It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.
I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...
Loved it.
Mawson had the field trip of a lifetime (for his two mates, it was the end of their lifetime!) and it didn't end his bug for the outside. I don't think he was made to sit in a lab.
I'd say your Mali trip was the same: it hasn't made you want to stop being outside from the sound of it.
I've "retired" to argriculture tech and labour support for W.Australian family grain production. We've almost finished harvest and I've been doing a lot of scrolling and posting here while hanging about near idle "on call" fire tenders (we had a hundred fires, mostly from lightening strikes, in a single week just recently)
* https://www.watoday.com.au/national/western-australia/wa-bus...
* https://www.youtube.com/watch?v=yulvSvtFVqc
^ Further south than I'm based, and a header fire, not a strike. Okay when caught early - life and town threatening if not.
Oh, yeah: Songhoy Blues: https://www.youtube.com/watch?v=BOValSt7YOY
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.
What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.
Finding creative ways to use existing data doesn't change that.
The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".
> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.
It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.
There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."
That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.
It’s so important that we write these down, so when these people have forgotten why they’re not making any progress and they’re searching for answers, they’ll find what we wrote down and say “ohhh, we had too much hubris thought we were smarter than everyone else and didn’t listen to how important actually going outside is.”
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.
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/
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.
90% of the time it is spend analyzing data or writing up proposals/grants/papers. i don't think AI was the turning point.
I always felt like one of the primary motivations to pursue science was being able to bail out of the office for the entire summer for "field work"...
Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.