MIT Technology Review profiles the Facebook Data Science Team, described as a gathering of grad students at a top school and headed by Cameron Marlow, the “young professor.”

Back at Facebook, Marlow isn’t the one who makes decisions about what the company charges for, even if his work will shape them. Whatever happens, he says, the primary goal of his team is to support the well-being of the people who provide Facebook with their data, using it to make the service smarter. Along the way, he says, he and his colleagues will advance humanity’s understanding of itself. That echoes Zuckerberg’s often doubted but seemingly genuine belief that Facebook’s job is to improve how the world communicates. Just don’t ask yet exactly what that will entail. “It’s hard to predict where we’ll go, because we’re at the very early stages of this science,” says ­Marlow. “The number of potential things that we could ask of Facebook’s data is enormous.”

In Newcastle, there’s a floating tide mill building on the River Tyne. The mill turns to generate power for the building, and in that flow of water are four sensors for oxygen, acidity, nitrates and salinity. Values for these metrics, along with wheel speed, are captured about every thirty minutes. Stephan Thiel of Studio NAND, in collaboration with Moritz Stefaner, visualized this data in an abstracted simulation of the flow through the tidemill.

Particles are continuously moving from right to left, being attracted or repelled by four circular zones representing the sensor values. The overall behavior of the particles is influenced by the turning speed of the waterwheel. If the value of one sensor is above its mean value, particles are repelled. If the value is below the mean, particles are attracted towards the center of the zone.

For example, if all four values are greater than the mean, you end up with four circular swells around these zones. In the above, oxygen is below the mean, so the simulated flows head towards the center of the oxygen zone instead of move around it like with the three zones before. So you end up with a sort of fingerprint for each window of data capture.

The data itself is probably of little interest to anyone who doesn’t work at the mill, but the aesthetics of the piece is calming and certainly evokes the context of what the data represents.

The wind map by Wattenberg and Viegas and Drawing Water by Wicks come to mind. Oh, and also perpetual ocean.

We’ve seen a number of looks at movie poster cliches, but this is the first time I’ve seen how the color of movie posters have changed over time. Vijay Pandurangan downloaded 35,000 poster thumbnails from a movie site, counted the color pixels in each image, and then grouped them by year and sorted by hue.

Some thoughts from Pandurangan’s designer friend Cheryle Cranbourne:

The movies whose posters I analysed “cover a good range of genres. Perhaps the colors say less about how movie posters’ colors as a whole and color trends, than they do about how genres of movies have evolved. For example, there are more action/thriller/sci-fi [films] than there were 50-70 years ago, which might have something to do with the increase in darker, more ‘masculine’ shades.”

There’s no mention of the blanked out 1924. That must’ve been a sad year. Oh wait, there were movies during that year, so there was either a massive ink shortage or it’s just missing data.

[via @DataPointed]

Developer Santiago Ortiz explores visualization references through Delicious tags and puts them in a discovery context. There are two views. The first is a network with tags and resources as nodes. At first it looks like a giant hairball, but mouseover and you get a fisheye effect to zoom in on nodes, which makes them more readable. Mouse over a tag, and the labels for related resources get bigger, and likewise, mouse over a resource, and the related tags get bigger.
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Remember geographer Kirk Goldsberry’s analysis of shot efficiency on the basketball court? Jeremy White, Joe Ward, and Matthew Ericson give it the New York Times treatment in this interactive version for this season’s finals teams, the Miami Heat and Oklahoma City Thunder. Like Goldsberry’s maps, the shooting area of the basketball court is treated as the region of interest where bin size represents shot frequency and color represents efficiency. Roll over the regions to see the exact numbers.
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Information visualization firm Periscopic, in collaboration with GE, explores the makeup of the American workforce, from 1960 to present.

Jobs are definitely a top of mind subject. Did you know that manufacturing jobs were the largest sector of employment in 1960, yet today the category has fallen to 6th place? In this interactive visualization, browse who has been working in America over the past 50 years by sector, gender or age.

As in other Periscopic projects, the interactive provides multiple views that let you see the data from different angles. The initial view is a current breakdown of sectors, and when you press play, the visual rewinds to 1960, animating forward in time. Faded people icons represent the peak of each sector for context. Then as you might guess, the people rearrange themselves accordingly when you select breakdowns by age or gender.
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Twitter engineers Miguel Rios and Jimmy Lin explored tweet volumes in different cities and found some interesting tidbits about how people use the service.

We see different patterns of activity between the four cities. For example, waking/sleeping times are relatively constant throughout the year in Tokyo, but the other cities exhibit seasonal variations. We see that Japanese users’ activities are concentrated in the evening, whereas in the other cities there is more usage during the day. In Istanbul, nights get shorter during August; Sao Paulo shows a time interval during the afternoon when Tweet volume goes down, and also longer nights during the entire year compared to the other three cities.

Notice the break during Ramadan in Istanbul?