Resident Datatoys researcher / Unity developer Robert Yang recently wrote about hooking Google Docs spreadsheet data into Unity, to better facilitate collaboration and make the data flow into our toys easier. Huzzah!
Misplaced Lens Cap

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roma★
let's talk about Bridgerton tea, my ask is open
he wasn't even looking at me and he found me
2025 on Tumblr: Trends That Defined the Year
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Product Placement

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$LAYYYTER

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@petlabdatatoys
Resident Datatoys researcher / Unity developer Robert Yang recently wrote about hooking Google Docs spreadsheet data into Unity, to better facilitate collaboration and make the data flow into our toys easier. Huzzah!
Amazing variant on a game that, well, stinks usually!
Lifeboat!
Lifeboat! is an audience experience meant to evoke some of the decision-making required by the Prisoner's Dilemma. In short, participants' individual consequences depend upon the actions of others. Selfish action seems like the most rational tack, but if multiple people choose the same path, the group as a whole suffers.
In Lifeboat!, the facilitator tells the audience a story, in which they are all participants. Everyone starts off standing. They are on a cruise ship that is suddenly caught in a squall, and they must form into lifeboats of seven people. The audience must then scramble to try to form boats with the people near them. Some people wind up in boats that are too small; other boats are overloaded. Those people all "drown," and they sit down.
In the next round, a helicopter comes to save the remaining survivors. However, the helicopter can save only a certain number of people, and if more people try to get onto the helicopter than it will hold, the helicopter will be pulled into the ocean, and all the people who went for it will be out. Likewise, the people who are left behind in boats must scramble to form boats of the appropriate size again after being abandoned by their boat-mates. One iteration includes the use of name-tags, whose color would be revealed to have some sort of effect on the size of the boat (e.g. a blue name tag means that person has hypothermia, and the boat needs an extra person to take care of them).
Rounds continue that force the audience to make similar decisions. Collective action is difficult to organize in a short time and with limited movement (assuming people are in a theatre with rows of seats). And the decision whether or not to go for a helicopter means that each person has to decide whether to take the route to immediate safety, which may wind up jeopardizing not only the people in their boat, but also all other audience members who make the same decision. Silently needing to convince others not to abandon you opens the door to conniving and backstabbing.
It's also fun.
We tested this on the second day of the Symposium for the MFA program in Design and Technology at Parsons. Despite being faced with imminent drowning, the audience enjoyed the experience.
Team: Jane Friedhoff, Alex Ackerman, Jennifer Presto
Additionally, here are a few audio samples that will be used while playing Lifeboat, to bring the narrative and experience to life! The sound effects were creating using Garage Band.
Crowdsourcing to Raise the Stakes
While brainstorming our Radiolab toy/experience, our group encountered a central problem: without raising the stakes for the audience, going through the paces of Golden Balls felt empty.
The central motivation, and suspense, behind the Golden Balls television program is money: participants will be engaged if there’s something in it for them.
With that in mind, our group felt that a Kickstarter (or some iteration of crowdfunding) could replicate the intensity that we were seeking.
Before the Radiolab’s national tour, the program would launch a Kickstarter campaign, with a catchy video. The details would be vague, but a few things would be made clear:
1. All cities, and audiences, will play against each other with the money. So donate now!
2. All money raised will go to charity, regardless of the results. So donate now!
At this point, we will imagine that Radiolab is embarking on a 10 city tour, and has successfully raised $100,000.
Now we come to the actual excercie of playing Golden Balls. Our group imagined a version of the television program, amplified to a scale of city vs. city.
The script would go as follows:
(Radiolab does a short segment on Golden Balls. Everyone in the audience understands how it works)
Ladies and gentleman! Now that we all understand the premise of Golden Balls, it’s time for you to play.
As many of you know, the course of the last few months, we’ve launched a successful kickstarter campaign to raise funds to “play” with, during our live show. All the money will go to charity — so we’re not taking it for ourselves — but there is something at stake: your city.
Before we embarked on our ten city tour, our Kickstarter raised $100,000. If you do that math, divided evenly, that’s $10,000 per city. However, since we’re playing Golden Balls, the rules are a bit different.
In this Radiolab edition of Golden Balls, you — as a city — are playing against the remaining cities in our tour. Together, as an audience, you have the choice to split (or keep) the money allotted to you, or steal from the larger pot. Stealing “steal” the money belonging to, say, your neighbors in Boston.
At the start of each show, we will divide the pot evenly, and the city we are in will “play” with the result of the equation. Because the pot sits at $100,000 now, you play with $10,000.
Under your seats, you will find two stickers. One says split, and one that says steal. Because you are playing as a city, when the time comes, you will place your individual choice on the back of the person sitting in front of you. If you’re sitting in the front row, you’ll put the sticker on your chest.
Together, you will vote, and then pass your remaining stickers to the left and into one of our collection boxes. From there, our trusty RadioLab intern will count your votes and your choice as a city — split or steal — will be revealed.
Now I’m pleased to introduce John Doe, from the Food Bank of New York. Tonight, whatever you split or steal — or lose, will go to the Food Bank.
These stakes are real. Like Golden Balls, if you’re feeling generous, you can split the pot. It’s very simple, if you split, the Food Bank of New York walks away — tonight — with a check for $10,000. John, what would that do for you?
“A donation of $10,000 would put food on the table for roughly 100 families of four, for a week. It would be such an excellent contribution!”
Now, of course you have the option to steal. If you steal, New York takes an additional 15% of the total pot — at this point and time that totals $23,500. John?
“A donation of that size would feed 250 impoverished families in New York. Of Four. That sum of money would be so incredible for our organization — I can barely put it into words! ”
So it’s very simple, you split, $10,000 — you steal $23,500.
However, in the spirit of Golden Balls, there’s just one catch: you can’t just get away will stealing! In this theater sits 1,000 audience members. In order to steal, the amount of “steal” stickers played must fall within the range of 100-200 votes. One sticker over, New York, and John get nothing.
So take a minute to discuss and when the music starts, you will have 30 seconds to place the sticker on the person in front of you — a decision that will stare back at you until the results are in.
Towards the end of the show the results will be revealed!
————
In short, our toy/exercise aims to do three things:
1. Create a competitive atmosphere between cities, in turn replicating the intense atmosphere that the original Golden Balls does so well
2. Have audience members “live” with their decisions throughout the show.
3. Create a dialoge that lasts outside of a single RadioLab show. Example: “New York stole?! Of course they did, those greedy bastards!”
Our toy/exercise is very malleable:
1. Representatives from the next city (the one that will be “stolen” from) could come to the show to implore the audience to split. “Boston needs it so much more! etc. etc. etc.)
2. Instead of stealing from the pot, cities could steal from each other (if New York were to split, and Boston steal, Boston would take New York’s money)
Miles Kohrman, Mike Susol, and Paul Cheng
The Balance of Power
Balance of Power is a toy that seeks to demonstrate how various forces — positive and negative — determine the climate of reception for immigrant populations as they arrive in American cities.
Players experience the challenge of managing a city that is “actively recruiting” immigrant populations, by manipulating factors of influence. Their ultimate goal is to achieve a balance in the city, and maintain incoming immigrant populations.
We imagined a few solutions that would represent flows and balance in a simple and straightforward way.
In our early prototypes, we used a round cardboard piece and office materials to represent various factors that influence the climate of reception in a fictional city.
We quickly moved from that a round wooden board, suspended in the air.
The board features randomly placed pegs and barriers. Players place weighted pieces (“factors”) on the pegs, changing the balance of the board for better or worse. There are also barriers on the board, which serve to temporarily prevent (or protect) pieces from falling off the board.
Smooth round pebbles represent members of the immigrant community. Players place these pebbles on the board, with the objective of keeping them there. They must anticipate the movement of the board’s changing orientation, and guide the pieces into specific areas to increase their influence.
The immigrant struggle for viability in a city — or balance — is exemplified by the pieces’ reaction to the changing balance of the board. If the board tips too far in one direction, they will symbolically slide off the edge.
How to Play:
When playing with the toy, three roles must be filled:
Person A: Places positive forces that improve the environment for immigrants and seek balance.
Person B: Places negative forces that disrupt the environment for immigrants and seek imbalance.
Person C: Places immigrants on the board in strategic locations. If the player’s immigrant pieces reach a certain location on the board, the player earns the ability to redistribute existing factors, in turn balancing the board. The goal is to keep as many immigrants on the board as possible.
The board starts at a balanced position.
Person C adds five immigrant pieces to the board, in any location.
Person B places a disruptive force (no more than three pieces) on the board
Person A places a balancing force (no more than three) on the board.
After a set amount of time, an external factor is revealed that influences the balance of the toy.
*External factors are events or circumstances that alter the balance of the toy by either increasing or removing factors that have been previously placed.
Examples:
GOOD: A new mayor is elected who campaigned on a pro-immigration platform. He pledges to bring new initiatives to the city and double the immigrant population. Person A: Remove eight factors in an effort to balance the board
BAD: A new mayor is elected who campaigned on an Anti-immigration platform. She pledges to reduce the amount of immigrants in the city in an effort to return “real american jobs” to real americans. Person B: remove eight factors in an effort to unbalance the board.
GOOD: Your city has a surplus this year, and the mayor chooses to implement new english language programs at the public library. Person A: remove three factors in an effort to balance the board.
BAD: A national financial crisis is affecting your city your negatively. Person B: remove ten factors in an effort to unbalance the board.
A Twist on Immigration
We used a set of data that listed unemployment levels among people of differing immigration status and education levels in certain cities. Our goal was to allow people to play with this data in a physical way, allowing them to feel the numbers with their bodies, and explore and contrast the challenges faced by each group.
We used a Twister board as inspiration, but rather than creating a static board on the ground, we used a vertical, digital play space, which a player could interact with using the Kinect. That way, the configuration of the dots could be adjusted instantly to represent the appropriate data, allowing the player to try out the data corresponding to several population groups, one after the other.
an early prototype
We designed our virtual play space such that, the higher the level of unemployment a population group faces in a certain city, the more the player will have to stretch to reach all the spots. Specifically, the angle of the leg out from the body is directly correlated to the percentage of that population group that is unemployed. Similarly, as unemployment increases, so does the distance of the arms from the body, as well as how high the player must lift them.
positioning for different unemployment levels
toy in action
In a future iteration, more information would reveal the story of the people underlying the data. After the player successfully reaches the spots on the screen, information would appear giving more precise information about the levels of unemployment, as well as the types of job that might be prevalent for that population group.
Team: Jane Friedhoff, Rey Mashayekhi, Jennifer Presto, Lauren Slowik
code for project available on GitHub
Material
A toymaker must seriously consider the material. The rules for play are embodied in the toy, not abstracted as they are with a game. Affordance rather than instruction.
Systems expressed via data are complicated and mysterious. As we work to understand these things and wonder how to translate them into a toy, remember this: the answer is in the material. Spend time with the material and time with the data. Don't push. Wait for one to appear within the other.
Immigration Auditization
Objective:
Utilizing the 2000-2010 dataset on immigration, our goal was to create a toy that abstracted the data through auditization.
Process:
Our original thoughts were to abstract and assign different data point to various elements of music (such as pitch, reverb, volume, key, etc.) This would give us a system map of how the data would turn into audio and also visualization.
Using a midi controller for inspiration, we experimented with the user experience of playing with buttons, knobs and sliders to manipulate the audio for each city, and each year represented in the data.
We were interested in taking a sampling of cities, and assigning a loop/sample to one aspect. For example: Low-Educated Immigrants or Native Unemployment. These loops/samples would be the same for each city, but manipulated slightly by the data. When a city is chosen, a composition is played. By choosing a city, a year, and playing/isolating the elements the player would start to hear the unique differences in the audio of each city.
In order to faithfully abstract the data, we embedded the lowest and highest values for each category in the data, turning them into values between 0 and 100. The different variables are assigned a volume level based on this. Certain data points, for instance a higher unemployment rate, would cause that track of the composition to be more prominent than others.
We created audio samples for Birmingham and Los Angeles (cities with polar immigrant populations) based on the data, manipulating BPM and volume levels for the points of low, mid and high educated immigrants.
For our final prototype, we decided to create a digital interface that combined the auditory and visual elements. The player is able to select one city at a time, toggle between the years, and isolate the different variables (.wav samples) that have been manipulated by volume based on the data.
The screen is divided into 6 sections and a ball (manipulated by the mouse) travels through these sections. The top sections are related to high education, the bottom sections are lower education. The left side is specific to education, the right side is specific to unemployment. As the ball passes through the sections an audio clip is played whose volume level corresponds to that data point percentage.
Players can control the ball and manipulate the velocity, and toggle through the data by year (2000/2005/2010).
posted by matthew willse, simone egipciaco, and paul cheng
Gear Up
Silent brainstorming notes from Gear Up.
“Gear Up” is a toy developed by team Aero, Alex, Robert, and Patricio.
The concept for Gear Up emerged from a silent brainstorming session. After sorting through many different ideas and the datasets provided by the Migration Policy Institute (MPI), the team agreed on using gears to depict how education levels of immigrants and natives impact employment opportunities. In the next meeting, Alex found an iPad application that demonstrated how gears could be used for this specific angle-players would use ‘factor’ gears to connect a spinning ‘job’ gear with an unmoving ‘immigrant’ or ‘native’ gear, so that the whole machine would function.
Gears in motion - prototype iteration #1 by Patricio
Patricio crafted the team’s first prototype in openFrameworks, showcasing the basic mechanics of the gears in motion. After presenting this iteration to the class, the next step became clear-the group had to start incorporating a data-based story into the toy. After doing some research, Alex noticed that, in most cities, low-educated immigrants had much lower unemployment rates than natives, whereas the opposite was true of highly educated immigrants. Alex spoke with Madeline Zavodny, an economics professor at Agnes Scott College who has published many articles on the topic of immigration, including some for MPI, to investigate this topic. The conversation sparked an interest on the subject of ‘brain waste,’ which occurs when highly educated immigrants are unable to obtain highly skilled jobs in America. I’m sure some of us have heard similar horror stories in which a foreign doctor ends up working as a taxi driver because they are unable to transfer credentials, among other things.
After reading over an article by MPI, “Uneven Progress: The Employment Pathways of Skilled Immigrants in the United States” (2008), the team decided to make their toy more specific, using the data found from the study by Jeanne Batalova and Michael Fix as a framework. For example, one key finding of the study is that highly skilled immigrants who had a limited knowledge of English were twice as likely to work in lower skilled jobs than their qualifications demand. Therefore, users of the toy can alter whether or not their immigrant has fluent, limited, or no proficiency in English. This decision impacts their likelihood of finding a highly skilled job.
Size and resistance are elements that influence and differentiate the factor gears. These elements depict the ease or difficulty of finding specific jobs. Immigrant gears that had favorable factors would be more likely to connect with the spinning gear (larger size), plus they spin faster. The toy was made to function with the following principles:
1. Two gears will be placed on the interface-they represent an immigrant (not spinning) and highly skilled job (spinning)
2. Users will place their own gears on the screen, one at a time. While doing so, a prompt will appear, describing their selection.
3. The size of the gear will be determined by the distance of the cursor to the previous gear. The size is flexible and chosen by the user, but each size represents particular factors that either help or hurt the chances of being employed
4. The larger each gear is, the more likely you are to get the job and the faster the machine will spin. For example, the gear size for an undocumented immigrant is small and will not spin, whereas an employment-based visa gear is large and spins fast.
The toy is meant to operate much like the Game of Life, as a choose your own pathway scenario. The factors that players can alter are as follows:
1. Visa Situation (no visa, here for a job, with family/spouse, greencard)
2. English Fluency (no English, some English, fluent English)
3. Education (high school diploma/GED, associate’s degree, degree from foreign university, degree from American university)
4. Origin (Europe or Asia, Latin America, Africa)
5. Time Spent in US (working here already, just arrived)
Looking ahead: If the group were to move forward with this project, they would investigate the employment opportunities of immigrants and natives seeking different level jobs in order to depict, on a larger scale, how education level impacts employment opportunity in America for both groups.
Find it on GitHub here.
Data Toys Demo Team 2013
Data Toys at SXSW
Colleen on the NewsBot panel at SxSW.
Donella Meadows, the patron saint of Data Toys.
Jay Wright Forrester, the fellow who brought us systems thinking.
How attractive are cities to immigrants? This is the question one of our early prototypes around the Making it in America project explores. This prototype abstracts the 25 city data down to just a few properties: whether or not a city is attracting immigrants or losing them, and the proportion of immigrants of different ethnicities that come to a given city. Ethnic proportions and diversity directly affects future immigration, the more diverse cities pulling more diverse immigrants. Cities are created by double-tapping, and then can be changed from a city that “pulls” or “pushes” immigrants, and moved around the space. People are created by dragging. Attractivate is not drawing upon the Making it in America API. As the prototype evolves, it may be fed data from the data set, but for the time being, it remains an exploration of systems modeling and interaction design.
Employable? is an early prototype for the Making it in America project based on the project API. The toy models the relationships between 25 cities in the US and the potential an immigrant has for finding employment in order to reflect on how likely immigrants of different education levels are to find employment.
A set of toy characters represent immigrants with three different education levels—no high school diploma, up to an associate’s degree, and a bachelor's degree or better. By placing a toy on the iPad, the player selects a city, then one of the three years for which we have data. The player can then see the employment pool within which the immigrant would compete. The competition conveys the percentage of potential applicant pool broken down by level of employment and employment status.
Data Toys @ SxSW
Thanks to the Knight Foundation, the Data Toys project is making its debut at SxSW Interactive. Colleen will be on the NewsBots panel on Saturday at 5pm, and then that evening, Colleen, Heather and John will be at the (private, unfortunately) Knight/Mozilla/MIT Media Lab/SoundCloud event that evening doing demos. They will be back at it at 11 am both Sunday and Monday at the Knight Foundation booth on the trade show floor. The initial prototypes we will be showing are the initial work we have done with our partners from Public Radio International and the Migration Policy Institute. We’re working together to explore how to create data toys that investigate the immigrant experience in the United States. MPI has prepared a set of data on 25 American cities collected in 2000, 2005 and 2010. So far, we have written an API to help tap into the data and then three different tablet-based experiments to model the underlying systems that impact the immigrant experience in the United States. We still have a long way to go with the project, but we are excited to share our work so far and to see what people at SxSW Interactive have to say. So if you are in Austin for SxSW Interactive, try to stop by and say hi and see what we’re up to.