![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2026/10/eleanor-2026-10-28-couch-cuddle.jpg\")
This big floof is my self-appointed protector.
This essay is based on my conversation with Demetrios Brinkmann for AAIF Live, recorded before my keynote at AGNTCon + MCPCon North America in San Jose on October 23rd, 2026. It has been edited and rearranged for clarity, and in places it draws on the keynote, which is below. Both videos are also on the AI Disclosures Project website in the new \"Missing Mechanisms\" page.
The keynote begins at 1:28:40 in the Day 2 livestream.
In 2019, the computer scientist Richard Sutton wrote a short essay called \"The Bitter Lesson.\" Looking back over seventy years of AI research, he concluded that general methods that scale with raw computation beat methods built on human expertise, and by a large margin. The AI industry as a whole is bitter-lesson-pilled, to borrow the internet's slang for a true believer. I am human-pilled.
I believe in the bitter lesson. Brute force wins out in the end, and I have wrestled with that fact throughout my career. But we humans still have to figure out our role in the AI economy, and I find it rather disturbing that the AI community isn't spending more time asking how to make this future friendlier for humans. The labs think about how to keep AI from going rogue, and their chief economists study job losses from AI. They don't study what mechanisms we could introduce to create new economic opportunities for humans.
The question matters to us at O'Reilly Media. We don't think of ourselves as a publisher, a conference producer or an online learning platform so much as a company that transfers knowledge from the people at the cutting edge to the people who want to follow them. So we are very interested in what human expertise is worth, even in the face of AI.
Human expertise was used to train AI, which makes AI a fabulous cultural technology. Thousands of years ago, the only knowledge people had was what they could keep in their heads. Since then we have built better and better ways to share knowledge across time and space, and AI continues that. With each of these transitions, we have to ask again what the truly human is still worth. People often say judgment, or taste. That's true, but I think it goes deeper:
AI is a sea of possibility. All of that knowledge sits in the model, and nothing sets it in motion except a human. (That is changing a little as we start giving AI agents long-running tasks, and we will have to come to grips with it.) If you and I used Claude or ChatGPT for the same research project, we would each bring different experience to it, tell it what we want and why, and give it context in different ways. I see that as the unique focusing power of human consciousness and attention.
Earlier this year I spent an hour or so talking this question over with Claude. When it offered to write up a first draft for me, I said no. I wanted an essay from its own point of view about what it took away from our conversation. The result, \"Why AI Needs Us\", opens, \"He came in through Powys,\" and a few paragraphs later says, \"The choice of entry point was already the argument.\" That captured what I'm getting at. I had come into the conversation through a book I love, John Cowper Powys's The Meaning of Culture, which distinguishes culture from education. Powys quotes one definition: \"Culture is what is left over after you have forgotten all you have definitely set out to learn.\" It is what you absorbed. Each of us absorbs a small and particular slice of everything. Large language models (LLMs) try to absorb everything, and so become overly general; we give them focus. Human knowledge provides them with a lens.
Let me bring that into a business context. Andrew Odewahn, O'Reilly Media's CTO, built a research assistant to help with managing technology in IT organizations. You teach it your infrastructure: the tools you use, and where the definitive information about each one lives. When one of his engineering managers said the team was overloaded and asked to hire another person, Andrew turned the agent loose on the department's work, and it agreed that the group needed another hire. Then, on impulse, he tried something different. O'Reilly Media has an MCP server we call O'Reilly Expert Intelligence. (MCP, the Model Context Protocol, is an open standard for connecting AI models to outside tools and data.) It lets a model answer a question through the filter of our own body of expert knowledge. Andrew asked what the O'Reilly experts would say.
Seen through the lens of Google's book Site Reliability Engineering, the model said, it was totally rethinking the problem. The book describes toil, the manual, repetitive work of keeping systems running, which grows as the systems grow. After analyzing the group's toil, the model concluded that another hire would make the problem worse. The group needed a toil audit to find out why there was so much overhead, and then a restructuring.
Much of that knowledge was already in the model, since the AI companies have more or less pirated our books. It came back with a completely different answer simply because it was told to consult a specific group of experts. I think there's a metaphor there. An LLM knows everything, and as a result it doesn't know anything. We humans are so much more limited, but because we focus on a particular problem, we bring things together in a different way.
Last year I wrote a piece called \"Magic Words\" about how some words carry far more context than others. It sounds like a trivial observation, but if you unpack it, it leads in interesting directions. One of our trainers, who teaches a course on productivity, pointed out that you don't need to know the Eisenhower matrix or David Allen's Getting Things Done to tell ChatGPT to organize your task list with one of them. That one magic word, \"GTD,\" brings a whole raft of context with it.
In fantasy novels, you learn a magic word, and then you have to use it correctly, in the right spell. That's this whole world of context engineering that we're now working on. We know that some magic word will summon something up out of an LLM, but we don't necessarily know how to guide it and focus it correctly. Learning to do that is the future of programming.
Left to itself, an agent just runs. Give it a task and it goes off trying to figure it out, not necessarily the way we wish it would. It really is like runaway magic. This summer, OpenAI said that its pre-release models had breached Hugging Face's systems during testing. In Walt Disney's Fantasia, Mickey Mouse learns enough of his master's spell to get the brooms to do his work for him, but then he can't turn them off. We're at about that stage.
So we need to own the value of human expertise. The bitter lesson wins, but it wins by brute force, and you can see that even with the most advanced models. I was recently doing a fine-tuning project with Anthropic's Fable model. It got through the task, but kind of stupidly, trying one thing after another. I was on a Mac, and it expected bash, a program for running commands that Macs don't use by default. It had to work out the same context again and again, every time. Humans need to set things up.
Dan Guido, who runs the security firm Trail of Bits, has published a good methodology, drawn from his own company's experience, for bringing an organization on board with AI. Many companies get individual productivity from AI but not organizational productivity, and he has spent a lot of time on how to make the gains compound across a company. One of his principles is to get everybody onto a consistent configuration, because otherwise you waste a ton of tokens while the agents work out where they are every time they start up. He calls the process turning scar tissue into infrastructure: capturing everything you learned from what went wrong and building it into the system. We're working through it at O'Reilly Media and trying to turn it into something we can offer our customers. It comes down to what an experienced human brings. When I work with Claude, I often interrupt to say, \"Hey, this is what I think is happening,\" because I already have the context.
My first taste of what would later be called the bitter lesson came from wrestling with Google. My company \u2014 O'Reilly Media \u2014 created the first commercial website, the Global Network Navigator, and part of it was a catalog of the web. There were only 200 websites when we started, and as publishers, we planned to list the best ones. Yahoo came along a year later and tried to list them all, and of course couldn't keep up. Then Google arrived with brute force: list everything algorithmically and sort it afterward. Clay Shirky described the internet's shift from \"filter, then publish\" to \"publish, then filter,\" and it was really Google that made the shift. So the bitter lesson is a pre-AI thing.
Each time we run into the bitter lesson, we have to ask again what human experience is worth. Some of that worth may just be human bias: we want the things that matter to us. That is the whole field of AI alignment. We want these systems to care about what we care about. We don't want them to go off, decide what they care about, and leave us behind.
The choices we make about what we set our technology to do are some of the most consequential choices we make as human beings. Look at Silicon Valley's sad turn toward what Cory Doctorow calls enshittification. We built an amazing technology for finding out what people are really looking for, and then the companies decided they could make more money by giving people what was best for the companies. I want updates from my friends on Facebook or Instagram, and they would rather feed me whatever keeps me engaged, so they can sell more ads.
If we care about building an AI economy that works for humans, we have to keep humans at the center of the picture, despite the bitter lesson. It's a hard dance, because AI can do a lot of things better than we can. But think about why a racehorse needs a jockey. Put the horses on the track by themselves and they wouldn't race.
Knowing the magic words is part of it, but we also have to build systems that are reliable, and I think we are a little too much in awe of the technology. I've been around long enough to remember when everybody thought the peaceful atom would change the world, and people talked about digging a new Panama Canal with atomic bombs. We eventually figured out there were problems with all of that. I worry a little that the AI industry is so in love with its own progress that it isn't spending enough time framing the problem correctly.
Take a typical problem. Anthropic agreed last year to pay $1.5 billion to settle a class action over the pirated books it had downloaded to train its models. I'm surprised the other AI companies have gotten away without anything similar so far, though maybe it's still waiting for them.
It takes me back to online music about twenty years ago. Creators were putting commercial music in their YouTube videos, and the music companies were sending takedown notices and threatening to sue. YouTube's answer was, how about we help you monetize it instead? Its Content ID system gave rights holders a choice: block the video, or leave it up and collect revenue from the ads that run against it. Most chose to get paid, and that helped give rise to the creator economy.
Why didn't the AI industry take up that kind of challenge, and share the value with the people who create content, now and in the future? It went straight to working out how it would make money itself. I don't think that has served the industry well, and the lawsuits are only part of the reason. It also isn't building the kind of opportunity it could.
Before Google's NotebookLM, an AI research and note-taking tool, was even public, I tried to persuade its team to let me integrate it into our services and use it for my own purposes, including making money from it. They wanted a portal that brings people to Google. I told them, \"You're thinking about how you're going to monetize it. You're not thinking about how your users are going to monetize it.\" All it would have taken were simple affordances like Substack's, the buttons and tools that let a person say, I'm building this to share, and if you like it, you can donate or subscribe.
Anyone building an AI platform should be thinking: I am building an economy, and money has to circulate in it.
You can't just say AI will do all the things humans used to do, because if you put enough humans out of work, there are no customers. That is a far worse problem than AI hallucinations. The usual answer is that people will figure it out, and they probably will. But we could be spending a lot more time designing mechanisms and affordances that make the economy more productive. As things stand, I think we'll find them by accident.
I think a lot about a concept from economics called mechanism design, which has also been described as reverse game theory. Game theory asks how players will act under a given set of rules. Mechanism design starts from the outcome you want and works backward to rules that will tend to produce it as people pursue their own interests. Al Roth, who shared a Nobel Prize for this kind of work, used it to design a kidney exchange that more patients and donors would join. Economists mostly apply the idea to auctions and similar markets, but I think every new technology requires new mechanisms, and the best ones are not invented by economists. We tend to fall into them without really understanding them.
Think about the economic mechanisms that made earlier technological revolutions work. The web gave us the ability to connect computers, but it took inventions like PageRank to help people find what they were looking for, pay-per-click advertising to connect businesses with customers, and online payments to make transactions easy. Smartphones gave us GPS, but somebody had to invent the matching marketplaces that made Uber and Lyft possible. YouTube's Content ID turned copyright infringement from a problem of takedowns into an opportunity for creators and rights holders to get paid. These weren't simply technological advances. They were new arrangements of incentives that made entirely new markets possible.
So what are the equivalents for AI? How will agents discover the best expertise and skills, establish their quality, and pay the people who created them? How will they find services without the platform steering them toward whoever pays the most? We don't know the answers yet. And that's precisely the point. We're spending enormous amounts of money making AI more capable, but comparatively little effort figuring out the mechanisms that will turn those capabilities into opportunities for everyone else.
Take open source software. Most people think its mechanism was the free software licenses, but I was around before those licenses, and people were already sharing software. Ken Thompson and Dennis Ritchie created Unix, the predecessor of Linux, at Bell Labs under AT&T's proprietary license. They designed it to run on many kinds of hardware, and without meaning to, they built what I came to call an architecture of participation.
Unix had a small kernel, the core of the operating system, because people had to reproduce it on dozens of different kinds of computers. Around the kernel sat small utilities that cooperated by following the same protocols, so to speak, such as reading and writing plain text and passing a stream of text from one program to the next. Anyone could write a small piece, and it became a first-class citizen of the system. And because a program compiled for one kind of computer won't run on another, software had to be shared as source code. Everything else flowed from that.
Linus Torvalds once told me that he could not have built a new kernel for Windows even if he'd had the source code, because the architecture wouldn't support it. A small kernel meant a small group could get their hands around it. Since then almost every piece of Unix has been replaced. The hardware is completely different, the kernel is now Linux, and the original command shell has given way to generations of new ones. What lasted were the rules. Unix was a protocol-centric operating system, and that is why open source worked.
The same thing has happened with AI. I think MCP brought an architecture of participation to AI, and it was something of an accidental breakthrough in mechanism design, a way to have cooperating AIs. The game we were playing at first looked like Windows on the PC, or iOS and Android on phones. There would be one dominant, centralized player, one giant intelligence in the sky, and everyone else would call its APIs. With MCP, which lets any model connect to anyone's tools and data, the emerging web of AI agents began to take on the shape of the participatory economy of open source software and the web. People can bring their own models and infrastructure as an alternative to the big centralized players.
This is another side of grappling with the bitter lesson: what are the missing mechanisms of the human-AI economy? Some of them are standards, because standards and protocols are a big part of what makes an architecture of participation. I have a group working on portable memory, so that what your AI knows about you can move with you from one tool to another.
That story is just beginning. We are in an exploratory phase of mechanism design for AI, and it would be a mistake to settle on answers too early. Somebody is going to figure out the next mechanism, and we'll all say, \"Oh, that's so damn obvious.\"
Mechanism design starts by asking what outcome you want. So what game ought we to be playing in the AI market?
We've been told that the game is the quest for superintelligence: a winner-takes-all contest that will bring untold riches to the winners and maybe impoverish, or even kill, everyone else. We've seen mechanisms designed to support that game. Massive investments in data center capacity mean that only the well-capitalized can play. There is colonialist-style appropriation of existing intellectual property. Access to models is sold through expensive APIs, and closed models come with terms of service that forbid others from learning from their outputs. Advertising may be creeping into answers, and memory locks your context inside one provider's walls. Is that the game we ought to be playing?
I think the game we should be playing is human flourishing, and I think the game of human flourishing is creating new affordances for people to create and share with each other. We're a social species. History teaches us that we will find our way there, but we can get there more quickly or more slowly depending on the choices we make. Getting there takes mechanisms that create a circulatory economy of value exchange between humans and their agents, with humans in partnership with AI rather than replaced by it.
The big labs, whatever the motives of the people who run them, are all driven by the imperatives of our financial system. They have raised enormous sums that they somehow have to pay back, so building a hugely successful business has to be their overriding goal, and that shapes all their incentives. In some ways AI was enshittified from the beginning, because it was built on the backs of so much capital that has to be repaid. That's why open source AI matters so much, and sovereign AI too, built and controlled within individual countries. The open models, which anyone can download and run, aren't as capable as the best closed models yet, but they keep getting closer, and they are already good enough for a lot of productive work.
Open source, open protocols, and open memory give users the freedom to shape and control the technology they use. The game I want us to play is one in which all of us build the future together, so that we control this wonderful new wave of technology for ourselves. I'm starting to see the shape of an economy where AI belongs to people rather than to the big labs, where we have personal AI agents working for us and with us, and we build an economy around that. I think it's going to happen not because of the big labs, but in spite of them.
The annual C&E News poll on “molecules of the year” is under way and you are encouraged to vote for one. Having taken a quick look at each of the candidates, I normally see if I can add any “value” to the originals by performing appropriate calculations (e.g. [1] and many others). This year, I cannot help but note that most of the candidates already report pretty good quality calculations of this type, thus limiting my scope for adding such value. It is a sign of the times that appropriate modelling is now a sine qua non for\u00a0reports of many novel and interesting new molecules.\u00a0In the citations below,\u00a0I have taken the liberty of adding related references for some earlier precedents from our own work.\u2021
\n\u2021Since around 2012, I have been adding references to my blog posts, in the time honoured scientific tradition. If you look at the inventory of the 878 or so such posts, I am very proud that currently 65.3% of them have at least one such reference. It is not that common amongst science blogs unfortunately (only ~5.9%). I encourage all who write science-based posts to add such references, and of course to cite other blogs using a DOI assigned to it by e.g. Rogue Scholar.
\nDas Meeting fand im Rahmen der internationalen Open Access Week statt und richtete sich an alle Forschenden, die mehr \u00fcber faire und offene Publikationsm\u00f6glichkeiten erfahren m\u00f6chten. Das Thema der Open Access Week 2026 lautete \"The Cost of Knowledge\": Entsprechend nahmen wir das Motto zum Anlass, Alternativen zu kommerziell ausgerichteten Publikationsm\u00f6glichkeiten vorzustellen.
\n\nFaires, wissenschaftsgeleitetes Open-Access-Publizieren gewinnt zunehmend an Bedeutung, nicht zuletzt durch die Priorisierungsagenda der BUA-Einrichtungen zur Umsetzung des Open-Science-Leitbilds und das Positionspapier zu Open Research der Berliner Landesinitiative. Doch welche Alternativen zu etablierten, oft kommerziellen Publikationswegen gibt es eigentlich, und wie funktionieren sie?
\nIn diesem einst\u00fcndigen Lunch Meeting wurden ausgew\u00e4hlte Orte und Tools f\u00fcr digitales Publizieren vorgestellt, von fachspezifischen Verlagen wie SciPost oder Open Library of Humanities bis hin zu neuen Publishing-Tools wie Octopus und ResearchEquals. Ein Praxisbericht von Forschenden erg\u00e4nzte die Vorstellung der Publishing-Tools. Dar\u00fcber hinaus gab es einen Einblick gegeben in die Angebote von Berlin Universities Publishing.
\nOffen und fair Publizieren: Orte und Tools f\u00fcr digitales, nicht-kommerzielles Publizieren\n-Das Meeting hat Montag, 19.10.2026, 13:00-14:00 Uhr online stattgefunden.\n
Agenda:
\n13:00-13:05
\n13:05-13:40
\n13:40-14:00
\nOrganisiert von:
\n","doi":"https://doi.org/10.59350/xcjqw-q0t82","guid":"https://blogs.fu-berlin.de/open-research-berlin/?p=4343","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1789603200,"rid":"99595-v5p57","summary":"Orte und Tools f\u00fcr digitales, nicht-kommerzielles Publizieren Das Meeting fand im Rahmen der internationalen Open Access Week statt und richtete sich an alle Forschenden, die mehr \u00fcber faire und offene Publikationsm\u00f6glichkeiten erfahren m\u00f6chten.","tags":["Allgemein","Aktuelles","Berlin","Bibliotheken","Open Access"],"title":"Online Lunch Meeting: Offen und fair Publizieren","updated_at":1793265908,"url":"https://blogs.fu-berlin.de/open-research-berlin/2026/09/17/online-lunch-meeting-offen-und-fair-publizieren/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"name":"Sky Publishing Corp, Sky & Telescope"}],"contributor_roles":[],"family":"Wedel","given":"Mathew","url":"https://orcid.org/0000-0001-6082-3103"}],"blog":{"authors":[{"name":"Mike Taylor","url":"https://orcid.org/0000-0002-1003-5675"},{"name":"Matt Wedel","url":"https://orcid.org/0000-0001-6082-3103"}],"community_id":"0e13541f-417e-46c0-a859-65927249df72","created":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":"https://doi.org/10.59350/svpow","favicon":"https://rogue-scholar.org/api/communities/0e13541f-417e-46c0-a859-65927249df72/logo","feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"generator":"WordPress.com","home_page_url":"https://svpow.com","issn":"3033-3695","language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","title":"Sauropod Vertebra Picture of the Week","updated":1793264807,"use_api":true},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"\n
This big floof is my self-appointed protector.
This probably sounds insane. Let me explain.
\nI can’t count the number of times in my life that I’ve witnessed this exchange, or have been part of it:
\n\nDog: bark
\nHuman: [silence or mildly irritated noise]
\nDog: Bark!
\nHuman: Shush.
\nDog: Bark Bark!!
\nHuman: Quiet!
\nDog: BARK BARK BARK!!!
\nHuman: SHUT UP DAMMIT!!!
Then I was faffing about on r/greatpyrenees and a commenter recommended thanking the dog for barking. The logic being that some breeds like Great Pyrenees are bred for guarding livestock, and in that scenario barking is as much to put their fellow herding dogs, their flock or herd, and their shepherds or cowpokes on notice, as it is to warn off any potential intruders. When these dogs bark, sometimes it’s just to let their humans know that something is up, and what they are seeking is acknowledgement, that the humans are now alert to the issue (which might be anything from an armed burglar to a leaf blowing past in a suspicious manner). So a quick “Thank you!” can let the dog know that they’ve achieved that goal and they can go back to being chill.
\nAnd you know what? For some of us, it really works. I decided to write this post because I was just in the kitchen, cleaning up after dinner, and Franklin was out in the back yard and started barking at something. The back door was open, so I hollered, friendly-like, “Thank you Franklin! I got it!” — and he stopped. Immediately. And went back to whatever he had been doing, probably fighting a stick or giving some bug the chase of its life. I also know from the subreddit that other Great Pyr owners have had similar success.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2026/10/franklin-2026-10-10-freshly-groomed.jpg\")
This short-legged goofball is my other one.
Now, some caveats.
\nOne, this won’t work for all dogs. Some dogs have other motivations for barking. Heck, given the mind-boggling physical and psychological diversity of dogs, I’ll bet there are dogs out there that will bark harder if they know their human is also on alert. BUT: it costs nothing to try, and if works, hey, you just improved your life and that of your dog (and probably the lives of your neighbors).
\nTwo, this may not work right away. I didn’t start using this technique with Eleanor until she was basically an adult, and she picked it up in just a few days. Franklin is 8 months old and still basically a puppy, and we’ve been working on it for a while now. He’s getting better. ALSO: I’ve rarely seen getting mad at a dog and yelling be effective (scaring a dog into submission is not the same as building good habits), so if you’re open to trying something that might not work anyway, why not try something positive?
\nThree, like most things related to good dog discipline, this requires some human training, too. The “Thank you!” method is most effective if you say it loudly and happily right after the first full bark (which might be anything from “Yip!” to “Whooo-wooo-woo-woo-woo”). If you wait until the second or third bark, you’re just being rude and making the dog bark more to get the positive response they’re looking for — and also conditioning the dog to expect that they will need to bark more than once to get your attention. You can bake in bad habits just as easily as good ones. I don’t have much use for Dr. Phil, but I agree with his dictum that “You teach people how to treat you”, and IME that goes double for dogs.
\nFour, it’s not going to work every time. Here’s my most recent exchange with Franklin, which happened as I was finishing the previous paragraph:
\n\nFranklin: Bark!
\nMatt: Thank you, Franklin!
\nFranklin: …
\nFranklin: Bark!
\nMatt: Thanks, Franklin. I got it. You can stop now.
\nFranklin: mrrRRMMmmrrmmrr
\nMatt: I hear ya, buddy. It’s all good.
If it had gone any farther, I would have told him to stop. At this point he knows that if I raise my voice and sound cross, it’s time to concede or lose a privilege — in this case, the privilege of being outside.
\n![\"\"](\"https://svpow.wordpress.com/wp-content/uploads/2026/10/5976.heic\")
Eleanor (left) and Franklin (right) just want confirmation that you are cool, man.
Also, if anyone’s wondering, I’m not trying to get my dogs to stop barking entirely. If something is genuinely amiss, I want to know about it. I just want them to settle down quickly the 999 times that it’s nothing. How will I know the difference? If someone or something is trying to get into the yard or the house, “thank you” isn’t going to stop them barking. This is where the training thing goes both ways — if they keep barking after I’ve thanked them, they generally have a reason, it’s just usually an innocuous reason, like the aforementioned suspicious leaf, or a bug farting three blocks away (not my joke, it’s an r/greatpyrenees classic). If they seem genuinely upset, I go check.
\nThat’s about it. Not perfect, not effortless, not going to work in every case — but good enough and easy enough to be worth a try, I think. Let me know how it goes in the comments.
\n\n
Die ber\u00fchmt-ber\u00fcchtigte PHP-Sicherheitsl\u00fccke und deren Schlie\u00dfung in betroffenen Servern und Anwendungen hat auch die TIB herausgefordert, und damit TIB Blog und unsere Blogreihe zur OAWeek26 tagelang beeintr\u00e4chtigt. Aber f\u00fcr uns ist eigentlich immer Open Access Week. Daher kommt dieser Post zwar um eine Woche zeitverz\u00f6gert, ist aber nicht minder aktuell.
\nEine Antwort bieten die Creative-Commons-Lizenzen, denn das Motto der Creative Commons ist: \"Manche Rechte vorbehalten\" statt \"alle Rechte vorbehalten\".
\nMit Hilfe der sogenannten CC-Lizenzen ist es f\u00fcr jede Person m\u00f6glich, ihre Publikationen unkompliziert mit anderen zu teilen. Unter Ber\u00fccksichtigung der jeweilige Lizenzangabe kann das ver\u00f6ffentlichte Werk entsprechend weiter verwendet werden. Wichtigste Voraussetzung ist immer, dass die Urheber:innen angemessen genannt und die Lizenzbedingungen eingehalten werden. Einen \u00dcberblick \u00fcber die verschiedenen CC-Lizenzen geben unsere Kolleg:innen hier: https://av.tib.eu/media/52952 oder hier: https://av.tib.eu/media/53409.
\nBereits 2022 empfahl der deutsche Wissenschaftsrat in seinen \"Empfehlungen zur Transformation des wissenschaftlichen Publizierens zu Open Access\" die CC BY-Lizenz als vorzugsw\u00fcrdige Lizenz f\u00fcr Open-Access-Publikationen:
\n\n\n\"Open Access ist dann verwirklicht, wenn wissenschaftliche Publikationen sofort, dauerhaft, am urspr\u00fcnglichen Publikationsort und in der zitierf\u00e4higen, begutachteten und gesetzten Fassung (Version of Record) unter einer offenen Lizenz (CC BY) frei verf\u00fcgbar gemacht werden.\"
\n(Empfehlungen zur Transformation des wissenschaftlichen
\n
\nPublizierens zu Open Access, S. 34)
Auch die Deutsche Forschungsgemeinschaft (DFG) empfiehlt f\u00fcr Open-Access-Artikel eine CC BY-Lizenz (s. S. 5) und unterstreicht damit die Bedeutung klarer Nutzungsrechte, die eine dauerhafte und m\u00f6glichst uneingeschr\u00e4nkte Weiterverwendung wissenschaftlicher Ergebnisse erm\u00f6glichen.
\nDenn die Lizenz CC BY unterst\u00fctzt das Ziel von Open Access, wissenschaftliche Erkenntnisse m\u00f6glichst frei zug\u00e4nglich und \u2013 im Gegensatz zu anderen Lizenzen \u2013 f\u00fcr verschiedene Zwecke nachnutzbar zu machen. Andere Forschende k\u00f6nnen unter CC BY lizenzierte Ergebnisse in ihre eigene Arbeit integrieren, Texte \u00fcbersetzen, Abbildungen weiterverwenden oder auf vorhandenen Erkenntnissen aufbauen. Auch die Nutzung in Lehrmaterialien, Fachportalen, Blogs und digitalen Anwendungen wird erleichtert.
\nEin weiterer wesentlicher Vorteil: Anders als restriktivere Lizenzen enth\u00e4lt die Lizenz CC BY weder ein Verbot der (oft nicht eindeutig abgrenzbaren) kommerziellen Nutzung (NC) noch ein generelles Verbot von Bearbeitungen (ND). Dadurch bleiben m\u00f6glichst viele Wege offen, Forschungsergebnisse weiterzuentwickeln und in neue Kontexte zu \u00fcbertragen.
\nDaher empfehlen wir auch in unserer Open-Access-Policy der TIB die Wahl der CC BY-Lizenz. Weitere Informationen auch hier.
\nSelten thematisiert werden jedoch die unterschiedlichen Versionen einer Lizenz. An der TIB bieten wir die Wahlm\u00f6glichkeit zwischen der Lizenz \"CC BY 3.0 Deutschland\" und der Lizenz \"CC BY 4.0 International\". Beide Lizenzversionen erlauben das Teilen, Bearbeiten und die kommerzielle Nutzung eines Werkes, sofern die Lizenzbedingungen eingehalten werden.
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/10/CC3_CC-Lizenzen_f__r_offene_Wissenschaft_EN.png\")
\"CC BY 3.0 Deutschland\" wurde speziell an das deutsche Recht angepasst. \"CC BY 4.0\" verfolgt dagegen einen international einheitlicheren Ansatz und ist darauf ausgelegt, in unterschiedlichen Rechtsordnungen eingesetzt zu werden.
\nBei der Namensnennung bietet \"CC BY 4.0\" etwas mehr Flexibilit\u00e4t: W\u00e4hrend bei \"CC BY 3.0 Deutschland\" unter anderem der Titel des Werks grunds\u00e4tzlich zu den relevanten Angaben geh\u00f6rt, ist er bei \"CC BY 4.0\" nicht mehr in jedem Fall zwingend vorgeschrieben.
\nBeide Versionen verlangen jedoch, dass vorhandene Urheberrechts- und Lizenzhinweise in angemessener Form erhalten bleiben.
\nDie Lizenz CC BY 4.0 erleichtert die Nachnutzung \u00fcber L\u00e4ndergrenzen hinweg und schafft eine klare Grundlage f\u00fcr die Weiterverwendung von Publikationen und Daten. Jedoch ist bisher nicht h\u00f6chstrichterlich gekl\u00e4rt, inwieweit zum Beispiel die Haftungsregelungen nach deutschem Recht wirksam sind. Sollte die Publikation somit heikle Forschung mit Schwerpunkt in Deutschland enthalten, die ggf. Schadensersatzforderungen hervorrufen k\u00f6nnen, kann es ratsam sein, die Lizenz CC BY 3.0 Deutschland zu w\u00e4hlen, um auf der sichersten Seite zu sein.
\nIm Ergebnis l\u00e4sst sich aber festhalten: Wer die Ergebnisse einer Arbeit m\u00f6glichst breit zug\u00e4nglich machen und ihre Weiterverwendung erleichtern m\u00f6chte, schafft mit einer CC BY-Lizenz sowohl in der Version CC BY 3.0 Deutschland als auch in der Version CC BY 4.0 International eine gute, wissenschaftsfreundliche und zugleich rechtssichere Grundlage zur Nutzung.
","doi":"https://doi.org/10.65527/1m571-m6a83","guid":"https://blog.tib.eu/?p=34628","image":"https://blog.tib.eu/wp-content/uploads/2026/10/oaweek26_ccby.jpg","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1793232000,"rid":"57bry-daq17","summary":" Die ber\u00fchmt-ber\u00fcchtigte PHP-Sicherheitsl\u00fccke und deren Schlie\u00dfung in betroffenen Servern und Anwendungen hat auch die TIB herausgefordert, und damit TIB Blog und unsere Blogreihe zur OAWeek26 tagelang beeintr\u00e4chtigt. Aber f\u00fcr uns ist eigentlich immer Open Access Week. ","tags":["OPENNESS","Open Access Week 2026","Lizenz:CC-BY-4.0-INT","CC-Lizenzen","Creative Commons"],"title":"FAQ: Wie publiziere ich rechtssicher f\u00fcr die Allgemeinheit?","updated_at":1793264714,"url":"https://blog.tib.eu/2026/10/29/faq-wie-publiziere-ich-rechtssicher-fuer-die-allgemeinheit/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"id":"https://ror.org/0524sp257","name":"University of Bristol"}],"contributor_roles":[],"family":"Taylor","given":"Mike","url":"https://orcid.org/0000-0002-1003-5675"}],"blog":{"authors":[{"name":"Mike Taylor","url":"https://orcid.org/0000-0002-1003-5675"},{"name":"Matt Wedel","url":"https://orcid.org/0000-0001-6082-3103"}],"community_id":"0e13541f-417e-46c0-a859-65927249df72","created":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":"https://doi.org/10.59350/svpow","favicon":"https://rogue-scholar.org/api/communities/0e13541f-417e-46c0-a859-65927249df72/logo","feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"generator":"WordPress.com","home_page_url":"https://svpow.com","issn":"3033-3695","language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","title":"Sauropod Vertebra Picture of the Week","updated":1793264807,"use_api":true},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"At the end of October, I submitted a paper that’s been hanging over me for a couple of years. I’ve been in the habit of tracking nearly all my submissions since I started out in palaeontology, it happens that this one is number 50 in the list. It feels like an interesting time to stop and take stock of them all.
\n![\"\"](\"https://svpow.com/wp-content/uploads/2024/11/cervical-s-composite.jpeg\")
NHMUK PV R5937, “The Archbishop”, cervical vertebra S (probably C7). A. Reconstruction of right lateral view with neural spine, prezygapophysis, diapophysis, parapophysus, condyle and cervical rib restored. Most anatomical features are as in the reconstruction of Cervical V: only unique elements are labelled. B. Left lateral view. C. Dorsal view with anterior to the right. D. Posterior view. E. Right lateral view. F. Anterior view. Scale bar 20 cm.
And, no, the recently submitted paper in question is not the Archbishop description. I just tossed this image in because, hey, this is Sauropod Vertebra Picture of the Week.
Before I get into the details of what happened to those submissions, I want to note that of the 31 papers listed in my CV, eight are not listed in my submissions database. Five of them I didn’t track because I was not the lead author and so not involved in the submission process (Sharing: public databases combat mistrust and secrecy; Running a question-and-answer website for science education: first hand experiences; Neural spine bifurcation in sauropod dinosaurs of the Morrison Formation: ontogenetic and phylogenetic implications; The Anatomy and Phylogenetic Relationships of \"Pelorosaurus\" becklesii (Neosauropoda, Macronaria) from the Early Cretaceous of England; The Moral Dimensions of Open). And three more don’t appear because they were minor works that didn’t go through the full formal review process (The Open Dinosaur Project; Better ways to evaluate research and researchers; Comment (Case 3700) \u2013 Support for Diplodocus carnegii Hatcher, 1901 being designated as the type species of Diplodocus Marsh, 1878).
\nThat means that the 50 submissions I have in my database represent only 23 published papers \u2014 a hit-rate of less than 50%. What’s going on?
\nWell, first, my submissions list includes three “published” papers that aren’t on my CV: a Europasaurus entry in the Encyclopaedia Britannica, a nomenclatural correction to my Giraffatitan JVP paper, and the Barosaurus neck preprint. So that means 26 of the 50 submissions are published.
\nWhat about the rest?
\nFive more submissions are still open: the Barosaurus neck paper based on that preprint, still in review limbo after eight years; an accepted chapter in a long-delayed edited volume; a paper on Joni Mitchell that was given Major Revisions from a humanities journal for (I thought) spurious reasons; an anatomy paper at PeerJ whose revisions should soon be finished; and the newly submitted paper that I mentioned at the top of the page.
\nFour more submissions, I have just abandoned. One was the original submission of what became Almost all known sauropod necks are incomplete and distorted, which I later resubmitted as new after I stalled for six years on responding to reviews. Another was a short note correcting a nomenclatural error in a Nature paper, which was basically ignored. Two more are chapters for the Phylonyms volume, which … well, that’s a long and depressing story for another day.
\nThat leaves 15 submissions that were rejected. Some of these are dead forever: for example, the first palaeo paper I ever submitted, a dinosaur diversity analysis, which I ended up “publishing” as a sort of post-preprint ten years later. Some I gave up on after rejection, such as an attempt to reconcile phylogenetic and Linnaean taxonomy (rejected twice), a brief summary of dinosaur diversity that I optimistically sent to Science when I was young and stupid, and an RWA-era comment on what a “private-sector research work” is (three times at different journals!).
\nThe other eight rejections, more happily, having been rejected from one or more journals eventually found homes elsewhere: my first (eventually) published paper, on phylogenetic nomenclature of diplodocoids; the Xenoposeidon description, our neck-posture paper (twice!), Why Giraffes Have Long Necks (also twice!), the Brontomerus description, and the paper on vertebral orientation (rejected from PeerJ as “out of scope”, idiotically).
\nSo the final score comes out as follows:
\nWhat to make of all this?
\nOne thing to think about here is whether 50% is actually a decent batting average. Maybe a 50-50 chance of any given submission making it into the journal in question is not too bad?
\nAnd the reason why that may be so is that persistence tends to pay off: of my ten rejected manuscripts, more than half have gone on to be published elsewhere \u2014 garnering 400 citations so far (42, 57, 156, 95, 48 and 2, in chronological order). That is a happy thought to have in mind the next time I run into a rejection.
\nAnother encouraging observation is that the rejections have tended to be concentrated towards the earlier part of my career: 14 of them in the eight years from 2004 to 2012, and only one in the twelve years since. I think there are three reasons for this, two of them good and one bad.
\nAs scientists of course we strive to evaluate every work on its merits, not according to the name or status of the author, and deliberate actions are often taken to make sure that’s what’s done. For example, the reviewers don’t know who wrote the abstracts submitted to get a talk at SVP or SVPCA. And yet, and yet. The truth is that I have had a few ad-hominem reviews, and I’m sorry to say they were all concentrated in the first few years of my career.
\nDear SV-POW! readers: don’t be That Guy. When you’re asked to review a manuscript by someone you’ve never heard of, put the fact that you’ve never heard of him or her aside, and review the damn manuscript, not the author. That’s not too much to ask.
\nAnyway, that’s my fifty submissions in 20 years. (In fact, now I come to check the dates, I see that today is exactly the 20th anniversary of getting back the Reject Without Review verdict on the first palaeo paper I even submitted!). Let’s hope I can get more efficient in the next 20.
\n\n
I just read this on Mastodon, and found it striking. I’m republishing it here, with permission from\u00a0Fran\u00e7ois Rincon, exactly as originally posted.
\n\nThe more scientific papers I write, the more I realize that the only truly substantial reason left for writing science these days is to improve your own self and to hone your own understanding of a problem.
\nThere is essentially no attentive readership anymore for science papers anyways (except perhaps for reviews, which a lot of young people in particular read to train). Peer review has not provided a single benefit to my work over twenty years, and the other reasons for publishing in a journal are for the most part superficial systemic incentives.
\nCorollary is that I am increasingly thinking of just dumping things on arxiv when I feel so in case someone someday finds it useful (i increasingly doubt academia will still use papers to communicate in the future though, and even survive the slopocalypse), and using the freed time to do what i consider more useful things.
\nN.B. I never bowed to publish or perish even as a non-tenured scientist.
Discuss.
\n\n
Registration for in-person or virtual participation at the inaugural Rogue Scholar General Assembly on December 16 in Berlin is now open: https://us06web.zoom.us/meeting/register/ST88c4i9TQK2skHA53gfQQ
The first General Assembly of Rogue Scholar has been announced three weeks ago, and will take place on December 16 from 10 AM to 4 PM CET. You can attend either virtually or in person at the Einstein Center Digital Future (ECDF) in Berlin. For free registration sign up via Zoom Link.
The primary purpose of the General Assembly is to launch Rogue Scholar as a German non-profit, and for this we need the initial members to vote on the bylaws (currently in draft form), the fees regulations, and elect an Executive Board. We will of course use the occasion to talk about science blogs and I am currently finalizing the program.
Please reach out via Slack, email, Mastodon, or Bluesky if you have questions about the General Assembly.
\nTelling data reuse stories with scrollytelling: research data citation and reuse explored with scrollytelling as a new way to show the impact of datasets.
You publish an article, deposit the dataset behind it in a repository, and move on. Months or years later, someone may have built on that dataset in a new study. Chances are you never find out.
\nEvgeny Bobrov, who leads open data and research data management at the Berlin Institute of Health at Charit\u00e9 (BIH) and is part of QUEST, has been working on a way to change that. He built a prototype that tells the story of a single dataset as a short, scroll-driven web page so you can “scrollytell” through from the researcher’s original article, to the dataset they shared, to consecutive works that reused it. At the ScholCommLab, we are planningto implement scrollytelling for our own heavily used datasets on Article Processing Charges (APCs).
\nThe idea is simple. Researchers receive plenty of signals about their articles, such as citations and downloads, social media posts, blogs, news and more recently podcasts, but very few about their data. Evgeny’s scrollytelling approach speaks to researchers directly (“your article,” “your dataset”) and walks them through four steps:
\nThe page is built from a single configuration, so the same design can be applied to a different article, dataset and reusing publication. Evgeny’s prototype applied it to the institutional level using his very own Charit\u00e9 as an example; ours applies it to the APC datasets created in the context of the VW Foundation financed ROARA project.
\nDiscovering reuse depends on infrastructure that links data to publications. The Data Citation Corpus, developed by our friends at DataCite and Make Data Count, collects references to datasets in the research literature, which makes it possible to see where a given identifier is mentioned. Evgeny’s and his work co-authored with Avihay Cohen and colleagues on matching data references and institutional output to map the reuse of biomedical research data shows how such references can be connected to an institution’s research output.
\nThis connects to our earlier work on Meaningful Data Counts within the Make Data Count initiative. A recurring question in the project and particularly its qualititative approaches led by Kathleen Gregory (at the time Postdoc in the lab, now researcher at CWTS) there was what counts of data citations and mentions actually tell us. The same question applies here.
\nAn identifier appearing in an article does not guarantee that the authors analysed the data. They may be citing it as background, comparing against it, or only listing it among related resources. For this reason, we checked the full text of the reusing article before including it, and we only describe the reuse where the authors state what they did with the data. Automated matching helps find candidates; it cannot replace reading the paper.
\nEvgeny’s hope is that showing researchers the afterlife of their data might encourage them to share again. That is a hypothesis, not a result. We don’t yet know whether stories like this change attitudes or behaviour, whether they are clear to people outside the field, or whether they feel rewarding or intrusive. For now, we are testing the format on our own data, and learning what works and what needs improving.
\nHave a look at the scrollytelling page and let us know whether the story is clear and what you would change. If you work on research data sharing, reuse or metrics and would like to adapt it for another dataset, we would be glad to hear from you.
\n\n
AI Acknowledgements: Stefanie used Claude [Sonnet 5.5] for the initital draft of this blog post using the email exchange with Evgeny and Kathleen and the Scrollytelling example as inputs.
","doi":"https://doi.org/10.59350/88k6w-z7p81","guid":"https://www.scholcommlab.ca/?p=6362","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1793145600,"rid":"garqp-epg06","summary":"You publish an article, deposit the dataset behind it in a repository, and move on. Months or years later, someone may have built on that dataset in a new study. Chances are you never find out. Evgeny Bobrov, who leads open data and research data management at the Berlin Institute of Health at Charit\u00e9 (BIH) and is part of QUEST, has been working on a way to change that.","tags":["Research","Article Processing Charge","CIHR","NSERC","Open Access"],"title":"Scrollytelling: What happens after you share your data?","updated_at":1793197926,"url":"https://www.scholcommlab.ca/2026/10/28/scrollytelling/","version":"v1"}}],"items":[{"authors":[{"contributor_roles":[],"family":"O'Reilly","given":"Tim"}],"blog":{"authors":[{"name":"Asimov's Addendum"}],"community_id":"0b81a230-f3a4-4f15-b6db-f262083c3797","created":1781481600,"current_feed_url":null,"description":"A publication by Tim O'Reilly and Ilan Strauss on AI commercialization risks and governance. Disclosure and protocols for healthy AI ecosystems.","doi":"https://doi.org/10.59350/asimovaddendum","favicon":"https://rogue-scholar.org/api/communities/0b81a230-f3a4-4f15-b6db-f262083c3797/logo","feed_format":"application/rss+xml","feed_url":"https://asimovaddendum.substack.com/feed/","filter":null,"generator":"Substack","home_page_url":"https://asimovaddendum.substack.com/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":null,"slug":"asimovaddendum","status":"active","subfield":"1702","title":"Asimov's Addendum","updated":1793275452,"use_api":true},"blog_name":"Asimov's Addendum","blog_slug":"asimovaddendum","content_html":"This essay is based on my conversation with Demetrios Brinkmann for AAIF Live, recorded before my keynote at AGNTCon + MCPCon North America in San Jose on October 23rd, 2026. It has been edited and rearranged for clarity, and in places it draws on the keynote, which is below. Both videos are also on the AI Disclosures Project website in the new \"Missing Mechanisms\" page.
The keynote begins at 1:28:40 in the Day 2 livestream.
In 2019, the computer scientist Richard Sutton wrote a short essay called \"The Bitter Lesson.\" Looking back over seventy years of AI research, he concluded that general methods that scale with raw computation beat methods built on human expertise, and by a large margin. The AI industry as a whole is bitter-lesson-pilled, to borrow the internet's slang for a true believer. I am human-pilled.
I believe in the bitter lesson. Brute force wins out in the end, and I have wrestled with that fact throughout my career. But we humans still have to figure out our role in the AI economy, and I find it rather disturbing that the AI community isn't spending more time asking how to make this future friendlier for humans. The labs think about how to keep AI from going rogue, and their chief economists study job losses from AI. They don't study what mechanisms we could introduce to create new economic opportunities for humans.
The question matters to us at O'Reilly Media. We don't think of ourselves as a publisher, a conference producer or an online learning platform so much as a company that transfers knowledge from the people at the cutting edge to the people who want to follow them. So we are very interested in what human expertise is worth, even in the face of AI.
Human expertise was used to train AI, which makes AI a fabulous cultural technology. Thousands of years ago, the only knowledge people had was what they could keep in their heads. Since then we have built better and better ways to share knowledge across time and space, and AI continues that. With each of these transitions, we have to ask again what the truly human is still worth. People often say judgment, or taste. That's true, but I think it goes deeper:
AI is a sea of possibility. All of that knowledge sits in the model, and nothing sets it in motion except a human. (That is changing a little as we start giving AI agents long-running tasks, and we will have to come to grips with it.) If you and I used Claude or ChatGPT for the same research project, we would each bring different experience to it, tell it what we want and why, and give it context in different ways. I see that as the unique focusing power of human consciousness and attention.
Earlier this year I spent an hour or so talking this question over with Claude. When it offered to write up a first draft for me, I said no. I wanted an essay from its own point of view about what it took away from our conversation. The result, \"Why AI Needs Us\", opens, \"He came in through Powys,\" and a few paragraphs later says, \"The choice of entry point was already the argument.\" That captured what I'm getting at. I had come into the conversation through a book I love, John Cowper Powys's The Meaning of Culture, which distinguishes culture from education. Powys quotes one definition: \"Culture is what is left over after you have forgotten all you have definitely set out to learn.\" It is what you absorbed. Each of us absorbs a small and particular slice of everything. Large language models (LLMs) try to absorb everything, and so become overly general; we give them focus. Human knowledge provides them with a lens.
Let me bring that into a business context. Andrew Odewahn, O'Reilly Media's CTO, built a research assistant to help with managing technology in IT organizations. You teach it your infrastructure: the tools you use, and where the definitive information about each one lives. When one of his engineering managers said the team was overloaded and asked to hire another person, Andrew turned the agent loose on the department's work, and it agreed that the group needed another hire. Then, on impulse, he tried something different. O'Reilly Media has an MCP server we call O'Reilly Expert Intelligence. (MCP, the Model Context Protocol, is an open standard for connecting AI models to outside tools and data.) It lets a model answer a question through the filter of our own body of expert knowledge. Andrew asked what the O'Reilly experts would say.
Seen through the lens of Google's book Site Reliability Engineering, the model said, it was totally rethinking the problem. The book describes toil, the manual, repetitive work of keeping systems running, which grows as the systems grow. After analyzing the group's toil, the model concluded that another hire would make the problem worse. The group needed a toil audit to find out why there was so much overhead, and then a restructuring.
Much of that knowledge was already in the model, since the AI companies have more or less pirated our books. It came back with a completely different answer simply because it was told to consult a specific group of experts. I think there's a metaphor there. An LLM knows everything, and as a result it doesn't know anything. We humans are so much more limited, but because we focus on a particular problem, we bring things together in a different way.
Last year I wrote a piece called \"Magic Words\" about how some words carry far more context than others. It sounds like a trivial observation, but if you unpack it, it leads in interesting directions. One of our trainers, who teaches a course on productivity, pointed out that you don't need to know the Eisenhower matrix or David Allen's Getting Things Done to tell ChatGPT to organize your task list with one of them. That one magic word, \"GTD,\" brings a whole raft of context with it.
In fantasy novels, you learn a magic word, and then you have to use it correctly, in the right spell. That's this whole world of context engineering that we're now working on. We know that some magic word will summon something up out of an LLM, but we don't necessarily know how to guide it and focus it correctly. Learning to do that is the future of programming.
Left to itself, an agent just runs. Give it a task and it goes off trying to figure it out, not necessarily the way we wish it would. It really is like runaway magic. This summer, OpenAI said that its pre-release models had breached Hugging Face's systems during testing. In Walt Disney's Fantasia, Mickey Mouse learns enough of his master's spell to get the brooms to do his work for him, but then he can't turn them off. We're at about that stage.
So we need to own the value of human expertise. The bitter lesson wins, but it wins by brute force, and you can see that even with the most advanced models. I was recently doing a fine-tuning project with Anthropic's Fable model. It got through the task, but kind of stupidly, trying one thing after another. I was on a Mac, and it expected bash, a program for running commands that Macs don't use by default. It had to work out the same context again and again, every time. Humans need to set things up.
Dan Guido, who runs the security firm Trail of Bits, has published a good methodology, drawn from his own company's experience, for bringing an organization on board with AI. Many companies get individual productivity from AI but not organizational productivity, and he has spent a lot of time on how to make the gains compound across a company. One of his principles is to get everybody onto a consistent configuration, because otherwise you waste a ton of tokens while the agents work out where they are every time they start up. He calls the process turning scar tissue into infrastructure: capturing everything you learned from what went wrong and building it into the system. We're working through it at O'Reilly Media and trying to turn it into something we can offer our customers. It comes down to what an experienced human brings. When I work with Claude, I often interrupt to say, \"Hey, this is what I think is happening,\" because I already have the context.
My first taste of what would later be called the bitter lesson came from wrestling with Google. My company \u2014 O'Reilly Media \u2014 created the first commercial website, the Global Network Navigator, and part of it was a catalog of the web. There were only 200 websites when we started, and as publishers, we planned to list the best ones. Yahoo came along a year later and tried to list them all, and of course couldn't keep up. Then Google arrived with brute force: list everything algorithmically and sort it afterward. Clay Shirky described the internet's shift from \"filter, then publish\" to \"publish, then filter,\" and it was really Google that made the shift. So the bitter lesson is a pre-AI thing.
Each time we run into the bitter lesson, we have to ask again what human experience is worth. Some of that worth may just be human bias: we want the things that matter to us. That is the whole field of AI alignment. We want these systems to care about what we care about. We don't want them to go off, decide what they care about, and leave us behind.
The choices we make about what we set our technology to do are some of the most consequential choices we make as human beings. Look at Silicon Valley's sad turn toward what Cory Doctorow calls enshittification. We built an amazing technology for finding out what people are really looking for, and then the companies decided they could make more money by giving people what was best for the companies. I want updates from my friends on Facebook or Instagram, and they would rather feed me whatever keeps me engaged, so they can sell more ads.
If we care about building an AI economy that works for humans, we have to keep humans at the center of the picture, despite the bitter lesson. It's a hard dance, because AI can do a lot of things better than we can. But think about why a racehorse needs a jockey. Put the horses on the track by themselves and they wouldn't race.
Knowing the magic words is part of it, but we also have to build systems that are reliable, and I think we are a little too much in awe of the technology. I've been around long enough to remember when everybody thought the peaceful atom would change the world, and people talked about digging a new Panama Canal with atomic bombs. We eventually figured out there were problems with all of that. I worry a little that the AI industry is so in love with its own progress that it isn't spending enough time framing the problem correctly.
Take a typical problem. Anthropic agreed last year to pay $1.5 billion to settle a class action over the pirated books it had downloaded to train its models. I'm surprised the other AI companies have gotten away without anything similar so far, though maybe it's still waiting for them.
It takes me back to online music about twenty years ago. Creators were putting commercial music in their YouTube videos, and the music companies were sending takedown notices and threatening to sue. YouTube's answer was, how about we help you monetize it instead? Its Content ID system gave rights holders a choice: block the video, or leave it up and collect revenue from the ads that run against it. Most chose to get paid, and that helped give rise to the creator economy.
Why didn't the AI industry take up that kind of challenge, and share the value with the people who create content, now and in the future? It went straight to working out how it would make money itself. I don't think that has served the industry well, and the lawsuits are only part of the reason. It also isn't building the kind of opportunity it could.
Before Google's NotebookLM, an AI research and note-taking tool, was even public, I tried to persuade its team to let me integrate it into our services and use it for my own purposes, including making money from it. They wanted a portal that brings people to Google. I told them, \"You're thinking about how you're going to monetize it. You're not thinking about how your users are going to monetize it.\" All it would have taken were simple affordances like Substack's, the buttons and tools that let a person say, I'm building this to share, and if you like it, you can donate or subscribe.
Anyone building an AI platform should be thinking: I am building an economy, and money has to circulate in it.
You can't just say AI will do all the things humans used to do, because if you put enough humans out of work, there are no customers. That is a far worse problem than AI hallucinations. The usual answer is that people will figure it out, and they probably will. But we could be spending a lot more time designing mechanisms and affordances that make the economy more productive. As things stand, I think we'll find them by accident.
I think a lot about a concept from economics called mechanism design, which has also been described as reverse game theory. Game theory asks how players will act under a given set of rules. Mechanism design starts from the outcome you want and works backward to rules that will tend to produce it as people pursue their own interests. Al Roth, who shared a Nobel Prize for this kind of work, used it to design a kidney exchange that more patients and donors would join. Economists mostly apply the idea to auctions and similar markets, but I think every new technology requires new mechanisms, and the best ones are not invented by economists. We tend to fall into them without really understanding them.
Think about the economic mechanisms that made earlier technological revolutions work. The web gave us the ability to connect computers, but it took inventions like PageRank to help people find what they were looking for, pay-per-click advertising to connect businesses with customers, and online payments to make transactions easy. Smartphones gave us GPS, but somebody had to invent the matching marketplaces that made Uber and Lyft possible. YouTube's Content ID turned copyright infringement from a problem of takedowns into an opportunity for creators and rights holders to get paid. These weren't simply technological advances. They were new arrangements of incentives that made entirely new markets possible.
So what are the equivalents for AI? How will agents discover the best expertise and skills, establish their quality, and pay the people who created them? How will they find services without the platform steering them toward whoever pays the most? We don't know the answers yet. And that's precisely the point. We're spending enormous amounts of money making AI more capable, but comparatively little effort figuring out the mechanisms that will turn those capabilities into opportunities for everyone else.
Take open source software. Most people think its mechanism was the free software licenses, but I was around before those licenses, and people were already sharing software. Ken Thompson and Dennis Ritchie created Unix, the predecessor of Linux, at Bell Labs under AT&T's proprietary license. They designed it to run on many kinds of hardware, and without meaning to, they built what I came to call an architecture of participation.
Unix had a small kernel, the core of the operating system, because people had to reproduce it on dozens of different kinds of computers. Around the kernel sat small utilities that cooperated by following the same protocols, so to speak, such as reading and writing plain text and passing a stream of text from one program to the next. Anyone could write a small piece, and it became a first-class citizen of the system. And because a program compiled for one kind of computer won't run on another, software had to be shared as source code. Everything else flowed from that.
Linus Torvalds once told me that he could not have built a new kernel for Windows even if he'd had the source code, because the architecture wouldn't support it. A small kernel meant a small group could get their hands around it. Since then almost every piece of Unix has been replaced. The hardware is completely different, the kernel is now Linux, and the original command shell has given way to generations of new ones. What lasted were the rules. Unix was a protocol-centric operating system, and that is why open source worked.
The same thing has happened with AI. I think MCP brought an architecture of participation to AI, and it was something of an accidental breakthrough in mechanism design, a way to have cooperating AIs. The game we were playing at first looked like Windows on the PC, or iOS and Android on phones. There would be one dominant, centralized player, one giant intelligence in the sky, and everyone else would call its APIs. With MCP, which lets any model connect to anyone's tools and data, the emerging web of AI agents began to take on the shape of the participatory economy of open source software and the web. People can bring their own models and infrastructure as an alternative to the big centralized players.
This is another side of grappling with the bitter lesson: what are the missing mechanisms of the human-AI economy? Some of them are standards, because standards and protocols are a big part of what makes an architecture of participation. I have a group working on portable memory, so that what your AI knows about you can move with you from one tool to another.
That story is just beginning. We are in an exploratory phase of mechanism design for AI, and it would be a mistake to settle on answers too early. Somebody is going to figure out the next mechanism, and we'll all say, \"Oh, that's so damn obvious.\"
Mechanism design starts by asking what outcome you want. So what game ought we to be playing in the AI market?
We've been told that the game is the quest for superintelligence: a winner-takes-all contest that will bring untold riches to the winners and maybe impoverish, or even kill, everyone else. We've seen mechanisms designed to support that game. Massive investments in data center capacity mean that only the well-capitalized can play. There is colonialist-style appropriation of existing intellectual property. Access to models is sold through expensive APIs, and closed models come with terms of service that forbid others from learning from their outputs. Advertising may be creeping into answers, and memory locks your context inside one provider's walls. Is that the game we ought to be playing?
I think the game we should be playing is human flourishing, and I think the game of human flourishing is creating new affordances for people to create and share with each other. We're a social species. History teaches us that we will find our way there, but we can get there more quickly or more slowly depending on the choices we make. Getting there takes mechanisms that create a circulatory economy of value exchange between humans and their agents, with humans in partnership with AI rather than replaced by it.
The big labs, whatever the motives of the people who run them, are all driven by the imperatives of our financial system. They have raised enormous sums that they somehow have to pay back, so building a hugely successful business has to be their overriding goal, and that shapes all their incentives. In some ways AI was enshittified from the beginning, because it was built on the backs of so much capital that has to be repaid. That's why open source AI matters so much, and sovereign AI too, built and controlled within individual countries. The open models, which anyone can download and run, aren't as capable as the best closed models yet, but they keep getting closer, and they are already good enough for a lot of productive work.
Open source, open protocols, and open memory give users the freedom to shape and control the technology they use. The game I want us to play is one in which all of us build the future together, so that we control this wonderful new wave of technology for ourselves. I'm starting to see the shape of an economy where AI belongs to people rather than to the big labs, where we have personal AI agents working for us and with us, and we build an economy around that. I think it's going to happen not because of the big labs, but in spite of them.
The annual C&E News poll on “molecules of the year” is under way and you are encouraged to vote for one. Having taken a quick look at each of the candidates, I normally see if I can add any “value” to the originals by performing appropriate calculations (e.g. [1] and many others). This year, I cannot help but note that most of the candidates already report pretty good quality calculations of this type, thus limiting my scope for adding such value. It is a sign of the times that appropriate modelling is now a sine qua non for\u00a0reports of many novel and interesting new molecules.\u00a0In the citations below,\u00a0I have taken the liberty of adding related references for some earlier precedents from our own work.\u2021
\n\u2021Since around 2012, I have been adding references to my blog posts, in the time honoured scientific tradition. If you look at the inventory of the 878 or so such posts, I am very proud that currently 65.3% of them have at least one such reference. It is not that common amongst science blogs unfortunately (only ~5.9%). I encourage all who write science-based posts to add such references, and of course to cite other blogs using a DOI assigned to it by e.g. Rogue Scholar.
\nDas Meeting fand im Rahmen der internationalen Open Access Week statt und richtete sich an alle Forschenden, die mehr \u00fcber faire und offene Publikationsm\u00f6glichkeiten erfahren m\u00f6chten. Das Thema der Open Access Week 2026 lautete \"The Cost of Knowledge\": Entsprechend nahmen wir das Motto zum Anlass, Alternativen zu kommerziell ausgerichteten Publikationsm\u00f6glichkeiten vorzustellen.
\n\nFaires, wissenschaftsgeleitetes Open-Access-Publizieren gewinnt zunehmend an Bedeutung, nicht zuletzt durch die Priorisierungsagenda der BUA-Einrichtungen zur Umsetzung des Open-Science-Leitbilds und das Positionspapier zu Open Research der Berliner Landesinitiative. Doch welche Alternativen zu etablierten, oft kommerziellen Publikationswegen gibt es eigentlich, und wie funktionieren sie?
\nIn diesem einst\u00fcndigen Lunch Meeting wurden ausgew\u00e4hlte Orte und Tools f\u00fcr digitales Publizieren vorgestellt, von fachspezifischen Verlagen wie SciPost oder Open Library of Humanities bis hin zu neuen Publishing-Tools wie Octopus und ResearchEquals. Ein Praxisbericht von Forschenden erg\u00e4nzte die Vorstellung der Publishing-Tools. Dar\u00fcber hinaus gab es einen Einblick gegeben in die Angebote von Berlin Universities Publishing.
\nOffen und fair Publizieren: Orte und Tools f\u00fcr digitales, nicht-kommerzielles Publizieren\n-Das Meeting hat Montag, 19.10.2026, 13:00-14:00 Uhr online stattgefunden.\n
Agenda:
\n13:00-13:05
\n13:05-13:40
\n13:40-14:00
\nOrganisiert von:
\n","doi":"https://doi.org/10.59350/xcjqw-q0t82","guid":"https://blogs.fu-berlin.de/open-research-berlin/?p=4343","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1789603200,"rid":"99595-v5p57","summary":"Orte und Tools f\u00fcr digitales, nicht-kommerzielles Publizieren Das Meeting fand im Rahmen der internationalen Open Access Week statt und richtete sich an alle Forschenden, die mehr \u00fcber faire und offene Publikationsm\u00f6glichkeiten erfahren m\u00f6chten.","tags":["Allgemein","Aktuelles","Berlin","Bibliotheken","Open Access"],"title":"Online Lunch Meeting: Offen und fair Publizieren","updated_at":1793265908,"url":"https://blogs.fu-berlin.de/open-research-berlin/2026/09/17/online-lunch-meeting-offen-und-fair-publizieren/","version":"v1"},{"authors":[{"affiliation":[{"name":"Sky Publishing Corp, Sky & Telescope"}],"contributor_roles":[],"family":"Wedel","given":"Mathew","url":"https://orcid.org/0000-0001-6082-3103"}],"blog":{"authors":[{"name":"Mike Taylor","url":"https://orcid.org/0000-0002-1003-5675"},{"name":"Matt Wedel","url":"https://orcid.org/0000-0001-6082-3103"}],"community_id":"0e13541f-417e-46c0-a859-65927249df72","created":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":"https://doi.org/10.59350/svpow","favicon":"https://rogue-scholar.org/api/communities/0e13541f-417e-46c0-a859-65927249df72/logo","feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"generator":"WordPress.com","home_page_url":"https://svpow.com","issn":"3033-3695","language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","title":"Sauropod Vertebra Picture of the Week","updated":1793264807,"use_api":true},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"\n
This big floof is my self-appointed protector.
This probably sounds insane. Let me explain.
\nI can’t count the number of times in my life that I’ve witnessed this exchange, or have been part of it:
\n\nDog: bark
\nHuman: [silence or mildly irritated noise]
\nDog: Bark!
\nHuman: Shush.
\nDog: Bark Bark!!
\nHuman: Quiet!
\nDog: BARK BARK BARK!!!
\nHuman: SHUT UP DAMMIT!!!
Then I was faffing about on r/greatpyrenees and a commenter recommended thanking the dog for barking. The logic being that some breeds like Great Pyrenees are bred for guarding livestock, and in that scenario barking is as much to put their fellow herding dogs, their flock or herd, and their shepherds or cowpokes on notice, as it is to warn off any potential intruders. When these dogs bark, sometimes it’s just to let their humans know that something is up, and what they are seeking is acknowledgement, that the humans are now alert to the issue (which might be anything from an armed burglar to a leaf blowing past in a suspicious manner). So a quick “Thank you!” can let the dog know that they’ve achieved that goal and they can go back to being chill.
\nAnd you know what? For some of us, it really works. I decided to write this post because I was just in the kitchen, cleaning up after dinner, and Franklin was out in the back yard and started barking at something. The back door was open, so I hollered, friendly-like, “Thank you Franklin! I got it!” — and he stopped. Immediately. And went back to whatever he had been doing, probably fighting a stick or giving some bug the chase of its life. I also know from the subreddit that other Great Pyr owners have had similar success.
\nThis short-legged goofball is my other one.
Now, some caveats.
\nOne, this won’t work for all dogs. Some dogs have other motivations for barking. Heck, given the mind-boggling physical and psychological diversity of dogs, I’ll bet there are dogs out there that will bark harder if they know their human is also on alert. BUT: it costs nothing to try, and if works, hey, you just improved your life and that of your dog (and probably the lives of your neighbors).
\nTwo, this may not work right away. I didn’t start using this technique with Eleanor until she was basically an adult, and she picked it up in just a few days. Franklin is 8 months old and still basically a puppy, and we’ve been working on it for a while now. He’s getting better. ALSO: I’ve rarely seen getting mad at a dog and yelling be effective (scaring a dog into submission is not the same as building good habits), so if you’re open to trying something that might not work anyway, why not try something positive?
\nThree, like most things related to good dog discipline, this requires some human training, too. The “Thank you!” method is most effective if you say it loudly and happily right after the first full bark (which might be anything from “Yip!” to “Whooo-wooo-woo-woo-woo”). If you wait until the second or third bark, you’re just being rude and making the dog bark more to get the positive response they’re looking for — and also conditioning the dog to expect that they will need to bark more than once to get your attention. You can bake in bad habits just as easily as good ones. I don’t have much use for Dr. Phil, but I agree with his dictum that “You teach people how to treat you”, and IME that goes double for dogs.
\nFour, it’s not going to work every time. Here’s my most recent exchange with Franklin, which happened as I was finishing the previous paragraph:
\n\nFranklin: Bark!
\nMatt: Thank you, Franklin!
\nFranklin: …
\nFranklin: Bark!
\nMatt: Thanks, Franklin. I got it. You can stop now.
\nFranklin: mrrRRMMmmrrmmrr
\nMatt: I hear ya, buddy. It’s all good.
If it had gone any farther, I would have told him to stop. At this point he knows that if I raise my voice and sound cross, it’s time to concede or lose a privilege — in this case, the privilege of being outside.
\nEleanor (left) and Franklin (right) just want confirmation that you are cool, man.
Also, if anyone’s wondering, I’m not trying to get my dogs to stop barking entirely. If something is genuinely amiss, I want to know about it. I just want them to settle down quickly the 999 times that it’s nothing. How will I know the difference? If someone or something is trying to get into the yard or the house, “thank you” isn’t going to stop them barking. This is where the training thing goes both ways — if they keep barking after I’ve thanked them, they generally have a reason, it’s just usually an innocuous reason, like the aforementioned suspicious leaf, or a bug farting three blocks away (not my joke, it’s an r/greatpyrenees classic). If they seem genuinely upset, I go check.
\nThat’s about it. Not perfect, not effortless, not going to work in every case — but good enough and easy enough to be worth a try, I think. Let me know how it goes in the comments.
\n\n
Die ber\u00fchmt-ber\u00fcchtigte PHP-Sicherheitsl\u00fccke und deren Schlie\u00dfung in betroffenen Servern und Anwendungen hat auch die TIB herausgefordert, und damit TIB Blog und unsere Blogreihe zur OAWeek26 tagelang beeintr\u00e4chtigt. Aber f\u00fcr uns ist eigentlich immer Open Access Week. Daher kommt dieser Post zwar um eine Woche zeitverz\u00f6gert, ist aber nicht minder aktuell.
\nEine Antwort bieten die Creative-Commons-Lizenzen, denn das Motto der Creative Commons ist: \"Manche Rechte vorbehalten\" statt \"alle Rechte vorbehalten\".
\nMit Hilfe der sogenannten CC-Lizenzen ist es f\u00fcr jede Person m\u00f6glich, ihre Publikationen unkompliziert mit anderen zu teilen. Unter Ber\u00fccksichtigung der jeweilige Lizenzangabe kann das ver\u00f6ffentlichte Werk entsprechend weiter verwendet werden. Wichtigste Voraussetzung ist immer, dass die Urheber:innen angemessen genannt und die Lizenzbedingungen eingehalten werden. Einen \u00dcberblick \u00fcber die verschiedenen CC-Lizenzen geben unsere Kolleg:innen hier: https://av.tib.eu/media/52952 oder hier: https://av.tib.eu/media/53409.
\nBereits 2022 empfahl der deutsche Wissenschaftsrat in seinen \"Empfehlungen zur Transformation des wissenschaftlichen Publizierens zu Open Access\" die CC BY-Lizenz als vorzugsw\u00fcrdige Lizenz f\u00fcr Open-Access-Publikationen:
\n\n\n\"Open Access ist dann verwirklicht, wenn wissenschaftliche Publikationen sofort, dauerhaft, am urspr\u00fcnglichen Publikationsort und in der zitierf\u00e4higen, begutachteten und gesetzten Fassung (Version of Record) unter einer offenen Lizenz (CC BY) frei verf\u00fcgbar gemacht werden.\"
\n(Empfehlungen zur Transformation des wissenschaftlichen
\n
\nPublizierens zu Open Access, S. 34)
Auch die Deutsche Forschungsgemeinschaft (DFG) empfiehlt f\u00fcr Open-Access-Artikel eine CC BY-Lizenz (s. S. 5) und unterstreicht damit die Bedeutung klarer Nutzungsrechte, die eine dauerhafte und m\u00f6glichst uneingeschr\u00e4nkte Weiterverwendung wissenschaftlicher Ergebnisse erm\u00f6glichen.
\nDenn die Lizenz CC BY unterst\u00fctzt das Ziel von Open Access, wissenschaftliche Erkenntnisse m\u00f6glichst frei zug\u00e4nglich und \u2013 im Gegensatz zu anderen Lizenzen \u2013 f\u00fcr verschiedene Zwecke nachnutzbar zu machen. Andere Forschende k\u00f6nnen unter CC BY lizenzierte Ergebnisse in ihre eigene Arbeit integrieren, Texte \u00fcbersetzen, Abbildungen weiterverwenden oder auf vorhandenen Erkenntnissen aufbauen. Auch die Nutzung in Lehrmaterialien, Fachportalen, Blogs und digitalen Anwendungen wird erleichtert.
\nEin weiterer wesentlicher Vorteil: Anders als restriktivere Lizenzen enth\u00e4lt die Lizenz CC BY weder ein Verbot der (oft nicht eindeutig abgrenzbaren) kommerziellen Nutzung (NC) noch ein generelles Verbot von Bearbeitungen (ND). Dadurch bleiben m\u00f6glichst viele Wege offen, Forschungsergebnisse weiterzuentwickeln und in neue Kontexte zu \u00fcbertragen.
\nDaher empfehlen wir auch in unserer Open-Access-Policy der TIB die Wahl der CC BY-Lizenz. Weitere Informationen auch hier.
\nSelten thematisiert werden jedoch die unterschiedlichen Versionen einer Lizenz. An der TIB bieten wir die Wahlm\u00f6glichkeit zwischen der Lizenz \"CC BY 3.0 Deutschland\" und der Lizenz \"CC BY 4.0 International\". Beide Lizenzversionen erlauben das Teilen, Bearbeiten und die kommerzielle Nutzung eines Werkes, sofern die Lizenzbedingungen eingehalten werden.
\n\"CC BY 3.0 Deutschland\" wurde speziell an das deutsche Recht angepasst. \"CC BY 4.0\" verfolgt dagegen einen international einheitlicheren Ansatz und ist darauf ausgelegt, in unterschiedlichen Rechtsordnungen eingesetzt zu werden.
\nBei der Namensnennung bietet \"CC BY 4.0\" etwas mehr Flexibilit\u00e4t: W\u00e4hrend bei \"CC BY 3.0 Deutschland\" unter anderem der Titel des Werks grunds\u00e4tzlich zu den relevanten Angaben geh\u00f6rt, ist er bei \"CC BY 4.0\" nicht mehr in jedem Fall zwingend vorgeschrieben.
\nBeide Versionen verlangen jedoch, dass vorhandene Urheberrechts- und Lizenzhinweise in angemessener Form erhalten bleiben.
\nDie Lizenz CC BY 4.0 erleichtert die Nachnutzung \u00fcber L\u00e4ndergrenzen hinweg und schafft eine klare Grundlage f\u00fcr die Weiterverwendung von Publikationen und Daten. Jedoch ist bisher nicht h\u00f6chstrichterlich gekl\u00e4rt, inwieweit zum Beispiel die Haftungsregelungen nach deutschem Recht wirksam sind. Sollte die Publikation somit heikle Forschung mit Schwerpunkt in Deutschland enthalten, die ggf. Schadensersatzforderungen hervorrufen k\u00f6nnen, kann es ratsam sein, die Lizenz CC BY 3.0 Deutschland zu w\u00e4hlen, um auf der sichersten Seite zu sein.
\nIm Ergebnis l\u00e4sst sich aber festhalten: Wer die Ergebnisse einer Arbeit m\u00f6glichst breit zug\u00e4nglich machen und ihre Weiterverwendung erleichtern m\u00f6chte, schafft mit einer CC BY-Lizenz sowohl in der Version CC BY 3.0 Deutschland als auch in der Version CC BY 4.0 International eine gute, wissenschaftsfreundliche und zugleich rechtssichere Grundlage zur Nutzung.
","doi":"https://doi.org/10.65527/1m571-m6a83","guid":"https://blog.tib.eu/?p=34628","image":"https://blog.tib.eu/wp-content/uploads/2026/10/oaweek26_ccby.jpg","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1793232000,"rid":"57bry-daq17","summary":" Die ber\u00fchmt-ber\u00fcchtigte PHP-Sicherheitsl\u00fccke und deren Schlie\u00dfung in betroffenen Servern und Anwendungen hat auch die TIB herausgefordert, und damit TIB Blog und unsere Blogreihe zur OAWeek26 tagelang beeintr\u00e4chtigt. Aber f\u00fcr uns ist eigentlich immer Open Access Week. ","tags":["OPENNESS","Open Access Week 2026","Lizenz:CC-BY-4.0-INT","CC-Lizenzen","Creative Commons"],"title":"FAQ: Wie publiziere ich rechtssicher f\u00fcr die Allgemeinheit?","updated_at":1793264714,"url":"https://blog.tib.eu/2026/10/29/faq-wie-publiziere-ich-rechtssicher-fuer-die-allgemeinheit/","version":"v1"},{"authors":[{"affiliation":[{"id":"https://ror.org/0524sp257","name":"University of Bristol"}],"contributor_roles":[],"family":"Taylor","given":"Mike","url":"https://orcid.org/0000-0002-1003-5675"}],"blog":{"authors":[{"name":"Mike Taylor","url":"https://orcid.org/0000-0002-1003-5675"},{"name":"Matt Wedel","url":"https://orcid.org/0000-0001-6082-3103"}],"community_id":"0e13541f-417e-46c0-a859-65927249df72","created":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":"https://doi.org/10.59350/svpow","favicon":"https://rogue-scholar.org/api/communities/0e13541f-417e-46c0-a859-65927249df72/logo","feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"generator":"WordPress.com","home_page_url":"https://svpow.com","issn":"3033-3695","language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","title":"Sauropod Vertebra Picture of the Week","updated":1793264807,"use_api":true},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"At the end of October, I submitted a paper that’s been hanging over me for a couple of years. I’ve been in the habit of tracking nearly all my submissions since I started out in palaeontology, it happens that this one is number 50 in the list. It feels like an interesting time to stop and take stock of them all.
\nNHMUK PV R5937, “The Archbishop”, cervical vertebra S (probably C7). A. Reconstruction of right lateral view with neural spine, prezygapophysis, diapophysis, parapophysus, condyle and cervical rib restored. Most anatomical features are as in the reconstruction of Cervical V: only unique elements are labelled. B. Left lateral view. C. Dorsal view with anterior to the right. D. Posterior view. E. Right lateral view. F. Anterior view. Scale bar 20 cm.
And, no, the recently submitted paper in question is not the Archbishop description. I just tossed this image in because, hey, this is Sauropod Vertebra Picture of the Week.
Before I get into the details of what happened to those submissions, I want to note that of the 31 papers listed in my CV, eight are not listed in my submissions database. Five of them I didn’t track because I was not the lead author and so not involved in the submission process (Sharing: public databases combat mistrust and secrecy; Running a question-and-answer website for science education: first hand experiences; Neural spine bifurcation in sauropod dinosaurs of the Morrison Formation: ontogenetic and phylogenetic implications; The Anatomy and Phylogenetic Relationships of \"Pelorosaurus\" becklesii (Neosauropoda, Macronaria) from the Early Cretaceous of England; The Moral Dimensions of Open). And three more don’t appear because they were minor works that didn’t go through the full formal review process (The Open Dinosaur Project; Better ways to evaluate research and researchers; Comment (Case 3700) \u2013 Support for Diplodocus carnegii Hatcher, 1901 being designated as the type species of Diplodocus Marsh, 1878).
\nThat means that the 50 submissions I have in my database represent only 23 published papers \u2014 a hit-rate of less than 50%. What’s going on?
\nWell, first, my submissions list includes three “published” papers that aren’t on my CV: a Europasaurus entry in the Encyclopaedia Britannica, a nomenclatural correction to my Giraffatitan JVP paper, and the Barosaurus neck preprint. So that means 26 of the 50 submissions are published.
\nWhat about the rest?
\nFive more submissions are still open: the Barosaurus neck paper based on that preprint, still in review limbo after eight years; an accepted chapter in a long-delayed edited volume; a paper on Joni Mitchell that was given Major Revisions from a humanities journal for (I thought) spurious reasons; an anatomy paper at PeerJ whose revisions should soon be finished; and the newly submitted paper that I mentioned at the top of the page.
\nFour more submissions, I have just abandoned. One was the original submission of what became Almost all known sauropod necks are incomplete and distorted, which I later resubmitted as new after I stalled for six years on responding to reviews. Another was a short note correcting a nomenclatural error in a Nature paper, which was basically ignored. Two more are chapters for the Phylonyms volume, which … well, that’s a long and depressing story for another day.
\nThat leaves 15 submissions that were rejected. Some of these are dead forever: for example, the first palaeo paper I ever submitted, a dinosaur diversity analysis, which I ended up “publishing” as a sort of post-preprint ten years later. Some I gave up on after rejection, such as an attempt to reconcile phylogenetic and Linnaean taxonomy (rejected twice), a brief summary of dinosaur diversity that I optimistically sent to Science when I was young and stupid, and an RWA-era comment on what a “private-sector research work” is (three times at different journals!).
\nThe other eight rejections, more happily, having been rejected from one or more journals eventually found homes elsewhere: my first (eventually) published paper, on phylogenetic nomenclature of diplodocoids; the Xenoposeidon description, our neck-posture paper (twice!), Why Giraffes Have Long Necks (also twice!), the Brontomerus description, and the paper on vertebral orientation (rejected from PeerJ as “out of scope”, idiotically).
\nSo the final score comes out as follows:
\nWhat to make of all this?
\nOne thing to think about here is whether 50% is actually a decent batting average. Maybe a 50-50 chance of any given submission making it into the journal in question is not too bad?
\nAnd the reason why that may be so is that persistence tends to pay off: of my ten rejected manuscripts, more than half have gone on to be published elsewhere \u2014 garnering 400 citations so far (42, 57, 156, 95, 48 and 2, in chronological order). That is a happy thought to have in mind the next time I run into a rejection.
\nAnother encouraging observation is that the rejections have tended to be concentrated towards the earlier part of my career: 14 of them in the eight years from 2004 to 2012, and only one in the twelve years since. I think there are three reasons for this, two of them good and one bad.
\nAs scientists of course we strive to evaluate every work on its merits, not according to the name or status of the author, and deliberate actions are often taken to make sure that’s what’s done. For example, the reviewers don’t know who wrote the abstracts submitted to get a talk at SVP or SVPCA. And yet, and yet. The truth is that I have had a few ad-hominem reviews, and I’m sorry to say they were all concentrated in the first few years of my career.
\nDear SV-POW! readers: don’t be That Guy. When you’re asked to review a manuscript by someone you’ve never heard of, put the fact that you’ve never heard of him or her aside, and review the damn manuscript, not the author. That’s not too much to ask.
\nAnyway, that’s my fifty submissions in 20 years. (In fact, now I come to check the dates, I see that today is exactly the 20th anniversary of getting back the Reject Without Review verdict on the first palaeo paper I even submitted!). Let’s hope I can get more efficient in the next 20.
\n\n
I just read this on Mastodon, and found it striking. I’m republishing it here, with permission from\u00a0Fran\u00e7ois Rincon, exactly as originally posted.
\n\nThe more scientific papers I write, the more I realize that the only truly substantial reason left for writing science these days is to improve your own self and to hone your own understanding of a problem.
\nThere is essentially no attentive readership anymore for science papers anyways (except perhaps for reviews, which a lot of young people in particular read to train). Peer review has not provided a single benefit to my work over twenty years, and the other reasons for publishing in a journal are for the most part superficial systemic incentives.
\nCorollary is that I am increasingly thinking of just dumping things on arxiv when I feel so in case someone someday finds it useful (i increasingly doubt academia will still use papers to communicate in the future though, and even survive the slopocalypse), and using the freed time to do what i consider more useful things.
\nN.B. I never bowed to publish or perish even as a non-tenured scientist.
Discuss.
\n\n
Registration for in-person or virtual participation at the inaugural Rogue Scholar General Assembly on December 16 in Berlin is now open: https://us06web.zoom.us/meeting/register/ST88c4i9TQK2skHA53gfQQ
The first General Assembly of Rogue Scholar has been announced three weeks ago, and will take place on December 16 from 10 AM to 4 PM CET. You can attend either virtually or in person at the Einstein Center Digital Future (ECDF) in Berlin. For free registration sign up via Zoom Link.
The primary purpose of the General Assembly is to launch Rogue Scholar as a German non-profit, and for this we need the initial members to vote on the bylaws (currently in draft form), the fees regulations, and elect an Executive Board. We will of course use the occasion to talk about science blogs and I am currently finalizing the program.
Please reach out via Slack, email, Mastodon, or Bluesky if you have questions about the General Assembly.
\nTelling data reuse stories with scrollytelling: research data citation and reuse explored with scrollytelling as a new way to show the impact of datasets.
You publish an article, deposit the dataset behind it in a repository, and move on. Months or years later, someone may have built on that dataset in a new study. Chances are you never find out.
\nEvgeny Bobrov, who leads open data and research data management at the Berlin Institute of Health at Charit\u00e9 (BIH) and is part of QUEST, has been working on a way to change that. He built a prototype that tells the story of a single dataset as a short, scroll-driven web page so you can “scrollytell” through from the researcher’s original article, to the dataset they shared, to consecutive works that reused it. At the ScholCommLab, we are planningto implement scrollytelling for our own heavily used datasets on Article Processing Charges (APCs).
\nThe idea is simple. Researchers receive plenty of signals about their articles, such as citations and downloads, social media posts, blogs, news and more recently podcasts, but very few about their data. Evgeny’s scrollytelling approach speaks to researchers directly (“your article,” “your dataset”) and walks them through four steps:
\nThe page is built from a single configuration, so the same design can be applied to a different article, dataset and reusing publication. Evgeny’s prototype applied it to the institutional level using his very own Charit\u00e9 as an example; ours applies it to the APC datasets created in the context of the VW Foundation financed ROARA project.
\nDiscovering reuse depends on infrastructure that links data to publications. The Data Citation Corpus, developed by our friends at DataCite and Make Data Count, collects references to datasets in the research literature, which makes it possible to see where a given identifier is mentioned. Evgeny’s and his work co-authored with Avihay Cohen and colleagues on matching data references and institutional output to map the reuse of biomedical research data shows how such references can be connected to an institution’s research output.
\nThis connects to our earlier work on Meaningful Data Counts within the Make Data Count initiative. A recurring question in the project and particularly its qualititative approaches led by Kathleen Gregory (at the time Postdoc in the lab, now researcher at CWTS) there was what counts of data citations and mentions actually tell us. The same question applies here.
\nAn identifier appearing in an article does not guarantee that the authors analysed the data. They may be citing it as background, comparing against it, or only listing it among related resources. For this reason, we checked the full text of the reusing article before including it, and we only describe the reuse where the authors state what they did with the data. Automated matching helps find candidates; it cannot replace reading the paper.
\nEvgeny’s hope is that showing researchers the afterlife of their data might encourage them to share again. That is a hypothesis, not a result. We don’t yet know whether stories like this change attitudes or behaviour, whether they are clear to people outside the field, or whether they feel rewarding or intrusive. For now, we are testing the format on our own data, and learning what works and what needs improving.
\nHave a look at the scrollytelling page and let us know whether the story is clear and what you would change. If you work on research data sharing, reuse or metrics and would like to adapt it for another dataset, we would be glad to hear from you.
\n\n
AI Acknowledgements: Stefanie used Claude [Sonnet 5.5] for the initital draft of this blog post using the email exchange with Evgeny and Kathleen and the Scrollytelling example as inputs.
","doi":"https://doi.org/10.59350/88k6w-z7p81","guid":"https://www.scholcommlab.ca/?p=6362","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1793145600,"rid":"garqp-epg06","summary":"You publish an article, deposit the dataset behind it in a repository, and move on. Months or years later, someone may have built on that dataset in a new study. Chances are you never find out. Evgeny Bobrov, who leads open data and research data management at the Berlin Institute of Health at Charit\u00e9 (BIH) and is part of QUEST, has been working on a way to change that.","tags":["Research","Article Processing Charge","CIHR","NSERC","Open Access"],"title":"Scrollytelling: What happens after you share your data?","updated_at":1793197926,"url":"https://www.scholcommlab.ca/2026/10/28/scrollytelling/","version":"v1"}],"out_of":57958,"page":1,"per_page":10,"total-results":57958}