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Intraocular Pressure: It's the data, stupid

Submitted by dave on Tue, 12/15/2009 - 10:09pm

The following post is adapted from "A Deluge of Data Shapes a New Era in Computing" By JOHN MARKOFF, published: December 14, 2009 in The New York Times.

Top computer scientists say we have entered the age of data-intensive scientific discovery. Dr. Gray, who was a database software pioneer and a Microsoft researcher,  called the shift a “fourth paradigm.” The first three paradigms were experimental, theoretical and, more recently, computational science.

Larger data sets are fundamentally transforming the practice of science. Dr. Gray foresaw an evolving era in which an massive amounts of observational data would power new scientific discoveries -- and he was aware of the need for new scientific computing tools to manage, visualize and analyze the data flood. This is a challenge for us in self-tonometry research as well.

In essence, computational power created computational science, which produced the overwhelming flow of data, which now requires a new computing tools. It is a positive feedback loop in which the data stream becomes the data flood and sculptures a new computing landscape.

With self-tonometry, we have overthrown the old paradigm where intraocular pressure was measured once every few months and where a typical patient would have intraocular pressure data over their lifetime that could -- in total -- be written on a single sheet of paper. In my own case, after just a few years of self-tonometry, I have over a million individual intraocular pressure measurements (including my Pascal DCT data). Self-tonometry has the potential to transform the field of intraocular pressure research from data-poor to data-rich.

In general, the shift from data-poor to data-rich science is giving rise to a computer science perspective, referred to as “computational thinking” by Jeannette M. Wing, assistant director of the Computer and Information Science and Engineering Directorate at the National Science Foundation.

Dr. Wing has argued that ideas like recursion, parallelism and abstraction taken from computer science will redefine modern science. Implicit in the idea of a fourth paradigm is the ability, and the need, to share data. In sciences like physics and astronomy, the instruments are so expensive that data must be shared. Now the data explosion and the falling cost of computing and communications are creating pressure to share all scientific data.

This transition is happening in all fields. “The advent of inexpensive high-bandwidth sensors is transforming every field from data-poor to data-rich,” Edward Lazowska, a computer scientist and director of the University of Washington eScience Institute, said in an e-mail message. The tonometer, in the hands of the glaucoma patient, is our sensor. If you have been reading for any length of time, you have heard me emphasize the need for high frequency self-tonometry. I have collected hundreds of intraocular pressure measurements per day for extended periods of time. And that's just the beginning of the revolution. This data-rich scientific transformation is even occurring in the social sciences, as the New York Times article describes.

“As recently as five years ago,” Dr. Lazowska said, “if you were a social scientist interested in how social groups form, evolve and dissipate, you would hire 30 college freshmen for $10 an hour and interview them in a focus group.”

“Today,” he added, “you have real-time access to the social structuring and restructuring of 100 million Facebook users.”

No where in ophthalmology is this transition more apparent than in the emergence of self-tonometry. Now we need to develop the computational tools to manage the intraocular pressure data we can generate from the latest tonometers that interface directly with computers.

Self-tonometry uniquely facilitates data-intensive scientific discovery in glaucoma research. And our self-tonometry group at is dedicated to sharing this data-centric knowledge.

In computing circles, Dr. Gray’s crusade was described as, “It’s the data, stupid.” It was a point of view that caused him to break ranks with the supercomputing nobility, who for decades focused on building machines that calculated at picosecond intervals.

He argued that government should instead focus on supporting cheaper clusters of computers to manage and process all this data. This is distributed computing, in which a nation full of personal computers can crunch the pools of data involved in the search for extraterrestrial intelligence, or protein folding.

The goal, Dr. Gray insisted, was not to have the biggest, fastest single computer, but rather “to have a world in which all of the science literature is online, all of the science data is online, and they interoperate with each other.” He was instrumental in making this a reality, particularly for astronomy, for which he helped build vast databases that wove much of the world’s data into interconnected repositories that have created, in effect, a worldwide telescope.

Both Microsoft and Google are hoping to entice scientists by offering cloud services tailored for scientific experimentation. Examples include Worldwide Telescope from Microsoft and Google Sky, intended to make a range of astronomical data available to all.

One day we need to have a comprehensive database of intraocular pressure data tailored for glaucoma research. At this moment, the key requirement is a software application that will facilitate transforming the raw tonometer output into formatted data that spreadsheets and databases understand. At the time the intraocular pressure data is collected, the software tools needs to ensure that the data is put into the appropriate context. We do this primarily by recording patient activities as well as patient physiological data and subjective experiences.

The promise of the shift described in the fourth paradigm is a blossoming of science. And it has been my own key to successfully managing my intraocular pressure.

NOTE: large sections of this text were extracted from "A Deluge of Data Shapes a New Era in Computing" By JOHN MARKOFF. See the original article at

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