I'm wondering: if I use someone else's data in my paper, should they be co-author? And if someone uses my model in their paper, should I be co-author? What about if all my analyses use brms? Should @paulbuerkner and @mcmc_stan always be co-authors?
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Both are hard, but experiments are riskier: you can spend years breeding and training animals and end up with nothing at all, not even an uninteresting or null result. It would take spectacularly bad planning for that to happen while modeling.
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I don't understand what you all mean when you say this, so I have at least 4 modeling projects over the past decade which are unpublished, some unpublishable. And I know many modelers in similar situations.
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I spent a year and a half training a monkey to perform a fairly complicated behavioral task. It died (of unrelated natural causes) before I had collected more than a handful of cells.
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That’s much different, IMHO, from working on something for a while, deciding its uninteresting, and moving on.
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I didn't say it's uninteresting. I said I can't publish it. Anyway, I don't understand why we have to talk like data collection is harder, riskier, etc. I don't think it's a comparison that makes sense.
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It’s not a total order or contest. I’m trying to explain why experimentalists are often a bit...protective of data—it feels like there’s a lot more room for things to go randomly and catastrophically wrong at any moment, wiping out all our effort so far.
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It would take years and $$$ to recover from a few TB bacteria in the animal house. OTOH, barring a worldwide collapse, I can reboot all of our analysis and modeling stuff with a ‘git clone’ and a coffee.
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Recovering code isn't the same as recovering computational experiments nor the same as publishing them. All I asked is why people think modelers are harming you, it's not zero sum AFAIK, when it's modelers who are at a disadvantage in terms of authorship and recognition?
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