SciPost wird von SCOSS unterstützt

Die Diamond-Open-Access-Plattform SciPost wurde von der Global Sustainability Coalition for Open Science Services (SCOSS) in ihre 6. Finanzierungsrunde aufgenommen. Damit sollen der Betrieb und die Weiterentwicklung von SciPost sichergestellt werden.

Was ist SciPost?

SciPost ist eine Plattform für Open-Access-Zeitschriften aus der Physik und anderen Fächern, die 2016 vom Festkörperphysiker Jean-Sébastien Caux von der Universität Amsterdam gegründet wurde. Die Plattform hat sich seither sehr erfolgreich entwickelt und im November 2024 den 3.000. Artikel veröffentlicht.

SciPost verlangt für die Veröffentlichung keine Gebühren (APCs) von den Autor:innen, ist nicht gewinnorientiert und wird von Wissenschaftler:innen für Wissenschaftler:innen betrieben. Damit steht es für Diamond Open Acccess im besten Sinne. Da SciPost sowohl für Leser:innen als auch für Autor:innen kostenfrei ist, wird es durch freiwillige Beiträge von Bibliotheken und Forschungseinrichtungen finanziert. Die Kosten pro Artikel werden mit rund 500 Euro kalkuliert – ein Bruchteil dessen, was kommerzielle Verlage an APCs verlangen.

Auch die TIB fördert seit mehreren Jahren SciPost, sowohl in ihrer Rolle als Universitätsbibliothek für die Leibniz Universität Hannover (LUH) als auch als zentrale Fachbibliothek für Technik und Naturwissenschaften. Mit seinem nicht-gewinnorientierten Geschäftsmodell und dem Verzicht auf APCs entspricht SciPost einer Variante von Open Access, die in den Open-Access-Policies von TIB und LUH als besonders unterstützenswert angesehen wird.

Was ist SCOSS?

Die 2017 gegründete Global Sustainability Coalition for Open Science Services (SCOSS) ist ein Netzwerk verschiedener Organisationen, das sich für die Sicherung der Infrastruktur für Open Access und Open Science auch in Zukunft einsetzt. Das Ziel ist, nicht-kommerzielle Dienste zu identifizieren, die für Open Science unverzichtbar sind. Diese werden dann für eine finanzielle Unterstützung im Sinne eines Crowdfunding empfohlen. SCOSS bietet den Rahmen und die Finanzierungsstruktur und prüft potenzielle Kandidaten anhand festgelegter Kriterien. In die laufende Finanzierungsrunde wurden neben SciPost die Infrastrukturen African Journals Online (AJOL), Episciences und Make Data Count aufgenommen.

SciPost und SCOSS

Auch wenn es aus der Wissenschaft und insbesondere auch der Wissenschaftspolitik zahlreiche Bekenntnisse zu Diamond Open Access gibt, bleibt die Finanzierung für viele Verlage und Plattformen, darunter auch SciPost, herausfordernd. Anders als kommerzielle Mitbewerber können sie sich nicht auf kontinuierliche Einnahmen durch Subskriptionen oder APCs verlassen, sondern sind auf freiwillige Unterstützung angewiesen.

Durch SCOSS ändert sich das zwar nicht, aber mit der Aufnahme in die „SCOSS family“ von unverzichtbaren Open-Science-Infrastrukturen soll die Aufmerksamkeit auf SciPost gelenkt und eine breitere Förderung sichergestellt werden. Als Finanzierungsziel für SciPost hat SCOSS 627.000 Euro angegeben. Es ist zu hoffen, dass möglichst viele Bibliotheken, Förderer und Forschungseinrichtungen den Wert von wissenschaftsgeleiteten Infrastrukturen wie SciPost erkennen und auch finanziell anerkennen.

#LizenzCCBY30DE #diamondOpenAccess #OpenAccessFinanzierung #SciPost #SCOSS

Michal Klodner wrote the following post Thu, 30 Jan 2025 14:59:12 -0800
@!Streams @!Hubzilla Support Forum

Announcing start of the project supported by @NGI Zero open source funding !

The goal of this development project is to create reproducible declarative NixOS configurations for modern CI/CD environments and stable, documented deployment of the Streams and Hubzilla. The deployment will support building the NixOS container images for OCI tools (Docker/Podman) and running multiple instances with HA Proxy.

Interoperability tests by posting in several interaction scenarios (Personal, group channel) with other fediverse servers should support FEPs standardization and bring wider interest and trust in these efforts.

Streams and Hubzilla are advanced fediverse social media servers with granular privilege system, allowing for private (followers only) conversations. This prevents unwanted replies, spam and abuse, surpassing weaknesses of other ActivityPub protocol implementations. Unique feature is support of complete channel migration and synchronized cloning among instances, which allows user control over personal data and choice of provider. Anybody can take out or move their channel data by instant online migration. This contributes to principles of service independence and data ownership.

NLNet is bringing support services with accessibility scan and a security audit. Especially since this project is about packaging different tools together and making it user-friendly, it seems important to make sure everything is usable and secure. Results of this will be communicated to source repositories.

NLNet project profile: nlnet.nl/project/Threadiverse-…

Project will run 5-7 months and repository of the deployment code and documentation will be announced with each finished phase.

This project is dveloped at @node9.org ;-) open culture ~ sustainable media

@Michal Klodner

Peering Inside a Hailstone

In spring and summer, major thunderstorms can include dangerous and destructive hailstones. In Catalonia, a group of scientists collected hailstones after a record-breaking 2022 storm, finding some as large as 12 centimeters across. Using a dentist’s CT scanner, they looked at the interior of the hailstones, uncovering layers that reveal how the hail grew. In the past, researchers have studied hail by slicing the ice; that method gives them only a single cross-section through the hailstone, which gets destroyed in the process. In contrast, a CT scan revealed the full interior of the ice.

The scientists found that, even though hail often appears spherical, the nucleus of the hail is not always located in the center. They saw that the hail grew in uneven layers that varied in density, depending on the storm conditions the hail experienced. To get to the enormous sizes seen here, hailstones have to travel up and down repeatedly through a storm, building up layer by layer. From the hail’s interior structure, the team could also tell what orientation the hail took its final fall in; the ice along the bottom of the hailstone was bubble-free, indicating that it collected as water drops hit the surface and froze. (Image credit: T. Ribas; research credit: C. Barqué et al.; via New Scientist)

#atmosphericScience #fluidDynamics #iceFormation #meteorology #physics #science #thunderstorm

#Dystopia instead of utopia

#German #politics managed in no time to become international #breakingNews at the hand of Friedrich #Merz of the #Merkel party #CDU just before the upcoming early elections provoked by the now a days ever more neoliberal turning #FDP.

Here a reshare of a #RSSfeed latin american article in spanisch about a vote in the German parliament in a Weimarerrepublik fashioned coalition of the '30s of the last century that led to the Third Reich between right wing centrists (CDU), neoliberals (FDP) and far right #AfD party of #theFascistHoecke to impose racist anti immigration legislation in a country that does need desperately immigration because of a population that becomes older and can't fill up the vacant employments.

Of course they do want to increase the influx of qualified immigrants, draining emerging countries of their most important labor force, while at the same time they desperately try to hold back people who flee their emerging countries because they coudn't get access to qualified education and a prosperous perspective at home. Fleeing even taking the risk of drowning in the mediterranean sea.

Missing educated labor force at home because of a lack of investment in education claiming that there is and was no money to finance the education of the own population, while fighting against immigration, trying to be attractive for educated immigrants?

Promoting the arguments and populist propaganda of the far right, trying to catch a disenchanted electorate already lost while courting the money and favors of billionaires created by the very own politics?

Sounds familiar?

.. stay tuned, the revolution will not be televised, in any case it will put you in the drivers seat of a #decentralizedNewsFeedFuture ..

#Politik #politica
@taz & @taz

Does Open Source AI really exist?

The Open Source Initiative (OSI) released the RC1 (“Release Candidate 1” meaning: This thing is basically done and will be released as such unless something catastrophic happens) of the “Open Source AI Definition“.

Some people might wonder why that matters. Some people come up with a bit of writing on AI, what else is new? That’s basically LinkedIn’s whole existence currently. But the OSI has a very special role in the Open Source software ecosystem. Because Open Source isn’t just based on the fact whether you can see code but also about the License that code is covered under: You might get code that you can see but that you are not allowed to touch (think of the recent WinAmp release debate). The OSI basically took on the role of defining which of the different licenses that were being used all over the place actually are “Open Source” and which come with restrictions that undermine the idea.

This is very important: Picking a license is a political act with strong consequences. It can allow or forbid different modes of interaction with an object or might put certain requirements to the use. The famous GPL for example allows you to take the code but forces you to also open your own changes to it. Other licenses do not enforce this demand. Choosing a license has tangible effects.

Quick sidebar: “Open Source” already is a bit of a problematic term, it’s (my opinion) a way to depoliticise the idea of “Free Software“. Both do share certain ideas but where “Open Source” frames things more in a pragmatic “corporations want to know which code they can use” kind of way Free Software was always more of a political movement arguing more from a standpoint of user rights and liberation. An idea that was probably damaged the most by the most visible figures in that space that probably should just walk into the sea.

So what makes a thing “Open Source”? Well the OSI has a brief list. You can read it quickly but let’s focus on Point 2: Source Code:

The program must include source code, and must allow distribution in source code as well as compiled form. Where some form of a product is not distributed with source code, there must be a well-publicized means of obtaining the source code for no more than a reasonable reproduction cost, preferably downloading via the Internet without charge. The source code must be the preferred form in which a programmer would modify the program. Deliberately obfuscated source code is not allowed. Intermediate forms such as the output of a preprocessor or translator are not allowed.

Open Source Initiative

To be Open Source a piece of software needs to come with the sources. Okay, that’s not surprising. But the writers have seen some shit so they added that obfuscated code (meaning code that has been mangled to be unreadable) or intermediate forms (meaning you don’t get the actual sources but something that has already been processed) are not allowed. Cool. Makes sense. But why do people care about sources?

Sources of Truth

Open Source is a relatively new mass phenomenon. We had software before, even some we didn’t have to pay for. We called it “Freeware” back then. Freeware is software you can use without cost but that you don’t get any source code to. You cannot change the program (legally), you cannot audit it, cannot add to it. But it’s free of charge. And there was a lot of that back in my younger days. WinAMP, the audio player I talked about above used to be Freeware and basically everyone used it. So why even care about sources?

For some it was about being able to modify the tools easier, especially if the maintainer of the software didn’t really work on it any more or started adding all kinds of stuff you didn’t agree with (think of all those proprietary software packages today you have to use for work that get AI stuffed in there behind every other button). But there is more to it than just feature requests. There’s trust.

When I run software, I need to trust the people who wrote it. Trust them to do a good job, to build reliable and robust software. To add only the features in the documentation and nothing hidden, potentially harmful.

Especially with so large parts of our real lives running on digital infrastructures questions of trust get more and more important. We all know that we want fully open sourced, peer-reviewed and battle-tested encryption algorithms in our infrastructures to our communication is safe from harm.

Open Source is – especially for critical systems and infrastructures – a key part of establishing that trust: Because you want (someone) to be able to verify what’s up. There has been a long push for more reproducible builds. Those build processes basically guarantee that given the same code input you get the same compiled result. Which means that if you want to know if someone really delivered you exactly what they said they would you can check. Because your build process would create an identical artifact.

Not everyone does this level of analysis of course. And even fewer people only use software from reproducible build processes – especially with a lot of software not being compiled today. But relationships are more nuanced than code and trust is a relationship: You being fully open book about your code and how exactly the binary version was built makes it a lot easier for me to trust you. To know what is in the software I am running on the machine that also has my bank statements or encryption keys on it.

What does this have to do with AI?

AI systems and 4 Freedoms

AI systems are a bit special. Because – especially the big ones everyone is so fascinated by – don’t really consist of a lot of code in comparison to their size. A neural network implementation is a few hundred lines of Python for example. An “AI system” consists not just of code but of a whole lot of parameters and data.

A modern LLM (or image generator) consists of some code. You also need a network architecture meaning the setup of digital neurons that are used and how they are connected. This architecture is then parameterized with the so-called “weights” which are the billions of numbers you need to get the system to do anything. But that is of course not all.

In order to translated syllables or words into numbers for an “AI” to consume you need and embedding, sort of a lookup table to tell you what “token” the number “227” stands for. If you took the same neural network but applied a different embedding than the one it was trained with, everything would fall to pieces. The structures wouldn’t match.

Then there is the training process meaning the process that created all the “weights”. In order to train an “AI” you feed it all the data you can find and in millions and billions of iterations the weights start to emerge and crystallise. The training process and which data it used and how is key to understanding the capabilities and issues a machine learning system has: If you want to reduce harm in a network you do need to know if it was trained on the Daily Stormer or not just to give an example.

And here’s the catch.

The OSI “The Open Source AI Definition – 1.0-RC1” demands an Open Source AI to provide four freedoms to its users:

1. Use the system for any purpose and without having to ask for permission
2. Study how the system works and inspect its components.
3. Modify the system for any purpose, including to change its output.
4. Share the system for others to use with or without modifications, for any purpose.

So far so good. That looks reasonable, right? You can inspect and modify and use and all that. Awesome. Nothing bad could happen in the fineprint, right? Let’s just quickly look what an AI system needs to offer. Code: Check. Model parameters (weights, configs): Check! We’re on a roll here. What about data?

Data Information: Sufficiently detailed information about the data used to train the system so that a skilled person can build a substantially equivalent system. Data Information shall be made available under OSI-approved terms.
In particular, this must include: (1) a detailed description of all data used for training, including (if used) of unshareable data, disclosing the provenance of the data, its scope and characteristics, how the data was obtained and selected, the labeling procedures and data cleaning methodologies; (2) a listing of all publicly available training data and where to obtain it; and (3) a listing of all training data obtainable from third parties and where to obtain it, including for fee.

Open Source Initiative

What does “sufficiently detailed information” mean? The Open Source definition never talks about “sufficiently detailed source code”. You need to get the source code. All of it. And not in obfuscated or mangled form. The actual thing. Because otherwise it doesn’t mean much, it doesn’t help you build trust.

The OSI’s definition of “Open Source AI” pokes a big hole into the idea of Open Source: By making a core part of the model – the training data special in this weird wibbly wobbly way they blessing all kinds of things as “Open Source” that really are not – based on their own definition of what Open Source is and what it’s for.

An AI system’s training data is for all intends and purposes part of its “code”. It is as relevant to the way the model functions as literal code, for AI systems probably even more because the code is just generic matrix operations with delusions of grandeur.

The OSI puts another cherry on top: Users deserve description of “unshareable data” that was used to train a model. What is that? Let’s apply that to code again: If a software product gives us a core part of its functionality just as a compiled artifact and then describes that it’s all totally cool and above board but that the code wasn’t “shareable” we would not call that piece of software open source. Because it does not open all the source.

Does a “description” of partially “unshareable” data help you to reproduce the model? No. You can try to rebuild the model and it might look a bit similar, but it’s significantly different. Does it help you to “study the system and inspect its components”? Only on a superficial level. But if you really want to analyse what’s in the magic statistics box you need to know what went into it. What was filtered out exactly, what went in?

This definition seems to be very weird coming from OSI, right? It very obviously goes against core ideas of what people think open source is and should be. So why do it?

(Un)Open AI

Here’s the thing. On the scale that we are talking about those statistical systems as “AI” today, open source AI cannot exist.

Many smaller models have been trained on explicitly selected and curated, public datasets. Those can provide all the data, all the code, all the processes and can be called Open Source AI. But those are not the machines that make NVIDIA’s stock go WEEEEEEEEEEEEE.

Those big systems that are called “AI” – whether they are for image generation, text generation or multi-modal – are all based on illegally acquired and used material. Because the data sets are too big to do actual filtering and ensuring their legality. It’s just too much.

Now the more naive people among you might wonder “Okay but if you cannot do it legally how can you claim that this is a legitimate business?” and you’d be right but we’re also living in a weird world where hoping that some magic innovation and/or money will come from reproducing Reddit posts saving our economy and progress.

“Open Source AI” is an attempt to “openwash” proprietary systems. In their paper “Rethinking open source generative AI: open-washing and the EU AI Act” Andreas Liesenfeld and Mark Dingemanse showed that many “Open Source” AI models offer hardly more than open model weights. Meaning: You can run the thing but you don’t actually know what it is.

Sounds like something we’ve already had: It’s Freeware. The Open Source models we see today are proprietary freeware blobs. Which is potentially marginally better than OpenAI’s fully closed approach but really only marginally.

Some models offer models cards or other docs but most leave you in the dark. Which stems from the fact that most of those models are being developed by VC funded companies that need some theoretical path towards monetization.

“Open Source” has become a sticker like “Fair Trade”, something to make your product look good and trustworthy. To position it outside of the evil commercial space, giving it some grassroots feeling. “We’re in this together” and shit. But we’re not. We’re not in this with Mark fucking Zuckerberg even if he gives away some LLM weights for free cause it hurts his competition. We, as normal people living on this constantly warmer planet, are not with any of those people.

But there is another aspect to it outside of doing an image makeover for tech bros and their corporations. It’s about legality. At least in Germany there are exceptions to some laws that normally would concern LLM makers: If you do it for research purposes you are allowed to scrape basically anything. You can then train models and release those weights and even though there’s Disney stuff in there you are in the clear. And this is where the whole Open Source AI thing plays a relevant role: This is a wedge to legitimise probably illegal behavior through openwashing: As a corporation you take some “Open Source AI” that is based on all the stuff you wouldn’t legally be allowed to touch and use that to build your product. Do some extra training with licensed data for example.

The Open Source Initiative has caught FOMO – just like the Nobel prize jury. They also want to be a part of the “AI” craze.

But for the systems that we are talking about today as “AI” Open Source AI isn’t practically possible. Because we’ll never be able to download all the actual training data.

“But tante, then we will never have Open Source AI”. Exactly. That’s how reality works. If you can’t fulfil the criteria of a category you are not in that category. The fix is not to change the criteria. That’s playing pigeon chess.

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#ai #open #openSource #osi #trust