OF WHAT NOT TO DO

20. AN EXAMPLE OF WHAT NOT TO DO #2

21. WHAT WE DO WANT

22. YOUR GOAL: SHARING A MESSAGE THE USER EXPLORES YOUR DATA AND CAN DRAW THEIR OWN CONCLUSIONS. YOU DECIDE THE STORY AND THE MESSAGE, GUIDING YOUR READER. YOUR GOAL: PROMOTING EXPLORATION

23. UNDERSTAND THE STORY YOU WANT TO TELL

24. THINK ABOUT HOW YOU WANT TO VISUALIZE THAT STORY CHART OR GRAPH? INFOGRAPHIC? FLOW CHART? WHAT’S YOUR “AH HA!” MOMENT FOR YOUR AUDIENCE?

25. START WITH A SKETCH Sketch to start… and you’ll work your way to something beautiful.

26. A STARTING GUIDE TO CHOOSING A CHART TYPE

27. CHART CHOOSER 2.O (JUICE LABS)

28. BEYOND THE BAR CHART: SOME IDEAS tree map network map bubble chart

29. JUST SAY NO TO PIE CHARTS

30. DESIGN CREATING FUNCTIONAL ART

31. DRAW ATTENTION TO WHAT’S IMPORTANT: PREATTENTIVE ATTRIBUTES

32. DRAW ATTENTION TO WHAT’S IMPORTANT: GESTALT PRINCIPLES

33. COLOR | SHAPES | SIZE MAPS | SHADING

34. NUMBERS | SHORT TEXT TIMELINE MOBILE TRADITIONAL GRAPHS AND CHARTS

35. A GUT-CHECK FOR VIZ JunkCharts Trifecta Checkup What is the practical question? What does the data say? What does the chart say?

36. A RANDOM EXAMPLE

37. SKETCH TIME AN ACTIVITY AN EXAMPLE

38. INSTRUCTIONS 1. Find a partner. 2. Review your data tables. 3. Sketch your viz for the audience that you’ve been assigned.

39. A STARTING GUIDE TO CHOOSING A CHART TYPE

40. TAILORING YOUR DATA VIZ TO A SPECIFIC AUDIENCE AND CONTEXT AN EXAMPLE FROM THE TANZANIA 2010 DEMOGRAPHIC AND HEALTH SURVEY ACTIVITY

41. Example 1: A condom distributor in Tanzania wants to improve marketing and asks: who are the main users of condoms in Tanzania? EXAMPLE 1

42. VERSION 1 EXAMPLE 1

43. VERSION 2 EXAMPLE 1

44. ATTEMPT 3 EXAMPLE 1

45. ATTEMPT 4

46. AN ACTIVITY SHARING TIME AN EXAMPLE

47. DATA VIZ RESOURCES PROCESS

48. RESOURCES WHERE TO FIND… BOOKS & ARTICLES EXAMPLES TOOLS COMMUNITIES

49. BOOKS PEOPLE TO READ Stephen Few Show Me the Numbers Edward Tufte The Visual Display of Quantitative Information Alberto Cairo The Functional Art: An Introduction to Information Graphics Cole Nussbaumer Storytelling withdata.com

50. ARTICLES NEW DIRECTIONS IN EVALUATION

51.

52.

53. TOOLS COLOR-HEX COLOR ORACLE COLOR BREWER Simple tool for picking palettes Originally for maps Simulates color blindness Great for checking your viz Technical info on color models Generates CSS codes

54. TOOLS INFOGR.AM

55. TOOLS PIKTOCHART

56. TOOLS EASEL.LY

57. TOOLS TABLEAU 8.1

58. BUT A WORD OF CAUTION… Just because you can built a graph doesn’t mean it’s the right fit.

59. NETWORK MAPS GEPHI NODE XL UCINET

60. COMMUNITIES DATAVIZHUB.CO

61. COMMUNITIES

62. QUESTIONS APLIC

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