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Scientist rarely keep the data secret. It's the basis of peer review. There's a reason why open access journals (PLoS One) are getting so popular, scientists wa
by olihb 15y ago
Scientist rarely keep the data secret. It's the basis of peer review. There's a reason why open access journals (PLoS One) are getting so popular, scientists want to be known and publish. In my experience, ego is a currency much more valuable than money in academic circles.
- Maro 15y agoIn Physics, published raw data is not the basis of peer review. We're not there yet.
- xbryanx 15y agoThe vast majority of the world's petroleum geologists keep their data secret.
- cpeterso 15y agoBut are they "doing science" or just doing a job?
- lutorm 15y agoPresumably, they are in private employment?
- gnosis 15y agoAre they concerned with the possibility of data falsification? This has happened in other fields.
- nl 15y agoIn related news most gold prospectors keep their gold strikes secret until they lodge the claim....
- tjic 15y ago> Scientist rarely keep the data secret. It's the basis of peer review. In a better world, that would be true. In this world, it's not. Most of the climate science data that Phil Jones and others have worked with, and most of their models are secret. It is THIS that makes me keep at least one ear open for the "anti-" side in the climate debate. Real science SHOULD be conducted out in the open.
- deleted 15y ago[deleted]
- Tycho 15y agoThe models are secret... wait, seriously??
- Retric 15y agoShort answer is no, long answer is sort of. The way in which data / code is released is a complex issue. However, if your willing to wait a reasonable amount of time AND pay for the costs involved you can get just about everything produced. Still, in the larger context validating someone's data is basically worthless activity. It's far more important to go out and collect new data, build a new model, run a new simulation, and report those results than it is to try and shortcut the process. The data deluge is making it way to easy for people to validate bad science using the same bad data / model as someone else. Fundamental assumptions in statistics break down when you use the same 10 coin flips to substitute for the 10 million coin flips that cost more than your willing to pay for. PS: The failure to collect significant quantity's of new data is a hallmark of fringe / bad science. If you really think global warming is a joke or you can be more healthy by eating coco puffs then go collect that data and do your analysis and see if someone can poke holes in your process.
- Tycho 15y agoWell the model is the thing that tells us what effect a rise in temperature/CO2 would actually have, no? Seems common sense to me that this should be open so that people can judge it. You could program a model to say anything regardless of the data.
- Retric 15y agoWhen it comes to global worming you have two models one of which is what happens after a fraction of a second when you have various levels of CO2 in a column of air over in daylight or darkness. That's a well understood thermodynamics problem and rarely debated, but you will have little trouble finding out the specific details on this issue. The larger model is based around how that the extra heat maintained gets distributed around the world and everyone knows this is far less accurate. The people building this are well aware it's highly limited but by trying to balance the errors in both direction they hope to create a good estimate. The problem is when you start tweaking the heat distribution equations it's easy to find errors in every direction because they are greatly simplified so it's easy for someone to introduce systematic errors by only correcting issues in one direction. Which is why you provide a high level description but when you release your source code you open yourself up to a lot of fruitless debate. Sort of like someone saying based on your methodology the rocks are really 1/1,000,000th a year older which means your science is a joke and less valid than my consistent theory that god created the world 8 weeks ago. But, if you actually build a model from scratch under similar assumptions it's going to have similar outputs and error bars or look really biased.