#scientific-publishing — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #scientific-publishing, aggregated by home.social.
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@oatp
Is this a thing? Is it real? I still search for words and phrases and authors in Google Scholar and elsewhere, and follow threads of citations from one paper to the next backwards to find relevant papers.Am I obsolete? Is the body of knowledge we create through publication now just grist for an AI search engine?
"Pinter: In 2026, Artificial Intelligence has become the primary consumer, parser, and filter of scholarly research. Large Language Models (LLMs) and specialized AI research assistants are entirely rewriting how scholars discover and synthesize literature (discoverability). If a monograph is not ingested into the core databases from which AI systems learn, it effectively ceases to exist for international science."
I begin to think we need a new global scientific effort to develop synthesize and integrate publications into a better catalog so that we can look papers up the same way we'd find a flight or a hotel reservation instead of hoping an AI will make the right connections.
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@oatp
Is this a thing? Is it real? I still search for words and phrases and authors in Google Scholar and elsewhere, and follow threads of citations from one paper to the next backwards to find relevant papers.Am I obsolete? Is the body of knowledge we create through publication now just grist for an AI search engine?
"Pinter: In 2026, Artificial Intelligence has become the primary consumer, parser, and filter of scholarly research. Large Language Models (LLMs) and specialized AI research assistants are entirely rewriting how scholars discover and synthesize literature (discoverability). If a monograph is not ingested into the core databases from which AI systems learn, it effectively ceases to exist for international science."
I begin to think we need a new global scientific effort to develop synthesize and integrate publications into a better catalog so that we can look papers up the same way we'd find a flight or a hotel reservation instead of hoping an AI will make the right connections.
-
@oatp
Is this a thing? Is it real? I still search for words and phrases and authors in Google Scholar and elsewhere, and follow threads of citations from one paper to the next backwards to find relevant papers.Am I obsolete? Is the body of knowledge we create through publication now just grist for an AI search engine?
"Pinter: In 2026, Artificial Intelligence has become the primary consumer, parser, and filter of scholarly research. Large Language Models (LLMs) and specialized AI research assistants are entirely rewriting how scholars discover and synthesize literature (discoverability). If a monograph is not ingested into the core databases from which AI systems learn, it effectively ceases to exist for international science."
I begin to think we need a new global scientific effort to develop synthesize and integrate publications into a better catalog so that we can look papers up the same way we'd find a flight or a hotel reservation instead of hoping an AI will make the right connections.
-
@oatp
Is this a thing? Is it real? I still search for words and phrases and authors in Google Scholar and elsewhere, and follow threads of citations from one paper to the next backwards to find relevant papers.Am I obsolete? Is the body of knowledge we create through publication now just grist for an AI search engine?
"Pinter: In 2026, Artificial Intelligence has become the primary consumer, parser, and filter of scholarly research. Large Language Models (LLMs) and specialized AI research assistants are entirely rewriting how scholars discover and synthesize literature (discoverability). If a monograph is not ingested into the core databases from which AI systems learn, it effectively ceases to exist for international science."
I begin to think we need a new global scientific effort to develop synthesize and integrate publications into a better catalog so that we can look papers up the same way we'd find a flight or a hotel reservation instead of hoping an AI will make the right connections.
-
@oatp
Is this a thing? Is it real? I still search for words and phrases and authors in Google Scholar and elsewhere, and follow threads of citations from one paper to the next backwards to find relevant papers.Am I obsolete? Is the body of knowledge we create through publication now just grist for an AI search engine?
"Pinter: In 2026, Artificial Intelligence has become the primary consumer, parser, and filter of scholarly research. Large Language Models (LLMs) and specialized AI research assistants are entirely rewriting how scholars discover and synthesize literature (discoverability). If a monograph is not ingested into the core databases from which AI systems learn, it effectively ceases to exist for international science."
I begin to think we need a new global scientific effort to develop synthesize and integrate publications into a better catalog so that we can look papers up the same way we'd find a flight or a hotel reservation instead of hoping an AI will make the right connections.
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In mid-February, I set up a small informal seminar in my department, the "Semiautomatic Lean Working Group", to actively test out new LLM-based Leaning capabilities of commercially available models.
Until then, I had refused direct engagement with such systems, for any purpose whatsoever, but I could not look away any longer, especially due to what I understood about its impending impact on the journal business.
We set out to share our experiences, exchange technical tips, discuss the latest news, and try to understand this better. Rather quickly, the seminar evolved into more of a "support group", where we would joke at the latest over-stretched analogy, speculate on optimistic/pessimistic futures, admit to our token dependencies, and express our grief.
The working group has reached a natural coda, as the department (with broader university backing) has just set up a broader committee to grapple with the big shifts underway.
I learned a lot through the working group, went through a lot of phases, and am grateful to the other members for walking together in this journey. Building and strengthening connections, sharing and reflection with others, this is how we move forward in this.
#generativeAI #formalization #semiautomatic #lean #scientificpublishing
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In mid-February, I set up a small informal seminar in my department, the "Semiautomatic Lean Working Group", to actively test out new LLM-based Leaning capabilities of commercially available models.
Until then, I had refused direct engagement with such systems, for any purpose whatsoever, but I could not look away any longer, especially due to what I understood about its impending impact on the journal business.
We set out to share our experiences, exchange technical tips, discuss the latest news, and try to understand this better. Rather quickly, the seminar evolved into more of a "support group", where we would joke at the latest over-stretched analogy, speculate on optimistic/pessimistic futures, admit to our token dependencies, and express our grief.
The working group has reached a natural coda, as the department (with broader university backing) has just set up a broader committee to grapple with the big shifts underway.
I learned a lot through the working group, went through a lot of phases, and am grateful to the other members for walking together in this journey. Building and strengthening connections, sharing and reflection with others, this is how we move forward in this.
#generativeAI #formalization #semiautomatic #lean #scientificpublishing
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In mid-February, I set up a small informal seminar in my department, the "Semiautomatic Lean Working Group", to actively test out new LLM-based Leaning capabilities of commercially available models.
Until then, I had refused direct engagement with such systems, for any purpose whatsoever, but I could not look away any longer, especially due to what I understood about its impending impact on the journal business.
We set out to share our experiences, exchange technical tips, discuss the latest news, and try to understand this better. Rather quickly, the seminar evolved into more of a "support group", where we would joke at the latest over-stretched analogy, speculate on optimistic/pessimistic futures, admit to our token dependencies, and express our grief.
The working group has reached a natural coda, as the department (with broader university backing) has just set up a broader committee to grapple with the big shifts underway.
I learned a lot through the working group, went through a lot of phases, and am grateful to the other members for walking together in this journey. Building and strengthening connections, sharing and reflection with others, this is how we move forward in this.
#generativeAI #formalization #semiautomatic #lean #scientificpublishing
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In mid-February, I set up a small informal seminar in my department, the "Semiautomatic Lean Working Group", to actively test out new LLM-based Leaning capabilities of commercially available models.
Until then, I had refused direct engagement with such systems, for any purpose whatsoever, but I could not look away any longer, especially due to what I understood about its impending impact on the journal business.
We set out to share our experiences, exchange technical tips, discuss the latest news, and try to understand this better. Rather quickly, the seminar evolved into more of a "support group", where we would joke at the latest over-stretched analogy, speculate on optimistic/pessimistic futures, admit to our token dependencies, and express our grief.
The working group has reached a natural coda, as the department (with broader university backing) has just set up a broader committee to grapple with the big shifts underway.
I learned a lot through the working group, went through a lot of phases, and am grateful to the other members for walking together in this journey. Building and strengthening connections, sharing and reflection with others, this is how we move forward in this.
#generativeAI #formalization #semiautomatic #lean #scientificpublishing
-
In mid-February, I set up a small informal seminar in my department, the "Semiautomatic Lean Working Group", to actively test out new LLM-based Leaning capabilities of commercially available models.
Until then, I had refused direct engagement with such systems, for any purpose whatsoever, but I could not look away any longer, especially due to what I understood about its impending impact on the journal business.
We set out to share our experiences, exchange technical tips, discuss the latest news, and try to understand this better. Rather quickly, the seminar evolved into more of a "support group", where we would joke at the latest over-stretched analogy, speculate on optimistic/pessimistic futures, admit to our token dependencies, and express our grief.
The working group has reached a natural coda, as the department (with broader university backing) has just set up a broader committee to grapple with the big shifts underway.
I learned a lot through the working group, went through a lot of phases, and am grateful to the other members for walking together in this journey. Building and strengthening connections, sharing and reflection with others, this is how we move forward in this.
#generativeAI #formalization #semiautomatic #lean #scientificpublishing
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I have a feeling that we finally are getting some movement in the #polsci, specifically the #policystudies sphere around taking back control over our journals (current state is pretty awful). Just learned about the new Public Policy Journal, which is run by respected scholars, independently, and seems to run on #openjournalsystems. Cool!
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I have a feeling that we finally are getting some movement in the #polsci, specifically the #policystudies sphere around taking back control over our journals (current state is pretty awful). Just learned about the new Public Policy Journal, which is run by respected scholars, independently, and seems to run on #openjournalsystems. Cool!
-
I have a feeling that we finally are getting some movement in the #polsci, specifically the #policystudies sphere around taking back control over our journals (current state is pretty awful). Just learned about the new Public Policy Journal, which is run by respected scholars, independently, and seems to run on #openjournalsystems. Cool!
-
I have a feeling that we finally are getting some movement in the #polsci, specifically the #policystudies sphere around taking back control over our journals (current state is pretty awful). Just learned about the new Public Policy Journal, which is run by respected scholars, independently, and seems to run on #openjournalsystems. Cool!
-
I have a feeling that we finally are getting some movement in the #polsci, specifically the #policystudies sphere around taking back control over our journals (current state is pretty awful). Just learned about the new Public Policy Journal, which is run by respected scholars, independently, and seems to run on #openjournalsystems. Cool!
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"by considering provenance of research, we risk moving back to a time when research was trusted only if it came from people with some kind of track record, and/or who were personally known to the reviewer or editor."
That has been my concern since LLMs started not being entirely nonsensical. I haven't yet seen any alternative to a web of trust.
All aggravated by soft money faculty positions, for-profit universities, and for-profit journals, which together create perverse incentives.
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"by considering provenance of research, we risk moving back to a time when research was trusted only if it came from people with some kind of track record, and/or who were personally known to the reviewer or editor."
That has been my concern since LLMs started not being entirely nonsensical. I haven't yet seen any alternative to a web of trust.
All aggravated by soft money faculty positions, for-profit universities, and for-profit journals, which together create perverse incentives.
-
"by considering provenance of research, we risk moving back to a time when research was trusted only if it came from people with some kind of track record, and/or who were personally known to the reviewer or editor."
That has been my concern since LLMs started not being entirely nonsensical. I haven't yet seen any alternative to a web of trust.
All aggravated by soft money faculty positions, for-profit universities, and for-profit journals, which together create perverse incentives.
-
"by considering provenance of research, we risk moving back to a time when research was trusted only if it came from people with some kind of track record, and/or who were personally known to the reviewer or editor."
That has been my concern since LLMs started not being entirely nonsensical. I haven't yet seen any alternative to a web of trust.
All aggravated by soft money faculty positions, for-profit universities, and for-profit journals, which together create perverse incentives.
-
"by considering provenance of research, we risk moving back to a time when research was trusted only if it came from people with some kind of track record, and/or who were personally known to the reviewer or editor."
That has been my concern since LLMs started not being entirely nonsensical. I haven't yet seen any alternative to a web of trust.
All aggravated by soft money faculty positions, for-profit universities, and for-profit journals, which together create perverse incentives.
-
Ars Technica: Peer review is overwhelmed—can it survive in the AI era?. “The number of papers indexed in the databases Scopus and Web of Science, for example, has recently been increasing exponentially, at a rate of 5.6 percent per year. By one estimate, researchers around the globe are devoting a collective 15,000 years of work to peer review every year—work that, if paid, would cost $1.5 […]
https://rbfirehose.com/2026/08/14/ars-technica-peer-review-is-overwhelmed-can-it-survive-in-the-ai-era/ -
Ars Technica: Peer review is overwhelmed—can it survive in the AI era?. “The number of papers indexed in the databases Scopus and Web of Science, for example, has recently been increasing exponentially, at a rate of 5.6 percent per year. By one estimate, researchers around the globe are devoting a collective 15,000 years of work to peer review every year—work that, if paid, would cost $1.5 […]
https://rbfirehose.com/2026/08/14/ars-technica-peer-review-is-overwhelmed-can-it-survive-in-the-ai-era/ -
Ars Technica: Peer review is overwhelmed—can it survive in the AI era?. “The number of papers indexed in the databases Scopus and Web of Science, for example, has recently been increasing exponentially, at a rate of 5.6 percent per year. By one estimate, researchers around the globe are devoting a collective 15,000 years of work to peer review every year—work that, if paid, would cost $1.5 […]
https://rbfirehose.com/2026/08/14/ars-technica-peer-review-is-overwhelmed-can-it-survive-in-the-ai-era/ -
Ars Technica: Peer review is overwhelmed—can it survive in the AI era?. “The number of papers indexed in the databases Scopus and Web of Science, for example, has recently been increasing exponentially, at a rate of 5.6 percent per year. By one estimate, researchers around the globe are devoting a collective 15,000 years of work to peer review every year—work that, if paid, would cost $1.5 […]
https://rbfirehose.com/2026/08/14/ars-technica-peer-review-is-overwhelmed-can-it-survive-in-the-ai-era/ -
Ars Technica: Peer review is overwhelmed—can it survive in the AI era?. “The number of papers indexed in the databases Scopus and Web of Science, for example, has recently been increasing exponentially, at a rate of 5.6 percent per year. By one estimate, researchers around the globe are devoting a collective 15,000 years of work to peer review every year—work that, if paid, would cost $1.5 […]
https://rbfirehose.com/2026/08/14/ars-technica-peer-review-is-overwhelmed-can-it-survive-in-the-ai-era/ -
The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]
https://rbfirehose.com/2026/08/12/the-state-scientific-and-technical-library-of-ukraine-mes-opens-interactive-dashboard-specialized-publications-of-ukraine/ -
The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]
https://rbfirehose.com/2026/08/12/the-state-scientific-and-technical-library-of-ukraine-mes-opens-interactive-dashboard-specialized-publications-of-ukraine/ -
The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]
https://rbfirehose.com/2026/08/12/the-state-scientific-and-technical-library-of-ukraine-mes-opens-interactive-dashboard-specialized-publications-of-ukraine/ -
The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]
https://rbfirehose.com/2026/08/12/the-state-scientific-and-technical-library-of-ukraine-mes-opens-interactive-dashboard-specialized-publications-of-ukraine/ -
The State Scientific And Technical Library Of Ukraine, and machine-translated from Ukrainian: MES opens interactive dashboard “Specialized publications of Ukraine”. “As part of the work of the Ministry of Education and Science of Ukraine to develop an open and transparent system of scientific professional publications, an interactive dashboard ‘Professional Publications of Ukraine’ was created. […]
https://rbfirehose.com/2026/08/12/the-state-scientific-and-technical-library-of-ukraine-mes-opens-interactive-dashboard-specialized-publications-of-ukraine/ -
Hundreds of free open access journals to receive significant financial support!
@ojcollective.bsky.social, which launched in January, has already raised £1.56 million of its £2.65 million target and will start to financially support 297 journals.These journals include many of the journals in the Free Journals Network, which I'm on the Advisory Board of, but we plan to wind down as our journals join OJC! https://blogs.lse.ac.uk/lsepress/2026/07/29/the-power-of-collective-funding-for-open-access-publishing/ #scholarlypublishing #scientificpublishing
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Hundreds of free open access journals to receive significant financial support!
@ojcollective.bsky.social, which launched in January, has already raised £1.56 million of its £2.65 million target and will start to financially support 297 journals.These journals include many of the journals in the Free Journals Network, which I'm on the Advisory Board of, but we plan to wind down as our journals join OJC! https://blogs.lse.ac.uk/lsepress/2026/07/29/the-power-of-collective-funding-for-open-access-publishing/ #scholarlypublishing #scientificpublishing
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Hundreds of free open access journals to receive significant financial support!
@ojcollective.bsky.social, which launched in January, has already raised £1.56 million of its £2.65 million target and will start to financially support 297 journals.These journals include many of the journals in the Free Journals Network, which I'm on the Advisory Board of, but we plan to wind down as our journals join OJC! https://blogs.lse.ac.uk/lsepress/2026/07/29/the-power-of-collective-funding-for-open-access-publishing/ #scholarlypublishing #scientificpublishing
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Hundreds of free open access journals to receive significant financial support!
@ojcollective.bsky.social, which launched in January, has already raised £1.56 million of its £2.65 million target and will start to financially support 297 journals.These journals include many of the journals in the Free Journals Network, which I'm on the Advisory Board of, but we plan to wind down as our journals join OJC! https://blogs.lse.ac.uk/lsepress/2026/07/29/the-power-of-collective-funding-for-open-access-publishing/ #scholarlypublishing #scientificpublishing
-
Hundreds of free open access journals to receive significant financial support!
@ojcollective.bsky.social, which launched in January, has already raised £1.56 million of its £2.65 million target and will start to financially support 297 journals.These journals include many of the journals in the Free Journals Network, which I'm on the Advisory Board of, but we plan to wind down as our journals join OJC! https://blogs.lse.ac.uk/lsepress/2026/07/29/the-power-of-collective-funding-for-open-access-publishing/ #scholarlypublishing #scientificpublishing
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“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.
Roll of modern agricultural barbed wire (source) #academicCommunications #academicResearch #AI #artificialIntelligence #barbedWire #ChesterHodge #commons #cooperation #culture #history #openRange #research #Science #scientificJournals #scientificPapers #scientificPublication #scientificPublishing #scientificResearch #Technology -
“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.
Roll of modern agricultural barbed wire (source) #academicCommunications #academicResearch #AI #artificialIntelligence #barbedWire #ChesterHodge #commons #cooperation #culture #history #openRange #research #Science #scientificJournals #scientificPapers #scientificPublication #scientificPublishing #scientificResearch #Technology -
“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 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.
Roll of modern agricultural barbed wire (source) #academicCommunications #AI #artificialIntelligence #barbedWire #ChesterHodge #commons #cooperation #culture #history #openRange #Science #scientificJournals #scientificPapers #scientificPublication #scientificPublishing #Technology -
“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 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.
Roll of modern agricultural barbed wire (source) #academicCommunications #AI #artificialIntelligence #barbedWire #ChesterHodge #commons #cooperation #culture #history #openRange #Science #scientificJournals #scientificPapers #scientificPublication #scientificPublishing #Technology -
“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.
Roll of modern agricultural barbed wire (source) #academicCommunications #academicResearch #AI #artificialIntelligence #barbedWire #ChesterHodge #commons #cooperation #culture #history #openRange #research #Science #scientificJournals #scientificPapers #scientificPublication #scientificPublishing #scientificResearch #Technology -
Our Managing Editor Dr. Barbara Hissa will talk about “A true open-access route: how to go diamond in the nanoscience field?” the 27th International Conference on Non-Contact Atomic Force Microscopy.
📍 Innsbruck, Austria
📅 August 4, 2026
🕔 5:20 pm CEST
➡️ https://www.beilstein-journals.org/bjnano/news/WC4JNQBXURQ2QECDBFX264QE3Y?M=y#EditorsTalk #EdiTours #ScientificPublishing #OpenScience #BJNANO 💎🔓
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Our Managing Editor Dr. Barbara Hissa will talk about “A true open-access route: how to go diamond in the nanoscience field?” the 27th International Conference on Non-Contact Atomic Force Microscopy.
📍 Innsbruck, Austria
📅 August 4, 2026
🕔 5:20 pm CEST
➡️ https://www.beilstein-journals.org/bjnano/news/WC4JNQBXURQ2QECDBFX264QE3Y?M=y#EditorsTalk #EdiTours #ScientificPublishing #OpenScience #BJNANO 💎🔓
-
Our Managing Editor Dr. Barbara Hissa will talk about “A true open-access route: how to go diamond in the nanoscience field?” the 27th International Conference on Non-Contact Atomic Force Microscopy.
📍 Innsbruck, Austria
📅 August 4, 2026
🕔 5:20 pm CEST
➡️ https://www.beilstein-journals.org/bjnano/news/WC4JNQBXURQ2QECDBFX264QE3Y?M=y#EditorsTalk #EdiTours #ScientificPublishing #OpenScience #BJNANO 💎🔓
-
Our Managing Editor Dr. Barbara Hissa will talk about “A true open-access route: how to go diamond in the nanoscience field?” the 27th International Conference on Non-Contact Atomic Force Microscopy.
📍 Innsbruck, Austria
📅 August 4, 2026
🕔 5:20 pm CEST
➡️ https://www.beilstein-journals.org/bjnano/news/WC4JNQBXURQ2QECDBFX264QE3Y?M=y#EditorsTalk #EdiTours #ScientificPublishing #OpenScience #BJNANO 💎🔓
-
Our Managing Editor Dr. Barbara Hissa will talk about “A true open-access route: how to go diamond in the nanoscience field?” the 27th International Conference on Non-Contact Atomic Force Microscopy.
📍 Innsbruck, Austria
📅 August 4, 2026
🕔 5:20 pm CEST
➡️ https://www.beilstein-journals.org/bjnano/news/WC4JNQBXURQ2QECDBFX264QE3Y?M=y#EditorsTalk #EdiTours #ScientificPublishing #OpenScience #BJNANO 💎🔓
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RE: https://post.lurk.org/@loriemerson/116935764227731524
The token [1] chasers creating more work for everybody. *You don’t say*
[1] Career progression tokens. Even this word has been usurped.
-
RE: https://post.lurk.org/@loriemerson/116935764227731524
The token [1] chasers creating more work for everybody. *You don’t say*
[1] Career progression tokens. Even this word has been usurped.
-
RE: https://post.lurk.org/@loriemerson/116935764227731524
The token [1] chasers creating more work for everybody. *You don’t say*
[1] Career progression tokens. Even this word has been usurped.
-
RE: https://post.lurk.org/@loriemerson/116935764227731524
The token [1] chasers creating more work for everybody. *You don’t say*
[1] Career progression tokens. Even this word has been usurped.
-
RE: https://post.lurk.org/@loriemerson/116935764227731524
The token [1] chasers creating more work for everybody. *You don’t say*
[1] Career progression tokens. Even this word has been usurped.
-
AI Made Up a Science Term — Now It’s in Dozens of Papers https://www.byteseu.com/2203410/ #AIHallucinations #AIInScience #AITrainingData #Claude35 #DigitalFossils #GPT4o #KnowledgeIntegrity #OCRErrors #PaperRetractions #RetractionWatch #Science #ScientificPublishing
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Congratulations to the team behind the journal Innovations in Graph Theory, hosted on the Centre Mersenne platform, whose application to the DOAJ has just been accepted!
https://doaj.org/toc/3050-743X
Innovations in Graph Theory is a mathematical journal publishing high-quality research in graph theory including its interactions with other areas. The journal is diamond open access, meaning there are no charges for authors and no charges for readers.
-
Congratulations to the team behind the journal Innovations in Graph Theory, hosted on the Centre Mersenne platform, whose application to the DOAJ has just been accepted!
https://doaj.org/toc/3050-743X
Innovations in Graph Theory is a mathematical journal publishing high-quality research in graph theory including its interactions with other areas. The journal is diamond open access, meaning there are no charges for authors and no charges for readers.
-
Congratulations to the team behind the journal Innovations in Graph Theory, hosted on the Centre Mersenne platform, whose application to the DOAJ has just been accepted!
https://doaj.org/toc/3050-743X
Innovations in Graph Theory is a mathematical journal publishing high-quality research in graph theory including its interactions with other areas. The journal is diamond open access, meaning there are no charges for authors and no charges for readers.
-
Congratulations to the team behind the journal Innovations in Graph Theory, hosted on the Centre Mersenne platform, whose application to the DOAJ has just been accepted!
https://doaj.org/toc/3050-743X
Innovations in Graph Theory is a mathematical journal publishing high-quality research in graph theory including its interactions with other areas. The journal is diamond open access, meaning there are no charges for authors and no charges for readers.
-
Congratulations to the team behind the journal Innovations in Graph Theory, hosted on the Centre Mersenne platform, whose application to the DOAJ has just been accepted!
https://doaj.org/toc/3050-743X
Innovations in Graph Theory is a mathematical journal publishing high-quality research in graph theory including its interactions with other areas. The journal is diamond open access, meaning there are no charges for authors and no charges for readers.
-
Making scientific knowledge free for all https://theconversation.com/making-scientific-knowledge-free-for-all-286857
The three main approaches are Green OA, Gold OA and Diamond OA:
- Green OA : publishing in a traditional journal and then depositing an accepted but unformatted version of the manuscript in an Open Access archive or an institutional repository
- Gold OA: final article immediately accessible to everyone, but involves publication fees amounting (i.e. Article Processing Charge, APC) to several thousand euros, paid by the authors or their institutions
- Diamond OA: allows authors to publish content for free and gives readers free access, thanks to journals that are backed by scientific communities, university libraries or non-profit organisations.
-
Making scientific knowledge free for all https://theconversation.com/making-scientific-knowledge-free-for-all-286857
The three main approaches are Green OA, Gold OA and Diamond OA:
- Green OA : publishing in a traditional journal and then depositing an accepted but unformatted version of the manuscript in an Open Access archive or an institutional repository
- Gold OA: final article immediately accessible to everyone, but involves publication fees amounting (i.e. Article Processing Charge, APC) to several thousand euros, paid by the authors or their institutions
- Diamond OA: allows authors to publish content for free and gives readers free access, thanks to journals that are backed by scientific communities, university libraries or non-profit organisations.
-
Making scientific knowledge free for all https://theconversation.com/making-scientific-knowledge-free-for-all-286857
The three main approaches are Green OA, Gold OA and Diamond OA:
- Green OA : publishing in a traditional journal and then depositing an accepted but unformatted version of the manuscript in an Open Access archive or an institutional repository
- Gold OA: final article immediately accessible to everyone, but involves publication fees amounting (i.e. Article Processing Charge, APC) to several thousand euros, paid by the authors or their institutions
- Diamond OA: allows authors to publish content for free and gives readers free access, thanks to journals that are backed by scientific communities, university libraries or non-profit organisations.
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Making scientific knowledge free for all https://theconversation.com/making-scientific-knowledge-free-for-all-286857
The three main approaches are Green OA, Gold OA and Diamond OA:
- Green OA : publishing in a traditional journal and then depositing an accepted but unformatted version of the manuscript in an Open Access archive or an institutional repository
- Gold OA: final article immediately accessible to everyone, but involves publication fees amounting (i.e. Article Processing Charge, APC) to several thousand euros, paid by the authors or their institutions
- Diamond OA: allows authors to publish content for free and gives readers free access, thanks to journals that are backed by scientific communities, university libraries or non-profit organisations.