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Scientific datasets are riddled with copy-paste errors

sciencedetective.org

31–40 of 68 posts

Re: Scientific datasets are riddled with copy-paste errors

#31

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

> The people who get caught red handed like this are lazy, incompetent and stupid. Being a cheat significantly correlates with laziness, incompetence and stupidity so there are probably very few cheats smart and diligent enough to not get caught.

> Being a cheat significantly correlates with laziness, incompetence and stupidity

There is no evidence for this.

Re: Scientific datasets are riddled with copy-paste errors

#32

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

It's likely easier to publish genuine research if you have knowledge and rigor to properly synthesize the data. It's not as common as you'd think, and a good simulator is easily publishable, at least was some years ago in domains I'm familiar with.

Re: Scientific datasets are riddled with copy-paste errors

#33

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

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Re: Scientific datasets are riddled with copy-paste errors

#34
post #29

It wouldn't surprise me one bit if many of these things can be attributed to Excel usage. I'm a "power user" of excel, and when working on larger problems with tens of sheets, smaller mistakes can easily carry on. Even more so if you're not a proficient user. 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.

Yes, immediately thought the same. CSV alone is a footgun and a half on any computer which doesn't have . as the decimal separator. Let alone column sorting and joining of data.

CSV occupies, even years after moving away from more raw data work, way too much of my brain is still dedicated to "ways of dealing with CSV from random places".

I can already hear people who like CSV coming in now, so to get some of my bottled up anger about CSV out and to forestall the responses I've seen before

* It's not standardised

* Yes I know you found an RFC from long after many generators and parsers were written. It's not a standard, is regularly not followed, doesn't specify allowing UTF-8 (lmao, in 2005 no less) or other character sets as just files. I have learned about many new character sets from submitted data from real users. I have had to split up files written in multiple different character sets because users concatenated files.

* "You can edit it in a text editor" which feels like a monkeys-paw wish "I want to edit the file easily" "Granted - your users can now edit the files easily". Users editing the files in text editors results in broken CSV files because your text editor isn't checking it's standards compliant or typed correctly, and couldn't even if it wanted to.

* Errors are not even detectable in many cases.

* Parsers are often either strict and so fail to deal with real world cases or deal with real world cases but let through broken files.

* Literally no types. Nice date field you have there, shame if someone were to add a mixture of different dd/mm/yy and mm/dd/yy into it.

* You can blame excel for being excel, but at some point if that csv file leaves an automated data handling system and a user can do something to it, it's getting loaded into excel and rewritten out. Say goodbye to prefixed 0s, a variety of gene names, dates and more in a fully unrecoverable fashion.

* "ah just use tabs" no your users will put tabs in. "That's why I use pipes" yes pipes too. I have written code to use actual data separators and actual record separators that exist in ASCII and still users found some way of adding those in mid word in some arbitrary data. The only three places I've ever seen these characters are 1. lists of ascii characters where I found them, 2. my code, 3. this users data. It must have been crafted deliberately to break things.

This, excel and other things are enormous issues. The fact that there any are manual steps along the path for this introduces so many places for errors. People writing things down then entering them into excel/whatever. Moving data between files. You ran some analysis and got graphs, are those the ones in the paper? Are they based on the same datasets? You later updated something, are all the downstream things updated?

This occurs in all kinds of papers, I've seen clear and obvious issues over datasets covering many billions of spending, in aggregate trillions. I can only assume the same is true in many other fields as well as those processes exist there too.

There is so much scope to improve things, and yet so much of this work is done by people who don't know what the options are and often are working late hours in personal time to sort that it's rarely addressed. My wife was still working on papers for a research position she left and was not being paid for any more years after, because the whole process is so slow for research -> publication. What time is there then for learning and designing a better way of tracking and recording data and teaching all the other people how to update & generate stats? I built things which helped but there's only so much of the workflow I could manage.

Re: Scientific datasets are riddled with copy-paste errors

#35

It wouldn't surprise me one bit if many of these things can be attributed to Excel usage. I'm a "power user" of excel, and when working on larger problems with tens of sheets, smaller mistakes can easily carry on. Even more so if you're not a proficient user. 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.

I think that's a little unfair. It really comes down to parsing text and you'll have similar issues even if you use a database or whatever you think the "real" solution is. I have a project I'm working on right now that stores dates, phone numbers, and website links. Cleaning/parsing has been 90% of the work and I still have edge cases that aren't fully solved. Every time I think I'm done, I find something else I haven't thought about. Local AI models have been a huge help though for sanitizing.

Re: Scientific datasets are riddled with copy-paste errors

#36

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

The other explanation is that often these are just mistakes that occur with a team of experts in their field but not data management, without a budget for building a more robust system, manually doing a lot of things with data. It's so easy to copy and paste something into the wrong place, to sort by a field and get things out of order, all kinds of issues like that.

Re: Scientific datasets are riddled with copy-paste errors

#37

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

Reminds me of a job I had doing QA at a pill factory in high school in the 90s. Weight, size, crush tests. After 3 months seeing 99.99% pass rates, I pulled out my statistics book and started generating fake data and just read books. If I could do it at 16...

Re: Scientific datasets are riddled with copy-paste errors

#38
post #34
post #29

Earlier quoted context omitted.

Yes, immediately thought the same. CSV alone is a footgun and a half on any computer which doesn't have . as the decimal separator. Let alone column sorting and joining of data.

CSV occupies, even years after moving away from more raw data work, way too much of my brain is still dedicated to "ways of dealing with CSV from random places". I can already hear people who like CSV coming in now, so to get some of my bottled up anger about CSV out and to forestall the responses I've seen before * It's not standardised * Yes I know you found an RFC from long after many generators and parsers were w…

Completely agree. If CSVs stay read only and are not user-submitted but computer generated they can be okay at best.

Anything else? Nope nope nope!

Re: Scientific datasets are riddled with copy-paste errors

#39

It wouldn't surprise me one bit if many of these things can be attributed to Excel usage. I'm a "power user" of excel, and when working on larger problems with tens of sheets, smaller mistakes can easily carry on. Even more so if you're not a proficient user. 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.

I think that's a little unfair. It really comes down to parsing text and you'll have similar issues even if you use a database or whatever you think the "real" solution is. I have a project I'm working on right now that stores dates, phone numbers, and website links. Cleaning/parsing has been 90% of the work and I still have edge cases that aren't fully solved. Every time I think I'm done, I find something else I hav…

that's a little unfair. It really comes down to parsing text and you'll have similar issues even if you use a database or whatever

Feel free to show a real-world example of a database or whatever that takes the input string "IGF1 SEPT2 PRX3 MARCH1" and writes that into storage as ["IGF1", "2026-09-02", "PRX3", "2026-03-01"].

Also with Excel, an inadvertent click+drag can move data between cells, and since the cells are uniform it's hard to see that anything unintended happened. I've seen people lose files in Windows Explorer the same way: double-click with a shaky hand can easily move a file into a subdirectory.

Re: Scientific datasets are riddled with copy-paste errors

#40
post #36

What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away. The people who get caught red handed like this are lazy, incompetent and stupid. Makes you won…

The other explanation is that often these are just mistakes that occur with a team of experts in their field but not data management, without a budget for building a more robust system, manually doing a lot of things with data. It's so easy to copy and paste something into the wrong place, to sort by a field and get things out of order, all kinds of issues like that.

On the other hand, any time a hypothesis appears significant, the first reaction should be to verify that all the data going into the calculation is correct, rather than just assume it is. In my day-to-day industry experience, significant results come far more often from incorrect data than an actual discovery.
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