Comparing Data in a Location vs. Rye Type Grid

By Taelynn Graham

These two programs let you compare 4 data points for each rye type at each location, laid out in a grid inspired by Russell’s project. To interact with it, choose which “quadrant” you want to adjust, and then click on the corresponding data point you would like represented there. There are still some bugs at this point, but I find the shapes that are generated are very interesting to look at.

For my first grid experiment, the distance of a corner point of the quadrilateral shape from its center is dependent upon the data (closer to center = smaller value).

My second grid experiment separated each quadrant data point into a circle of varying diameter, and each data point is represented by a different colour as well, to show more distinction between each data type. This is definitely a work in progress, and a concept I intend on refining further

Plant Height 3D Print

By Corwin Smith

This was the result of the plant height being printed on the 3D printer. I experimented with whether having the plant height really tall and far apart, or shorter and more together would give better results. I feel the right one with the taller and more spread out data points turned out to be more successful in this situation.

Hagberg Falling Number Lattice

By Corwin Smith

For my second project where I created a lattice structure based off of the Hagberg Falling Number. What I did was use the Grasshopper definition used to calculate averages (created by Denton Fredrickson) and added on to it to make a definition used to create these graph structures for both location, and rye type. I then lined them up so that the graphs for location and rye type lined up and it created this interesting lattice structure which I then made into a physical object.

Grasshopper Sketch

By Corwin Smith

This is the grasshopper definition in Rhino which was used to create the graphs for the lattice structure. In the green area is where the agronomic data is pulled in and averages are generated (The Agronomic and Grain Quality Averages portion of these Grasshopper sketches were done by Denton Fredrickson, and I utilized the framework he had created to add on and create my projects). In the grey area to the right is the stuff I did to take those averages and create the agronomic data. I created points which were placed from this data, and then lines were drawn between the points to create the final result before lining them all up. This could have been more efficient but it worked for what I wanted to do and I was happy with it.

This is the result of a suggestion made by Denton to use ranges in my grasshopper definition. This led to some research on how the range node worked, and I was able to make this definition which was much smaller and more efficient than what I have shown before in the media samples. The nodes in the grey area were what I put in to make those graphs, and you can see that it is much smaller and to the point that what I was working with before.

Hagberg Falling Number Simulation

By Corwin Smith

This visualization was created using the data gathered for Hagberg Falling Number. It is used to determine the grain quality by making a thick gravy like mixture, and letting the plungers fall. The time it takes for the plunger to fall in the machine is recorded for the data point. When the falling number is higher it allows for better bread to be created since the starch content is much higher, so a higher number is looked for in the rye.

This project was done in collaboration with Taelynn Graham. We both had a very similar idea on how we wanted to visualize the Hagberg falling number through a simulation, so to help make the workload a little lighter for both of us we split it up. Taelynn worked at simulating the rye types showing plungers falling for each location. I simulated locations simulating the rye types falling. For this visualization I worked in Blender, and I felt it was very successful since I was able to show the falling number in an effective way. Not only that, but since I used 3D I was able to give a sense of the machines that are actually used to calculate this data, as well as having the metallic parts change colour for the different soil types of the locations.

Musical Mapping in Relation to Hagberg Falling Number

By Will Austin

The Long Drawing functions similarly to my first project, but instead graphs both ergot and protein percentage in response to a movement through decreasing Hagberg Falling Numbers. The cultivars and locations were again gridded, this time in order of average Hagberg Falling Number. The data is drawn with a point for each of the fourteen locations repeating through all fifteen cultivars (unlike in the painting, which is a movement through all 15 cultivars in each location sequentially). The song sounds with two overlaid melodies of 14 notes offset slightly, with the melody responding to protein % one octave higher than that of ergot %. Each note in the melody corresponds in frequency to relative protein/ergot percentage, within a specific location. The fourteen note order of locations stays the same while proceeding through each cultivar. All data is sounded relative to the whole set within two octaves of the B-flat minor key.

Musical Mapping Ergot and Protein

By Will Austin

The painting contains a graph of % ergot ordered from average highest to lowest in both dimensions (cultivar vs. location), and a graph of % protein following the same order of cultivars and locations. The song which it accompanies is in the key of E minor, with fourteen 15-note melodies each divided by a full note rest. The variable of time is the movement through the 15 ‘cultivars’ (breeds) of rye from the least prone to ergot (fungal disease) to the most prone, repeating in structure through all of the studied locations from least prone to ergot to most (14 locations). The notes are determined by the relative percentage of protein divided through three octaves of E minor. Each registered breed is given a half note sustain, and each non-registered breed is given a quarter note. There is a bass drone under each 15-note melody which is the average for the current location across all breeds, dropped two octaves. It is meant to function generally as a graph of the correlation between ergot incidence (increasing as time) versus protein percentage (of grain).

Hagberg Falling Number Simulation

By Taelynn Graham

The Hagberg Falling Number is important in determining grain quality, especially for bread-making. This test is done by churning the flour with warm water (essentially making gravy) and timing how long a plunger takes to sink to the bottom of the mixture. A higher falling number reflects higher quality, and I wanted to create a simulation to show the testing process. Corwin also had the same idea, so we split up the work into two parts: he would split simulations up based on location, and I would split mine up by rye type.

This interactive simulation allows you to choose which rye type you’d like to look at, and then use the buttons in the bottom right to play, pause, and rewind the simulation. The average for that rye type is also shown, so it is interesting to see how much higher Vauxhall is than any other location. There is also an option to see each rye type beside one another.

This simulation is interesting in the fact that it is “soft-coded”- it calculates everything you see based on the numbers in the data table. If another experiment was conducted, it would simply be a matter of putting the file into the program and everything would automatically be recalculated (number of rye types, names of each rye type, averages for each, etc.).

Exploring Heading Date, Maturity Date, and Grain Yield

By Taelynn Graham

This was my first attempt to visualize this new data set. I was initially interested in making some of the time-based data appear in a linear fashion, as this was something I hadn’t done yet in my data viz experiments. I decided to look at if there was a relationship between the date the heads emerged to its maturity date, and if that had an impact on the yield of those crops.

The overlapping circles are drawn for each rye type at each location, and the size of the circles are reflective of the yield. The four rows represent each province the rye was planted. From top to bottom: Alberta, Saskatchewan, Manitoba, and Ontario. I think it’s interesting how Alberta is much more consistent in maturity date and has higher yields than Saskatchewan or Manitoba.

Environmental Impact on Rye Yield and Grain Quality

 

 

 

By Russell Mcmurty and Taelynn Graham

[Russell] This project was the first time I delved into something that you could hold this semester and I’m glad I had the help of Taelynn Graham. We started with lengthy conversations about how people interact with data and the impact of small multiples on the field of Data Visualization. With this conversation in our back pocket, we moved into brainstorming ideas that would display the most meaningful relationships within the data.

[Taelynn] Each transparency layer corresponds to a location, which then has four pieces of data being represented on two graphs: mean daily temperature and mean daily precipitation on the top, and then falling number and grain yield on the bottom. Since the Hagberg Falling Number and yield are important factors when considering a new plant breed for registration, we wanted to see how much of an impact the environment would play.

[Russell] I think both of us would love to expand this idea in various ways. I have an interest in etching the graphic on to glass or large sheets of plastic so that someone could interact with them in more dynamic ways while Tae has lots of plans as well. I think that this is an expansive idea we stumbled upon and I hope to reinvent it a few more times before we put it to rest.

[Taelynn] The transparent sheets were very flimsy, and the data would benefit from a solid, clear surface that would also be able to be mounted, in order to accurately line up the graphs if you wanted to. An installation piece where glass layers could be moved horizontally might make for some interesting interaction, and increasing the scale by at least double turns it into a full-body immersive experience.

The final transparency sheets being layered together

Kernel Size Grid with Ergot Percentage Colour Overlay

by Russell McMurty

This project was originally an experiment that revolved around showing data for every cultivar and every city at the same time. This, much like my first project, is written in Java on a program called “Processing”. This image created a sense of scale for a viewer of the size of rye kernels across the entire data set. Then using colour, I add another level of data by comparing instances of ergot growth across all of the cities and cultivars.

On the whole I was very impressed with this project. I was excited that it showed relationships between kernel size and location/cultivar that the scientist we had be working with confirmed are well known in rye breeding. I was extremely cool to learn that I was uncovering other, new relationships as well.

I would like to expand this project to cover more portions of the data. I was very content to have the size of the ellipses have a spatial connection with the size of the kernels. Upon more learning, I’ve decided that this visualization would be equally impactful when it contains other bits of data too.

“Biocontrol” Simulation

By Kiri Stolz

The goal of this sketch was to make the data “invisible”, so that the viewer doesn’t even know that it’s being shown to them. Behind the scenes are many pieces of code that calculate the likelihood that a piece of the digital plant will be galled by the digital wasps. These calculations are based entirely on the data itself, taking into account the age of the plant, it’s species, and how many galls were created on it during the actual experiment.

This style of Data Visualization is an effective way of introducing the viewer to a set of data that is not overwhelming or intimidating, but also allows them to construct their own questions about what they are seeing, and (hopefully) seek to have them answered, either by careful observation, or by speaking with an expert in the field.

Interactive Scatterplot

By Kiri Stolz

A scatterplot is a fairly basic form of data visualization. It is, however, incredibly legible and can be very easy on the eyes. In this example, the user is able to change what values are being represented on the X and Y axes, as well as the values being represented by the size of the squares themselves. The user is invited to explore the data, and make their own representation of it.

In the early stages of this sketch, I realized that there was more to my little scatterplot than I had originally thought. The spray of pixel-like squares on the display created a captivating pattern that I wanted to expand upon. Now, the user has the ability to create these abstract scattered designs that have a deeper layer of meaning to those who view it in the context of the data itself.

Number of Galls on Main and Lateral Stolons

By Linda Shi

This model represents data collected by counting the number of galls found on main and lateral stolons of Whiplash. The four sizes of rings represent four stages of stolon growth from largest to smallest; we see the stolons in their old to super young stages. I have also chosen green zip ties to be the colour for main stolons whereas black ones represent lateral stolons. 
By building this physical model, I’m able to see, group by group, on what type of stolon or which stage of stolon growth is the wasp more prone to lay their eggs. I’ve noticed that on the main stolon, wasps are more attracted to the young and super young stages of the stems, whereas on the lateral ones, more galls are found in the oldest stage of growth. One thing to keep in mind is that the lateral stolons sprout later than the main stolons. This difference in time could potentially result in the differences we see between galled stolons. I’m interested in the potential architectural installation of this model. In a built project, I want to see people become part of the data and have a 1:1 scale interaction with it.

Cloud of Rings

By Linda Shi

In my final project, I’m taking the data I learned from Project one and the skills I’ve accumulated from Project two, to put together a larger skill installation in the presentation space. 
My Cloud of Rings, instead of using the different sizes of rings to represent different stages of plant growth, I’m using the thickness of the rings to do so. By reversing the dark and light colors of the stolons, this color representation of the main and lateral stolons are more accurately displayed. Since the main stolons have grown for longer periods of time, they are thus represented by the darker colors. 
Having the Cloud hang right above the hallway, I invite visitors to walk under the installation. The six strings hanging the are proportional to the number of total galls collected from each study. 
The rings above overlap each other, creating various densities in the surface mesh. I hope this creates an interesting experience for those who walk below it. I hope to bring this project further by becoming more proficient at Rhino and Grasshopper. I would also like to use the 3D printer to construct smaller mock-up models of future interactions.

Galls on Main and Lateral Stolons Voronoi Map

By Linda Shi

Taking the physical mock-up model one step further, my second project renders a pattern that evokes the same set of data from my first project.


Using 3D modelling software, Rhinoceros, and its plug-in parameter modifier, I was able to manipulate geometries on the plane. Using the parameter Voronoi, the maximum area is drawn around each point defined on the plane. I’m drawn to the mathematical and structural properties of the pattern, creating interesting dynamics between each of the defined points:

I want to use this pattern to represent the data collected. This pattern can easily be translated into a surface or screen that defines a specific architectural space. This will allow people to interact with data while navigating around the architectural installation.

Greenhouses

By Kiri Stolz

This sketch represents the average number of galls in each of the six greenhouses that the bio-control experiments were carried out in. Each square represents one of the greenhouses, and the saturation of the colour is indicative of the average number of galls that had been created by the gall wasps at the end of the experiment. The idea here was to take the data back into the environment from which it came, and possible reveal new information about the space itself, and how that could have contributed to the production of galls or “fertility” of a particular greenhouse.

I was later made aware that some of the experiments were started later than others, which contributed to the low fertility rate in the top-leftmost greenhouse square. However, this data visualization led me to ask the questions necessary to learn more about the experiment than I had expected.

Galled vs. Ungalled Portions of Hawkweeds

By Morgan Bath

All of the projects that I created this semester dealt with only the galled vs ungalled and did not focus on the lateral and the stolen figures. The works on paper are drawings of hawkweed seeds which were then manipulated to represent the ungalled portions of the plant that still have the ability to grow and the holes represent the lack of growth that the gulls created. The colours of the papers represent different plant growth stages as well as in the colour of the warp strings on the weavings I started.

The sounds piece that accompanies the works on paper is a creation from using the data provided for each plant stage and using the frequency of each number to create ten-second interval sounds of each plant stage and type. This piece I think was very successful, it is straight forward but I don’t think it is too literal, like the other pieces, right up front. I used two types of sounds wave one to represent galled and one ungalled and then just inserted the data set into the program to generate the sound clips.

Re-Assessing Stolon Growth with Attention to Galling in Hawkweeds

By Keith Morgan

My previous visualizations had given me a few assumptions about the trial that seemed to go against some of the hypotheses of the researchers. I had come across a trend that seemed to show that when introduced to the biocontrols at certain ages, the hawkweeds tended to grow more stolons rather than fewer. What I had not taken into account when exploring this was what the galls themselves were doing: how were the galls dispersed on the plants, and did this have an effect? Again, I severely averaged out the data, this time combining all of the trials and all of the ages for the test group as one entity, and the control group as another. The key metrics for this visualization were the total average number of main and lateral stolons that grew on one plant, the average number of main and lateral stolons that had galls, and finally the average total number of galls per plant. Assuming that a galled stolon is considered ‘dysfunctional’, these new numbers actually reversed my original assumption that while galled plants tended to have more stolons on average, they actually had fewer ‘functional’ stolons. In my visualization, only these functional stolons grow to full length.

Stolon Counts Between the Control and Test Trials of Whiplash and Mouse Ear Hawkweeds in Response to Galling

By Keith Morgan


Having worked with the data more, I was curious as to how much of an effect the galling process had on the quantity of stolons that grew on each species at the different trial ages. To achieve this, I compared the test group against a set of control samples as well. One of the primary questions for the original experiment was to test if the biocontrol agents could prove to be a natural method to control the hawkweed’s spread. 
With this visualization, I maintained the age distinctions as it became clear when going through the data that the different species were more affected at different ages. I instead distilled the lateral and main stolon metrics together in order to have an average number of total stolons per species per age for both the control and test trials. With these numbers, I found the ratio of galled plants to the control plants in terms of the number of stolons they had. This ratio – while potentially misconstrued due to the averaging process – showed that only the Very Young group in Whiplash and Very Young and Young groups in Mouse Ear had reduced stolon counts. It appeared in the other groups that galling had in fact stimulated stolon production. It is this difference that I chose to express in my visualization. 
A number of metrics are represented in this sculpture. The monoliths are 16’ high, 9.6” deep, 6’ wide, and have 48 spines. This represents the 16 plants per replicate, the 96 total plants in the trial, the 6 different replicates conducted, and the 48 plants per species used in the trial respectively. 
The tree structures in this project were an addition inspired by Linda Shi’s first visualization where she modeled a series of abstracted trees using the data points to constrain the height of the trees and leaf population. When she explained that her model represented a large-scale installation that someone could walk under, my mind was completely and utterly blown wide open. It was the first data visualization that I resonated strongly with, and her architectural approach inspired me to create my own large-scale installation piece. Running within the Blender Game Engine, I wanted to honour Linda’s piece by recreating it in a setting more suited to her original vision alongside my own visualization.