#scientific-papers — Public Fediverse posts

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of current (and dramatic) example: as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

“Science is a cooperative enterprise spanning the generations… a community of minds, reaching back to antiquity and forward to the stars”*…

The sharing of experimental results and the underlying data is critical to the advance of science. Indeed, when I had the chance to do a scenario planning exercise with a collection of the leading research university librarians in the U.S. a couple of decades ago, the biggest threat/fear they surfaced was the concern that the free and open exchange of ideas and data, as manifest formally in scientific publication and informally in the collegial cooperation among scientists, would be occluded by an increasing proprietary embrace of knowledge.

73% of geneticists surveyed in an article in the 23/30 January 2002 issue of the Journal of the American Medical Association agreed that although keeping data private may help the individual researcher, data hoarding is detrimental to the progress of science Still, sadly, that threat has grown since the turn of the millennium.

By way of current (and dramatic) example: as Celina Zhao reports, more than half of AI “unicorns” have never published a paper or preprint…

Today’s biggest artificial intelligence (AI) startups make no shortage of bold promises. Their technologies, some boast, will revolutionize software development, drug discovery, and scientific research.

Yet a new preprint posted on 16 July on bioRxiv suggests many of these firms barely participate in one of science’s most fundamental practices: publicly documenting discoveries in scientific literature so other researchers can evaluate and build on them. More than half of AI unicorns—private companies valued at more than $1 billion—have never played a leading role in publishing a scientific paper or preprint, according to the new analysis. Collectively, they accounted for just one in every 1000 AI papers published in 2025.

“For a field that is supposedly reshaping science and is so advanced in terms of scientific potential, not having any scientific documentation seems like a very weird paradox,” says paper co-author John Ioannidis, a metascientist at Stanford University [see here]. “How can you judge that what they say is real, validated, and reproducible?” The scarcity of publications, others say, also makes it harder to assess AI’s social impacts, including energy use and safety.

But University of Alberta AI ethicist Mohamed Abdalla says the findings reflect the incentives facing commercial AI developers, rather than solely a failure to uphold scientific norms. “It’s not the company’s job to advance science, right?” he says. “The company’s job is to advance money.”

Ioannidis has long studied how unicorns, particularly in biotech, engage with the scientific literature. (In 2015, he was the first to publicly scrutinize the lack of peer-reviewed studies produced by Theranos, the blood testing startup that proved to be based on fraudulent data.) He wondered whether AI unicorns would show similar patterns.

To find out, he and his team first identified all 317 unicorn AI companies that have existed from 1998 to 2025. Then, they searched for publications affiliated with these startups—including journal articles, conference papers, reviews, and preprints. They selected those where a company researcher played a leading role as a first or last author, indicating the startup had made a substantial contribution to the work. The final data set included 2077 final publications, comprising 1389 peer-reviewed papers and 688 preprints.

More than half of the startups had never produced a single qualifying paper, the analysis revealed. Scientific influence proved even more concentrated, with the top 5% of firms accounting for greater than 90% of all citations. OpenAI alone was responsible for nearly 40% of all citations in the data set, followed by the Chinese computer vision company Megvii and the platform Hugging Face. And even at the most prolific companies, much of the output came from the same small group of repeat authors. For example, despite OpenAI employing roughly 4500 people, only eight researchers had authored five or more qualifying papers.

The findings are unsurprising to some AI researchers given how the industry is structured. For example, unlike the pharmaceutical industry, where published discoveries can be protected by patents, AI companies have learned they often gain little from publicly disclosing technical advances, says Nur Ahmed, an AI researcher at the University of Arkansas. Google’s landmark 2017 paper on the transformer—the architecture that underpins today’s large language models—has become a classic cautionary example, Abdalla adds. Although Google patented aspects of the technology, “I don’t think anybody’s paying Google for that,” he says.

Startups also operate on much faster timelines than academia, where peer review can lumber on for months or even years. That’s why many AI companies have embraced what Avijit Ghosh, an AI policy researcher at Hugging Face, calls the “blogification” of research: announcing new models and releasing code or data sets through blog posts and technical reports rather than scientific journals. The new analysis didn’t track those outputs, he points out.

For Ghosh, the debate shouldn’t center on publishing in journals versus blogs. What matters is whether companies are releasing enough code, data sets, or model weights (the numbers that determine how a model interprets and responds to a prompt) for others to independently verify and build on their work, he says.

The preprint also found that firms based in China consistently published more papers than their counterparts based in the United States. Whereas leading U.S. frontier labs have increasingly kept the details of their most capable models secret or “closed sourced,” leading Chinese companies have embraced “open-source” models. Moonshot AI, one of the Chinese startups included in the study, recently unveiled Kimi K3—one of the strongest open models to date—and publicly released its model weights through Hugging Face today.

But whether models are open or closed, the rapid pace toward increasingly powerful generalist AI worries Emma Pierson, a computer scientist at the University of California, Berkeley. She argues AI research—whether published freely or kept secret—risks accelerating models that pose serious societal and safety concerns, including supercharging cyberattacks. “If we were racing forward on cancer-curing AI, I would be like, ’Fantastic, full steam ahead,’” she says. “But that’s not what we’re racing toward, right?”…

The secretive unicorns: “AI’s top startups are barely publishing their research,” from @science.org.

By way of example? In order to have a broader footprint in AI for (default proprietary) scientific discovery, Google moves away from a successful AI effort (that did publish): “Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift” (gift article from the FT). One wonders: when these LLMs run out of published papers on which to train, where (and how) will they source the knowledge they need to stay useful?

* Neil deGrasse Tyson

###

As we share and share alike, we might recall that it was on this date in 1887 that Chester A. Hodge of Beloit, Wisconsin received patent No. 367,398 for ‘spur rowel’ barbed wire (consisting of spur shaped wheels with 8 or 10 points mounted between 2 wires).  It was one of many patents for barbed wire (e.g., here), which spread across the American West rapidly (thanks, in no small measure to the guy featured in the almanac entry here)– and (by protecting farmers from foraging free-ranging cattle) paved the way for the expansion of wheat (and other kinds of) farming… even as it spelled the doom of a commons– the open range.

According to a new paper in The Lancet, the rate of made-up citations in biomedical papers has increased by more than 12x since 2023. #AI #Biomedical #ScientificPapers #Medicine https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(26)00603-3/fulltext

According to a new paper in The Lancet, the rate of made-up citations in biomedical papers has increased by more than 12x since 2023. #AI #Biomedical #ScientificPapers #Medicine https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(26)00603-3/fulltext

TikTok but for Scientific Papers

https://andreaturchet.github.io/website/index.html

#HackerNews #TikTokScience #ScientificPapers #ResearchInnovation #AcademicSharing #ScienceCommunication

TikTok but for Scientific Papers

https://andreaturchet.github.io/website/index.html

#HackerNews #TikTokScience #ScientificPapers #ResearchInnovation #AcademicSharing #ScienceCommunication

UC San Francisco: Announcing the Open Access UC-Authored Monographs Pilot Project. “The University of California (UC) Libraries are supporting several open access pilot projects intended to broaden access to UC research and scholarship by making UC-authored books freely available online.”

UC San Francisco: Announcing the Open Access UC-Authored Monographs Pilot Project. “The University of California (UC) Libraries are supporting several open access pilot projects intended to broaden access to UC research and scholarship by making UC-authored books freely available online.”

Paper2Video: Automatic Video Generation from Scientific Papers

https://arxiv.org/abs/2510.05096

#HackerNews #Paper2Video #Automatic #Video #Generation #ScientificPapers #ResearchInnovation

Paper2Video: Automatic Video Generation from Scientific Papers

https://arxiv.org/abs/2510.05096

#HackerNews #Paper2Video #Automatic #Video #Generation #ScientificPapers #ResearchInnovation

Retraction Watch: Exclusive: Journal bans drug safety database papers as they flood the literature. “Starting around 2023, a curious trend took hold in papers on drug safety monitoring. The number of articles published on an individual drug and its link to specific adverse events went from a steady increase to a huge spike. The data source in most of those articles was largely the same: The FDA […]

https://rbfirehose.com/2025/09/17/exclusive-journal-bans-drug-safety-database-papers-as-they-flood-the-literature-retraction-watch/

Scientific papers: where great ideas go to die a slow death by copy-paste 🧟‍♂️📝. Wayne claims it's a battle between #innovation and #imitation, but let's be real, it's mostly just a tedious cycle of academic déjà vu 📚🔁. Who knew groundbreaking #research could be so... groundbreaking? 🙄🔬
https://www.johndcook.com/blog/2025/06/05/scientific-papers-innovation-or-imitation/ #scientificpapers #academia #HackerNews #ngated

Scientific papers: where great ideas go to die a slow death by copy-paste 🧟‍♂️📝. Wayne claims it's a battle between #innovation and #imitation, but let's be real, it's mostly just a tedious cycle of academic déjà vu 📚🔁. Who knew groundbreaking #research could be so... groundbreaking? 🙄🔬
https://www.johndcook.com/blog/2025/06/05/scientific-papers-innovation-or-imitation/ #scientificpapers #academia #HackerNews #ngated

Scientific Papers: Innovation or Imitation?

https://www.johndcook.com/blog/2025/06/05/scientific-papers-innovation-or-imitation/

#HackerNews #ScientificPapers #Innovation #Imitation #Research #ScienceDiscussion

Scientific Papers: Innovation or Imitation?

https://www.johndcook.com/blog/2025/06/05/scientific-papers-innovation-or-imitation/

#HackerNews #ScientificPapers #Innovation #Imitation #Research #ScienceDiscussion

The term "vegetative electron microscopy" keeps showing up in scientific papers

https://www.sciencealert.com/a-strange-phrase-keeps-turning-up-in-scientific-papers-but-why

#HackerNews #vegetativeelectronmicroscopy #scientificpapers #research #trends #sciencealert

The term "vegetative electron microscopy" keeps showing up in scientific papers

https://www.sciencealert.com/a-strange-phrase-keeps-turning-up-in-scientific-papers-but-why

#HackerNews #vegetativeelectronmicroscopy #scientificpapers #research #trends #sciencealert

Paper2Code: Automating Code Generation from Scientific Papers

https://arxiv.org/abs/2504.17192

#HackerNews #Paper2Code #Automating #Code #Generation #ScientificPapers #AIinResearch

Paper2Code: Automating Code Generation from Scientific Papers

https://arxiv.org/abs/2504.17192

#HackerNews #Paper2Code #Automating #Code #Generation #ScientificPapers #AIinResearch

#Academic Publication question: say you are writing a review that involves really old papers (i.e. published <1950). Most of the papers you're citing were quite hard to obtain, but you managed to get access to them somehow so you have a scanned version of all of these.

Is there a legal / official way to provide a folder with all the cited papers together with your review once it's published? And by legal I mean a way that the journal that you're publishing in will be happy with?

If these were old enough they would probably fall into the public domain but I don't think they're old enough for that (this says copyright expires from the death of the author +70y: https://copyright.gov/help/faq/faq-duration.html#duration)

#ScientificPapers #Academia #AcademicPublication

#Academic Publication question: say you are writing a review that involves really old papers (i.e. published <1950). Most of the papers you're citing were quite hard to obtain, but you managed to get access to them somehow so you have a scanned version of all of these.

Is there a legal / official way to provide a folder with all the cited papers together with your review once it's published? And by legal I mean a way that the journal that you're publishing in will be happy with?

If these were old enough they would probably fall into the public domain but I don't think they're old enough for that (this says copyright expires from the death of the author +70y: https://copyright.gov/help/faq/faq-duration.html#duration)

#ScientificPapers #Academia #AcademicPublication

FDA advisers to weigh first psychedelic therapy in early June—Sorcero genAI conjures plain language from scientific papers —How mRNA vaccines could be personalized cancer cures --http://bit.ly/w28kSd #fda #psychedelics #genAI #ai #scientificpapers #mrna #cancer #oncology #personalizedmedicine #pharma #pharmanews #biotech #biopharma #biotechnology #cafepharma

Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

https://arxiv.org/abs/2403.16887

1st in a series of 2 articles on the use of generative AI when evaluating scientific papers or studies.

This publication is not intended to give a moral lesson on the use of generative AI in research papers. It's more about raising awareness of how to use them better, or not.

#ChatGPT #PeerReviews #GenerativeAI #ScientificPapers #Phd

The Guardian:
Fake #scientificpapers push #research #credibility to crisis point
https://www.theguardian.com/science/2024/feb/03/the-situation-has-become-appalling-fake-scientific-papers-push-research-credibility-to-crisis-point

New ways to pay for #research could boost #scientificprogres
"the “disruptiveness” of both #scientificpapers & #patents, as measured by citation patterns, fell by over 90% for papers, & more than 80% for patents, btw 1945 & 2010 (see chart) .. w its mix of short timelines & small grants, the system leaves researchers “constantly thinking” abt where their next cheque is coming fr. But skill at #raisingmoney is not necessarily correlated w the usefulness of one’s research"
https://www.economist.com/science-and-technology/2023/11/15/new-ways-to-pay-for-research-could-boost-scientific-progress

Newly added to the Trade-Free Directory:

open access journals and books

Articles published open access are peer-reviewed and made freely available for everyone to read, download and reuse in line with the user license displayed on the article.

#open-access-articles #scientific-papers

More here:

https://www.directory.trade-free.org/goods-services/open-access-journals-and-books/

"Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it’s appropriate to cite the bot as an author. Publishers are racing to create policies for the chatbot, which was released as a free-to-use tool in November by tech company OpenAI in San Francisco, California.""

https://www.nature.com/articles/d41586-023-00107-z

#Science #Research #ChatGPT #Citations #Authors #ScientificPapers

"Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it’s appropriate to cite the bot as an author. Publishers are racing to create policies for the chatbot, which was released as a free-to-use tool in November by tech company OpenAI in San Francisco, California.""

https://www.nature.com/articles/d41586-023-00107-z

#Science #Research #ChatGPT #Citations #Authors #ScientificPapers

Hey scientists, here's a fun game for your readers: include a scale bar in a figure, but don't say how long it is!

1cm seems sensible? No that would make this thing to small ... 10cm?... no way too big. Okay, it's a bit of a strange number but 5cm? Nope still too big. I think it's 3cm.

The great thing about this game is that you don't know you've got the right answer! Hours of fun!

#ScientificPapers #Papers #MeasureYourDamnDinosaur

Hey scientists, here's a fun game for your readers: include a scale bar in a figure, but don't say how long it is!

1cm seems sensible? No that would make this thing to small ... 10cm?... no way too big. Okay, it's a bit of a strange number but 5cm? Nope still too big. I think it's 3cm.

The great thing about this game is that you don't know you've got the right answer! Hours of fun!

#ScientificPapers #Papers #MeasureYourDamnDinosaur

Excellent article:

https://experimentalhistory.substack.com/p/the-dance-of-the-naked-emperors

One innovative idea that the scientific method brought forth: even though you think you know what will happen, try it and see what actually happens.

Isaac Asimove pointed out that the comment that actually accompanied scientific discovery was not so much "Eureka!" as "Huh. That's funny..."

#publishing #scienceJournals #scientificPapers #peerReview #innovation

Neat. A service to convert PDF scientific papers to HTML for easier reading on web browsers and mobile devices.

"This is an experimental prototype that aims to render scientific papers in HTML so they can be more easily read by screen readers or on mobile devices."

https://papertohtml.org/

Via Hacker News [ https://news.ycombinator.com/item?id=28542977 ].

#PDF #HTML #ScientificPapers