How information is difficult to capture in the form of standards – WP5 interview fragments

A selection of fragments of interviews published inWP5 are made available in this document. In the interviews the participants describe how information is difficult to capture in the form of standards and they refer to different types of contexts: biological, socio-cultural and technical to explain why that is the case.

Biological contexts

Biology 1/Emergence

«So, I think that the very [unintelligible 00:41:23] step in bringing standards to biology is to focus in metrology, and in this area of engineering that has to do with how to measure things, how to measure functions, how to measure properties. So, even if we agree in a standard to measure things in biological systems, to me that will be a fantastic achievement. But if you go further than that then you have to go back and rethink a few questions, because then things become very complicated, or very intricate. I mean, not impossible to tackle, but certainly it’s not straightforward. And also in biology you have this phenomenon that we call emergence. So, that means that two plus two is not always four. It can be less than four, it can be more than four, and that’s something that engineers cannot live with. So, [unintelligible 00:42:10]. That’s the basis of biology».

Biology 2/ Understanding complexity

«It is partly about annotation, but also about how that part is defined. Within our basic DNA assembly format, we have defined our own promoter architecture which is quite specific. We have multiple families of those promoters. So we have an upstream terminator, and then we have an up-element core promoter sequence and then a right <11:52>. So this is a very specific architecture. When we talk about our promoter, we have a whole context surrounding that promoter part. Most people don’t do that. They talk about the core promoter sequence. Then if you put that core promoter sequence next to a different upstream space <terminator?>. That promoter would behave differently. It is about core definition and description and characterisation, and understanding what is important within those architectures».

Biology 3/flexibility and evolution:

«That is all directly related to the way we do science. As much as I understand standardisation as a concept, let’s say from physical and engineering disciplines…. We should not forget that biology is a little bit different. In physical terms a transistor is a transistor, right? It will never evolve into something new. But in biology a transistor is something that has the potential to evolve into something new. It is only as accurate as it needs to be. There is always this flexibility built into it to do something new. Because only if you do something new in a given backbone<05:58?> of your protein can it evolve new functionality. So typically proteins, but also actually many other things like genes are evolved to be as efficient and as correct or specific as it needs to be. But not more. Because otherwise you sacrifice the potential to evolve (…) We take this as an essential part of a biological part: that it has flexibility built in, due to the fact it needs to evolve further. This is one of the problems I see with you, it is meta-data annotation. It is how can you assess the full potential of a biological part, because you focus on the main function of the main part, but you very often do not focus on the other functionality. And those are pretty hard to grasp. Because every part has a different evolutionary history. It comes with a different context».

Biology 4/Biological complexity is so huge that some information is always missing

«In biology, the amount of information that we have to deal with, it’s so huge, that if I can find somewhere the information about a protein, and a couple of comments by a researcher, let’s say “this protein works very well in E. coli but very badly in pseudomonas”, or some additional data “yes but maybe this is because of the strains of E. coli at used, and if you use a slightly different method it is going to work in pseudomonas”. The complexity is so large that I am not sure that most of the researchers, at least me, will exactly trust what you hear from others. Some people prefer to create their own data and constructs from scratch because of the lack of reliable, complete or good enough data annotations.»

Socio-cultural contexts

Socio-Cultural 1/Annotations need to be tuned in particular contexts of reception/publics

Q: What do you do in order to try to make your data reusable?

A: «We try to define sort of minimal sets of additional information that needs to be associated to data. This is different if you want to share it in your lab, if you want to share in your community. Say colleagues working in the same area and with the same organism, having quite some experience, they need perhaps a little bit less information to make sense out of it, or if you want to share with the whole world” (…) And it is also the question, what to share with the whole world? At the current situation I cannot imagine that you could share this data in a way that everybody could make sense of it, and everybody could use it. Because the complexity of the systems we are working with is far too high. I could imagine that for some model organisms and for some broader systems biology approaches or some basic tools of model organisms the community could agree on making kind of big data sets under exactly the same conditions…but as soon as you spread something in a community and you have different hands, different labs, different media, different glass, different dishwashers for your labware, it could make already a difference…»

Adapting to particular publics and technical contexts

«It is the same if I ask a colleague for a tool, I am not using his data, basically before we are doing our work, we (re)do some characterization to see if in our setting it is doing the same. It is not because you mistrust the colleague.»

Adjusting or repurposing tools for new lab contexts and research questions. Reusability as a matter of context rather than mainly a matter of trust.


Socio-Cultural 2/Good reporting as a matter of personal skills, experience and training.

«You have people that are more careful than others in keeping records of the experiment. But it is not about adding a lot of detail, but about finding the right level of detail. This depends also very much on the experience of a person. If you have worked for twenty years in the lab, you get a sort of feeling, and I say feeling because it is really feeling it is not knowledge» (…)

«You need to understand the process to see what steps do matter and what steps do not really matter (and also knowing who is going to read it). So it is a question of how a person makes the right decision on where to add a high level of detail and when it is not necessary (…) Beginners have to learn how to transfer situations, from one situation to another one. There are people who can do this, and there are others who never learn it. Because they simply cannot understand how we can transfer one experience in one setting to another different problem. I think this happens everywhere, its nothing specific for the lab»

Intimate knowledge of lab materials and tools and discrimination skills.

«Sometimes you are missing information, and then you need to contact the person who produced the data. Sometimes information is missing because you didn’t know that it mattered, and nobody in the world could have known that it mattered»


Socio-Cultural 3/Learning and knowledge transferring beyond words.

«That is not, well, you can try to make a protocol but it is not always easy to tell that. Sometimes it is also hard to know what you have to tell. So if you make an SOP, standard operation procedure, of how you have to grow cells. Or how you have to transform them. You can put all the elements that you know, or that you think is logical, but somtimes you do something that you don’t realise, but that is important wihtin the SOP. Most of the time it is these little things that are not obivous that you can only transfer or communicate when you do it together with someone. That they can see it. As it is very hard to put that on paper. Because you don’t realiase it. It is only because they mimic you… They take over that same behaviour.  I don’t know if I am explaining it well, but it is sometimes really small things you do. Putting something on ice or not. Or, it is on ice, but before you flip it, and it isn’t really described in the SOP. But maybe that flipping is important. That is something that you see when you are communicating informally by showing it. That you shadow someone that is explaining it. It is always easier, the transfer of information is always easier, especially if you want to transfer a SOP. It is always easier if someone can follow in reality, someone who is familiar with that procedure. It is awlays a standard procedure that we try to do».

Situated and tacit knowledge is hard to capture and code


Socio-Cultural 4/ Personal communication and relations count when deciding which data to use

«Imagine that I have to use a specific part for some microbiology experiment. Of course in the bibliography I can find hundreds of possibilities. But then close to my lab there is one researcher I trust and like personally. I think that he or she is a good researcher. Then that person says “look, you should use this one, because it works super well! And all my students are using it, it works super well!” I would use that one. Because it is there. It is free. And I have the experience of that person, so in case I am in trouble I can go back to that person and ask for specific and direct help. That I in my case I would strongly appreciate the possibility of having a person. It is like when you  have trouble and you call your phone company. Do you prefer a robot or a human being? Human beings can be nasty, a robot is never nasty, but I prefer a human being, because of the flexibility, of course»


Socio-cultural 5/Some people actually prefer to annotate experiments on paper. They talk about emotions and memory getting activated.

«To write (laboratory notes) on paper is very comfortable, if you make a mistake, you can just cross those lines out or you can make a remark on the side, and you have it always on paper (…). You get like an emotional relation (to your notebook).  Sometimes I do more informal notes, like annotations for myself, like “this is crap” or “this does not work”, then you see all those crossing-outs on your paper, or you see something there that makes you remember that you had problems doing that. To me it is also easier than to find things in digital formats, and one can be scared also that everything disappears (in digital formats). On paper things remains always there».


Socio-cultural 6/Science as freedom and discovery.

«If everything in the lab was standardized “there would be also something to lose because some great discoveries and surprises have been made because of unwanted variations. That someone did something different by accident or because he didn’t know and then you get an unusual result, you follow up and it turns out that you have discovered something really exciting… robots could not know that that was exciting …You need a person to understand that that was exciting»


Socio-cultural 7/Personal notebooks, scientific freedom, inspiration and discovery

«I have given a lot of scientific freedom (to his PhDs for deciding how to report experiments), and I do not think we should be too restricted by being too standardised. We are not industry, we are still discovery driven. And there might be things which cannot report easily, right? The kind of science we’re doing is very much explorative. It is very much discovery driven. If you do not know how big this field this is, you cannot draw a map. If you don’t know there is a new force of nature, you cannot describe it. I think you should keep this flexibility, but I think for understanding and comparing data, it is helpful. But over standardisation might be a little bit problematic (…) “this is a little bit of gut feeling.  The same holds true when we want to find a new function of a given part. So you have this enzyme and we think the enzyme has the capability to do another reaction. Now you can test one enzyme or you can test five different enzymes. It is a little bit of which enzymes you test. If you see a little bit of activity and then you try to intuitively improve it. That is where it gets a little bit wizardry and experience based– lets put it this way. It is probably experience based».

There is a lot that you do not know and sometimes you act intuitively, but that indeterminacy is a condition of scientific discovery.


Socio-cultural 8/Data sharing standard formats are not enough and Important information still shared through informal channels

«So the sequence data for one part, one DNA part, that one is okay and we can find it anywhere. But sometimes people want to know more, how they will put three or four together. A few things may change there when they are put together. Especially they want to know which (lets say) chassis, which < uniteligible 10:09> was tested. Sometimes that data is missing.  Then they will want to know how we tested; which temperature, which time, how long.  All that will affect the performance.  That type of data, people are always asking. The way we share it is still by email.  So this is information that is emailed, it would be nice if there was a way to put it in a file– but all that is done the old way. The way we do it is that we have our parts in Benchling. We have our constructs in Benchling. But the rest is still shared the one way: email»

Technical contexts

Technical 3/Cultural contexts matter. Tools and measurements can mean different things in different latitudes/cultures

«You take a very smart student, with quite a lot of experience in molecular biology, and give that person a protocol, and very likely that is not going to work. Because it is very difficult to translate into words and instructions all the small details that are involved in molecular biology experiments. So, you know, “hold the tube like that” — that isn’t going to be written.  Or what about, “vortex gently”, what does that mean? Gently means not too hard, but what does that mean, not to hard? There are a lot of things which are explained in a pseudo-scientific way. So if you say 37 degrees celsius, this is exact. It is thirty seven point zero zero zero zero degrees of it. But if you say vortex gently, it means nothing. “Incubate overnight”, what does incubate overnight mean? How long is your night there? At what time do you go home? In other countries? Or in Spain where people work a lot to very late. There are a lot of things there where no one thinks carefully about. An overnight, this is a typical thing in microbiology, you led bacteria grow over night».

But can every single description be turned into a formal measurement? Can you eliminate cultural contexts for absolute universal measuring?


Technical 2/ Experimental tools and equipment are use dependent.

«There are a few things. First, there are the conditions of the experiments. That is many times forgotten. How did they time it? How did they test it? How did they grow the cell? How did they grow the strain? Sometimes it could also be that people do not sequence! Or do not verify their constructs. They do something. They build it. They think it is okay. But maybe there is a mutation already. They share it… Many times it has happened to us that somebody sent us a gene-part, we sequence it and it was different from what was supposed to be there. So that is also something. Also there is a big lack of standards to calibrate equipment. My equipment works totally differently! It could be the same equipment, but because of different use»

Equipment is used differently in different contexts, and it is also evolving and updated.

New measurement units will be required as equipment is updated. Measurements can also change as instruments change. And additional problem is that Not everyone has access to the same equipment context has also to do with inequalities.


Technical 3/Relative unit systems

«The other important aspect in this is the lack of units on data. So you might take something from another lab, and they might have published what their data Need to re-adjust data to specific research labs with specific equipment and research objectives.»  

Specific units and technical tools maybe required in accordance to specific research questions and research fields.


Summing up. Emerging topics in these interviews were: changing metrics, interpretation, and incomplete annotations (the dream of complete annotations), differing contexts of reception/fields, evolution and emergency, experience and a feeling for the experiment (notes as memory), freedom and discovery.

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