 Good morning and welcome to this week's edition of Encompass Live. I am Krista Burns, your host here at the Nebraska Library Commission. Encompass Live is the Commission's weekly online event where we cover various commission activities and any library topics of interest to Nebraska librarians. We have commission staff and guests sometimes speak. We do these sessions every Wednesday morning at 10 a.m. central time. They're about an hour long and they are recorded so if you are not available to watch, able to watch one of our live sessions, you can view the recording. This morning we have John Felton from here at the Library Commission who's going to talk about presenting data for libraries. So I'm going to hand over the mouse and control of the interface to John. Okay, good morning. I spend a lot of time collecting and analyzing data, but today I'm going to talk about the next step. How do you present that data to your target audience? I'm not going to do a step-by-step how to using Excel to make charts. I think you can figure that out or there are a lot of tutorials on the web that will show you how to do that. In fact, while I will show you how charts can be a good method of presenting data, I plan to go a little further than that and go beyond those techniques and demonstrate some other means of making data that kind of come alive for your audience. Here's our agenda for the day or the hour at least. We're going to talk about what you should do to prepare for your presentation, figure out for yourself, ask yourself what is your message, then ask who is your audience. I've put together some rules of data presentations that I think would be good to follow. And then I'll go into how you make your data speak by using the right method of presentation. And sometimes that's a table as I've mentioned at the top there. I'm going to show you a few tools for how you can get some assistance in selecting which chart is best if you decide to go that way. Talk about some charting basics and then just show you some examples of some charts and graphs you can use. Then as I promised, beyond that, we're going to talk about dashboards and data summaries. We're going to talk about map mashups because I just love them. We're going to talk a little bit about how you can do a narrative, but really emphasize the numbers in those narratives to make them stand out. And then talk about how you might want to put together a pocket guide as a handy hand up. And in the end, I'm just going to go give you a few caveats about how to use Excel charts with care. So let's get ready. But first, help yourself with some coffee and donuts if you haven't already. You know, there's a little luller that you've already got your coffee. I've already had mine. So I just figured, you know, if we were here together, this is what I'd be doing. It's showing you, giving you some coffee and donuts to settle down. So get comfortable and we'll get going. First of all, as I said, you really have to ask yourself, what is my message before you start going around with charts and graphs or whatever you're going to use to present the data. You need to really become familiar with your subject and decide, you know, what am I going to talk to these people about? What is it about the data that's important? So once you know what story to tell, then you can shape your presentation around it to help your audience make sense of the data that you're going to be showing. For instance, your message might be to show that more residents per capita in your community make regular use of the library than in similar communities. That's your message. Now you can plan how best to convey it. The quote here is from Steven Fugh, who's a data presentation expert and one of my gurus, who really says it very well. There's information in all this data we have, but it's up to you to get that data out of there and show it to your audience. Next, you need to know your audience. Who are they? You have to understand who you're presenting this data to because it really makes a difference in how you shape your story. Are they going to be familiar with really complex data visualization techniques, for instance? Or would it be best to just stick with something familiar, like a line chart, a bar graph? What will hold their interest and help them focus on your data? Who are they? Are you talking to your library board? Is it the city council or village board you're talking to? Is it county commissioners or is it a group of your customers? Are they stakeholders in your library's services? This is something you need to know so you can really present the right story to them. Next, here are a few rules of data presentations. And actually, these are rules that apply to just about any presentation you're going to be making. Be clear. Keep it simple. Keep it brief. We're going to say make sure it's accurate and frame it with some context and meaning. These last two are particularly significant when you're presenting data to people. You don't want it to be inaccurate. And we'll talk a little bit more about how important context is when you're showing statistics. So, clarity. The quote on this slide is from another expert, Edward Tufti, who is probably everyone's guru in the data presentation field. What he's saying here is make sure that your core message is the focus of what you're showing your audience. Be direct and clear about it. Don't lose the numbers in an overly complicated graphic like a chart or a graph. Just be very clear. I love DaVinci's quote here. And again, this applies to a lot of presentations. Simplicity is the ultimate sophistication, like this clock. It couldn't be much simpler than that. So, I'm sure you've seen some really fancy, colorful charts that use these great backgrounds, like the sun or something, in publications, on websites, or even on television. But next time you see one of those, take a close look at it and decide how easy it is to figure out the message from that chart. And identify which parts of that are really just distractions. Because, you know, sometimes being cool doesn't really translate into being effective, hard to say. I love this one. What do I need to say about this? I mean, if you want your audience to pay attention and remember what you're presenting, just give them what is needed and really know more. One of my former English professors from college gave me the best advice I've ever received about writing clearly. She simply said, pack meaning into your verbs. When she said that, it didn't really strike me as that significant at the time, but it has stayed with me for longer than I'm going to mention. Because it expresses the point in two ways. First of all, that's all she said to me when I showed her my paper. Pack meaning into your verbs. But the meaning of what she said has stayed with me for a long time. And if any of you out there don't know who this quote is from, you haven't been reading your Shakespeare, because this is from Hamlet, spoken by Lord Polonius. Gravity is the soul of lips. Oh, no, okay, here. I'm going to shift gears. Now, don't do this. This is one right there. Don't follow this example. The media has really tried to redefine the term accuracy and reliability, I think, sometimes by going a little too wild. This graphic's been all over the internet since it was actually used in a live broadcast. And here's a tip about what's wrong with this. My charts can only add up to 100%, not 193. So the point I'm trying to make here is, before you present your data, first of all, check your spreadsheet or your database. Make sure that the data is correct, that it's accurate, that you've double checked it. And then after you produce a graph from it, go back and check that too. Don't just rely on the program that you're using to produce the graph to figure out all the numbers for you. Make sure that it makes sense. Otherwise, as soon as they see something like this, you're going to lose all your credibility. Now, you've probably heard someone say that content is king on the internet. Well, for presentation of data, I would offer that context is king. Because really, statistics have no meaning without context. You can say, oh, our circulation last year was 60,000. But so what? What does that mean? Unless you provide a context like, what was the total last year? Or what kind of circulation do libraries of similar size have for that same year? See, without that, it doesn't really mean anything. So for context and meaning on this slide, for instance, we have a graphic that everyone in Nebraska has seen. I don't have to tell you that this is Memorial Stadium on Game Day with over 80,000 big red fans. But if you want to relate it to your audience and give them some context, it's a great graphic. Because you could say, oh, gosh, we had over 8 million people visit libraries last year and your audience might be saying, yes, so what? But if you think about it, if your audience thinks about how that would fill Memorial Stadium 106 times, that's context. That gives them some meaning. And finally, you know what? It all boils down to communication, because that's what you're doing here. You're trying to communicate and translate your data into something familiar for your audience. Now, the zebra on the right, you know, you might be telling his friend there's something really important like, hey, you know, there's a pride alliance behind you sneaking up on us. So you want the message to get delivered clearly, and you want the audience to really understand it quickly. This is the crux of what I'm talking about today. What's the correct method of presenting your data? And what is the method? Well, it kind of depends. Well, like everything else. Because sometimes all you really need is the data itself. Now, this is an example I got from a mutual fund brochure. And when I first saw this, I said, well, gosh, look at how they've done this. I've got all the information I need right here because they emphasized what they wanted you to see in this rather small group of numbers. Now, the next page of this brochure, they had a bunch of fancy graphs, but I just paged through it because I'd already found out what I needed to know just from this quite easily. So, you know, if you have a small amount of data like this and you can easily highlight it like this, go ahead and use it. It's okay to not be fancy with a graph. In fact, tables are best when what you really need to do is look up or compare individual values. And when you need those values to be very precise. So, here's a good table. Now, I told you I was going to show you some ways to pick the right chart. So, once you decide, wow, I don't want to use a table on this one. I don't think they'll get the idea. How do you pick the right one? There are some tools on the web. And Chris, it's going to help me get to that. Oh, you want to get to that? Oh, I get to that. Yeah. Do you have that as a hotlink? Actually, maybe a little bit. Can we do it from there? Actually, because that was a hotlink right in the PowerPoint, it will jump right to it in a new tab. Hey, it wasn't that smart to do that. Okay, here's the chart chooser. Chooser, I say chooser because it's from Juice Analytics. It's one of my favorite sites. You know, it's kind of fun to use, too. And it takes it from the point of what kind of message are you trying to convey? Are you going to compare some data, for instance? You would pick comparison. And it says, okay, here's some ways you can do that. You can use a line chart like this. Same thing works with a bar chart. Or, you know, you can just use a table. Use a table that's pretty clear. So it gives you some choices there. If you wanted to show a trend over time, click this one. This gives you some ideas. I don't have all the ones I might pick here. But here's another line chart with multiple data sets shown. Or, you know, it also works with stacked column chart and various things. So it's kind of fun. Again, relationship works with these different charts. If you want to show parts of a whole, you might pick this. And you can see that that's really what we're doing here. And that's probably the only thing pie charts are good for. I'll be talking about pie charts later. Oh, well. Unkind terms. So, let me get back. Just click that. Nope. Yes? That. Yes. Okay. So that's juice analytics giving you their chart chooser. You might want to give that a try. Here's another one I found. And I kind of like this because it's really simple. You can print it out and stick it on your bulletin board next to your computer when you're doing this kind of work. Because it gives you some, again, the same idea. Do you want to show relationships? Try a bubble chart or a standard chart. Do you want to show a comparison? Look at all the choices you might have depending on what kind of comparison you're showing. Comparisons over time or trends. Line charts, column charts, multiple line charts, many categories. This is really kind of a fun and very useful tool for deciding how to present that particular message. Here's another one. I'm not going to go and show you this one because it's kind of lame actually. When you get down to actually using it live. But I thought it was just so clever. And here's somebody who decided let's take the periodic table of elements and make it into data visualizations. So they go way beyond data. Only the yellow here is the ways you present data. And all it really shows you is examples of those particular things. I'll show you an area chart. But they don't tell you when to use it. So it's not as useful as the ones I showed you earlier. And bring it all back home. Here's a real simple one I put together that will serve you just as well as those things. You just take a look at what kind of charts you have at your disposal. And this is certainly not all of them. These are the ones you're probably going to use most often. Because when we talk about context and familiarity, these are the things that people see all the time and recognize. So there's a comfort level there. So line charts, they find and compare trends very well. And they're particularly good at showing change in direction. Which becomes like a data series over time. You can compare more than one data series over time with different line charts. And they're very simple and easy to see. Show correlation. They're just great at showing how values of a data set will change over time. In fact, you should only use line charts when the X axis, and we'll talk about that, is continuous. Like every measuring events over time or distance, changes in temperature and things like that. Area charts, I'll show you one of those. They're really just a different way of using line charts. But instead of showing direction, they really show magnitude better. Column graphs or vertical bar charts, as they're also known. Pretty good at showing frequency distribution, if that's what you're trying to present. But I would avoid stacked bar charts. I find them, and many people find them very hard to decipher. Let's just avoid those. There are other ways to show the data. For instance, if you're into a bar chart and you've got more than one data set in a grouping, it's better to have them side by side than stacked, in my opinion. Bar charts are really good for ranking data sets. I'll show you an example of that. And they can also show comparison. Pi charts deserves a whole session in itself. I'll show you some of the things in more detail. I don't particularly like what pie charts. You can still use them, but you have to be careful how you use them. Since we're going to start talking about charts, here's a little primer. And I did this almost more for me than other people, because I always forget when I'm trying to build a chart, it says, well, what values do you want in your x-axis? And I go, okay, which one is that? So this is a way to kind of help you. Usually, the x-axis will be the one that holds your categorical information. This is the one where you provide your context. That, and of course, the title of your graph. And you notice how spare this graph is. I don't have a lot of stuff here, but this really makes it less distracting for your viewer. Now, of course, the y-axis, which is normally vertical, and this will change sometimes with some charts, but the y-axis is where your data shows up. This is your quantitative information in your graph. See, these are the real basic things you can use before you start out, especially if you're like me and you just can't remember these things. Here's a little levity. You think data geeks don't have a sense of humor? Sure we do. Here's something I stumbled on that just shows you, no matter what you do with a chart, someone's going to complain about it. Well, this is kind of a silly thing about the chance of people complaining about it and the reason for complaining. You see the high one here is typos, which is a type of, or too simple, too complex. Your spacing's wrong. I don't know what to mean by Murphy's law. I guess because everything goes wrong. So anyway, it's just kind of a funny way to keep this in mind that, you know, no one's ever going to be completely satisfied with your graph. But if you make them simple and you make them clear, you'll be better off. Now I'm finally showing you an actual chart. This is a very simple line chart and you'll see that my title says that it's a time series and that with the line chart what we're really stressing is the direction, the trend. Line charts are really quite common. I suppose because they're really easy to produce. They just use a line to connect these plot lines because there's, you know, there's a point right here, there's a point right here. It's just showing, you know, where your data points are on your graph. But they're really good at showing fluctuations in value over time, obviously. As you can see here, the emphasis is not so much on these numbers as it is the direction. Those numbers, where they've gone over time. They're very good for that. And remember these are the, as I said earlier, these are the only charts that display data contiguously and you can only use a line chart when the variable on the x-axis, and remember it's this one, has to be continuous like time, like distance or temperature. They have to be contiguous and continuous or don't use a line chart. However, here we go. This is the same data as in the previous line chart. Now we're showing how it might be used in a vertical bar chart or column chart. You've got the same information, but this one does stress the numbers a little more carefully and it's really better at showing size or magnitude. Because what you don't notice as you look at this, you don't notice so much the trend as you do the magnitude of each year's figures. So, you know, you can use these two graphs for basically the same purpose, but it depends on what you're trying to emphasize. And that depends on what your message is. You're trying to show a trend, or you're trying to show how many uses of those computers you had. Here's another line chart, and as I mentioned in that sort of table, was that line charts can also be used as a comparison tool. You want to compare two data sets over time. They can correlate them and they can also compare them. So again, you know, again, it stresses direction, a trend, but you can easily see from this that children's circulation over these six years has been leveling off, whereas the adult circulation continues to climb. Just, I like this because it's very simple and see it at a glance. You could also, sometimes I don't like legends like this, this is your legend. Sometimes unless it gets too crowded, I like to take the legend and actually put it on the data set so that it's really, you don't have to decode anything. It's very quite easy. So that's the way you might want to customize a graph like this. But still, because you have so few items, it still works pretty well. Now, area charts. Area charts are really just line charts with the data filled in, but they are good at showing, again, more magnitude than direction. You can see here, and they really, I think they really add interest, because you've really got more to look at here, and you can see here what your eye is drawn to is the size of this data. How much it fills that graph. So it's just another use for the same kind of change in data over time. Now, I'm not really an expert on histograms, which show frequency distribution, because it's not something I do a lot of my work. But it's another thing that bar charts can be very good at. For instance, in this one, they wanted to see the distribution of scores among students. So they took ranges of scores on a test. That's your x-axis, that's your context. And then they showed how many students fall into those ranges here. So that's what we know, what is known as frequency distribution, and what a lot of people call histograms. You'll see more of these in scientific graphs than in statistical graphs. Now, bar charts. Boy, bar charts are really great at showing rank. In this case, it's men's rural test rankings, but not the latest, because I'm sure things have changed since then. Catherine Brockmore would tell me, wait a minute, that's not right. That's not right now. Although Roger Federer might still be on top. But I like this graph because they kept the bars the same color, except the one they really wanted to emphasize. And they sorted them by size, which of course you'd want to do in a ranking. Now, if you were showing golf scores, for instance, you'd flip this, wouldn't you? You'd want to emphasize those scores, and so you'd flip around the top. Or the way I might use this one, a bar graph, is if I were showing the results of a survey. So here I would have the possible questions that people could answer. And here I'd have the number of people that I answered in these particular ways. So that's another way that it's used. Now we finally get to our pie charts. Pie charts are just wildly popular. I'm sure you've seen them all over the USA Today, television, magazines. They're often misused and misunderstood, however, because they're really only appropriate for showing the parts of a hole. So you've got your hole, in this case, would be the total number of libraries in the state. And you're just showing how they're divided up by population. So you have a slice for each one of these population ranges. Now, when I first started doing charts and graphs with my data, I thought pie charts were really cool, and they were fun to make. But the more I played around with them, the less enamored I came, because depending on the data you're presenting, you might find a lot of problems with them. And the basic problem is you want your data to speak. But sometimes, even though a pie chart looks really good, it mumbles when it tries to talk. So first of all, they're only good for a small set of values. You'll notice here, if you go much more than five or six slices or datasets, it can easily get too crowded, so it's harder to distinguish the differences. Especially, that's especially true if you have a lot of them that are similar in size like we have here. Now, if these numbers weren't here, you'd have a tough time figuring out which one of these was the greater. The other thing about this particular one is, again, I moved that legend, because it's very hard to go boom, boom, boom. Your eyes just keeps going from the data to the chart. I'd go ahead and put this as a legend in here, for instance. But the big problem with pie charts is that it is with our eyes. The human eye just doesn't discern angles and estimate them as easily as it does distances like line lengths, and that's where we have a big problem. So, to demonstrate that, this is the same data in a bar chart. Now, you can see here that, especially when you get to these values that are very close, it's not too hard to figure out which one's the larger because of the way they're organized. It's easier for you to distinguish these line lengths than from angles and sizes. And again, another problem with that pie chart is, if these get really small, like you've got really small values, you're going to have to do weird stuff like taking your legend and pointing to it. And I've got something like that. I admit I have done something like that where the value was so small, I couldn't actually put the legend inside it. I had actually made the little arrow and pointed it in really hard to see. I don't know. There are alternatives, however. Now, this is when I started showing up Owen publications on the web. It's called a waffle chart or some people call it a square pie chart. And really, it's just using squares to represent the data. You add up the number of squares to figure out what the total number is. But the thing I like about this is, I think it's a lot easier to see the differences in values that might be fairly close. Like these two. I mean, just, hey, just count the squares, man. And what it's really good at then is if you have very small values, like alas, the amount of our savings in this country, you can see there's only one square. And it really sticks out as very small. In a pie chart, that'd be lost. That'd be absolutely the tiniest, tiniest little slice, and you'd never notice it. So what I haven't figured out yet is how to make these. That's something you can do with Excel. And so I'm trying to figure out, although some people have tried to figure out formulas. I looked at them and worked very well. But so I'm trying to figure out an easy way to make these. And if I do, I'll post it. Oh, now here. I'm just stuck on pie charts, aren't I? I just got enough of them. This is just incredible. This is, yeah, I think this might be the world's worst pie chart. Someone wanted to show the 100 most active tweeters, people using Twitter. And so they made this abomination of a craft here. And, you know, look at the, look at the index here. It actually goes down further. If this were live, you've got a hundred different ones. Well, this doesn't really mean anything, does it? I mean, how are you going to find Tim Howler over here in all this business? It's just, I just did to show you how awful pie charts can become. Because this one doesn't really mean anything. I love it. This is great, great crap. Oh, and this one's, oh my, oh dear. Okay, this, this is actually the pie charts food metaphor cousin, the donut chart. It's really kind of the same, same idea, trying to show parts of a whole. But this one succeeds in violating the rules of clarity, simplicity and brevity all at once. It's just amazing. You know, it really isn't necessary that people see your, your, and figure out your graph at a glance. But goodness, how long does it take to figure this out? Personally, I'd rather just see a table with these numbers at it. And I think if they were presenting it that way, I think people would get it a little easier. So this is really what we know as chart junk. And look, one final part about pie charts. This, this kind of sums it up. Number of occasions for which you pie charts are a good choice. And unfortunately the number of occasions when people are used. So be careful. Now we're going to go away from charts, traditional charts at any rate. Now this, this, I know this looks a bit busy and I wouldn't really make one like this. But I wanted to show you this because this is kind of a new visualization that businesses are using when the, well like the manager or the CEO wants to keep track of data continually and wants it all in one place. So generally how these are done is that you tie the, the data or what they call key performance indicators to computers that will track and automatically update all these little things that really matter to them like sales and expenses, etc. And you know if this were live, these would be changing all the time. So whenever the CEO clicks on this page, he sees the very latest things. It's a pretty useful example actually of what data dashboards look like, because you can actually understand most of it. Although I've seen a lot of them where they take the metaphor a little too literally, and instead of having just simple bar charts or line charts, they don't try to make fuel gauges, speedometers, stuff like that. They're really, they all work too well. They're creative, but they don't work very well. Now what I did here, this is something you might have seen on my website if you ever go there. What I decided to do is make a hybrid. I thought, gee, I thought data dashboards were really cool, but I don't have any data that has to be updated all the time. What I really need is a snapshot. So that's what this is. I saw someone else who had done a summary like this of the survey. And I thought, well, that would be really cool for showing annual statistics. So I combined the charts and graphs of the data dashboards with some tables that are very simple and just show in a nutshell what happened in 2007, 2008. And added some more interest in some context by showing then what that meant, what was the change over time to compare with previous years. So I'm still working on a really better one of these, and you guys might want to do this too. It's kind of fun because all this really is, it's an Excel spreadsheet. Each one of these little things are cells. And we just go in and kind of white out some of the grid lines. And there you have it. So I think I'm going to do another one of these. They're kind of cool. You can tell me if you think so too. If you don't like them, tell me. Oh, somebody has a question. Okay, a question. They're going to hang with my man. A UNL Stadium library example. Well, actually, the figure I was using for the stadium was 81,000 something. That's why I did. So what you missed was the figure I was using. I guess maybe I missed the last stadium edition. I don't know. Sorry about that. Did you get the idea? I should go on that chart of complaints about your... Yeah, get that chart complaint thing back up here. Okay, here we have Matthew might have seen before. Sometimes geographic information is important. And fortunately, we now have map mashups that make it relatively easy to show data by location. It tells the viewer at a glance just how public libraries are distributed across the state. Let me show you the actual map here so we can play around. Because it is the interactive. It isn't just a static thing here. It takes you all for this to plot. Not too long. So what I did here is I took the fiscal year 2008 statistical survey and converted it. I just took my spreadsheet and geo-coded it. I just took the addresses and ran them through a program that assigns latitude and longitude to them to come up with this little map mashup of where things are in Nebraska. And then what I did is I kind of assigned the color categories by population range. So you can see from the colors very quickly where different size libraries are located across the state. Then, of course, you can also go into each map marker and get more information about what's going on with that library. That is very cool, I think. I love this. This is fun to make. But, you know, it's not just fun. It's really kind of interesting because what I discovered when I was showing this to the library commissioners was, you know, you can say, and it's true, that 58% of Nebraska's public libraries serve communities of under a thousand people. But what does that really look like? Well, you can go into this legend down here and eliminate all the libraries and you see what an impact that has. Look what's left. Much fewer libraries. So I think that this is a way to show some context and some impact and maybe surprise your users, too. So I really like maps. I like them so much that I made another one. I'll show you one that I made as we work on broadband issues in Nebraska libraries. On this one, I took the same libraries course, but the legend actually shows the speed of that library's internet connections. We can kind of get an idea of how well we're doing in terms of broadband across the state. What's kind of fun about this one is you can see how many slow connections we have or no connections we have. I'm fooling around. Let's take the ones that have over five megabits per second. Well, it didn't change much. Most of them are below that. And this is an easy way to see that we have a ways to go to really get fast broadband in Nebraska libraries. So that's why I did that. And I'll show you one more map because I really love this one. This was done by David Dras from the State Data Center at UNO. He does one of these. He has a new one. I didn't happen to find that one, but he does one every year. He's been tracking this population change in Nebraska. And gosh, you look at this map and when I first saw it, I was just taken aback by how the population is changing in Nebraska. I don't want to scare you that you can see that the red ones here are counties where the population increased both from 1990 to 2000 for the 2000 census and also increased during the 2000 to 2007 annual estimates. So these are the ones that are really growing. You can kind of see the pattern where they're distributed along the interstate and along this north-south or eastern quarter north, probably the eastern fourth of the state. And the real impact is these vanilla ones that decreased over both of those periods of time. So it's a real good way for him to show this, all his data that he's been collecting. I really like this one. So the idea about this is that sometimes you can use maps to really bring home your point. Here's one I really like that's different. This was done by LA. It helped me. I don't even remember when I found this, but I just loved it. I still have it on my bulletin board because I love the way they did this. And you may have seen similar things like this, maybe in text only and things like Time Magazine. What they did is they said, okay, we're going to bring the reader's eyes to this page. And the first thing we're going to show them is these big numbers. But if they want to know the context for that number, they've got to go closer and read the accompanying text to see what that number means. And of course, the graphics help illustrate even further what we're talking about. But I think what's great about this is that it holds the reader's attention. It is interesting. It says, look at that huge number. What the heck are they talking about? And so it pulls them into the page and then provides them an opportunity for some self-discovery. They can discover for themselves what this means. And I think what that means for the presenter is that the information is going to tend to stay with the reader. It was a very surprising in many cases. So you'd want to use this for some of your most surprising or significant data. Another one we have done here a couple of years does a similar kind of thing. I mean, the graphics tell you, oh, this is libraries. And then we're taking familiar things. Remember that picture of Memorial Stadium where it was very familiar to people. What we did is we said, well, let's take a look at the visits to the libraries in Nebraska. And let's relate that to something familiar. So Henry George Zoo, the questioner. A lot of people have been there and they know, gee, it looks awfully crowded here. But you can see that in comparison, the annual attendees at libraries is much greater than either of these two very popular events. And then again down here, we did a similar thing. We said, well, the number one paid attendance attraction in Nebraska is the zoo. But hey, for free attendance, it's Nebraska libraries. Just a way to kind of bring things home and then compare them. Like down here we compared the circulation with the national average. And what are we doing here? We're supplying context because by itself, who would know that 7.4 was better than the national average. And so it was therefore very good. Now here are pocket guides. I like pocket guides because they're like the text version of an elevator speech. If you don't know what an elevator speech is, it's something that public relations people give their employees and say, okay, if you're stuck in an elevator with someone and they would say, well, what do you do? And what does the organization, what's their mission? What do they do? You'd have a quick little elevator speech that would sum up all the good things that you do. So in this case, this is a little pocket guide. And the size is kind of like when it's all folded up and it'll be like one of these panels. And it's just a little bigger than a business card. So it's a very easy way to distribute the most important parts of your statistics. So those are the kind of charts I wanted to show you and the means of presenting data. So now I'm down to my caveat about Excel. Now I use, I love Excel. I use it every day and I wouldn't be able to do my job without it. But this is a portion of the chart choices in Excel 2007. It goes down further. And the problem with this is there are only about 10% of these that I would actually use. And I would only use those because they're the ones that actually present the data in a clear and simplistic way, not simplistic but simple way. And you don't see it here, but when you're choosing them, when you're creating a graph, you'll see that they'll group these by 2D and 3D. Well, I'm sure a way to just leak 3D, which is all of these. Because do you really want to show a bar chart with pyramids? Do you really want a line chart with this kind of wavy thing? There's just no way you can look at this and see the data as easily as you can with this one. So if you're using Excel, my advice is ignore a lot of them. I wish that what some of these software developers would do is give you some very basic tools, like maybe these three here, although I don't like stacked column charts, and give you more information on how to select the right choice for your data. They would take a look at your spreadsheet or your database and say, okay, you know, here's what I'd do. I'd present it like this. Because it would be a lot more interesting. Because what you have to remember here is what you're trying to do is what? You're trying to be clear. You're trying to keep it simple. You're trying to be brief, accurate, and provide context. Now, I'm going to skip here over the slide. Before I leave you, I want to do a shameless plug for the library commission. Because I've got you here. You know, I want to encourage people to visit these sites. Because gosh, you know, we all work really hard to provide up-to-date information and newsworthy information that you guys can all use. If you go to our homepage here, the first thing you're going to see is announcements of what's going on and what you need to know about. If you go to the blog, we do all kinds of things in the blogs. What are the new online classes you might see? I might do something about explaining a tool like data.gov or talk about the LJ index of libraries, something like that. There will often be very interesting and topical information here. So you should take a look at it. And if you're interested in statistics in the Public Library Survey and national statistics, you can go to the Data Services page here where a lot of that stuff resides. And where you can actually go to get past years of annual data. This also can be a very useful tool. And of course, if you love the Encompass Online, as I'm sure you do, this is where you go to find out what's going to come up in the future. And it's a way to go to the archives. So if you miss something really neat, you can go back and take a look at it. So, you know, you've got to bookmark all these, a couple of these, put them on your RSS feed. I think all of these are very, very useful. So after that shameless plug, I will stop. I hope you don't look like this at this time. I can't see you, so it's not going to hurt my feelings. Does anybody have any questions about anything? Thanks, John. That was a very clear explanation of using charts. I really appreciated it. Is the chart that you made, not the chart, but the display with the statistics on both the chart in the middle, is that on the web someplace? You did that. It will be. It will be. In fact, this, are we going to put this on the slide, Sharon? Yeah. Well, I think it means the actual chart where you did the one of the three different sections. Yes, I'll be putting that on the, well, I just showed you the slide. I'll be putting that on the data services page very shortly. Yes. And also, this PowerPoint presentation where we put up, when I put up the recording of the session probably tomorrow, to be able to download it from our slide share account. And all the links that John talked about, link to or just showed will also be, already actually in our delicious account here for the commission. So you can access all of those from there. Any other questions from anybody? Okay. I know we ran a little off on our time because we had some technical difficulties at the beginning, but I'm glad some of you came back and stuck around. Thank you very much, John. That was very interesting and useful, I think. I don't know a lot about this stuff. I don't do statistics. I took one statistics class in library school. That was it. That's enough. Yeah. I hope you'll join us next week when we will be doing a behind-the-scenes look at the Talk and Book and Braille service, meeting the volunteers and staff who work down there. So hopefully you'll join us next week at 10 a.m. Wednesday, 10 a.m. Central Time. And this has been recorded and will be available, as I said, probably tomorrow. So thank you very much for attending. Bye-bye. Bye-bye.