1. Should it be required data be available for any data visualization in publications or even newspaper articles? Links could prevent publications from getting too long but allow for a sense of transparency and give the reader the ability to reach their own conclusions.

2. How can the public become more experienced at not just reading but also thinking critically abouta graphical work? (for example understanding that different circumstances can greatly impact data as week in the South Africa example on page 107).

3. Could the inclusion of one graphical representation in article abstracts encourage authors to communicate visually, help educate readers on how to read graphs, and give the reader a sense for the type of evidence supporting the article's claim?

1. This is a good point:

"If data are accessible as needed, using figures instead
of tables becomes much easier."

Why isn't this the case in reality? In most cases, visuals are more easily accessible than data.

1. Regarding this argument: The best data visualization " consists
   of complex ideas communicated with clarity,
   precision, and efficiency. ... [It] is that which
   gives to the viewer the greatest number of
   ideas in the shortest time with the least ink
   in the smallest space. ...", what I found is that data viz generated with processing/code to be a complex graph showing a complex issue in a complex way. Do designers rely on code so much that they stop caring about graphic design decisions?

2. Are bar charts and scatter plots easier to understand because they are taught or because they are intuitive?

"[W]e argue that it is time for these methods to be fully integrated into sociology’s research process"
Is data visualization used for confirming theories or for exploring them?

Would/could too much of a heavy focus on two-dimensional data and visualization distract the research process of sociology, which could require a more dimensional process?

Should the complex data models be accessible to the public? How would you differentiate between data that is used to confirm, explore, and present?

1. Healy and Moody talk about how audiences of data viz are usually well versed in the information being presented, which gives them a context and background knowledge to be able to view the visualization with far more comprehension than the average viewer but how do we incorporate these nuances to make the dv more accessible to audiences without this knowledge while still maintain the impact of the visualization and the data?

2. Why are some graphs/charts/designs more intuitive than others? Is it because we already have visual cues that inform us or is it because it is an inherent nature?

3. When is it appropriate to be interactive in dv? Is it more important to use aesthetically pleasing design or more displaying the data in the most impactful way that may downplay the physical numbers?

"Materials vary wildly between and even within articles" What's the best and consistent method that fits most data analysis? Is it really enough to have just the axis labeling,and gridlines?

Is the example 9b on 'Attitudes toward Europe' an appropriate example to show data with standard errors? Some data gets lost when they overlap one another. Is it significant enough to make a change?

According to Keynes, figures can become much easier in visualizing data rather than tables. What are some good examples of this? How can these figures be represented in a clear way? Wouldn't they be in a table of themselves?

Will sociology ever accept 'big data's move from causality to correlation? Now that we have enough data to make really good correlational models, will causality still be studied as intensely?

What are the main barriers to using data visualisation before deciding on a statistical model? To me, this seems the perfect way to work out what you're looking at, and indeed for. (I have in fact just done this at length for a Sociology class)

Not really a question, but I'd really like some discussion of 3D data representation where the three dimensions are x,y, and TIME instead of z. (I personally think that every layer of interaction you can cohesively add is almost as valuable as another axis).

1. Aesthetic features of visualizations have been flourishing in recent decades, but has the degree to which they suit the data deteriorated? What factors have led up to the convergence on certain visual tropes in different fields, and is cross-pollination between them important?

2. How is visual communication prioritized and taught in sociological fields today, if at all, and how can it be improved upon? Why aren't designers taught statistics, and should they be?

3. What are key things researchers should know about differences between communicating to each other and the public?

4. What are the best ways of representing categorical data?

1. Can better tools & software help drastically improve the general practice?

2. Technology is becoming more and more advanced at collecting big data. How can design make it easier to analyze?

3. How should visualizations differ when being communicated to a different audience? (researchers vs general public)