This fabric work represents the gene expression for Ta_ChSp_TFL1 (Chinese Spring Wheat). This work explores a combination of my experience at the Agricultural Research Centre with an exploration in understanding Jamie Larsen’s data set. By enlarging and simplifying the 522 character long gene expression I seek to make the data tangible to the viewer by inviting exploration through touch. Each pocket will be stuffed with a set weight of cracked wheat to represent the A,T,G, or C in the sequence.
My first work determined itself to be based exclusively on my first response. It is my way of activating my own investigation towards taking in entirely new scenery. My initial reaction to the tour and the lectures at the Lethbridge Research Station was: How am I going to cross over to all of this new information when it is not in a language that I can easily understand? I began to consider ways in which this cross over becomes manifested. To me, the answer that arrived was about a personal limited understanding but couldn’t it be simultaneously about unlimited possibilities? How might those two notions arrive at the same place and at the same time? I decided that I would set myself parameters and create a work that I hoped reflected processes that showed limits. I limited myself to one material that also has limited abilities of use. I limited the supports used and I chose limited steps through which to take the material through. These limited processes that I had taken the material through, at the end, were placed in an eclectic installation. The installation was very important to this piece. I wanted to evoke a sense that there is still so much possibility that is, even within limitations.
For my piece I have decided to explore the effect of stripe rust on different wheats. My initial form is stretched and twisted in relation to the resistance and susceptibility of stripe rust genes on wheat. While my initial studies are in wood, I would like to progress into either metal or acrylic/glass.
My first project represents the activity of the VRN1 gene in different zadock stages of perennial plants. The circles that form at the beginning of the animation represent the different zadock stages from the data I chose (Z22,Z39,Z47, and Z65). As the animation continues stalks of wheat grow out and represents the growth of wheat for each zadock stage chosen. At the end of the wheats growth it will sprout out a coloured semi circle to show the activity of the VRN1 gene at that stage of the plants growth.
Inspired by late 20th century video game pixel graphics, I looked to work with a pseudo three-dimensional isometric art style, similar to the graphics seen in Chris Sawyer’s Roller Coaster Tycoon (1999). In addition, I felt that the emphasis on building, construction, and micromanagement in the game tied in well with the goals of the research data. I began with a simple bar graph mapping the presence of the tested gene in a particular part of the plant at different life stages. I then adapted that graph to the isometric style and experimented with the result, considering the potential problems with each iteration. Although still in the experimental stage, the “diorama” appears to be the most successful, and will likely be the biggest influence on the direction of the final project.
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.
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.
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.
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.
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).
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.
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.
Competition surrounds us. It is the driving force of the capitalist economy, it is often fuel for nationalistic, civic and institutional pride, and, from a Darwinian perspective, it is the reason for the existence of all living organisms. For Jamie Larsen, Crop Geneticist and Breeder with Agriculture and Agri-Food Canada, competition takes the form of expedited evolution through the selection and controlled breeding of wheat, rye, and triticale plants. In the case of his Fall Rye Co-operative study, when a particular fall rye variety performs well, such as high winter survival rates, high grain yields, and low amounts of ergot (a fungus toxic to humans), its seeds move forward to be used in the next year’s crop, which can then be bred with other top performers. By controlling the pollination of each crop of fall rye, Larsen is able to foster the development of a rye variety with increasingly ideal qualities over time. Competition at its finest!
Following this breeding process (which can take up to 12 years) the seed selection culminates most dramatically at the yearly Prairie Recommendation Committee meetings. Here, Larsen and his colleagues present the agronomic and grain quality data of their most promising seeds for evaluation, the goal being to have them ‘supported’ for registration with Canadian Food Inspection Agency, who gives the final approval for them to enter the marketplace. In the past, seeds would be supported or rejected based on a majority vote, but these days decisions are informed largely by the Agronomic Evaluation Team’s “Merit Assessment Tool” which scores each variety according to how a seed’s data measures up against other control varieties.
Once on the market, a fall rye variety will face a new set of competitive challenges from its new audience – the farmers of the Canadian Prairies. Even the most impressive seeds on paper must overcome the critical eye of the seasoned farmer and have enough appeal to inspire him or her to take a chance on the unfamiliar crop. Often, it is also a matter of convincing farmers of the merits of winter cropsor planting rye in general.
So, where does data visualization connect with the tumultuous world of competitive crop breeding? For myself, the Data Artist-in-Residence at the Disruptive Imaginings Data Visualization Lab at the University of Lethbridge, the connection materialized through a visual form familiar to modern sports enthusiasts – the bracket tournament.
Bracket tournaments are a pervasive type of competition in professional and league sports where all the winners of a set round of games move forward to the next round, and so on until the best team prevails through all rounds. During my research of bracket tournaments, I found a few delightful cross-overs with agriculture, most notably the reference to “planting” the “seeds,” which refers to the process of arranging the players/teams in the first set of brackets so that the strongest competitors do not meet until later in the tournament (1)(2). The practice of predicting the outcomes of bracket tournaments, known as bracketology, has become a sport in itself, taking over many offices and friendship networks during NCAA March Madness or NFL Fantasy Football. Since their domination of the sporting world, bracket tournaments have also become popular in many other domains, like movies, music, books, public radio, and now… fall rye!
The Fall Rye Leaguedata visualization will be shown on April 20th at 2PM at the University of Lethbridge. Incorporating interactive paint, a micro-controller, and lights, the multimedia project presents the statistical champion of Jamie Larsen’s Fall Rye 2013 – 2014 Cooperative study through a touchable cork interface. Location TBA.
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.
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.
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.
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.
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.
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.
This was my first encounter with the data set, so in order to get familiar with the metrics and patterns in the data, I chose a straightforward representation to keep myself grounded. I attempted to show the number of main and lateral stolons that had been galled, in addition to the total number of galls that had formed on each species. These sets were expressed as the height of each collection of blocks in relation to the top of the clear walls on the sculpture. My process involved combining the data from all of the ages of the plants in each trial so that the comparison was left to differences between species instead of age. What this allowed me to do was show that proportionally speaking, the Whiplash species had more main stolons galled, while Mouse Ear had more lateral stolons galled. Still, when we look at the total number of galls that grew, it became apparent that both species grew an almost identical number of galls.