One of my first jobs as an analyst was to clean up messy spreadsheets made by people, even very senior employees, who never bothered to learn excel properly.

The people who get caught red handed like this are lazy, incompetent and stupid. Makes you wonder what about the ones not getting caught.

Expensive tools, expensive test setups, live, gene-altered animals, etc.

In fields such as deep learning or other more digital fields (my field is using a lot of freely available satellite data) replication is often cheaper and actual application of research outcomes is a lot more common.

A LOT of labour goes into making it work. Most scientists I know and work with are very diligent people who care a lot about the outputs being as correct as possible, but wow, their workflows aren't great.

My job is to try and address this in whatever ways are practical for the data and the people doing the science, and it's kind of like Saas in that you think it should be easy enough to spot problems, solve them, and carry on/become a billionaire, but... The world is much more complicated than that, and it's easier to fail in this endeavour than it is to break even.

The classic "DropBox is just rsync" or "I could build Airbnb in a weekend" sentiments have their commonalities and counterparts in science, and the reality is similarly defeating and punishing on both sides. Making science go faster while maintaining correctness is exceedingly difficult. There are so many moving parts. So many disparate participants who are wildly technical and capable, or brilliant at studying bacteria in starfish yet terrified to run a command in a terminal. Your user base has virtually nothing in common in terms of ability and willingness to do anything other than get their own work done. It's brutal.

So, I sympathize with the authors of these papers and I hope readers don't assume they're bad at what they do or that it's done in bad faith. It's genuinely difficult.

An anecdote: I created a tool for validating biodiversity data against a specification called Darwin Core. Initially our published data was failing to validate so much that I thought I'd made the tool wrong. Rather, the spec is so complex and vast that the people I work with were unable to manage to get valid data into the public repositories. And yet! They were able to publish, because the public repositories' own validation is... Invalid. That's the state of things.

Granted, the data is still correct enough to be useful, and the errors don't cause the results to indicate anything that they shouldn't. It's more like minor metadata issues, failures to maintain referential integrity across different datasets, etc. But it's a very real, very difficult problem.

Science isn't easy at all. So many hoops to jump through, so much rigor, so much data. Mistakes are inevitable.

I can easily imagine after spending years or decades devoted to discovering a scientific breakthrough that some could be tempted to slightly alter the data. I believe there was some scandal about this a few years back with climate data. Fixing this is however something that AI would do fairly well.

That's how I feel about copy-paste. Nothing is ever so janus-faced.

> [Paper author:] Englund's claim that the Model 680 "records raw light measurements as it sees them without any post-processing" is incorrect. [...] It converts analog intensity signals to absorbance values using Beer's law, rounding results to the nearest 0.001 OD.

> [OP author:] Ok, that was incorrect on my part and shows my lack of knowledge about photometers. I was attempting to paraphrase an email from Bio-rad where they said: “The system [Bio-rad 680] was very basic and recorded OD as seen, it did not do any onboard manipulation of the data, it gave raw results for the user to interpret.”

For someone focusing on accuracy and sloppy work, that's a significant problem. How much of the OP is based on their "lack of knowledge" and reckless application of ignorance?

For the first issue, the section titled "Verdict" is followed by,

> the authors have so far not responded.

I would not issue verdicts without making sure I understand the other's perspective (note that it is a requirement in courts of law). That applies especially when I lack direct knowledge or expertise.

When I haven't taken that step, I've learned a thousand times that my certainty usually reflects my lack of imagination, knowledge, or careful thought. Even when I'm 'right', I'm 'wrong': even if the 'verdict' is the same, the truth differs from what I was so certain about. I used to express my certainty prematurely; now I know to keep it to myself until I know what I'm talking about, which frequently saves me from major and/or embarassing errors.

Recent example I found (semi-accidentally, I was only looking for microscopy related courses):

https://ufind.univie.ac.at/de/course.html?lv=301053&semester...

At the end of the description it has:

"Übersetzt mit DeepL.com (kostenlose Version)"

This means, in english, "translated via DeepL.com (free version)" aka the not-paid-for version. What I found baffling is that even for a single paragraph, some are too lazy to write stuff on their own - or, at the least, remove that disclaimer. Other people also pointed out that they saw this in autogenerated brochures/booklets, in the USA for instance; think I saw this about 3 months ago but I forgot which booklet it was. But the whole booklet was AI-autogenerated. To me this is all spam. I can not want to be bothered to read AI "content" when it is really just glorified slop-spam.