balls” approach to massive datasets.

However, even massive datasets are sometimes not complete enough to deliver definitive results. Recent discoveries in biomedical research have revealed that even a complete index of the human genome’s three billion pairs of chemical bases has not greatly accelerated breakthroughs in health care, because other crucial medical data is missing. A study of 19,000 women, led by researchers at Brigham and Women’s Hospital in Boston, used data constructed from the National Human Genome Research Institute’s catalog of genome-wide association study results published between 2005 and June 2009—only to find that the single biggest predictor of heart disease among the study’s cohort is self-reported family history. Correlating such personal data with genetic indexes on a wide demographic scale today is nearly impossible as an estimated 80% of U.S.-based primary-care physicians do not record patient data in electronic medical records (EMRs). Recent government financial incentives are meant to spur EMR adoption, but for the immediate future, crucial data in biomedical research will not exist in digital form.

Another issue in biomedical research is the reluctance of traditionally trained scientists to accept datasets that were not created under the strict parameters required by, for example, epidemiologists and pharmaceutical companies.

CMU’s Mitchell says this arena of public health research could be in the vanguard of what may be the true crux of the new data flood—the idea that the provenance of a given dataset should matter less than the provenance of a given hypothesis.

“The right question is, Do I have a scientific question and a method for answering it that is scientific, no matter what the dataset is?” Mitchell asks. Increasingly, he says, computational scientists will need to frame their questions and provide data for an audience that extends far beyond their traditional peers.

“We’re at the beginning of the curve of a decades-long trend of increasingly evidence-based decision-making across society, that’s been noticed by people in all walks of life,” he says. “For example, the people at the public policy school at CMU came to the machine learning department and said,

“the right question is,
Do i have a scientific
question and a
method for answering
it that is scientific,
no matter what
the dataset is?”
asks tom mitchell.

‘We want to start a joint Ph.D. program in public policy and machine learning, because we think the future of policy analysis will be increasingly evidence-based. And we want to train people who understand the algorithms for analyzing and collecting that evidence as well as they understand the policy side.’” As a result, the joint Ph.D. program was created at CMU.

 

Further Reading

Duda, S.N. Cushman, C., and Masys, D.R. An XML model of an enhanced dictionary to facilitate the exchange of pre-existing clinical research data in international studies, Proceedings of the 12th World Congress on Health Informatics, Brisbane, Australia, August 20–24, 2007. Mitchell, T.M., Shinkareva, S.V., Carlson, A., Chang, K.-M., Malave, V.L., Mason, R.A., and Just, M.A. Predicting human brain activity associated with the meanings of nouns, Science 320, 5880, May 30, 2008. Murray-Rust, P. Data-driven science—a scientist’s view, nSF/JISC Repositories Workshop position paper, April 10, 2007.

Newman, H.B.

Data intensive grids and networks for high energy and nuclear physics: drivers of the formation of an information society, World Summit on the Information Society, Pan-European Ministerial Meeting, Bucharest, Romania, november 7–9, 2002.

Thakar, A.R.

The Sloan Digital Sky Survey: drinking from the fire hose, Computing in Science and Engineering 10, 1, Jan./Feb. 2008.

Gregory Goth is an oakville, ct-based writer who specializes in science and technology.

© 2010 acm 0001-0782/10/1100 $10.00

Research;&;Development
Paper
Chase

Due to enormous governmental investments in research and development, scientists in many asian countries are steadily increasing their number of papers published in scientific journals.

The asia-Pacific region increased its total of published science articles from 13% in the early 1980s to slightly more than 30% in 2009, according to the Thomson reuters National Science Indicators, an annual database of the number of articles published in about 12,000 internationally recognized journals. China leads the pack with 11% in 2009, up from 0.4% in the early 1980s, followed by Japan with 6.7% and India with 3.4%. In contrast, the ratio of articles from scientists in the u. S. decreased to 28% in 2009, down from 40% in the early 1980s.

In all, 25 nations have increased their research, but none more so than Singapore. With a population of just five million, the nation published 8,500 articles in 2009, compared with only 200 in 1981. Singapore now allocates 3% of its gross domestic product to research and development, a figure expected to rise to 3.5% by 2015.

The increase in scientific publications, especially in east asian countries, reflects a “phenomenal” increase in funding, Simon Marginson, a professor of higher education at the university of Melbourne, told The New York Times. Marginson attributed the increase in research output to governments’ commitment to establishing knowledge-intensive economies. “It’s very much not simply about knowledge itself—it’s about its usefulness throughout the economy. I think that that economic vision is really the principal driver,” Marginson said.

another reason for increased publications is that many asian universities now receive additional funding to have their papers translated into english, the language used by the majority of academic journals.

—Phil Scott