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Comment by tony_cannistra | Hacker News Reader

I have a PhD in Ecology and a BS in CS. I find the bifurcation portrayed here exaggerated. The best modern ecologists merge rigorous fieldwork with advanced modeling; we need to harness vast, underutilized datasets, not just generate new ones.

The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.

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Seems to me there are potentially opportunities for greater returns to data gathering work as quality data can inform many more papers in the future. How that will work still needs to be brokered…

Absolutely. There are a few excellent projects in this vein, as well - where deeper investment in data gathering, done in ways to optimize its broad use in research, is occurring.

An example is the National Science Foundation NEON project, which is a long-term ecological monitoring initiative with common field methodologies across 81 North American sites. https://www.neonscience.org/

Huh. I weirdly enough have worked with a lot of those sites from the remote sensing side, but never really know what the overall project was. Just "use the NEON sites for examples". I should have looked it up more at the time. Thanks for sharing!

It leads to hoarding data (or artifacts) within labs for exclusive analysis.

This. It's this broken publishing model we have at the moment that is the real problem.

You should be able to publish data as a paper and get academic credit for doing that. Then others can publish analyses of that data, crediting you.

To an extent there are incentive structures that support what you're describing, but the reality is that they just don't hold the same amount of value as "results-based" publishing does.

Ecology PhD turned data scientist, I was looking to respond and you summed up my thoughts really well!

I will add that funding can complicate things a bit, funding sources often get wowed by more "advanced" methods, while the underlying science might be less than stellar. There are important questions that can be answered by small, elegant field studies, and there are questions that require larger datasets and more computation. When we start putting the methodological cart before the scientific horse, that's where we run into problems.

I'd also add that the best scientists I know have, for the duration of their careers, put the question first and pursued methods to fit. I know folks who have the wildest set of skills, from next-gen sequencing to fish tattooing and all sorts of random engineering skills. Willingness to learn new skills in the pursuit of worthwhile questions is one of the hallmarks of a good scientist, in my experience.

100 percent. I'm guilty of doing this wrong in the beginning of my PhD, and it was the biggest hurdle I needed to learn how to overcome. It's easy to try to force problems onto methodologies; it's much harder (and more interesting!) to try to solve real problems with the best available tool for the job.

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