All posts by chorasimilarity

can do anything

More experiments with Open Science

I still don’t know which format is better for Open Science. I’m long past the article format for obvious reasons. Validation is a good word and concept because you don’t have to rely absolutely on opinions of others and that’s how the world works. This is not all the story though.

I am very fortunate to be a mathematician, not a biologist or biochemist. Still I long for the good format for Open Science, even if, as a mathematician, I don’t have the problems biologists or chemists have, namely loads and loads of experimental data and empirical approaches. I do have a world of my own to experiment with, where I do have loads of data and empirical constructs. My mind, my brain are real and I could understand myself by using tools of chemists and biologists to explore the outcomes of my research. Funny right? I can look at myself from the outside.

That is why  I chose to not jump directly to make Hydrogen, but instead to treat the chemlambda  world, again, as a guinea pig for Open Science.

There are 427 well written molecules in the chemlambda collection on Github. There are 385 posts in the chemlambda collection on Google+, most of them with animations from simulations of those molecules. It is a world, how big is it?

It is easy to make first a one page direct access to the chemlambda collection. It is funnier to build a phylogenetic tree of the molecules, based on their genes. That’s what I am doing now, based on a work in progress.

Each molecule can be decomposed in “genes” say, by a sequencer program. Then one can use a distance between these genes to estimate first how they cluster and later to make a phylogenetic tree.

Here is the first heatmap (using the edit distance between single occurrences of genes in molecules) of the 427 molecules.


Is a screenshot, proving that my custom programs work 🙂 (one understands more by writing some scripts than by taking tools ready made from others, at least at this stage of research).

Moreover, I see structure! The 427 molecules are made of copies of  605 different linear “genes” (i.e. sticks with colored ends)  and 38 ring shaped ones.  (Is easy to prove that lambda terms have no rings, when turned into molecules.) There are some interesting curved features visible in the edit distance of the sticks.


They don’t look random enough.

Is clear that a phylogenetic tree is in reach, then what else than connecting the G+ collection posts with the molecules used, arranged along the tree…?

Can I discover which molecules are coming from lambda terms?

Can I discover how my mind worked when building these molecules?

Which are the neglected sides, the blind places?

I hope to be able to tell by the numbers.

Which brings me to the main subject of this post: which is a good format for an Open Science piece of research?

Right now I am in between two variants, which may turn out to not be as different as they seem. An OS research vehicle could be:

  • like a viable living organism, literary
  • or like a viable world, literary.

Only the future will tell which is which. Maybe both!

Chemlambda will be curated

Chemlambda appeared out of frustration that nobody understands and see what I do, so I had to write it. The same with the chemlambda collection, I’ll curate it and put it in one easy to figure out place. It’s doable. The only fear I have about this is to be sucked again in this highly hallucinatory universe, which is almost real now and will be really real soon.

So for the moment here’s a page which allows you to go directly to any of the chemlambda collection post.

Maybe this will improve my karma so I’ll be prepared to do pure hydrogen. The initial trials look very promising and despite the apparent simplicity (what? make required mathematics, space, physics and this simple atom, all invoked from abstract nonsense like in a super geometric Lisp) the hydrogen project is more difficult because there is no precedent.

So wish me luck 🙂

Update the Panton Principles please

There is a big contradiction between the text of The Panton Principles and the List of the Recommended Conformant Licenses. It appears that it is intentional, I’ll explain in a moment why I write this.

This contradiction is very bad for the Open Science movement. That is why, please, update your principles.

Here is the evidence.

1. The second of the Panton Principles is:

“2. Many widely recognized licenses are not intended for, and are not appropriate for, data or collections of data. A variety of waivers and licenses that are designed for and appropriate for the treatment of data are described [here]( Creative Commons licenses (apart from CCZero), GFDL, GPL, BSD, etc are NOT appropriate for data and their use is STRONGLY discouraged.

*Use a recognized waiver or license that is appropriate for data.* ”

As you can see, the authors clearly state that “Creative Commons licenses (apart from CCZero) … are NOT appropriate for data and their use is STRONGLY discouraged.”

2. However, if you look at the List of Recommended Licenses, surprise:

Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0) is recommended.

3. The CC-BY-SA-4.0 is important because it has a very clear anti-DRM part:

“You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.” [source CC 4.0 licence: in Section 2/Scope/a. Licence grant/5]

4. The anti-DRM is not a “must” in the Open Definition 2.1. Indeed, the Open Definition clearly uses “must” in some places and “may” in another places.  See

“2.2.6 Technical Restriction Prohibition

The license may require that distributions of the work remain free of any technical measures that would restrict the exercise of otherwise allowed rights. ”

5. I asked why is this here. Rufus Pollock, one of the authors of The Panton Principles and of the Open Definition 2.1, answered:

“Hi that’s quite simple: that’s about allowing licenses which have anti-DRM clauses. This is one of the few restrictions that an open license can have.”

My reply:

“Thanks Rufus Pollock but to me this looks like allowing as well any DRM clauses. Why don’t include a statement as clear as the one I quoted?”


“Marius: erm how do you read it that way? “The license may prohibit distribution of the work in a manner where technical measures impose restrictions on the exercise of otherwise allowed rights.”

That’s pretty clear: it allows licenses to prohibit DRM stuff – not to allow it. “[Open] Licenses may prohibit …. technical measures …”


“Marius: so are you saying your unhappy because the Definition fails to require that all “open licenses” explicitly prohibit DRM? That would seem a bit of a strong thing to require – its one thing to allow people to do that but its another to require it in every license. Remember the Definition is not a license but a set of principles (a standard if you like) that open works (data, content etc) and open licenses for data and content must conform to.”

I gather from this exchange that indeed the anti-DRM is not one of the main concerns!

6. So, until now, what do we have? Principles and definitions which aim to regulate what Open Data means which avoid to take an anti-DRM stance. In the same time they strongly discourage the use of an anti-DRM license like CC-BY-4.0. However, on a page which is not as visible they recommend, among others, CC-BY-4.0.

There is one thing to say: “you may use anti-DRM licenses for Open Data”. It means almost nothing, it’s up to you, not important for them. They write that all CC licenses excepting CCZero are bad! Notice that CC0 does not have anything anti-DRM.

Conclusion. This ambiguity has to be settled by the authors. Or not, is up to them. For me this is a strong signal that we witness one more attempt to tweak a well intended  movement for cloudy purposes.

The Open Definition 2.1. ends with:

Richard Stallman was the first to push the ideals of software freedom which we continue.

Don’t say, really? Maybe is the moment for a less ambiguous Free Science.

The price of publishing with GitHub, Figshare, G+, etc

Three years ago I posted The price of publishing with arXiv. If you look at my arXiv articles then you’ll notice that I barely posted on since then. Instead I went into territory which is even less recognized as serious by a big part of academia. I used:

The effects of this choice are put in front of my homepage, so go there to read them. (Besides, it is a good exercise to remember how to click on links and use them, that lost art from the age when internet was free.)

In this post I want to explain what is the price I paid for these choices and what I think now about them.

First, it is a very stressful way of living. I am not joking, as you know stress comes from realizing that there are many choices and one has to choose. Random reward from the social media is addictive. The discovery that there is a way to get out from the situation which keeps us locked into the legacy publishing system (validation). The realization that the problem is not technical but social. A much more cynical view of the undercurrents of the social life of researchers.

The feeling that I can really change the world with my research. The worries that some possible changes might be very dangerous.

The debt I owe concerning the scarcity of my explanations. The effort to show only the aspects I think are relevant, putting aside those who are not. (Btw, if you look at my About page then you’ll read “This blog contains ideas from the future”. It is true because I already pruned the 99% of the paths leading nowhere interesting.)

The desire to go much deeper, the desire to explain once again what and why, to people who seem either lacking long term attention capability or having shallow pet theories.

Is like fishing for Moby Dick.

Synergistics talks through his chemlambda Haskell version

… in a very nice and clear, 9:30 presentation. I especially enjoyed from 5:32, when he describes what enzymes are and further, but all of the presentation is instructive because it starts from 0.

The video talk is this

His github repository chemlambda-hask is this

Thank you J, very nice!

Pharma meets the Internet of Things

Pharma meets the Internet of Things, some commented references for this future trend. Use them to understand

[0] After the IoT comes Gaia

There are two realms of computation, which should and will become one: the IT technology and biochemistry.

General stuff

The notion of computation is now well known, we speak about what is computable and about various models of computation (i.e. how we compute) which always turned out to be equivalent in the sense that they give the same class of computable things (that’s the content of the Church-Turing thesis).

It is interesting though how we compute, not only what is computable.

In IT perhaps the biggest (and socially relevant) problem is decentralized asynchronous computing. Until now there is no really working solution of a model of computation which is:
– local in space (decentralized)
– local in time (asynchronous)
– with no pre-imposed hierarchy or external authority which forces coherence

In biochemistry, people know that we, anything living, are molecular assemblies which work:
– local in space (all chemical interactions are local)
– local in time (there is no external clock which synchronizes the reactions)
– random (everything happens without any external control)

Useful links for an aerial view on molecular computing, seen as the biochemistry side of computation:


Some history and details provided. Quote from the end of the section “Biochemistry-based information technology”

“Other experiments have shown that basic computations may be executed using a number of different building blocks (for example, simple molecular “machines” that use a combination of DNA and protein-based enzymes). By harnessing the power of molecules, new forms of information-processing technology are possible that are evolvable, self-replicating, self-repairing, and responsive. The possible applications of this emerging technology will have an impact on many areas, including intelligent medical diagnostics and drug delivery, tissue engineering, energy, and the environment.”


A detailed historical view (written in 2000) of the efforts towards “molecular electronics”. Mind that’s not the same subject as [1], because the effort here is to use biochemistry to mimic silicon computers. While [1] also contains such efforts (building logical gates with DNA, etc), DNA computing does propose also a more general view: building structure from structure as nature does.


Two easy to read articles about real applications of molecular computing:
– “Microscopic machine mimics the ribosome, forms molecular assembly line”
– “Biological computer can decrypt images stored in DNA”


Article about Craig Venter from 2016, found by looking for “Craig Venter Illumina”. Other informative searches would be “Digital biological converter” or anything “Craig Venter”


Interesting talk by an interesting researcher Lee Cronin

[6] The Molecular Programming Project

Worth to be browsed in detail for seeing the various trends and results

Sitting in the middle, between biochemistry and IT:

[1] Algorithmic Chemistry (Alchemy) of Fontana and Buss

Walter Fontana today:

[2] The Chemical Abstract Machine by Berry and Boudol

[3] Molecular Computers (by me, part of an Open Science project, see also my homepage and the chemlambda github page )

On the IT side there’s a beautiful research field, starting of course with lambda calculus by Church. Later on this evolved in the direction of rewriting systems, then graph rewriting systems. I can’t even start to write all that’s done in this direction, other than:

[1] Y. Lafont, Interaction Combinators

but see as well the Alchemy, which uses lambda calculus!

However, it would be misleading to reduce everything to lambda calculus. I came to the conclusion that lambda calculus or Turing machines are only two among the vast possibilities, and not very important. My experience with chemlambda shows that the most relevant mechanism turns around the triple of nodes FI, FO, FOE and their rewrites. Lambda calculus is obtained by the addition of a pair of A (application) and L (lambda) nodes, along with standard compatible moves. One might use as well nodes related to a  Turing Machine instead, as explained in

Everything works just the same. The center, what makes things work, is not related to Logic or Computation as they are usually considered. More later.