I did marine biology field work almost 5 decades ago as a lowly junior lab tech. Work always has downsides, for me it was not really the Scots winter, cold feed, chapped hands, the land-rover having to reverse up steep icy roads to get back from the harbourside: it was washing the glassware and dealing with sodium hydroxide weighing (it absorbs moisture from the air so its a fools game). But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
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.
I've always enjoyed field work, much of the code I've written has been well outside of any office.
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 ...
Douglas Mawson ("home of the blizzard") had a rich life after Antartica as a field geologist, exploring the flinders ranges. He found a radium mine and was shipping ore to Europe for a while. He led students on field trips, one of whom, Reg Sprigg caught the bug, explored as much as he could, persuaded the Australian petro and uranium sector to fund pushing tracks into his favourite spots, and then converted the landscape into the Arkaroola Wilderness Sanctuary. I got to spend a night there last year on a flight safari to Lake Eyre, it's an amazing place, dark sky with a big telescope, wildlife, well worth a visit.
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)
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearence - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
I also have spent quite awhile as an exploration geophysicist. I miss it! I work purely with satellite data now, which is decidedly less tangible.
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.
I worked in a research lab like 30 years ago and it was all on computers. We had loads of generic data collected by someone somewhere and we just looked for patterns to infer sequences. I wrote Java and C++ and got my name on a paper. There were maybe a dozen scientists in the lab and they were all just coders with expertise in one or another field of biology. It was called a "dry lab".
As a kid I had problems with Foundation (Asimov) premise that loss of scientific knowledge can be the trigger not just the result of civilizational collapse - not anymore.
Machine learning and data science are not new things in science. It's great that we have the ability to share and work with existing data sets, collect data remotely with sensors, and build software to create models, but we'll always need people to go out and collect updated data, place censors and verify that what models predict is actually happening.
> 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.
I'm a scientist in industry. It's remarkable how many smart people think that science can be done without data. I've heard managers ask: "Why do we need to gather data? Can't we just model it? The customer doesn't want to see data. They just want an answer."
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.
People that I'm currently working with are using AI to try to extract data from the text of published papers, getting access to raw data sets doesn't seem to be a priority.
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.
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'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 clearence - 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.
> 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.
90% of the time it is spend analyzing data or writing up proposals/grants/papers. i don't think AI was the turning point.
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.
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"...