Tag: data formats

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The trouble with semantics…

…is knowing what you mean…

I posted last week about the spontaneous CMLReact hackfest held around Peter Murray-Rust’s dining room table the day after Science Blogging in London. There were a number of interesting things that came out of the exercise for me. The first was that it would be relatively easy to design a moderately strict, but pretty standard, description format for a synthetic chemistry lab notebook that could be automatically scraped into CMLReact.

Automatic conversions from lab book to machine readable XML

CMLReact files have (roughly) three sections. In the first, all the molecules that are relevant to the description are described, or in the ideal semantic web world pointed to at an external authority such as Chemspider, PubChem, or other source. In the second section the relationships between input materials, solvents, products, and samples are described. In general all of these will be molecules which are referred to in the first session but this is not absolutely required (and this will be important later). The final section describes observables, procedures, yields, and other descriptions of what happened or what was measured.

If we take a look at the UsefulChem experiment that we converted to CMLReact you can see that most of this information is available in one form or another. The molecules are described via InChi/InChiKey at the bottom of the page. This could be used as they are to populate the molecules section. A little additional markup to distinguish between reactants, solvents, reagents, and products would make it possible to start populating the second section describing the relationships between these molecules.

The third section is the most tricky, and this will always be an 80:20 game. The object is to abstract as much information as can be reasonably garnered without putting in the vast amount of work required to get close to 100% retrieval. At the end of the day, if someone wants the real detail they can go back to the lab book. Peter has demonstrated text scraping tools that do a pretty good job of extracting a lot of this information. In combination with a bit of markup it is reasonable to expect that some basic information (amounts of reagents, yield, temperature of reaction, some descriptive terms) could reasonably be extracted. Again, getting 80-90% of a subset of regularly used terms  would be very powerful.

But what are we describing?

There is a problem with grabbing this descriptive information from the lab notebook however, and it is a problem that is very general and something I believe we need to grapple with urgently. There is a fundamental question as to what it is that this file is describing. Does it describe the plan of the experiment? The record of carrying out a specific example of this experiment? An ‘averaged’ description of a set of equivalent experiments? A general description of the reaction? Or a description of a model of what we expect or think is happening?

If you look closely at the current version of the CMLReact file you will see that the yield is expressed as a percentage with a standard deviation. This is actually describing the average of three independent reactions but that is not actually made explicit anywhere in this file. Is this important? Well I think it is because it has an effect on what any outward links back to the lab book mean. There is a significant difference between – ‘this link points to an example of this kind of reaction’ (which might in fact be significantly different in the details) and ‘this link points to this exact experiment’ or indeed ‘this link points to an index of relevant experimental results’. Those distinctions need to be encoded in the links, or perhaps more likely made explicit in the abstracted file.

The CMLReact file is an abstraction of the experimental record. It is therefore important to make it clear what the level of abtraction is and what has been abstracted out of that description. This relates to the distinction I have made before between the flexibility required to record an experiment versus the ability to use a more structured vocabulary to describe the experiment after it has happened. My impression is that people who work in developing these controlled vocabularies are focussed on description rather than recording and don’t often make the distinction between the two. There is also often a lack of distinction between describing an experiment and describing a model of what happened in that experiment.  This is important because the model may need to be modified in the future whereas the description of the experiment should be accurate.

Summary

My view remains that when recording an experiment the system used should be as flexible as possible. Structure can be added to this primary record when convenient to make the process of abstracting from this primary record to a controlled vocabulary easier. The primary goal for me, for the moment, remains making a human readable record available. The process of converting the primary record into a controlled vocabulary, such as CMLReact, FuGE, or workflow system such as Taverna, should be enabled via domain specific automated or semi-automated tools that help the user to structure their description of the experiment in a way that makes it more directly useful to them but maintains the links with the primary record. Where the same controlled vocabulary is used for more abstracted descriptions of studies, experiments, or the models that purport to describe them, this distinction must be made clear.

Semantics depends absolutely on being clear about what you are describing. There is absolutely no point in having absolute clarity about the description of an object if the nature of that object is fuzzy. Get it right and we could have a very sophisticated description of the scientific record. Get it wrong and that description could be at best unclear and at worst downright misleading.

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Policy and technology for e-science – A forum on on open science policy

I’m in Barcelona at a satellite meeting of the EuroScience Open Forum organised by Science Commons and a number of their partners.  Today is when most of the meeting will be with forums on ‘Open Access Today’, ‘Moving OA to the Scientific Enterprise:Data, materials, software’, ‘Open access in the the knowledge network’, and ‘Open society, open science: Principle and lessons from OA’. There is also a keynote from Carlos Morais-Pires of the European Commission and the lineup for the panels is very impressive.

Last night was an introduction and social kickoff as well. James Boyle (Duke Law School, Chair of board of directors of Creative Commons, Founder of Science commons) gave a wonderful talk (40 minutes, no slides, barely taking breath) where his central theme was the relationship between where we are today with open science and where international computer networks were in 1992. He likened making the case for open science today with that of people suggesting in 1992 that the networks would benefit from being made freely accessible, freely useable, and based on open standards. The fears that people have today of good information being lost in a deluge of dross, of their being large quantities of nonsense, and nonsense from people with an agenda, can to a certain extent be balanced against the idea that to put it crudely, that Google works. As James put it (not quite a direct quote) ‘You need to reconcile two statements; both true. 1) 99% of all material on the web is incorrect, badly written, and partial. 2) You probably  haven’t opened an encylopedia as a reference in ten year.

James gave two further examples, one being the availability of legal data in the US. Despite the fact that none of this is copyrightable in the US there are thriving businesses based on it. The second, which I found compelling, for reasons that Peter Murray-Rust has described in some detail. Weather data in the US is free. In a recent attempt to get long term weather data a research effort was charged on the order of $1500, the cost of the DVDs that would be needed to ship the data, for all existing US weather data. By comparison a single German state wanted millions for theirs. The consequence of this was that the European data didn’t go into the modelling. James made the point that while the European return on investment for weather data was a respectable nine-fold, that for the US (where they are giving it away remember) was 32 times. To me though the really compelling part of this argument is if that data is not made available we run the risk of being underwater in twenty years with nothing to eat. This particular case is not about money, it is potentially about survival.

Finally – and this you will not be surprised was the bit I most liked – he went on to issue a call to arms to get on and start building this thing that we might call the data commons. The time has come to actually sit down and start to take these things forward, to start solving the issues of reward structures, of identifying business models, and to build the tools and standards to make this happen. That, he said was the job for today. I am looking forward to it.

I will attempt to do some updates via twitter/friendfeed (cameronneylon on both) but I don’t know how well that will work. I don’t have a roaming data tariff and the charges in Europe are a killer so it may be a bit sparse.

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A new type of chemistry journal: Nature Chemistry requests input

As has been noted in a few places, Neil Withers, one of the editors of soon to be newest Nature journal, Nature Chemistry put out a request last week for input on a range of issues to do with how people use journals, formats, and technical widgets. Egon Willighagen, Rich Apodaca, and Oscar the Journal Munching Robot (masquerading as Peter Murray-Rust, or is that the other way around?) have already posted responses. Here I want to add my own thoughts and possibly amplify some of the points others have made. Continue reading “A new type of chemistry journal: Nature Chemistry requests input”

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More on the science exchance – or building and capitalising a data commons

Image from Wikipedia via ZemantaBanknotes from all around the World donated by visitors to the British Museum, London

Following on from the discussion a few weeks back kicked off by Shirley at One Big Lab and continued here I’ve been thinking about how to actually turn what was a throwaway comment into reality:

What is being generated here is new science, and science isn’t paid for per se. The resources that generate science are supported by governments, charities, and industry but the actual production of science is not supported. The truly radical approach to this would be to turn the system on its head. Don’t fund the universities to do science, fund the journals to buy science; then the system would reward increased efficiency.

There is a problem at the core of this. For someone to pay for access to the results, there has to be a monetary benefit to them. This may be through increased efficiency of their research funding but that’s a rather vague benefit. For a serious charitable or commercial funder there has to be the potential to either make money, or at least see that the enterprise could become self sufficient. But surely this means monetizing the data somehow? Which would require restrictive licences, which is not at the end what we’re about.

The other story of the week has been the, in the end very useful, kerfuffle caused by ChemSpider moving to a CC-BY-SA licence, and the confusion that has been revealed regarding data, licencing, and the public domain. John Wilbanks, whose comments on the ChemSpider licence, sparked the discussion has written two posts [1, 2] which I found illuminating and have made things much clearer for me. His point is that data naturally belongs in the public domain and that the public domain and the freedom of the data itself needs to be protected from erosion, both legal, and conceptual that could be caused by our obsession with licences. What does this mean for making an effective data commons, and the Science Exchange that could arise from it, financially viable? Continue reading “More on the science exchance – or building and capitalising a data commons”

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More on FuGE and data models for lab notebooks

Frank Gibson has posted again in our ongoing conversation about using FUGE as a data model for laboratory notebooks. We have also been discussing things by email and I think we are both agreed that we need to see what actually doing this would look like. Frank is looking at putting some of my experiments into a FUGE framework and we will see how that looks. I think that will be the point where we can really make some progress. However here I wanted to pick up on a couple of points he has made in his last post. Continue reading “More on FuGE and data models for lab notebooks”

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Semantics in the real world? Part II – Probabilistic reasoning on contingent and dynamic vocabularies

Rendering of human brain.And other big words I learnt from mathematicians…

The observant amongst you will have realised that the title of my previous post pushing a boat out into the area of semantics and RDF implied there was more to come. Those of you who followed the reaction [comments in original post, 1, 2, 3] will also be aware that there are much smarter and more knowledgeable people out there thinking about these problems. Nonetheless, in the spirit of thinking aloud I want to explore these ideas a little further because they underpin the way I think about the LaBLog and its organization. As with the last post this comes with the health warning that I don’t really know what I’m talking about. Continue reading “Semantics in the real world? Part II – Probabilistic reasoning on contingent and dynamic vocabularies”

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Friendfeed, lifestreaming, and workstreaming

As I mentioned a couple of weeks or so ago I’ve been playing around with Friendfeed. This is a ‘lifestreaming’ web service which allows you to aggregate ‘all’ of the content you are generating on the web into one place (see here for mine). This is interesting from my perspective because it maps well onto our ideas about generating multiple data streams from a research lab. This raw data then needs to be pulled together and turned into some sort of narrative description of what happened. Continue reading “Friendfeed, lifestreaming, and workstreaming”

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Data models for capturing and describing experiments – the discussion continues

Frank Gibson has continued the discussion that kicked off here and has continued here [1, 2, 3, 4] and in other places [1, 2] along the way. Frank’s exposition on using FuGE as a data model is very clear in what it says and does not say and some of his questions have revealed sloppiness in the way I originally described what I was trying to do. Here I will respond to his responses and try to clarify what it is that I want, and what I want it to achieve. I still feel that we are trying to describe and achieve different things, but that this discussion is a great way of getting to the bottom of this and achieving some clarity in our description and language. Continue reading “Data models for capturing and describing experiments – the discussion continues”

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The structured experiment

More on the discussion of structured vs unstructured experiment descriptions. Frank has put up a description of the Minimal Information about a Neuroscience Investigation standard at Nature Precedings which comes out of the CARMEN project. Neil Saunder’s has also made some comments on the resistance amongst the lab monkeys to think about structure. Lots of good points here. I wanted to pick out a couple in particular;

From Neil;

My take on the problem is that biologists spend a lot of time generating, analysing and presenting data, but they don’t spend much time thinking about the nature of their data. When people bring me data for analysis I ask questions such as: what kind of data is this? ASCII text? Binary images? Is it delimited? Can we use primary keys? Not surprisingly this is usually met with blank stares, followed by “well…I ran a gel…”.

Part of this is a language issue. Computer scientists and biologists actually mean something quite different when they refer to ‘data’. For a comp sci person data implies structure. For a biologist data is something that requires structure to be made comprehensible. So don’t ask ‘what kind of data is this?’, ask ‘what kind of file are you generating?’. Most people don’t even know what a primary key is, including me as demonstrated by my misuse of the term when talking about CAS numbers which lead to significant confusion.

I do believe that any experiment [CN – my emphasis] can be described in a structured fashion, if researchers can be convinced to think generically about their work, rather than about the specifics of their own experiments. All experiments share common features such as: (1) a date/time when they were performed; (2) an aim (”generate PCR product”, “run crystal screen for protein X”); (3) the use of protocols and instruments; (4) a result (correct size band on a gel, crystals in well plate A2). The only free-form part is the interpretation.

Here I disagree, but only at the level of detail. The results of any experiment can probably be structured after the event. But not all experiments can be clearly structured either in advance, or as they happen. Many can, and here Neil’s point is a good one, by making some slight changes in the way people think about their experiment much more structure can be captured. I have said before that the process of using our ‘unstructured’ lab book system has made me think and plan my experiments more carefully. Nonetheless I still frequently go off piste, things happen. What started as an SDS-PAGE gel turns into something else (say a quick column on the FPLC).

Without wishing to pick a fight, most people with a computer science background who lean towards the heavily semantic end of the spectrum are dealing with the wet lab scientists after the data has been taken and partially processed. I don’t disagree that it would help the comp sci people if the experimenters worked harder at structuring the data as they generate it, and I do think in general this is a good thing. The problem is that it doesn’t map well onto how the work is actually carried out. The solution I think is a mixture of the free form approach combined with useful tools and widgets that do two things: firstly they make the process of capturing the process easier; secondly the encourage the collection and structuring of data as it comes off. This is what the templates in our system do, and there is no reason in principle why they couldn’t be driven by agreed data models.

Actually the Frey group (who have done the development of the LaBLog system) already have a highly semantic lab book system developed during the MyTea project. One of our future aims is to take the best of both forward into a ‘semi-semantic’ or ‘freely semantic’ system. One of the main problems with implementing the MyTea notebook is that it requires data models. It was developed for synthetic chemistry but it would make sense, in expanding it into the biochemistry/molecular biology area to utilise existing data models with FuGE the obvious main source.

One more point: we need to teach students that every activity leading to a result is an experiment. From my time as a Ph.D. student in the wet lab, I remember feeling as though my day-to-day activities: PCR reactions, purifications, cloning weren’t really experiments […] Experiments were clever, one-shot procedures performed by brilliant postdocs to answer big questions […] Break your activities into steps and ways to describe them as structured data should suggest themselves.

This is very true, and harks back to my comment about language. A lot of the issues here are actually because we mean very different things by ‘experiment’. We probably should use better words, although I think procedure and protocol are similarly loaded with conflicting meanings. Control of language is important and agreement on meaning is, after all, at the root of semantics (or is that semiotics, I’m never sure…)

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The heavyweights roll in…distinguishing recording the experiment from reporting it

Frank Gibson of peanutbutter has left a long comment on my post about data models for lab notebooks which I wanted to respond to in detail. We have also had some email exchanges. This is essentially an incarnation of the heavyweight vs lightweight debate when it comes to tools and systems for description of experiments. I think this is a very important issue and that it is also subject to some misunderstandings about what we and others are trying to do. In particular I think we need to draw a distinction between recording what we are doing in the lab and reporting what we have done after the fact. Continue reading “The heavyweights roll in…distinguishing recording the experiment from reporting it”