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I am an engineer working in molecular neuroscience field, hence getting exposure in both solving practical problems (how to image fish brain) and molecular bio questions (do neurons X connect to neurons Y?). Our field spend a lot of time highlighting reproducibility crisis but I don't seem to be able to find references or writing on the following problem of resources allocation:

  • funding experiments vs
  • funding better experimental design and data shepherding

As I see it today (from the perspective of R1 US institutions) most resources are given to independent professors who then give it to students to work on either novel problems (which leads to waste of time because students are not professionals yet) or well-established problems (which leads to producing a lot of new data about old molecule X that nobody cares about). Some are arguing that shepherding data is a good investment, but it is rare.

Which are the philosophers or theories discuss aspects of experimental design, particularly in regards to value-theoretic or economic considerations?

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  • This is quite a narrow focus. Are you sure there's a subdiscipline that addresses the narrow interpretation? Maybe you can also attack this in another forum here on SE? Perhaps Data Science? I'm a business analyst, and this seems like it might be an IT management question in terms of ROI? – J D Nov 9 '20 at 5:39
  • Edited to avoid 'off-topic objection'. – J D Nov 9 '20 at 5:40
  • @JD I think that's a good suggestion to look into IT world. In some sense, I am asking about "philosophy of balancing development versus design and paying technical debt" or along those lines. We discuss that in IT a lot, but not in science IMHO – aaaaa says reinstate Monica Nov 9 '20 at 14:56
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Answer

I'm no specialist, but I'll give it a stab. First, you might be interested in SEP: Measurement in Science. Some other possible resources that you can follow up with are SEP: Experiment in Biology, PhilPapers: Experimental Philosophy of Science, and Blackwell's Companion to the Philosophy of Science. I have an older edition, but the article Experiment by David C. Gooding contains the following references:

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