[HN Gopher] More Fakery ___________________________________________________________________ More Fakery Author : rossdavidh Score : 116 points Date : 2022-04-11 13:07 UTC (9 hours ago) (HTM) web link (www.science.org) (TXT) w3m dump (www.science.org) | qchris wrote: | My favorite article on this topic is "Escaping science's paradox" | by Stuart Buck[1]. I'm, in particular, interested by the idea (at | least within the United States) of "red teaming" science. This | would involve having an independent agency funding attempts to | replicate (and to find/publish flaws in) NSF- or NIH-funded | projects, and publishing those. Ideally, the history of | replication for authors' papers could then be part of the | criteria for receiving funding for more novel research in the | future. | | Obviously, there's a few fields where this might not work (you | can't just create a second Large Hadron Collider for validation), | but in areas from sociology to organic chemistry to environmental | science, I think there's a lot of promise in that method for | helping to re-align incentives around producing solid, replicable | research. | | [1] https://www.worksinprogress.co/issue/escaping-sciences- | parad... | a-dub wrote: | it's the same as everything. there should be more and easier | money for the less rewarding task of verification/replication. | some people actually enjoy this sort of work just as much as | some people enjoy being on the bleeding edge... but there are | probably less of them. | | where it would get complicated is also the same as everything. | when the verification effort neither supports nor refutes the | original one. many would argue that it means it wasn't done | right, but lots of things aren't done right in life. | | then there can be the triple replication revolution! so it | goes... | Beldin wrote: | Funding replication is a great idea, but cannot solve this. | It would require roughly add much funding add now goes to | science merely to replicate results produced now. That still | leaves a rather hefty backlog. Moreover, the pace with which | scientific output doubles is increasing. From top of my head | out would be below a decade nowadays. Even if 90% of those | would not need replication (new algorithms that work), then | merely keeping pace would basically require 1 in 10 | institutions to devote itself fully to replication studies. | Even then we'd need more capacity to look at previous results | - that 10% is fully needed to investigate new results. | | Note that this is optimistic: I'd expect the percentage of | publications where a reproducibility study makes sense to be | above 50%. | qchris wrote: | This isn't intended as snarky, but I don't understand what | "it's the same as everything" is supposed to mean. What is | "it"? What is "everything"? Why are they the same? | | I'd also argue that your reduction of this problem sort of | misses the point. One of the big problems with the way that | studies are done is not that replication efforts aren't | conclusive (it's very difficult to prove something doesn't | exist), it's that a) non-replicable studies are generally | considered as valuable as replicable ones, and b) as a | result, it's extremely difficult to replicate many studies to | begin with, because there's no incentive to take the time to | make it possible. Even if the end result of a replication | paper is "we couldn't produce the same results", the people | working on it can say "this author's experiments were | exceedingly difficult to even try to reproduce," or | conversely "we didn't get the same results, but their data | collection methods and analysis code were well-documented and | accessible." That has a lot of value! | | If you tried doing triple replication for every paper, I | agree that maybe wouldn't be the best use of resources. But | the current state of affairs is so bad that a well-organized | drive to create single-attempt replication on a fraction of | publicly-funded projects has the potential to be a | significant driver of change. | a-dub wrote: | "the same as everything" is an observation that often times | verification/correctness/accuracy efforts are tossed aside | in favor of new development and this is a truism across | many fields. in science you see this as funding being | committed to shiny new nature and science cover stories, | with replication being left as an afterthought. in software | you see this as heavy commitments to new features that | drive revenue, with security/compliance/architecture and qa | remaining underfunded and less respected. (until, of | course, the problems that result from underfunding them | make themselves apparent). | 0x0203 wrote: | One thing I'd like to see is a requirement that for all | government funded research, a certain percentage of that | funding, say 30%, must go toward replicating other publicly | funded research that has had less than 2 independent and non- | affiliated labs replicate. Any original research couldn't be | published until at least two independent and non-affiliated | labs replicate based on the submitted paper and report on the | results that can then be included with the original research. | I'd like to see this across all of academia, but I imagine | there are enough challenges with enforcing this in a productive | manner already that doing it across all research becomes both | impractical and difficult to prevent abuse. But at least with | public funds, it would be nice to put in some checks to reduce | the amount of fraudulent or sloppy research that tax payers pay | for. | the_snooze wrote: | I should point out that the notion of "replication" can often | be way more difficult and nuanced than people expect. For | one, what is the scope of the replication? Would it be simply | to re-run the analysis on the data and make sure the math | checks out? Or would it be to re-collect the data according | to the methods described by the original researchers? | | The former is pretty easy, but only catches errors in the | analysis phase (i.e., the data itself could be flawed). The | latter is very comprehensive, but you essentially have to | double up the effort on re-doing the study---which may not | always be possible if you're studying a moving target (e.g., | how the original SARS-CoV-2 variant spread through the | initial set of hosts). | OrderlyTiamat wrote: | > re-run the analysis on the data and make sure the math | checks out? | | That isn't a replication in any meaningful sense. But a | replication can certainly take many forms. An exact | replication is one, another could be to do a conceptual | replication, so studying the same effect but with a | different design, or combining these with a new analysis | pooling the data from both the study and the new study with | (possibly) improved statistical analysis. | epgui wrote: | Here's an even easier set of requirement to simplify the | first case: | | - Require all research to publish their source code. | | - Require all research to publish their raw data minus | "PII". | | * Note: I use "PII" here with the intention of it taking | the most liberal meaning possible, where privacy trumps | transparency absolutely and where de-anonymization is | impossible. This would rule out a lot of data, and | personally I think we could take a more balanced approach, | but even this minimalist approach would be a vast | improvement on the current situation. | bjelkeman-again wrote: | When I learned at university that not all published | research, especially government funded, didn't do this | already I was dumbfounded. | epgui wrote: | "Not all" is a big understatement... I would estimate | that less than 0.00001% of published research does this. | Every time I talk about this to someone (colleagues in | adjacent fields, PIs...), they seem to give zero pucks. | It's really mind-boggling. | mike_hearn wrote: | Be aware that despite how much focus replicability gets, it's | only one of many things that goes wrong with research papers. | Even if you somehow waved a magic wand and fixed | replicability perfectly tomorrow, entire academic fields | would still be worthless and misleading. | | How can replicable research go wrong? Here's just a fraction | of the things I've seen reading papers: | | 1. Logic errors. So many logic errors. Replicating something | that doesn't make sense leaves you with two things that don't | make sense: a waste of time and money. | | 2. Tiny effect sizes. Often an effect will "replicate" but | with a smaller effect than the one claimed; is this a | successful replication or not? | | 3. Intellectual fraud. Often this works by taking a normal | English term and then at the start of your paper giving it an | incorrect definition. Again this will replicate just fine but | the result is still misinformation. | | 4. Incoherent concepts. What _exactly_ does R0 mean in | epidemiology and _precisely_ how is it determined? You can | replicate the calculations that are used but you won 't be | calculating what you think you are. | | 5. A lot of research isn't experimental, it's purely | observational. You can't go back and re-observe the things | being studied, only re-analyze the data they originally | collected. Does this count? | | 6. Incredibly obvious findings. Wealthy parents have more | successful children, etc. It'll replicate all right but so | what? Why are taxpayers being made to fund this stuff? | | 7. Fraudulent practices that are nonetheless normalized | within a field. The article complains about scientists | Photoshopping western blots (a type of artifact produced in | biology experiments). That's because editing your data in | ways that make it fit your theory is universally understood | to be fraud ... except in climatology, where scientists have | developed a habit of constantly rewriting the databases that | contain historical temperature records. And by "historical" | we mean "last year" here, not 1000 years ago. These edits | always make global warming more pronounced, and sometimes | actually create warming trends where previously there were | none (e.g. [1]). Needless to say climatologists don't | consider this fraud. It means if you're trying to replicate a | claim from climatology, even an apparently factual claim | about a certain fixed year, you may run into the problem that | it was "true" at the time it was made and may even have been | replicated, but is now "false" because the data has been | edited since. | | Epidemiology has a somewhat similar problem - they don't | consider deterministic models to be important, i.e. it may be | impossible to get the same numbers out of a model as a paper | presents, even if you give it identical inputs, due to race | conditions/memory corruption bugs in the code. They do _not_ | consider this a problem and will claim it doesn 't matter | because the model uses a PRNG somewhere, or that they | "replicated" the model outputs because they got numbers only | 25% different. | | What does it even mean to say a claim does or does not | replicate, in fields like these? | | All this takes place in an environment of near total | institutional indifference. Paper replicates? Great. Nobody | cares, because they all assumed it would. Paper doesn't | replicate, or has methodological errors making replication | pointless? Nobody cares about that either. | | Your proposal suggests blocking publication until replication | is done by independent labs. That won't work, because even if | you found some way to actually enforce that (not all grants | come from the government!), you'll just end up with lots of | papers that can be replicated but are still nonsensical for | other reasons. | | [1] https://nature.com/articles/nature.2015.17700 | Beldin wrote: | One problem is that the amount of scientific output is | increasing at an increasing rate. | | This means that the vast, vary majority of works will never be | considered for replication - even with a dedicated replication | institute. So for most applicants, the amount of replicated | results will be 0. | bee_rider wrote: | Being on the science red team could also be really cool and | fun. Since the goal is to explore the type of error or lie that | gets through reliably, put new scientists on a team with some | old greybeard, let's pass along that hard earned "how to screw | up cleverly" experience. | JacobThreeThree wrote: | >Being on the science red team could also be really cool and | fun. | | I think it depends on what you're investigating, and how much | is at stake. I doubt it would be much fun to be put on a | corporate hit list. | | >The court was told that James Fries, professor of medicine | at Stanford University, wrote to the then Merck head Ray | Gilmartin in October 2000 to complain about the treatment of | some of his researchers who had criticised the drug. | | >"Even worse were allegations of Merck damage control by | intimidation," he wrote, ... "This has happened to at least | eight (clinical) investigators ... I suppose I was mildly | threatened myself but I never have spoken or written on these | issues." | | https://www.cbsnews.com/news/merck-created-hit-list-to- | destr... | mike_hearn wrote: | Talk to people who have actually done it. Not one will tell | you it's cool or fun. Here's how science red teaming actually | goes: | | 1. You download a paper and read it. It's got major, obvious | problems that look suspiciously like they might be | deliberate. | | 2. You report the problems to the authors. They never reply. | | 3. You report the problems to the journals. They never reply. | | 4. You report the problems to the university where those | people work. They never reply. | | 5. Months have passed, you're tired of this and besides by | now the same team has published 3 more papers all of which | are also flawed. So you start hunting around for people who | _will_ reply, and eventually you find some people who run | websites where bad science is discussed. They do reply and | even publish an article you wrote about what is going wrong | in science, but it 's the wrong sort of site so nobody who | can do anything about the problem is reading it. | | 6. In addition if you red-teamed the wrong field, you get | booted off Twitter for "spreading misinformation" and the | press describe you as a right wing science denier. Nobody has | ever asked you what your politics are and you're not denying | science, you're denying pseudo-science in an effort to make | actual science better, but none of that matters. | | 7. You realize that this is a pointless endeavour. The people | you hoped would welcome your "red teaming" are actually | committed to defending the institutions regardless of merit, | and the people who actually do welcome it are all | ideologically persona non grata in the academic world - even | inviting them to give a talk risks your cancellation. The | End. | | An essay that explores this problem from the perspective of | psychology reform can be found here: | | https://www.psychologytoday.com/us/blog/how-do-you- | know/2021... | mherdeg wrote: | I took a science journalism class in college where our | instructor had us read a paper and then write the news story | that explained what was interesting about the result. | | "You all got it wrong," he said, "the news is not that Amy | Wagers could not make things work with mouse stem cells the way | this prior paper said this one time. The news is that Wagers- | ize is becoming a verb which means 'to disprove an amazing | result after attempting to replicate it'. The lab has Wageres- | ed another pluripotent stem cell result. The news is about how | often this happens and what it means for this kind of science." | | This class was in 2006 and this later profile in 2008 seemed to | bear things out the way he said: | https://news.harvard.edu/gazette/story/2008/07/amy-wagers-fo... | j7ake wrote: | As long as there is some quantitative criteria on which jobs and | promotions depend, there will be people gaming the system. | | One solution is to couple this quantitative criteria with | independent committees that assess people beyond the metrics, but | that requires a lot of human effort and not scalable. | | Assessing people in ways that don't scale seem to be the way to | avoid this gaming trap in academia. | cycomanic wrote: | I'd argue that it's not just that the metrics don't scale but | the problem is that we are trying to find quantitative metrics | for something that can't be easily quantified. The worst | outcome is not even the forgeries and fakes as in this article, | but more that even the vast majority of ethical academics are | being pushed into a direction that is detrimental to longterm | scientific progress, in particular short term outcomes instead | of longterm progress. | lutorm wrote: | _even the vast majority of ethical academics are being pushed | into a direction that is detrimental to longterm scientific | progress, in particular short term outcomes instead of | longterm progress_ | | I agree. The egregious fraud is just the high-sigma wing. | It's a symptom, but the real problem is how it affects the | majority. | rossdavidh wrote: | Interesting point; it is much like the problems of trying to | assess programmer productivity. | _tom_ wrote: | I was thinking it's much like Google trying to deal with SEO. | Most people optimize for high google ranking, not quality | content. | | Google periodically changes the evaluation, in theory to | reward good content and penalize bad, but people still try to | game the system, rather than improving content. | _tom_ wrote: | And non-quantitive evaluations are prone to favoritism and | prejudice. AKA people gaming the system. | | I doubt there's an easy answer. | | Trying to better align the short term objectives with longer | term ones could help, but that just makes it harder to game, | doesn't eliminate it. | j7ake wrote: | It's why one needs both. You need both undeniable | productivity by quantitative metrics, as well as glowing | reviews from independent panels that are not influenced by | favoritism (almost like an audit). | epolanski wrote: | Data fabrication is sadly the norm nowadays. | | I was a chemistry researcher working on renewables, and during my | master's thesis 9 months were spent validating fake results (from | a publication of a scientist who worked in our group moreover). | some_random wrote: | It's crazy to me that academic fraud isn't a more pressing | concern to society in general and academia in particular. The | scientific process as currently implemented is broken across | every single discipline. Even subjects like CS that should in | theory be trivially reproducible, are rarely so. The reproduction | crisis is still going on, but only nerds like us care. | derbOac wrote: | There are many causes of the lack of concern, but I think at | the heart the problem, at least in the US, is that science has | become politicized such that attempts at reform are | mischaracterized for political gain. There's also a bit of | ignorance in the general public, but that's only part of it. | | For example, if some on the right suggest some difficult but | needed reforms, it tends to be spun as an attack on science. Or | complaints that trivial projects are being overfunded get | misinterpreted by the right and they try to make an example of | the wrong studies for the wrong reasons. | | The pandemic was a good example of this in my mind, in that I | think there were some serious systematic problems in academics | and healthcare that were laid brutally bare, and many people | suffered or died as a result. But then the whole thing got | misidentified and sucked into the political vortex and all you | end up with are hearings about how to rehabilitate the CDC, as | if that is the problem and not a symptom of even bigger | problems. | | I still think there are ways for things to change, but the most | likely of them involve unnecessary suffering and chaos. | N1H1L wrote: | I can give a different perspective. It is not because of | politicization IMO - at least not in the hard sciences. The | problem comes from way up, from Congress because the | immediate impact of science is not obvious. Especially, for | basic sciences the impact takes decades to be really felt. | | But then how do you do promotions? How do you judge output? | Worse still, how does US Congress justify spending taxpayer | dollars. Rather than acknowledging that any short term | measurement of the quality of science is a fool's errand, we | have doubled down on meaningless metrics like impact factors | and h-indices. And this is what we have as a result. | ArnoVW wrote: | Aside from reproducibility issues in ML, what sort of issue did | you have in mind in CS? | | Most CS work is 90% maths, I don't see how you can have | reproducibility issues? | the_snooze wrote: | Take, for example, network measurement research: | https://conferences.sigcomm.org/imc/2021/accepted/ | | One of those papers is about counting the scale and scope of | online political advertising during the 2020 election. How | does one reproduce that study? The 2020 election is long | past, and that data isn't archived anywhere other than what | the researchers have already collected. This is a pretty | simple empiracal data collection tastk, but you can't just | re-measure that today because that study is about a moving | target. | tlb wrote: | I did my dissertation on this problem 25 years ago. It hasn't | gone away. | | In general, performance comparisons are hard to reproduce. | For instance, when benchmarking network protocols, often a | tiny change in configuration makes a big change in a results. | You might change the size of a buffer from 150 packets to 151 | packets and see performance double (or halve.) | | Instead of making measurements with some arbitrary choices | for parameters, you can take lots of measurements with | parameters randomly varied to show a distribution of | measurements. It's hard work to track down all the possible | parameters and decide on a reasonable range for them, so it's | rarely done. I found many 10x variances in network protocol | performance (like how fairly competing TCP streams can | sharing bandwidth). | | The big idea was to show that by randomizing some decisions | in the protocol (like discarding packets with some | probability as the buffer gets full) you can make the | performance less sensitive to small changes. ie, more | reproducible. Less sensitivity is especially good when you | care about the worst-case performance rather than average. It | can also make tuning a protocol much easier, since you aren't | constantly being fooled by unstable performance. | | Performance sensitivity analysis is hard work, so most papers | are just like "we ran our new thing 3 times and got similar | numbers so there you go." | thechao wrote: | If you're any good at your chosen specialty you get a "feel" | for the bullshit. I know this doesn't help the public. My | experience is in medical research, crystallography, and | computer science. Here's an example for detecting "bullshit" in | cardiology: call up the MD PI from the published paper and ask | to review anonymized charts from patients targeted with the | procedure. Are there any? Then, the research is probably good; | are there none? It's probably because it'd kill the patient. | Similarly, in Programming Language Theory: we'd just ask which | popular compilers added the pass. Is it on in -O3 in LLVM? | Serious fucking result; is it in some dodgy branch in GHC? Not | useful. | rossdavidh wrote: | I think there's two problems impeding our ability to focus | better on this: | | 1) for many people, the idea that science has widespread fraud | is just hard to accept; in this respect it is similar to the | difficulties that many religious communities have in accepting | that their clergy could have a corruption problem | | 2) the solutions require thinking about problems like | p-hacking, incentives, selection effects, and other non-trivial | concepts that are tough for the average person to wrap their | heads around. | derbOac wrote: | I've often thought religious corruption is a good analogy, in | that many of the societal dynamics are very similar. As I'm | writing this the parallels are interesting to think about | relative to US politics. | throwawayboise wrote: | It it is a good analogy. For most lay people, science is a | religion. They lack the expertise to understand the theory, | but they unquestioningly accept the explanations and | interpretations of the so-called experts. | | Most people don't understand astronomy and physics well | enough to prove to themselves that the earth orbits the sun | and not vice-versa. Yet they believe it does, with | certainty, because they have been taught that it is true. | SubiculumCode wrote: | Also: I have not seen evidence of widespread fraud. Evidence | of fraud,yes. Evidence of widespread fraud no. | rossdavidh wrote: | Agreed it's an important point that fraud is only a | fraction of the problem. | derbOac wrote: | That's a fair point, although fraud per se is only a small | part of all the problems. There's other forms of corruption | than fraud, and a lot of it falls into this zone of | plausible deniability rather than outright fraud. Also, I | think the problems tend to find most weight with higher | concentration of power, so what matters isn't as much "how | widespread is corruption?" but rather "how is corruption | distributed among power structures in academics and what is | rewarded?" | bhk wrote: | I have. According to [1], "1 in 4 cancer research papers | contains faked data". As the article argues, the standards | are perhaps unreasonably strict, but even by more favorable | criteria, 1/8 of the papers contained faked data. | Interestingly, [2] using the same approach, found fraud in | 12.4% of papers in the International Journal of Oncology. | More broadly, [2] found fraud in about 4% of the papers | studied (782 of 20,621). I'd say that's pretty widespread, | but you further have to consider that these papers focused | narrowly on a very specific type of fraud that is easy to | detect (image duplication), so we would expect the true | number of fraudulent papers to be much higher. | | [1] https://arstechnica.com/science/2015/06/study- | claims-1-in-4-... | | [2] https://www.biorxiv.org/content/biorxiv/early/2016/04/2 | 0/049... | mistermann wrote: | Don't forget though: events proceed evidence, and evidence | doesn't always follow events. | | Also: perception is ~effectively reality. | javajosh wrote: | Could it be there's just too much science being done for much of | it to be any use? And that this oversupply causes these schemes, | as a side-effect? If so, selling authorship is merely a symptom | of the worthlessness of most modern science. | | For much of human history, science was something you did in your | spare time - or, if you were exceptional, you might have a | patron. Then nation states discovered the value of technology and | science, and wanted more, and so have created science factories. | But, perhaps unsurprisingly, the rate of science production | cannot really be improved in this way, and yet the economics of | science demand that is does. This disconnect between reality and | expectation is the root of this problem, and many others. | rossdavidh wrote: | Oof. Good point. I feel like there's a similar pattern to | having too much VC money chasing too few actually good ideas to | invest in. | pphysch wrote: | Or a government printing money to hire private contractors, | completely disregarding its ability do anything on its own. | | To some extent, this is the curse of being the creator of the | global reserve currency. The US government can, in theory, | print as much money as it wants and pay off whoever it wants | to do whatever it wants. This also extends to the academic | and financial (VC) sectors, because a lot of that liquidity | comes directly from the Government/Fed. | | Unfortunately this leads to a culture of corruption (who gets | the grants/contracts/funding?) and widespread fraud. This | causes the ROI of money printing to go down, the money | printer accelerates and we get inflation too. | SubiculumCode wrote: | This is in fact incredibly wrong. At least in my field, there | is so much more data than there are qualified experts to | analyze it. For one reason, academia pays so much less than the | private sphere that post docs are leaving. | seiferteric wrote: | Something I was wondering is if faking results is so common, | then surely these things they are researching must never be | used in any application right? If they were, it would quickly | be found that it does not actually work... | HarryHirsch wrote: | This is exactly how it works in practice. Anyone who works at | the bench learns quickly to spot the frauds and fakes and | avoids them. That's the "replication" everyone talks about, | no special agency to waste funds on boring stuff needed. | gwd wrote: | > If they were, it would quickly be found that it does not | actually work... | | Unfortunately some of the effect sizes are so small that it's | hard to tell what's working or not. The results of papers on | body building, for instance, are definitely put into practice | by some people. If the claim of the paper is that eating | pumpkin [EDIT] decreases muscle recovery time by 5%, how is | an individual who starts eating pumpkin supposed to notice | that he's not getting any particular benefit from following | its advice? Particularly if he's also following random bits | of advice from a dozen other papers, half of which are valid | and half of which are not? | btrettel wrote: | One problem I've observed is that people applying things | often cargo-cult "proven" things from the scientific | literature that aren't actually proven. It's easier to say | that you're following "best practices" than it is to check | that what you're doing works, unfortunately. | twofornone wrote: | Maybe it's a deeper problem related to western liberal notions | that anyone can do anything if they just "set their mind to | it". We have a glut of "professionals" across industries and | institutions who don't really have any business being there, | but the machine requires that they appear to be useful, and so | mechanisms emerge to satisfy this constraint. A consequence in | science is a long list of poor quality junk publications, and | few people are willing to acknowledge the nakedness of the | emperor for fear of losing their positions, but because doing | so may betray their own redundancy. | photochemsyn wrote: | My own rather short academic career involved doing lab work with | three different PI-led groups. One PI was actually excellent, and | I really had no idea how good I had it. I caught the other two | engaging in deliberately fraudulent practices. For example, data | they'd collect from experiments would be thrown out selectively | so that they could publish better curve-fits. Another trick was | fabricating data with highly obscure methods that other groups | would be unlikely to replicate. They'd also apply pressure to | graduate students to falsify data in order to get results that | agreed with their previously published work. | | The main difference between the excellent PI and the two | fraudsters was that the former insisted on everyone in her lab | keeping highly accurate and detailed daily lab notebooks, while | the other two had incredibly poor lab notebook discipline (and | often didn't even keep records!). She actually caught one of her | grad students fudging data via this method, before it went to | publication. Another requirement was that samples had to be | blindly randomized before we analyzed them, so that nobody could | manipulate the analytical process to get their desired result. | | If you're thinking about going into academia, that's the kind of | thing to look out for when visiting prospective PIs. Shoddy | record keeping is a huge red flag. Inability to replicate | results, and in particular no desire to replicate results, is | another warning sign. And yes, a fair number of PIs have made | careers out of publishing fraudulent results and never get | caught, and they infest the academic system. | georgecmu wrote: | I would say that this applies even more so outside of academia. | At early stages of development, a research group's or company's | product is by necessity a report or a presentation rather than | a physical plant's or process's real, quantifiable performance. | No malicious intent is required; it's just all too easy to fool | yourself or cherry-pick data to support desired conclusions | when the recordkeeping is poor. | | In my hard-tech experiment-heavy start-up there's no way we | could have made any actual technical progress without setting | up a solid data preservation and analysis framework first. For | every experimental run, all the original sensor data are | collected and immediately uploaded along with any photos, | videos, and operator comments to a uniquely-tagged confluence | page. Results and data from any further data or product | analysis are linked to this original page. | | As an anecdotal example, we recently caught swapped dataset | labels in results from analysis performed on our physical | samples by a third-party lab. We were able to do this easily | just because we could refer back to every other piece of | information regarding these samples, including the conditions | in which they were generated months prior to this analysis. As | soon all the data were on display at once, the discrepancies | were obvious. | ketanmaheshwari wrote: | [PLUG] Some of what you mention are "negative results" that are | quite prevelant and a necessary part of any research. However, | the expected mold at publishing venues is such that they are | not considered worthwhile. | | My colleagues and I are trying to address this by creating a | platform to discuss and publish such "bad" or "negative" | results. More info here: | | https://error-workshop.org/ | EamonnMR wrote: | Does the new In The Pipeline blog have an RSS feed? I haven't | been able to find it. | bannedbybros wrote: | Enginerrrd wrote: | I always thought it would be a good idea to start a journal that | has a lab submit their methods and intent of study for peer | review and approval / denial PRIOR to performing the work. Then, | if approved, and as long as they adhere to the approved methods, | they get published regardless of outcome. That would really | encourage the publishing of negative results and eliminate a lot | of the incentive to fudge the numbers on the data. It would | probably overly reward pre-existing clout, but frankly that's a | problem ANYWAY. | Guybrush_T wrote: | This is done with clinical trials (or at least it's | recommended). Many researchers register their study at | https://clinicaltrials.gov/ before data collection starts. I'm | not sure if something similar exists for lab based research. | francislavoie wrote: | Reminds me of Bobby Broccoli's video series on Jan Hendrik Schon | who almost got the Nobel Prize in Physics fraudulently. Extremely | good watch: | | https://www.youtube.com/playlist?list=PLAB-wWbHL7Vsfl4PoQpNs... | slowhand09 wrote: | Worked on a NASA program once, about measuring Earth Science | data. We built a database application to gather suggested | requirements from members of the earth science community. One | such member from our own team helped develop specs for our | system. After we built it, she wanted to measure its utility and | usability. She watched as users navigated and entered data into | the system. She also asked myself and members of my team who | developed the developed the software to use it and be measured. I | and one other developer (2 of 3 members) explained why we | implemented each feature as we were utilizing the system. The | "scientist" measuring us all promptly published as a conclusion | in her paper "The usability of the system was better for | inexperienced users than it was for experienced users. The | experienced users took nearly 50% longer to navigate and enter | similar requirements". She basically made up an "interesting" | conclusion by omitting characterization of our testing session, | where we explained how we implemented her requirements. ___________________________________________________________________ (page generated 2022-04-11 23:00 UTC)