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Laying the Groundwork For Data-Driven Science
aarondubrow writes The ability to collect and analyze massive amounts of data is transforming science, industry and everyday life. But what we've seen so far is likely just the tip of the iceberg. As part of an effort to improve the nation's capacity in data science, NSF today announced $31 million in new funding to support 17 innovative projects under the Data Infrastructure Building Blocks (DIBBs) program, including data infrastructure for education, ecology and geophysics. "Each project tests a critical component in a future data ecosystem in conjunction with a research community of users," said said Irene Qualters, division director for Advanced Cyberinfrastructure at NSF. "This assures that solutions will be applied and use-inspired."
If the NSF grant process is like the one for NASA, there's still a little bit of flexibility for the program manager after they've gotten the scores.
I know because I was on a panel that specifically gave two proposals 'poor' reviews (the lowest possible), and the program manager asked us to consider changing it. In this case, he's a rather nice guy, and it may just be that he didn't want to have to write the 'your proposal sucks' letter to them ... but those of us on the panel knew that there is _no_ way for them to fund a 'poor'. They have leeway with any other score, and could give something with a marginal rating some seed money (fund 'em for a year, so they might be able to put in a more competitive bid next round).
We told the program manager that no, we wanted to make sure that there was no possible way that those two proposals could get funded.
Build it, and they will come^Hplain.
The problem with data driven science... is that data isn't evidence.
Correlative statistics are not evidence.
I think you are confusing "evidence" with "proof". Data, and more specifically, the patterns in data, most certainly are evidence. If that were not true, then there would be no reason to even try doing science.
Having data isn't an accomplishment.
Any scientist who has spent years obtaining a hard-won dataset would strongly disagree with you. Consider, for example, the ground-breaking data generated a few years ago by the Human Genome Project, or the current explosion of data about exoplanets. These data most certainly do represent substantial intellectual and technical accomplishments. Now, if what you mean is that simply downloading someone else's data from the Web is not an accomplishment, then I agree with you.
Scientists need to be willing to get their hands dirty and get the data themselves.
I think you will find that, in the hard sciences at least, that's usually how it's done. The researchers who write the papers are usually the same people who were involved in collecting the data. However, for very large-scale studies (e.g., global biodiversity research), there is no way that a single scientist, or even a single research team, could gather all of the necessary data. In these cases, the only way to make the research tractable is to integrate multiple datasets.
Your points about the importance of understanding where the data one uses in a study came from, how they were collected, and any potential biases are all well taken. However, ignoring any of these factors is simply sloppy science, no matter whether the researcher collected the data him or herself, or if someone else collected it.
All science is data driven. Without data there is no hypothesis, and without hypothesis there is nothing to test (falsify). This is just another hype, like nanotechnology or now nanobiotechnology etc. Nearly all molecules are nanoscale: their size is measured in nanometers, and in the same way all science is data driven.
There is nothing wrong with good old "science driven science" where people think, do experiments, and think again.