JazzGraphs – Visualizing Music and Musicians

Visualizing Musical Genres, Part 2

Posted on March 20, 2024 by jazzman

My last post showed a little bit about the musical genre data structure in the MusicBrainz database. In this post we’ll expand our view to include all genres and sub-genres, and look at a few visualization approaches using Flourish.

Flourish provides several options for visualizing hierarchical data; in this post we’ll look at some of the advantages and disadvantages of each approach. Ultimately, my goal is to categorize all my CDs and vinyl using this approach, but for now we’ll work with the MusicBrainz genre data.

We had a quick look at a sunburst chart in the prior post, so we’ll begin there with the much larger dataset we now have. How does it work?

Sunburst chart displaying all genres and sub-genres

Hmmm…it’s a little challenging to see the data beyond the first few genres (these are the ones with the most sub-genres). We can narrow our focus by using the filter or by clicking on one of the inner circle genres. Let’s look at the rockgenre:

That’s a bit better; note that each sub-genre has an identical size here, something that will change once I feed my own music collection into Flourish. At least we can now identify all the sub-genres in the data.

What about a treemap approach? Treemaps can be useful in showing categories and sub-categories, sized by count or some other value (revenue, sales, profit, etc.). Here’s a look at all the data:

Once again, it’s a challenge to see anything beyond the most frequently occurring genres; even if we provide a pop-up label it’s not very user-friendly. Let’s filter down, this time in the electronicgenre:

Here we get a similar result to the sunburst, albeit in a different layout. Again, this could be more interesting with an actual record collection, where each sub-genre would potentially be sized differently, with some not even appearing (i.e.- no recordings in a sub-genre).

Our next example will use circles, an approach sometimes known as circular packing. All genres will be arranged in a somewhat random layout, rather than the radial or rectangular formats we have just seen. Here is a look at all genres:

Once more, we have a similar issue to the sunburst and treemap displays, although it is fairly easy to see the highest frequency genres in the center. Filtering on the popgenre yields a series of identical sized circles for all pop sub-genres:

The circle approach is perhaps my least favorite of the three we have seen thus far, due to the seemingly more random placement of the individual circles.

At the opposite end of the spectrum we can use bars to view the same data. Here we are able to clearly see the rank order and relative frequency for each genre:

This looks really good for the high frequency genres – clear labels with easy to distinguish relative frequencies. The downside is when we have hundreds of genres; our bar chart becomes incredibly tall from top to bottom. In short, this approach will be effective for a limited number of genres, although the same could be said for the other methods.

Our final approach uses a radial tree option in Flourish. This method most closely mimics the sunburst option, with results laid out in a circle; genres can then be clicked on or filtered to get to the sub-genre level. Here are all genres:

Not exactly helpful, is it? There are simply too many genres and sub-genres to display; even the sunburst chart provided more information at first glance. But what about when we select a single genre, such as reggae?

That’s better! We now have a clear, concise display to work with. This could prove to be useful when we have different size values for each sub-genre; in essence it will merge the best aspects of the sunburst and bar displays. I’ll be interested in seeing this sort of display when my music collection data is complete to see how well it handles differing sizes.

So which approach is best? I’m going to say that it depends on the underlying data; none of these charts was great when we attempted to view all genres at once, but they do appear to offer potential when the data has fewer categories (genres). Personally I like the sunburst and radial methods for the clarity of their display coupled with the visible connection between the sub-genres and the parent genre. I’m eager to see how they work with a more typical dataset.

That’s it for now – hope you enjoyed this, and thanks for reading!

Musical Genres via MusicBrainz Data

Posted on March 14, 2024March 14, 2024 by jazzman

Once again I have pulled the core MusicBrainz tables into my local version of PostgreSQL, where I can start exploring all sorts of musical data – recordings, releases, places, artists, and much, much more. The database is large, totaling nearly 17 gigabytes of data across 171 tables, so there is no shortage of potential topics to explore.

One of the areas that intrigues me the most is an exploration of musical genres, created in MusicBrainz for contributors to categorize recordings. While the genres don’t currently tie in to individual recordings or releases in the database, I hope to use them with my own collection of music to create some potentially interesting visualizations. For now, let’s undertake an exploration of the raw data on genres, using the DBeaver database tool.

Our first table is simply named genre; let’s look at a screenshot of some of the data:

Each genre has a unique id and gid value linked to a distinct genre name such as acid house, arena rock, or bebop. As you can tell, we’re going to a very specific level here, not simple classifications like pop, rock, or jazz. This should make it quite interesting (but not so easy!) when I start tagging my own music collection.

A second table is named genre_alias; here we find some examples where distinct names are rolled up to a single genre id to join to the genre table we just saw. For instance, have a look at some of the entries below:

Data from the MusicBrainz genre_alias table

We see multiple rows pointing to a single genre id (the genre column), largely based on alternative spellings or differing punctuation. The last three rows display one such case – alternative rap, alternative hip-hop, and alternative hiphop all have a genre value of 10; in the genre table this classifies all three as alternative hip hop. In other words, these entries are three possible variations on the original alternative hip hop genre; they all represent the same musical genre. In a sense, this is some data cleansing that I won’t need to perform.

A third table is named l_genre_genre; it ties together sub-genres with a higher level ‘umbrella’ genre. Using the alternative hip hop example from above, let’s dive into this table, where we can see the 10 value in the entity1field:

Alternative hip hop id in the entity1 column

Note the 199 value in the entity0 column; if we refer back to the genre table, here’s what we see:

The 199 id value corresponds to hip hop, which contains the alternative hip hop genre, as well as any other sub-genres related to hip hop. So entity0represents the higher level grouping, with entity1 representing the next level down (a sub-genre). In terms of classifying music, we can now use two levels, which may prove useful when it comes to building visualizations.

Is there anything we can visualize at this early stage? How about a very simple sunburst chart? These will become far more interesting when I can tag my own collection with genre info, but for now, here’s a conceptual look using Flourish. You can use the filter or click on a genre to focus the display.

I hope you can see the potential here; ultimately each genre and sub-genre will be sized based on the number of albums (vinyl & CD) in a collection. This will provide a quick visual indication for where someone’s musical preferences lie. There may even be the possibility to take it down to the single recording level, but we’ll have to test that idea.

That’s it for now; looking forward to doing some more fun stuff with the MusicBrainz data. Thanks for reading!

Checking Out the MusicBrainz API

Posted on February 15, 2024February 18, 2024 by jazzman

For the last few year at JazzGraphs I’ve depended on the MusicBrainz database to draw data that populates many of the network graphs and other visualizations on the site. It’s an immense dataset that can be downloaded (as I’ve previously done) or accessed via the MusicBrainz API (Application Programming Interface). For upcoming visualizations, I’ve elected to go the API route, but simply as a means to access data, not to use in traditional API fashion. This approach allows for querying the topics I choose – specific artists, genres, recordings, etc. and pull down only the data needed to create a visualization.

Part 1A: Cataloging My Music

Posted on March 25, 2023February 18, 2024 by jazzman

Well, that was fun! Just finished capturing all my vinyl recordings using the dictate functionality in Microsoft Word; what could be better on a cloudy, windy day in the Motor City? This is merely the first step in capturing all the music I have accumulated over the last 25-30 years; unfortunately some old albums from my youth are long gone 🙁

Getz, Parker, Henderson Networks Updated

Posted on March 8, 2023February 18, 2024 by jazzman

I’ve added three more jazz artist networks following the same template I used for the initial group I added earlier in the week.

https://jazzgraphs.com/graphs/charlie_parker/

https://jazzgraphs.com/graphs/stan_getz

https://jazzgraphs.com/graphs/joe_henderson

Each of these network graphs show the connections between an artist, his releases, and the songs on each release. The graphs are easy to zoom, pan, and click, with an information panel opening on the left side of the screen. Enjoy!

Shorter, Coltrane, Gordon Networks deployed

Posted on March 7, 2023February 18, 2024 by jazzman

After a weekend trying multiple approaches at incorporating images of jazz musicians as backgrounds to their network graphs, I finally hit on a good solution – create icon-like images that display in the upper left corner of the graph background. This allows the network to be easily viewed on multiple devices ranging from phones to widescreen monitors, without interfering with any of the network elements.

Here’s the solution, as shown for the Dexter Gordon network:

Nice and tidy, with an icon-like image created in Powerpoint; the outer circle around the image is colored to match the artist node at the center of the graph for a consistent visual appearance. Even when the sidebar is shown and the graph is zoomed in, this approach works well:

Note that the musician image is combined with the background color and the circle shape in Powerpoint, and is then saved as a picture we can use in our CSS file for the network. Here’s a look at the styling elements for this graph:

#carte {    
    position: absolute;  
    background-image: url('../img/Gordon_20230306.png');
    background-repeat: no-repeat;
    background-size: cover;
    left: 0px;
    width: 100%;
    height: 100%;
}

We ensure that the background image is set to cover the entire screen by using the background-size attribute and setting it to ‘cover’. This enables the background to adjust to larger screen sizes seamlessly – no awkward edges to be seen!

From my perspective, this approach solves two issues in a very nice way:

First, it provides a consistent look & feel to the networks, regardless of which artist we select
Second, it is very production-friendly; I can use the same background and circle while adding in a new artist picture. This provides a very efficient solution for creating future graph networks

I’ll be adding more permanent links to all of these networks, but for now, here are the initial three:

https://jazzgraphs.com/graphs/dexter_gordon

https://jazzgraphs.com/graphs/wayne_shorter

https://jazzgraphs.com/graphs/john_coltrane

Thats’ it for now – have fun with the networks and thanks for reading!

Refining the Musician Networks, Part 1

Posted on February 18, 2023February 4, 2024 by jazzman

I recently posted about the six new saxophonist networks I created using MusicBrainz data and Gephi, and have subsequently created another four, including networks for two of the acknowledged giants of the instrument – Charlie Parker and John Coltrane. However, as I was digging deeper into the data I realized that there are a lot of redundancies in the data due to a couple of grammatical issues. There are two major issues I have now addressed that will make for cleaner networks.

Here’s what these networks currently look like:

I expect to replace these with cleaner, more logical graphs after making this pair of changes. The end result will have fewer nodes and fewer edges crossing each other to connect nodes.

The first change to address is the subtle but important difference between an apostrophe character ( ‘ ) and the similar yet slightly different grave ( ` ) character. Each one is used to represent an apostrophe in the MusicBrainz release.name field, leading to duplicate entries that are actually the same song. For example, ‘Round Midnight versus `Round Midnight. Subtle difference, right? But one that my postgres queries and ultimately Gephi see as two unique songs, cluttering the network graph unnecessarily. So how do we fix this issue in the data?

I first created a new version of the releases table, just in case something went wrong as I tried to make any updates. We now have an empty table with all the same attributes as the original. Step 2 is to populate the new table with a simple SELECT INTO statement:

select * into public.release_new  from public.release r

The next step is a bit trickier since it involves an apostrophe character, which postgres treats as quotation marks for other characters. We have to use some additional formatting to convince postgres that we really do want to replace all our ` characters with ‘ characters. Here’s the code I used (there are several ways to do this):

UPDATE 
   public.release_new
SET 
   "name"  = REPLACE(name,'`',E'\'')
WHERE 
"name" > '0'

Without going into too much detail, we are telling our query to find all ` characters and replace them with an apostrophe. I recognize this might mess up some cases where there is an actual grave accent on a song name, but we will now have a consistent approach rather than two slightly different characters throwing us off. The goal is to ensure that we recognize a given song as a single node for our graph as much as humanly possible.

We have a second issue to correct for, but this one can be done within our query rather than updating the database. In this case, some songs are listed in upper case in one place, and then as lower case in another. We could force all text to lower case for a match, but that is less than ideal. The same holds true for upper case; we don’t want our graph labels to be all caps. A third solution is to use the INITCAP function in postgres, like this:

select b.id, b.name, b.label, b.type, SUM(b.size)as size
FROM
(SELECT DISTINCT INITCAP(a.id) as id, INITCAP(a.name) as name, INITCAP(a.label) as label, a.type, a.size

INITCAP forces all first letters to upper case while leaving the other letters as they were. It’s not a perfect solution; apostrophes cause us a problem here too, but it’s perhaps a 99% solution. By correcting the apostrophe format and then using INITCAP, we now have a much cleaner query result for Gephi. As an example, the nodes query for Joe Henderson now returns 269 records, versus the original 285, an improvement of > 5%. This should certainly help clean up our graphs, as it will also reduce the number of edges connecting the nodes.

In part 2 of this series I’ll show the impact these changes have on our network graphs. The beauty of these changes is that I can apply the logic to all future graphs. Thanks for reading, and see you soon.

Six New Saxophonist Networks

Posted on February 6, 2023February 4, 2024 by jazzman

Decided to play with the latest version of Gephi by creating a new musician network, and wound up creating six using MusicBrainz data. This was a fun project and will be followed by additional work covering more great jazz musicians. Here’s a quick screenshot of one of the graphs showing the artist, album releases, and songs associated with those releases:

I’ll post the links to each network below and then take a walk through the creation process. Note that the graphs are all interactive, with panning, zooming, edge removal, and other features all available. More on those features later in the post. Here are the links to each network graph:

Note that there are at least two major omissions among saxophonists – Charlie Parker and John Coltrane, and of course some other notable names. I’ll plan to address those omissions in a future post.

To create each of these graphs I followed a simple set of steps and then used the same settings to create graphs with a consistent look & feel. The goal is to have users focus on the structure and content of the network as opposed to having to deal with changing shapes, sizes, and colors. Perhaps I’ll alter this for different instruments – piano or trumpet, for example may have a different color palette. For now, the color palette I have used conveys an appropriately jazzy aura, with the dark background and contrasting pastel-like node colors and subtle gray edges connecting the nodes.

The data for this project is sourced from the impressive MusicBrainz database. Note that MusicBrainz data covers many genres beyond jazz, but for my current purposes the focus is on jazz. I have created a local version of the data using DBeaver for writing and running SQL queries to retrieve data for ingestion by Gephi. DBeaver is a great solution for me – all of my other databases are in MySQL, while the MusicBrainz data is in PostgreSQL format. No problem, as DBeaver can handle both types (as well as many other data formats) with ease.

Here’s an example of the code used for node creation for Sonny Rollins:

SELECT a.* 
FROM
((SELECT CONCAT(ac.name, ' (Artist)') AS id, ac.name AS name, ac.name AS label, 'Artist' AS type, 50 AS size

FROM public.artist_credit ac

WHERE ac.id = 21832)

UNION ALL

(SELECT CONCAT(r.name, ' (Release)') AS id, r.name AS name, r.name AS label, 'Release' AS type, COUNT(DISTINCT rl.release) AS size

FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m 
ON r.id = m.release
INNER JOIN public.medium_format mf 
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id

WHERE r.artist_credit = 21832 
GROUP BY r.name
)

UNION ALL 

(SELECT CONCAT(ta.name, ' (Song)') AS id, ta.name AS name, ta.name AS label, 'Song' AS type, COUNT(DISTINCT rl.release)
AS Size
FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m 
ON r.id = m.release
INNER JOIN public.medium_format mf 
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.track_aggregate ta
ON t.name = ta.name

WHERE r.artist_credit = 21832 
GROUP BY ta.name
)) a

While the code may appear complex, it’s goal is simple – retrieve all releases and songs for the artist Sonny Rollins, who has the ‘21832’ id. This code creates nodes for the artist (first section), all releases (second section) and all songs (third section). It uses the UNION ALL statement to combine the three sections into a single output file.

We then run similar code to create an edges source file:

SELECT a.*
FROM
((SELECT CONCAT(ac.name, ' (Artist)') AS source, CONCAT(r.name, ' (Release)') AS Target, 'Artist' AS source_type, 'Release' AS target_type
FROM public.artist_credit ac
INNER JOIN public.release r
ON ac.id = r.artist_credit
INNER JOIN public.release_label rl
ON r.id = rl.release
WHERE ac.id = 21832)

UNION ALL

(SELECT CONCAT(r.name, ' (Release)') AS Source, CONCAT(ta.name, ' (Song)') AS Target, 'Release' AS source_type, 'Song' AS target_type
FROM public.release r
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.track_aggregate ta
 ON t.name = ta.name
 
WHERE r.artist_credit = 21832)) a
GROUP BY a.source, a.target, a.source_type, a.target_type

This output will instruct Gephi to use the artist as a source node and all releases as target nodes (first section) and then to use all releases as source nodes with songs as target nodes. Think of this as a hierarchy of Artist –> Releases –> Songs where individual songs are associated with the release they appeared on. Of course, in jazz, many of the most popular songs will appear connected to multiple releases, ultimately making for a more interesting graph.

Now that we have created the source files, let’s shift to Gephi to see how we use them.

Gephi allows us to pull in spreadsheet files as long as they meet certain criteria. Node files should have a name, label, id, and preferably a size attribute, although this can be created within Gephi based on the data. Edge files must have source and target fields, and ideally a weight value corresponding to the strength of network connections.

Here’s our data after ingestion, starting with the nodes:

I forget to mention the usefulness of having a ‘type’ column; this will make it simple to set node colors in Gephi. Now the edges file:

You can see the source and target values, which are critical to how the graph will be displayed. Our edge weights are all set to 1 in this network, but frequently we will have varying numbers to indicate stronger versus weaker connections.

Here’s our completed graph in Gephi, after using a number of settings:

Setting the node colors by type in the Partition tab
Sizing the nodes in the Ranking tab
Choosing a layout algorithm – Force Atlas 2 is a popular choice
Scaling the graph to an appropriate size
Preventing overlap of nodes

This process can be very iterative, playing with different settings until you are pleased with the results. For graphs like this with hundreds of nodes, different options can be tried very quickly.

The next step is to export the underlying data as a graph file – .gexf is my choice for the web template I use. Here’s a small subset of the Dexter Gordon .gexf file showing the name, type, and size associated with each node.

Next, we can update settings in the config.js file; These will adjust the display parameters for the nodes and edges; note that there is also a .css (Cascading Style Sheet) file where many more modifications can be made.

Finally, we have the index.html file that contains links to several scripts as well as the config file. This is where we can also add a title and small bits of information about the graph content.

The index.html file is used for titles, script references, and other display options

I’ll be creating additional network graphs using this same end to end approach. The process becomes easier once the code has been tested and validated, and the settings have been standardized in Gephi and the resulting output files; much of the effort will simply involve copying and pasting existing settings. Watch this space for new graphs, and thanks for reading!

Miles Davis Song Plots

Posted on January 12, 2023February 18, 2024 by jazzman

In this blog we’re going to use Flourish with more MusicBrainz data to plot the length of Miles Davis songs on a range of vinyl releases. This type of data often suggests the use of a scatter plot with an x-y axis to best visualize the information. For instance, we could place record labels on the x-axis, and the length of each song (in seconds) on the y-axis. However, with record labels being a categorical variable (i.e.- discrete values such as Sony, Columbia, etc.) there are better options for understanding the data versus a true scatter plot.

The first of these is a boxplot, which provides the ability to see the distribution of data (song lengths) by record label. Let’s take a look at this data in Flourish:

Here we have limited the data display to a single label (showing all was quite messy!). Select CBS or Columbia to see labels with many Miles Davis releases. We now see the median length of a recording, as well as the 25th percentile (bottom of the box) and the 75th percentile (top of the box). It’s also easy to see individual songs that lie below or above the typical range; in statistical terms, these are called outliers. On our plot, they represent songs that are either much shorter than normal (below the extended line) or longer than normal (above the extended line).

This is all useful information, but presents some limitations. Boxplots are very good at doing the aggregations for us while obscuring the individual data values, especially values that lie inside the box. To improve our ability to see those values we turn to a violin plot, which excels at showing the shape of a distribution, rather than the fixed shape provided by the boxplot. We have also combined a beeswarm plot with the violin plot so we can see every individual value:

Again, select CBS or Columbia to view a label with many releases/songs to understand why we elected to use this approach. Hover over individual points to learn more about an individual song – it’s length, release, artist, label, and song title. For me, this approach is best if I’m trying to explore the data; the boxplot is great when I’m interested in overall patterns. Both are powerful tools suited to their individual strengths.

I’ll be using Flourish to interrogate the MusicBrainz data further in future posts, but that’s it for now. Thanks for reading!

Miles Davis Sunburst Visualization

Posted on December 26, 2022February 5, 2024 by jazzman

With the Christmas holiday chaos (somewhat literally this year) in the rearview, I’ve been playing a bit with the MusicBrainz data and the Flourish visualization library. First up was using some repurposed code to visualize Miles Davis recordings. I thought a sunburst diagram might be an interesting way to show album releases and the songs on each release. Turns out it wasn’t quite as simple as I thought…it never is!

After multiple query tweaks and iterations, I’ve got something fun and interesting. Miles produced so much music, with much of it re-released in multiple formats (think vinyl vs. cd) and in various collections, factors that wound up influencing my query and chart logic. As is the case for many jazz artists, multiple labels are an issue, so why not create a filter to view releases for each label (Columbia, Blue Note, etc.)? And many songs turn up on multiple releases (studio, live, collections), so we need to account for that as well.

So my thought with using a sunburst was to group songs and releases together, and allow filtering by label. Mind you, it took multiple attempts to get the data in the best format, but we eventually wound up with something workable to feed the sunburst chart.

If you aren’t familiar with the sunburst chart, here’s a quick primer. The goal of a sunburst chart is to display hierarchical information in a circular layout with 2 or 3 levels (typically). The outer layer has more surface area to work with, and successive inner layers each have less visual space to use. For this reason, I wound up using individual songs in the outermost layer, with their respective albums as the inner layer. With an average of perhaps 5-10 songs per album, this takes advantage of the sunburst hierarchy framework.

Here’s what the code eventually became, after multiple iterations:

SELECT distinct ac.name AS artist, l.label_code, l.name AS label_name, r.name AS release, mf.name AS format, t.name AS id, t.name AS label, t.name AS name,
r.name AS recording,
CASE WHEN t.length < 180000 THEN ‘< 3 Minutes’ WHEN t.length < 300000 THEN ‘3-5 Minutes’ WHEN t.length < 420000 THEN ‘5-7 Minutes’ WHEN t.length < 600000 THEN ‘7-10 Minutes’ WHEN t.length > 600000 THEN ’10+ Minutes’
ELSE ‘No Length’ END category

FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.medium_format mf
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.recording re
ON t.recording = re.id

WHERE r.artist_credit = 1954
and mf.name = ’12” Vinyl’

ORDER BY l.name, r.name

What we’re doing here, in a nutshell, is retrieving all the information for Miles Davis’ 12″ vinyl releases; many of these recordings were eventually released on CD, so we’re attempting to avoid duplication here. The ‘r.artist_credit = 1954’ line refers to Miles Davis and his MusicBrainz artist ID, while the medium_format name field is set to grab just 12″ vinyl releases.

Enough of the technical details – let’s view some results:

Here’s a look at the dropdown filter we created using labels:

Note that we ordered our query by both label name and release name; this translates to an alpha sorted dropdown on labels, making it much more intuitive to select a specific label. We can choose to display all labels, but that gets rather messy for an artist like Miles who recorded for or was re-released by many companies. Let’s filter it down to Columbia, a major label who Miles recorded for many times:

The inner circle displays individual releases, of which there are many, while the outer ring displays the songs on each release. The Flourish sunburst charts are interactive, but it’s a challenge to see what’s going on in our static image. Let’s move to the Blue Note label, a major force in jazz, but one where Miles was not a major player:

Now we can see the layout, with album releases surrounded by individual songs. We can go a step further by clicking on the Miles Davis, Volume 1 layer, which reveals the following:

Now we are focused strictly on that release and can easily view the songs on that album. Hope you get the general idea for how the sunburst charts work. Now have a go at it yourself with the live version:

I’ll have more of these to come, as it feels like a great way to capture a lot of information in a fun, interactive layout. See you soon, and thanks for reading!