10 ms·

Scientific datasets are riddled with copy-paste errors

158 points by jruohonen 6mo ago

steve_adams_86 6mo ago
This is legitimately so challenging to avoid, because loads of scientific processes are—to some degrees or others—bespoke and difficult to fully streamline and introduce efficient, well-structured, comprehensive QA. 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.
- cyanydeez 6mo ago
  just imagine you scan private insustry. this is a generic problem that LLMs wont solve in generative capabilities.
- TheTaytay 6mo ago
  Yes…mistakes are inevitable, and I get not expecting or demanding perfection. But the subtext here is that this is unlikely to be a mistake, and much more likely to be fraud. There are incentives for these spreadsheets having the values that they do, and also there is no conceivable way that the values are correct, and on top of that, the most likely ways to get these values are to copy and paste large amounts of numbers, and even perturb some of them manually. If you see this in accounting,(where there are also mistakes), it’s definitely fraud. (Awww man - we accidentally inflated our revenue and profit to meet expectations by accidentally duplicating numerous revenue lines and no one internally caught it! Dang interns!) If you see it in science, you ask the authors about it and they shrug and mumble a semi plausible explanation if you’re lucky? I can totally imagine a lab tech or grad student making a large copy paste mistake. I can’t imagine them making a series of them in such a way that it bolsters or proves the author’s claim AND goes completely undetected by everyone involved.
  - SubiculumCode 6mo ago
    well, in that case, its bad. Obviously.
  - energy123 6mo ago
    > I can’t imagine them making a series of them in such a way that it bolsters or proves the author’s claim AND goes completely undetected by everyone involved. The small minority of cases that do fit this pattern get selected to be on the front page of HN. So we aren't drawing from a random sample of mistakes. All the selection effects work against the more common categories of mistakes showing up on the HN front page, such as author disinterest, reader disinterest, to rejection by the journal, to a lack of publicity if the null result is published. The more reliable tell that it's a fraud is that the authors didn't respond when the errors were discovered.
- 6mo ago
evolighting 6mo ago
[dead]
aaron695 6mo ago
[dead]
l5870uoo9y 6mo ago
> It could be either a fat-finger mistake when editing the Excel file or deliberate tampering to cover up real data that didn't tell the right story. 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.
- shevy-java 6mo ago
  > AI would do fairly well But AI can also hallucinate data. I am not sure this is an area for an automatic "AI is better than humans". Honesty is very important in science. There were even fake articles generated: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00166-5/abstract https://www.thelancet.com/journals/lancet/article/PIIS0140-6... And some other article I forgot, about arsene or some other ion being used in/for DNA or so. Turned out to be totally fabricated. Right now I don't remember the name of the article; was from some years ago.
- devmor 6mo ago
  I don’t believe fixing this is not something AI would do well. Identifying it is something AI could do well, though. It’s very good at finding patterns - that’s kind of essential to how it works.
- CWwdcdk7h 6mo ago
  > some could be tempted to slightly alter the data We even suspect the G. Mendel of altering his pea data, it is unlikely he got results so close to predicted ratios. So it is not "some", everyone is tempted to "clean up" data, be it by removing outliers or by duplicating "good" rows.
shevy-java 6mo ago
Not only that but sometimes at universities, they use AI to generate descriptions. 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=2026S 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.
coppsilgold 6mo ago
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 wonder what about the ones not getting caught.
- scotty79 6mo ago
  > 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.
  - tpoacher 6mo ago
    Based on my experience of the field, I very much challenge that assumption.
    - coppsilgold 6mo ago
      Indeed, the niches of smart cheats or smart criminals have a lot of room. Because the trajectories to reach that stage without being caught by a legal (good legal work) or moral (good person) attractor are sparse and that makes them somewhat rare. Handwaving correlations between cheating/criminality and most personality/intelligence aspects is an error, not least because there is a selection bias problem (eg. who gets caught).
  - isolli 6mo ago
    The sample of cheaters that we know about is biased towards cheaters who get caught.
    - BobaFloutist 6mo ago
      To those, yes, but also to the weirdly high number of cheaters that can't help bragging to everyone that'll listen about their cheating.
X1a0Ch3n 6mo ago
[dead]
TrackerFF 6mo ago
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.
- jcattle 6mo ago
  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.
  - IanCal 6mo ago
    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.
jcattle 6mo ago
Just a thought: This data engineering can only really occur in sciences with a significant "moat". 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.
- mattkrause 6mo ago
  I used to think that but.... I've reviewed for a few "replication tracks" at ML Conferences and there are a surprising number of reports where people are simply unable to replicate published results. The reasons are all over the map: sometimes the original authors' code just needs to be fixed (new libraries, different environments), but other results simply don't seem to hold up.
kriro 6mo ago
Innocent mistakes and frustrating back and forth are also very common, especially for interdisciplinary teams. The mismatch in tooling and workflows and manual copy & paste conversion is a thing to behold. Add multiple countries and Excel to the mix (dot vs comma, formulas being language specific), maybe have a couple of Chinese, Japanese, Russian or Arabic speking researchers in the group for some extra UTF-8 magic. Line endings on Linux vs. OSX vs. Windows.
cowartc 6mo ago
The real rate is certainly higher because this only catches the laziest form of error. The harder problem is the same one we see in production ML. Your pipeline can produce confident results on garbage data and nothing in the system tells you. The first step isn't better models or better tools, its profiling the input before you trust anything downstream of it.
hackeraccount 6mo ago
I always think about that Simpsons quote about alcohol "The cause of... and solution to... all of life's problems" That's how I feel about copy-paste. Nothing is ever so janus-faced.
mmooss 6mo ago
Note this exchange with the OP author: > [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.

158 points by jruohonen 6mo ago

This is legitimately so challenging to avoid, because loads of scientific processes are—to some degrees or others—bespoke and difficult to fully streamline and introduce efficient, well-structured, comprehensive QA. 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.

cyanydeez 6mo ago
just imagine you scan private insustry. this is a generic problem that LLMs wont solve in generative capabilities.
TheTaytay 6mo ago
Yes…mistakes are inevitable, and I get not expecting or demanding perfection. But the subtext here is that this is unlikely to be a mistake, and much more likely to be fraud. There are incentives for these spreadsheets having the values that they do, and also there is no conceivable way that the values are correct, and on top of that, the most likely ways to get these values are to copy and paste large amounts of numbers, and even perturb some of them manually. If you see this in accounting,(where there are also mistakes), it’s definitely fraud. (Awww man - we accidentally inflated our revenue and profit to meet expectations by accidentally duplicating numerous revenue lines and no one internally caught it! Dang interns!) If you see it in science, you ask the authors about it and they shrug and mumble a semi plausible explanation if you’re lucky? I can totally imagine a lab tech or grad student making a large copy paste mistake. I can’t imagine them making a series of them in such a way that it bolsters or proves the author’s claim AND goes completely undetected by everyone involved.
- SubiculumCode 6mo ago
  well, in that case, its bad. Obviously.
- energy123 6mo ago
  > I can’t imagine them making a series of them in such a way that it bolsters or proves the author’s claim AND goes completely undetected by everyone involved. The small minority of cases that do fit this pattern get selected to be on the front page of HN. So we aren't drawing from a random sample of mistakes. All the selection effects work against the more common categories of mistakes showing up on the HN front page, such as author disinterest, reader disinterest, to rejection by the journal, to a lack of publicity if the null result is published. The more reliable tell that it's a fraud is that the authors didn't respond when the errors were discovered.
6mo ago

evolighting 6mo ago

[dead]

aaron695 6mo ago

[dead]

l5870uoo9y 6mo ago

> It could be either a fat-finger mistake when editing the Excel file or deliberate tampering to cover up real data that didn't tell the right story. 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.

shevy-java 6mo ago
> AI would do fairly well But AI can also hallucinate data. I am not sure this is an area for an automatic "AI is better than humans". Honesty is very important in science. There were even fake articles generated: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00166-5/abstract https://www.thelancet.com/journals/lancet/article/PIIS0140-6... And some other article I forgot, about arsene or some other ion being used in/for DNA or so. Turned out to be totally fabricated. Right now I don't remember the name of the article; was from some years ago.
devmor 6mo ago
I don’t believe fixing this is not something AI would do well. Identifying it is something AI could do well, though. It’s very good at finding patterns - that’s kind of essential to how it works.
CWwdcdk7h 6mo ago
> some could be tempted to slightly alter the data We even suspect the G. Mendel of altering his pea data, it is unlikely he got results so close to predicted ratios. So it is not "some", everyone is tempted to "clean up" data, be it by removing outliers or by duplicating "good" rows.

shevy-java 6mo ago

Not only that but sometimes at universities, they use AI to generate descriptions. 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=2026S 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.

coppsilgold 6mo ago

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 wonder what about the ones not getting caught.

scotty79 6mo ago
> 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.
- tpoacher 6mo ago
  Based on my experience of the field, I very much challenge that assumption.
  - coppsilgold 6mo ago
    Indeed, the niches of smart cheats or smart criminals have a lot of room. Because the trajectories to reach that stage without being caught by a legal (good legal work) or moral (good person) attractor are sparse and that makes them somewhat rare. Handwaving correlations between cheating/criminality and most personality/intelligence aspects is an error, not least because there is a selection bias problem (eg. who gets caught).
- isolli 6mo ago
  The sample of cheaters that we know about is biased towards cheaters who get caught.
  - BobaFloutist 6mo ago
    To those, yes, but also to the weirdly high number of cheaters that can't help bragging to everyone that'll listen about their cheating.

X1a0Ch3n 6mo ago

[dead]

TrackerFF 6mo ago

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.

jcattle 6mo ago
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.
- IanCal 6mo ago
  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.

jcattle 6mo ago

Just a thought: This data engineering can only really occur in sciences with a significant "moat". 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.

mattkrause 6mo ago
I used to think that but.... I've reviewed for a few "replication tracks" at ML Conferences and there are a surprising number of reports where people are simply unable to replicate published results. The reasons are all over the map: sometimes the original authors' code just needs to be fixed (new libraries, different environments), but other results simply don't seem to hold up.

kriro 6mo ago

Innocent mistakes and frustrating back and forth are also very common, especially for interdisciplinary teams. The mismatch in tooling and workflows and manual copy & paste conversion is a thing to behold. Add multiple countries and Excel to the mix (dot vs comma, formulas being language specific), maybe have a couple of Chinese, Japanese, Russian or Arabic speking researchers in the group for some extra UTF-8 magic. Line endings on Linux vs. OSX vs. Windows.

cowartc 6mo ago

The real rate is certainly higher because this only catches the laziest form of error. The harder problem is the same one we see in production ML. Your pipeline can produce confident results on garbage data and nothing in the system tells you. The first step isn't better models or better tools, its profiling the input before you trust anything downstream of it.

hackeraccount 6mo ago

I always think about that Simpsons quote about alcohol "The cause of... and solution to... all of life's problems" That's how I feel about copy-paste. Nothing is ever so janus-faced.

mmooss 6mo ago

Note this exchange with the OP author: > [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.