An arrangement that would make sense would be to separate data collection from publication and make the academic success of data collectors predicated on how many publications use their data. That would incentivize data collectors to make their data as widely useful and available as possible, which is very counter to the current situation. I imagine data collection groups talking to a number of data analysis groups and figuring out what to collect that would be useful for the largest number of interesting experiments. This might also make data collected intentionally or accidentally to give a specific outcome less likely.

Unfortunately, it's absurdly hard to change what counts as credit towards a successful academic career, so this seems like a pipe dream. Academia is still waging a very uphill battle to get Research Engineer to be a long-term viable career path, despite the absolutely critical need for software engineering in all areas of modern science.

Cannot agree more. My experience: 50 to 60% of experiments are collecting data, 20 % doing the actual experiment (data processing) and 20% writing the paper (and going through peer review).

Now think about what "collecting the data" means in the medical field:

- Preparing protocols and getting them through ethical review committees

- Finding suitable patients and getting them on board

- 1 on 1 patient consultations, oftentimes multiple of those for each patient and spread through a few years.

I'd say the balance can easily turn to 90% data collection, 10% everything else in many cases...

I don't see how there's any way 90% of the recognition and reward of published research can go to the data collectors over everybody else combined, even in the cases where data collection is really that onerous.

Which I guess is why all efforts to try to split up the work in a more efficient manner have failed and the current model persists. This means that open academic research effectively remains a cottage industry compared to industrial, military, and other research types that can be kept secret.

This is a great suggestion. Data collection is itself a (sometimes big) contribution to science and humanity's state of knowledge, and we should reward it accordingly.

Data collection is more expensive, more time consuming, and usually more tedious than data analysis and paper writing. The people good at collection are not necessarily good at analysis and vice versa. More people enjoy analysis than collection. The results of the analysis are what is useful to the rest of society. Also, the people doing the analysis need to have very detailed input to its collection.

I was hoping stating it all together like that would make a solution more obvious, but I don't think it did. Having researchers commission data collection and every user having to pay would create barriers to entry we don't want.

You can't exactly decide what to reward. A lot of the important aspects of "reward" are implicit, non-legible, unstructured, informal. It's about how the community perceives you. About how many people walk up to you to chat with you at a conference. About how many requests you get to visit another lab. It's about intangible "status" and respect within the community. And it so happens that this informal respect is mostly given to people who make exciting discoveries. You can't top-down force the criteria for this. Data collection doesn't make you an exciting personality to invite to give a keynote speech or something. And those opportunities lead to more visibility, more collaborations, more papers, more grants, more recognition, more fame etc.