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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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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Why do non-modelling people feel the need for example to tell me that modelling is easy? When they are probably from the same group who say modelling is hard? Why? I don't say anything is easy about data collection.
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Again, it’s not the difficulty level. BOTH ARE HARD. It’s the level of control. If your code is hard to debug, that’s on you—you wrote it! If you have a hard time publishing something, you picked the topic. Exp have these problems too, obviously.
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