I did not have a chance to read all of the Design Challenge 3 Critiques. But I did read many – and they were almost all really good.

Here is some commentary on them, that might help for phase 2:

1. People took a wide range of level of abstraction to consider. Some people critiqued the specific pictures I made, while others considered the most abstract task (looking at a ranked list), and other people picked things in between. All of this is OK: I didn’t really specify which level of abstraction to consider. Each level has its challenges. (I was going to say that the specific examples are easiest to pick apart, but they also suffer most from lack of context).
2. It is good that people called me out on some very basic graph making things. Guilty as charged! My defense would  say “if we were actually to use one of these diagrams, it would have a good figure caption that explained it, and a story in the text of the paper.”
3. There is a whole host of issues that relate to what the Explainers are, and how they are computed. Some of these come from trying to interpret them out of context (if you’re not a literary scholar, some of these might not make any sense – and even then, you need to be familiar with the Docuscope varaibles). In general, I’d say these are not really Visualization issues – but maybe they are? Maybe there are ways to make the viewer familiar with the meaning of the variables, or other pieces of context.
4. Many people pointed out an entire class of tasks these diagrams are bad at: dealing with specific items. (try to find your favorite play in this, or the city you want to visit in this). I couldn’t label all the points (in order to save space). But what could I have done? (even labeling them all doesn’t completely help)
5. People had mixed opinions on my color choices. Hopefully, you can find more interesting things to comment on too – but the colors are fair game. The colors for the genre experiments do come from a Brewer qualitative ramp, but these are not perfect.
6. Scale is certainly the biggest challenge. The big examples (figures 8-10) show lots of items. (getting to over 1000 – but you can imagine cases where you have 10,000 – or more!) But you could also imagine more than 4 classes (for Figure 9, there are actually 12 genres). You could imagine wanting to compare more than a handful of rankings. …
7. As I think I mentioned, there is VERY limited interaction in these. That’s a potential source of improvement. How would you use interaction to address some of the problems with Explainers.
8. I was thinking that people would focus on the “explainers diagram” (the thing for showing scored/ranked lists). But some people had interesting comments on other diagrams (like the scagnostics-like thing), and some people even had interesting comments on the Explainers concepts themselves. All is fair game, but I don’t want people to feel like they need to understand all the math behind explainers in order to do the assignment. Of course, if you want to improve the math behind them, it needs some help too!
9. It might not be obvious, but the Y dimension is used consistently in the diagrams. For the list, its used to show the rank. For everything on the right of the splines, it is the value on the scale (everything uses the same scale).
10. Many people commented that it is hard to learn to read these diagrams. The fact that I needed to make a special extra-simple example with a long explanation for “training” is proof of these points. Some people pointed out that the task of some of the examples was to explain the visualizations for later tasks, …
11. There is no discussion of how you specify what Explainer you want to see, how to put them together in the pictures, how you specify what is in the picture, … If you really want to know… I have to write Python scripts. My collaborators send me email with questions, and I send them back pictures.
12. It is actually possible to allow for more detailed queries “Find me an explainer of American-ness where Minneapolis is the most American city” – or “Find me an explainer of tragicness where Hamlet is the most tragic of all plays.” There is no interface for doing this (something you could try to add). (the math behind the scenes is also a little ugly, but that’s another story)
13. Understanding the variables is a problem. And one that I think actually could be addressed with some visualization.
14. Comparing orderings is hard. It isn’t shown at all in the bigger diagrams (look at figure 8 – there is no sense that the ordering of the documents is completely different, even if the scores are the same). In the smaller diagrams, you are kindof on your own to make the comparisons. This is a hard general problem you might want to solve. It generalizes well beyond Explainers.

Here’s another (hard) example: 550 plays. (figure10) – I am mainly including it to show that I had already fixed some of the little things some people pointed out.

Due Date: Wednesday, May 6th.

Canvas Link: Seek and Find 14 on Canvas

One last seek and find.

This week, it’s your choice: pick a visualization. I’d like you to pick something that shows off the principles we’ve discussed over the semester. Preferably, something good: where the designer has made use of the things we’ve learned to make something that is effective.

In your write-up, provide a critique. How have the principles we’ve discussed influenced the design? How could things we’ve learned about been applied to make it better?

Reading/Initial Posting due: Monday, May 4th Wednesday, May 6th

Canvas Link: Reading 23 on Canvas

OK, I had promised 1 reading a week, and last week we had 2. So this week, it’s not really a reading. (or, the reading is optional). And since you’re hopefully still thinking about last week’s discussions, we can delay starting this one a little.

The main topic (scientific visualization) is one for which I have no good reading for. There are whole books on it and tons of technical papers, but no concise introductions. So there’s no required reading on it. If you’re really interested in volume visualization (for simulations or medical imaging or …), I’d recommend the chapter from the Visualization Handbook (available via the library, but we grabbed it and put it into the reader here, or the author has a preprint here).

• Arie Kaufman and Klaus Mueller. Overview of Volume Rendering. Chapter 7 of The Visualization Handbook (Hansen and Johnson eds), Academic Press, 2005.

Instead, I want you to watch a video. Well, two videos. They relate to two topics that I wanted to talk about in class, but probably won’t get to: presentations, and animation.

The first is about the role of animation in visualization.

You don’t have to read the paper, but you do have to watch the video:

Heer, Jeffrey, and George Robertson. “Animated Transitions in Statistical Data Graphics.” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (January 2007): 1240–47. doi:10.1109/TVCG.2007.70539.

http://vis.berkeley.edu/papers/animated_transitions/

Reading the paper is optional.

Second, I want you to watch a video of Hans Rosling, who uses animated visualizations in quite a compelling way. If you have already seen a Hans Rosling video, find another one. There are lots of videos of rosling presentations – here’s one I have handy, or here’s another one.

The actual point of Rosling is not his visualizations (which are not necessarily that unusual), but rather as a way to talk about presentations. I’d like you to read through last year’s assignment (you don’t have to do it – other than watch Rosling), and read my old rant on presentations.

So, the requirements are: (1) watch the Heer and Robertson video, (2) watch a Rosling video, and (3) read the 2012 assignment and my web page about presentations.

For the discussion…

I’m not sure there’s too much to say about the animated transitions video (so I’ll trust you watched it). Actually there is a lot to say about it – especially given a recent paper that suggests that the staggering and other effects may not work that well. But there isn’t too much to say just about the video. In fact, think about the video in terms of “how well does it explain the ideas of the paper.”

In your initial posting, comment on:

Rosling’s presentation. You can say why you think he’s good (or not), but also comment on why he is often used as an example for presentation style. What can you learn from him (positive or negative)? There are lots of hints in what I wrote.

The transitions video – why is this a good presentation of the ideas (or not)?

For the discussion: consider the idea of presentations in general. How does what we’ve learned in class connect with giving presentations? How does my advice on presentations (which is 4 years old, by the way, and is actually a moving target as my thoughts evolve) apply (or not) to the kinds of presentations you have to give? (it was written for CS students originally, specifically, ones who do the kinds of stuff I do)

Initial posting due: Monday May 4, additional discussion through the end of class

Turn-in: 838-Only Assignment on Canvas

Many of you 838 students (especially ones who are not CS students) are engaged in some field of research – probably one where visualization is either used, or needed.

For this discussion, I’d like people to think about how visualization applies in their field.

1. How can what we learned in class be applied in your field?
2. Is there some other aspect of visualization that we haven’t gotten to that would have been more useful? (for example, a particular kind of data we didn’t talk about)
3. Give an example of visualization in your field, and critique it.

For the critique part, please give:

• A link to a paper (or website) with a visualization that you think is a good example (either of the need, of the current practice, or of people doing a good job). It is totally OK (prefered even) if you have an example from your own work (or your lab’s work) – especially if it leads to a “here’s how I can do better in the future given things we’ve learned in class.”
• A snapshot of an image of the visualization
• A brief description of the “science” – what is the visualization about (for those of us who are not-experts). What is the data? What is the visualization trying to say?
• A critique of the visualization (remember our principles of critique: consider purpose, and what is working, before pointed at what isn’t). What might you improve about it (especially now that you’ve learned something in class)?

If you don’t feel like you have “your field” (for example, if you’re a computer science student who isn’t involved in research): it’s OK to say so, and to pick something from another class (where you have hopefully read a paper that involves some data and visualization). If that fails, you can pick something from this class, or from my work (but that is only a last resort).

Due Date: Wednesday, April 29th

Turn in link: Seek and Find 13 on Canvas

In this seek and find, you need to find a visualization that shows something in 3D. Preferably something where the author has put some effort into designing it so that 3D works in the 2D image (but if 3D is failing to show depth, then you might discuss why).

In your discussion, be sure to discuss why this is (or isn’t) a good use of 3D, and how the author has tried to make it easier to perceive depth (or if they haven’t tried, what they might do instead).

Reading Due: I would like you to do some of the reading before class on April 30, but I understand that there is another reading (and the design challenge) already due then. So, have your initial posting on or before Friday May 1st.

Canvas Discussion: Reading 22B on Canvas

There is my explanation of why we’re not really “teaching D3” in class (that was written before I really new what this class way). It might give you a sense of what you’re up against if you choose to learn D3. In class, we’re going to focus on learning “about” D3 (why it is the way it is, not necessarily how to use it).

The D3 paper is an important starting point. It’s the “academic document” that tries to explain why D3 is what it is, and why it’s a good idea. It’s a weird mix of an academic CS paper, with lots of specific implementation details (which are less common in academic CS papers). The paper really is the best way to get the rationale and the key ideas, you just have to skip over a lot of acronyms and buzz-words.

If you want to understand what D3 can do, there is a huge gallery of examples. Although, the most interesting examples are where it gets used in practice – many of the visualizations you seen in the web browser (that are of the form that D3 can do well) are done with D3

D3 is part of a long line of tools that Jeff Heer (and his students/collaborators) have built. Some of the best ways to understand “why” D3 is what it is is to see what didn’t work (as well). Protovis (the thing that came before D3) is, ironically, a better idea for a class (since it was designed to make simple things simple). It just doesn’t let you get much beyond doing the basic stuff. The Protovis paper is interesting since it provides a set of abstractions that seem to be good ones for building (or thinking about) visualizations.

If you actually want to learn to use D3…

On the D3 web page, there is a huge list of tutorials.

The O’Reilly Book “Interactive Data Visualization for the Web” by Scott Murray is available on line for free.

http://chimera.labs.oreilly.com/books/1230000000345/index.html

This is more of a “here’s how to use D3” book (which might be what you want), but its decent for that. It has an overview of the underlying technologies that you need to know. But Chapter 2 can give you a sense of what D3 is roughly about.

The reading/discussion:

I’d like everyone to try to read the D3 paper. If your eyes glaze over in parts, that’s to be expected. You should be able to get something about the general ideas. Based on that, you might want to either get some more general sense of what’s going on (from Chapter 2 of the D3 book or the D3 web page) – or you might want to dig in and look at some of the tutorials. Given the range of people in the class, different people will look at this at different levels. And everyone should definitely take some time to play with examples.

Based on what you’ve read, seen on the web, heard in class, … I’d like you to think about / discuss the following 3 things:

1. What is D3 good for? When would you want to use it? When would you not want to use it?
2. Why is D3 good for the things it’s good for? Why is it so successful?
3. How would you go about figuring it out? If we had looked at it earlier in the semester would you have tried it yourself?

In your initial posting, say what you tried to read. If you’re stumped, ask questions! At least try to get at the three discussion topics above.

Hopefully, in the discussion, you can get to the heart of those discussion topics. Also, some of the more CS oriented people can hopefully decode some of the things in the readings. If you find particularly nifty demos, say so!

Reading Due: Monday, April 27, 11:59pm (for class discussion April 28)

Canvas Discussion: Reading 22A on Canvas

We’ve been avoiding 3D for most of class. We can’t do it forever. While using 3D for visualization has its problems, sometimes its important (if we’re trying to show 3D phenomena), and sometimes it can be useful.

The required readings will give you a sense of how we see 3D. The focus is on the perception part. What cues do we use? What can we or can’t we measure visually?

We don’t really have much time in class to discuss how to make pictures that best help people see depth. I have some readings, but we won’t get to them. They are listed below in case you are interested.

Required Readings:

• Chapter 5 of Ware’s Visual Thinking for Design (this covers more than 3D, but the non-3D parts are short).
• James Todd. The Visual Perception of 3D Shape. Trends in Cognitive Science. 2004. A nice, compact article. It focuses on a few cues.

For the discussion… In your initial posting discuss why showing 3D is hard in a picture, but more significantly, what this should mean for making visualizations of 3D things. (when to use 3D? when to avoid it?) In the discussion, try to think of what to do in those cases when you need to show things in 3D. You’ll figure some out, you’ll see some in the seek and find, you’ll hear some ideas in class, or you can look at some of the things below. If you read some optional readings, say so.

As far as optional readings… artists have dealt with the problem of trying to convey depth in a picture since, well, I’ll let an art historian answer that, but let’s just say a long time. Painters and illustrators have all kinds of tricks. Photographers and filmmakers use light and camera position and other things. Computer Scientists have tried to pick up some of those tricks and systematize them.

This is a chapter of the “Guild Handbook of Illustration” that helps illustrators learn to convey 3D shape in their drawings. A lot of it is about how to think about how light helps you perceive shape (and it does so with fabulous examples). When they start talking about the actual techniques (like how to use charcoal to make the pictures), it’s a little less interesting.

• Light on Form (Chapter 4 of the Guild Handbook of Illustration) by Jessup and Mascaro. (CS protected reader) (box)

Some things that apply well to Vis:

• Amy and Bruce Gooch. Using Non-Photorealistic Rendering to Communicate Shape. SIGGRAPH ‘99 course notes here. (this is better than the original, but seminal paper. you don’t need to read it in detail – just skim through the motivation and look at the pictures).
• Cipriano and Gleicher. Molecular Surface Abstraction.
• Look at the light collages web page (but it links to the initial version of the paper – if you want to read more, check below).

I really wanted to add a few things that show how to effectively use the cues in visualization. But this is just so huge and broad that I don’t know where to start. I’ll mention some of my favorites (some of these are seminal pieces, where there is lots of follow on. some of these are:

• Lee, et al. Geometry-Dependent Lighting. IEEE Trans of Vis and Comp Graphics. (ieee official version). Note: this paper is the extended version of the original Light Collages paper.
• SIGGRAPH 2008 Course notes “Line Drawings from 3D Models” http://www.cs.princeton.edu/gfx/proj/sg08lines/ – These are nice slides that summarize the topic very well.
• DeCarlo, et al. Suggestive Contours for Conveying Shape. Proc. SIGGRAPH 2003. (pdf) (project). The 2003 paper is really seminal, the web page lists some of the follow-ons.
• Linedemann and Ropinski.  About the Influence of Illumination Models on Image Comprehension in Direct Volume Rendering. IEEE Vis 2011. (page here)

Due Dates:

Initial Analysis (critique, task analysis): Wednesday, April 29th — Turn-in on Canvas

Re-Designs: due Wednesday, May 6 Friday, May 8 — Turn-in on Canvas

Description

For our last design challenge, I wanted to give you a real problem. A problem that I care about. A problem I need help with…

The challenge is: re-design the Explainers diagrams!

We’ve seen Explainers briefly in class. Explainers are a way of using machine learning techniques to create projections (reductions from high-dimensions to a single dimension). The exact techniques of Explainers aren’t important for the design challenge (but you are welcome to learn about them!). For the purpose of this assignment, you are only responsible for displaying the output of the process.

An Explainer takes a set of “items” (say, cities or plays), and scores them (gives them a single number). We usually also know what the “property” of the object is. So, the trick is to see how the objects score (and do objects with certain properties score higher or lower). And then things get trickier, because we might want to look at multiple Explainers. And there might be many items. And …

The online “supplement” for explainers: (http://graphics.cs.wisc.edu/Vis/Explainers/) has a link to the paper, as well as many examples using the simple diagram I designed (as well as some scatterplots and parallel coordinates diagrams). If you look at Figure 0, there is a full explanation of the diagram, as well as a discussion of some of the problems.

You do not need to understand what an Explainer is, or how they are generated in order to succeed at this assignment. You can view it as scoring data (each object gets measured), and each object has some discrete properties. So, your data might tell you whether the object is green or not, and a measure of how green it is (or multiple measures of how green it is). And the goal is to help the user understand how the score relates to the concept. (replace “green” with “comedy” or “European” or …)

Start by looking through the examples online – these should give you a sense of some of the things one might want to do with Explainers. By the time you see all of these examples, you’ll probably have a good sense of what these diagrams are used for, and what is good and bad about them.

My initial design has very limited interaction (since interaction is hard to implement!). And there is no interaction for specifying either how the explainers are built, or how the pictures get made (well, there is an interface – but it involves me writing Python programs to make the pictures).

Your challenge? Come up with better explainer diagrams! (if you want to address other aspects of the user experience of explainers, great – but that’s not required)

How can they be better? Well, that’s for you to figure out. Some ideas (coming soon – link the ideas to examples):

• Scale to more items.
• Scale to allow for comparing more explainers.
• Scale to allow for seeing how well an explainer correlates with a more complex relationship (like, predicting the year of a play, or a conjunction/disjuntion of category properties)
• Scale to deal with combinations of properties. If you have something that scores for a property (is the play a comedy? is the city European?), do certain subgroups of the items have the scoring work well or not (do the plays written by Shakespeare work with the same comedy relationship? are cities that hosted the Olympics more or less likely to have their “Europeanness” score be an indicator of their actual location).

You could even address some of the other aspects of Explainers, not dealt with in the diagrams (note: this is optional, since it requires you to understand more about explainers, and some of the statistical/machine learning underpinnings):

• How to specify the relationships that get trained on
• How to pick which explainers to show (out of the whole ensemble of explainers that get generated)
• How to get a sense as to whether or not the explainer you are looking at is doing better than chance (is the difference between classes statistically significant)?
• How to specify different aspects of the diagrams so they are better suited to specific situations (without having to change the program each time)

These are just examples… You should think of more.

Note: you are not expected to implement anything. Just to critique the current design, and to “describe” (which probably means give pictures) your alternatives. Describing interaction can be tricky (but that’s part of the challenge here – how to “sketch prototype”).

If you want to try to prototype something, I will happily give you the data. But beware: prototyping is more work. Functioning prototypes will be appreciated, but its better to do a good “sketched” design (even using computer drawing tools to mock-up what things look like) than to do a mediocre, but functional, design.

There are two parts to the assignment:

Phase 1: Critique.
In this phase, you will analyze the task and the existing design.

• What is the task? What might the user be trying to do?
• How does the design address the task(s)? What is good about the design?
• How have the ideas we’ve learned about in class been applied in the design?
• What does the design not do well? What kinds of tasks should things be improved for?

Phase 2: Re-Design
In this phase, your goal is to propose some alternative designs. You will probably want to pose multiple designs. You might want to show how your designs work in multiple settings. You might also try to come up with designs that address specific examples. (e.g. I use the same design for almost all examples, but you might want to have a specialized design for something like Figure 3).

But wait, this is hard!
Yes, it’s hard. The best I came up with is flawed, but it does work. So you have a pretty high bar to beat. But you have taken a Visualization class and I haven’t so you should be able to do better than me.

What we expect

For the Phase 1 (Critique) deadline, we expect each person to turn in a PDF document with a discussion of the task, and your analysis of my design. Be sure to relate the concepts you learned in class to your analysis. This is due on Wednesday, April 29th.

Note: we may not be able to provide feedback on your assignment Phase 1 before Phase 2 is due. (we will probably try)

However, if you would like feedback from your classmates, and are willing to provide feedback to them, we will arrange for this. We will allow people to “sign up” to participate (it’s totally optional). If you opt-in, we will give your critique to 2 people, and give you 2 others to look at and provide feedback for. This feedback trade is optional and ungraded.

For Phase 2 (Re-Designs), we expect each person to turn in a PDF document describing their proposed re-designs. Each person should propose at least 2 different designs. (don’t go too crazy coming up with lots of alternatives – we’ll only look at your 2-4 best ones). With your designs, be sure to discuss their goals, and give them a self-critique. Even if you come up with things that are better than the original, you should be able to articulate why the design is great, as well as point to its drawbacks.

With all critiques (both your assessment of my design, as well as your self critiques) be sure to consider where the design is (and is not) appropriate. My design tries to work for all explainers problems – and maybe I should have used different designs in different situations?

You can try to take on the challenge of showing multiple explainers at once. For example, in the paper/talk/web page, I show scatterplots (for showing two explainers), parallel coordinates (for 3 explainers), and simply putting different explainer diagrams side-by-side to give a sense of multiple different explainers. At least one of your designs should try to take on the challenges of a single explainer though (with an eye towards juxtaposing multiple ones)

If you want to turn in something more than a PDF for phase 2 (you still need to turn in a PDF) – be it a working prototype, a movie that illustrates your interaction, … Contact Alper and make arrangements. Note that you still need to turn in a PDF – even if there is a program for us to play with, we will want to read about the design and its rationale/critique.

Due Date: Wednesday, April 22, 11:59pm

Turn-in-Link: S&F 12 on Canvas

For this seek and find, you need to find a visualization that deal with data with uncertainty, and presents that uncertainty (along with the data). It may be a traditional uncertainty visualization, or it might be a case where the uncertainty is conveyed in a non-standard way.

In your discussion, be sure to articulate what the uncertainty is, and how it is being conveyed.

As a hint… Weather forecasts are a form of uncertainty visualization. Here is is visualization that I use a lot. You can’t pick this one, because I already did (and I’ll probably use it as an example in class)

Initial posting due: Wednesday, April 22nd, 11:59pm (note: this is Wednesday, not Monday, since I was slow at getting the assignment out). Additional postings required later.

Turn in Link: Reading 21 on Canvas

Uncertainty is a really important topic in Visualization. Really important. It’s really hard. And I don’t have any readings that I am totally happy with.

There are 6 things listed here. You need to read 1 or 2 (probably 1, since 2 is long – but feel free to pick it if you find the topic interesting). Then read either 3 or 4 (quickly – just get the ideas of how to present uncertainty, not necessarily the details of the experiment). Similarly, skim #5.

The Readings

The first paper is short (it’s an extended abstract), but it gets at a lot of the issues (in an unexpected way).

1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM) (PDF in Box)

In contrast, this is a thorough survey – too much for me to ask everyone to read, but it has a nice diversity.

2. Ken Brodlie, Osorio, R. A., & Lopes, A. (2012). Expanding the Frontiers of Visual Analytics and Visualization. In J. Dill, R. Earnshaw, D. Kasik, J. Vince, & P. C. Wong (Eds.), Expanding the Frontiers of Visual Analytics and Visualization (pp. 81–109). London: Springer London. doi:10.1007/978-1-4471-2804-5 (Springer) (PDF in Box)

I like this next paper because it gets at a variety of different ways to show uncertainty, and points at some of the different strategies. The evaluation aspect is less important for class.

3. MacEachren, A. M., Roth, R. E., O’Brien, J., Li, B., Swingley, D., & Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2496–2505. doi:10.1109/TVCG.2012.279  (PDF)

This one focuses on a single kind of visual technique, but goes a little deeper…

4. Wood, J., Isenberg, P., Isenberg, T., Dykes, J., Boukhelifa, N., & Slingsby, A. (2012). Sketchy Rendering for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2749–2758. doi:10.1109/TVCG.2012.262 (web)

We just wrote a paper that gets at the issues in a different way, but it isn’t published yet – and I am not sure its ready for people to read. So, instead, I’ll point to our recent paper which deals with a very common case of uncertainty visualization, and one of the most standard visualizations. (we have discussed this in class, early on)

5. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)

The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).

6. Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)

What you need to read…

Everyone must read #1 or #2. Each person should read 3 or 4 – but don’t worry about the details of the experiments. (hopefully within each discussion group, there will be a mix). Everyone should look at 5 (but again, get the gist, don’t worry about the details of the experiments). 6 is optional.

For your initial posting, give a sense of the kinds of challenges in visualizing uncertain data. And then describe how the methods in the technique paper you read (3,4) address these. In the follow on discussion, think about how the range of techniques you see (in 3,4,5 and other places) both make use of the various concepts from class,  but also, might be applied to the various problems we’ve talked about.