Steve Ruark for The New York Times

Jimmy Lin is an associate professor at the University of Maryland.

At the heart of this criticism is data. Researchers and workers in fields as diverse as bio-technology, astronomy and computer science will soon find themselves overwhelmed with information. Better telescopes and genome sequencers are as much to blame for this data glut as are faster computers and bigger hard drives.

While consumers are just starting to comprehend the idea of buying external hard drives for the home capable of storing a terabyte of data, computer scientists need to grapple with data sets thousands of times as large and growing ever larger. (A single terabyte equals 1,000 gigabytes and could store about 1,000 copies of the Encyclopedia Britannica.)

The next generation of computer scientists has to think in terms of what could be described as Internet scale. Facebook, for example, uses more than 1 petabyte of storage space to manage its users’ 40 billion photos. (A petabyte is about 1,000 times as large as a terabyte, and could store about 500 billion pages of text.)

It was not long ago that the notion of one company having anything close to 40 billion photos would have seemed tough to fathom. Google, meanwhile, churns through 20 times that amount of information every single day just running data analysis jobs. In short order, DNA sequencing systems too will generate many petabytes of information a year.

“It sounds like science fiction, but soon enough, you’ll hand a machine a strand of hair, and a DNA sequence will come out the other side,” said Jimmy Lin, an associate professor at the University of Maryland, during a technology conference held here last week.

The big question is whether the person on the other side of that machine will have the wherewithal to do something interesting with an almost limitless supply of genetic information.

At the moment, companies like I.B.M. and Google have their doubts.

For the most part, university students have used rather modest computing systems to support their studies. They are learning to collect and manipulate information on personal computers or what are known as clusters, where computer servers are cabled together to form a larger computer. But even these machines fail to churn through enough data to really challenge and train a young mind meant to ponder the mega-scale problems of tomorrow.

“If they imprint on these small systems, that becomes their frame of reference and what they’re always thinking about,” said Jim Spohrer, a director at I.B.M.’s Almaden Research Center.

Two years ago, I.B.M. and Google set out to change the mindset at universities by giving students broad access to some of the largest computers on the planet. The companies then outfitted the computers with software that Internet companies use to tackle their toughest data analysis jobs.

And, rather than building a big computer at each university, the companies created a system that let students and researchers tap into giant computers over the Internet.

This year, the National Science Foundation, a federal government agency, issued a vote of confidence for the project by splitting $5 million among 14 universities that want to teach their students how to grapple with big data questions.

The types of projects the 14 universities have already tackled veer into the mind-bending. For example, Andrew J. Connolly, an associate professor at the University of Washington, has turned to the high-powered computers to aid his work on the evolution of galaxies. Mr. Connolly works with data gathered by large telescopes that inch their way across the sky taking pictures of various objects.

The largest public database of such images available today comes from the Sloan Digital Sky Survey, which has about 80 terabytes of data, according to Mr. Connolly. A new system called the Large Synoptic Survey Telescope is set to take more detailed images of larger chunks of the sky and produce about 30 terabytes of data each night. Mr. Connolly’s graduate students have been set to work trying to figure out ways of coping with this much information.

Purdue, meanwhile, looks to carry out techniques used to map the interactions between people in social networks into the biological realm. Researchers are creating complex diagrams that illuminate the links between chemical reactions taking place in cells.

A similar effort at the University of California, Santa Barbara, centers on making a simple software interface — akin to the Google search bar — that will let researchers examine huge biological data sets for answers to specific queries.

Mr. Lin has encouraged his students to illuminate data with the help of Hadoop, an open-source software package that companies like Facebook and Yahoo use to split vast amounts of information into more manageable chunks.

One of these projects included a deep dive into the reams of documents released after the government’s probe into Enron, to create an analysis system that could identify how one employee’s internal communications had been connected to those from other employees and who had originated a specific decision.

Mr. Lin shares the opinion of numerous other researchers that learning these types of analysis techniques will be vital for students in the coming years.

“Science these days has basically turned into a data-management problem,” Mr. Lin said.

By donating their computing wares to the universities, Google and I.B.M. hope to train a new breed of engineers and scientists to think in Internet scale. Of course, it’s not all good will backing these gestures. I.B.M. is looking for big data experts who can complement its consulting in areas like health care and financial services. It has already started working with customers to put together analytics systems built on top of Hadoop. Meanwhile, Google promotes just about anything that creates more information to index and search.

Nonetheless, the universities and the government benefit from I.B.M. and Google providing access to big data sets for experiments, simpler software and their computing wares.

“Historically, it has been tough to get the type of data these researchers need out of industry,” said James C. French, a research director at the National Science Foundation. “But we’re at this point where a biologist needs to see these types of volumes of information to begin to think about what is possible in terms of commercial applications.”

GENETICS An I.B.M. simulation of the “DNA transistor” it hopes will sequence genomes by reading DNA pulled through an atomic-size hole.

One of the oldest names in computing is joining the race to sequence the genome for $1,000. On Tuesday, I.B.M. plans to give technical details of its effort to reach and surpass that goal, ultimately bringing the cost to as low as $100, making a personal genome cheaper than a ticket to a Broadway play.

I.B.M. already has a wide range of scientific and commercial efforts in fields like manufacturing supercomputers designed specifically for modeling biological processes. The company’s researchers and executives hope to use its expertise in semiconductor manufacturing, computing and material science to design an integrated sequencing machine that will offer advances both in accuracy and speed, and will lower the cost.

“More and more of biology is becoming an information science, which is very much a business for I.B.M.,” said Ajay Royyuru, senior manager for I.B.M.’s computational biology center at its Thomas J. Watson Laboratory in Yorktown Heights, N.Y.

DNA sequencing began at academic research centers in the 1970s, and the original Human Genome Project successfully sequenced the first genome in 2001 and cost roughly $1 billion.

Since then, the field has accelerated. In the last four to five years, the cost of sequencing has been falling at a rate of tenfold annually, according to George M. Church, a Harvard geneticist. In a recent presentation in Los Angeles, Dr. Church said he expected the industry to stay on that curve, or some fraction of that improvement rate, for the foreseeable future.

At least 17 startup and existing companies are in the sequencing race, pursuing a range of third-generation technologies. Sequencing the human genome now costs $5,000 to $50,000, although Dr. Church emphasized that none of the efforts so far had been completely successful and no research group had yet sequenced the entire genome of a single individual.

The I.B.M. approach is based on what the company describes as a “DNA transistor,” which it hopes will be capable of reading individual nucleotides in a single strand of DNA as it is pulled through an atomic-size hole known as a nanopore. A complete system would consist of two fluid reservoirs separated by a silicon membrane containing an array of up to a million nanopores, making it possible to sequence vast quantities of DNA at once.

The company said the goal of the research was to build a machine that would have the capacity to sequence an individual genome of up to three billion bases, or nucleotides, “in several hours.” A system with this power and speed is essential if progress is to be made toward personalized medicine, I.B.M. researchers said.

At the heart of the I.B.M. system is a novel mechanism, something like nanoscale electric tweezers. This mechanism repeatedly pauses a strand of DNA, which is naturally negatively charged, as an electric field pulls the strand through a nanopore, an opening just three nanometers in diameter. A nanometer, one one-billionth of a meter, is approximately one eighty-thousandths the width of a human hair.

The I.B.M. researchers said they had successfully used a transmission electron microscope to drill a hole through a semiconductor device that was intended to “ratchet” the DNA strand through the opening and then stop for perhaps a millisecond to determine the order of four nucleotide bases — adenine, guanine, cytosine or thymine — that make up the DNA molecule. The I.B.M. team said that the project, which began in 2007, could now reliably pull DNA strands through nanopore holes but that sensing technology to control the rate of movement and to read the specific bases had yet to be demonstrated.

Despite the optimism of the I.B.M. researchers, an independent scientist noted that various approaches to nanopore-based sequencing had been tried for years, with only limited success.

“DNA strands seem to have a mind of their own,” said Elaine R. Mardis, co-director of the genome center at Washington University in St. Louis, noting that DNA often takes a number of formations other than a straight rod as it passes through a nanopore.

Dr. Mardis also said previous efforts to create uniform silicon-based nanopore sensors had been disappointing.

One of the crucial advances needed to improve the quality of DNA analysis is to be able to read longer sequences. Current technology is generally in the range of 30 to 800 nucleotides, while the goal is to be able to read sequences of as long as one million bases, according to Dr. Church, who spoke in July at a forum sponsored by Edge.org, a nonprofit online science forum.

Other approaches to faster, cheaper sequencing include a biological nanopore approach being pursued by Oxford Nanopore Technologies, a start-up based in England, and an electron microscopy-based system being designed by Halcyon Molecular, a low-profile Silicon Valley start-up that has developed a technique for stretching single strands of DNA laid out on a thin carbon film. The company may be able to image strands as long as one million base pairs, said Dr. Church, who is an adviser to the company, and to several others.

“To bring about an era of personalized medicine, it isn’t enough to know the DNA of an average person,” said Gustavo Stolovitzky, an I.B.M. biophysicist, who is one of the researchers who conceived of the I.B.M. project. “As a community, it became clear we need to make efforts to sequence in a way that is fast and cheap.”