Freeing and Forgetting Data With Science Commons

blackbearnh writes "Scientific data can be both hard to get and expensive, even if your tax dollars paid for it. And if you do pay the big bucks to a publisher for access to a scientific paper, there's no assurance that you'll be able to read it, unless you've spent your life learning to decipher them. That's the argument that John Wilbanks makes in a recent interview on O'Reilly Radar, describing the problems that have led to the creation of the Science Commons project, which he heads. According to Wilbanks, scientific data should be easy to access, in common formats that make it easy to exchange, and free for use in research. He also wants to see standard licensing models for scientific patents, rather than the individually negotiated ones now that make research based on an existing patent so financially risky." Read on for the rest of blackbearnh's thoughts. "Wilbanks also points of that as the volume of data grows from new projects like the LHC and the new high-resolution cameras that may generate petabytes a day, we'll need to get better at determining what data to keep and what to throw away. We have to figure out how to deal with preservation and federation because our libraries have been able to hold books for hundreds and hundreds and hundreds of years. But persistence on the web is trivial. Right? The assumption is well, if it's meaningful, it'll be in the Google cache or the internet archives. But from a memory perspective, what do we need to keep in science? What matters? Is it the raw data? Is it the processed data? Is it the software used to process the data? Is it the normalized data? Is it the software used to normalize the data? Is it the interpretation of the normalized data? Is it the software we use to interpret the normalization of the data? Is it the operating systems on which all of those ran? What about genome data?'"

Comments

Re:What's most important to keep.

[mako1138]

Let's say the LHC publishes its analysis, and then throws away the data. What happens when five years later it's discovered that a flawed assumption was used in the analysis? Are we going to build another LHC any time soon, to verify the result?

For a billion-dollar experiment like the LHC, that dataset is the prize. The dataset is the whole reason the LHC was built. Physicists will be combing the data for rare events and odd occurrences, many years down the road.

Re:What's most important to keep.

[Mr Z]

With a large and expensive dataset that can be mined many ways, yes, it makes sense to keep the raw data. This is actually pretty similar to the raw datasets that various online providers have published over the years for researchers to datamine. (AOL and Netflix come to mind.) Those data sets are large and hard to reproduce, and lend themselves to multiple experiments.

But, there are other experiments where the experiment is larger than the data, and so keeping the raw data isn't quite so important as documenting the technique and conclusions. The Michelson-Morley interferometer experiments (to detect the 'ether'), the Millikan oil-drop experiment (which demonstrated quantized charges)... for both of these the experiment and technique were larger than the data, so the data collected doesn't matter so much.

Thus, there's no simple "one size fits all" answer.

When it comes to these ginormous data sets that were collected in the absence of any particular experiment or as the side effect of some experiment, their continued existence and maintenance is predicated on future parties constructing and executing experiments against the data. This is where your LHC comment fits.