MishMash

OS data to be made freely available?

Potentially big news, even if limited: some Ordnance Survey data (you know, the stuff the tax payer paid for, directly or indirectly) is to be made freely available. The free our data blog has some comments. Importantly:

Key points: it involves “mid-scale” maps from 1:10,000 upwards; and it kills off the “derived data” rows that government departments and everyone else has been having for so long. Derived data will have a stake through its heart.

Oh, and – this “free” will extend to being free for commercial use. That’s right, you’ll be able to build a business with it. Though it’s not clear yet whether you’d be able to take the maps and create *printed* ones. Must ask about that.

I hope that means it will become possible to obtain e.g. landranger mapping for the Thames tidal floodplain without making a dozen requests through Digimap. That was a painful process.

I suppose it also means that all of the wierd and arcane restrictions on use will be lifted (including different restrictions for printed and online papers, as far as I could interpret the Ts and Cs for digimap).

It's only a step, but it sounds like a significant step. It's significance goes beyond Ordnance Survey, too. It seems to me to undermine the argument that government requires that data be charged for. Other goverment agencies take note ...

Cameron Neylon, Capturing process

Just been at a seminar organised by the Comp Sci people here in Newcastle. Cameron Neylon, currently of the Science and Technology Facilities Council on:

Capturing process: The promise and challenges of connecting experimental records to the semantic web

The promise of the semantic web in its broadest terms to integrate structured data, and in particular scientific data, is breathtaking. Yet our ability to deliver on that promise remains severely limited. On one side, battles are fought over vocabularies and structured description languages, while on the other computer scientists argue over design and architecture. In the middle the experimental scientist is usually left bemused as to what all the fuss is about and how it can have any relevance to their research, which is naturally far too specialised and ground-breaking to fit into any framework that an outsider could ever provide. The challenge lies in finding a pathway that moves towards satisfying the needs and interests of these disparate groups while providing both value and a usable interface to the end user.

We have adopted a blog based approach to capturing the record of research in an experimental laboratory. The use of a free form framework along with templates encourages the user to structure the description of their work in a way that matches their needs. Cycles of template design and optimisation lead to structures with strong similarities, and occasional important differences, with designed ontologies and vocabularies. The challenge for the future lies in determining how best to guide the actions of users towards the use of appropriate controlled vocabularies and structured descriptions, while using their choices to inform the design of these systems. The potential for Google Wave to provide a framework to enable local structuring as well as integration with global structured descriptions will be explored.

Some interesting ideas, as abused by my memory:

• People tell stories, they don't organise stuff the way it needs to be organised for machines to process. A big part of the disconnect between scientists and the folks building "e-science" tools is that these tools don't help people tell stories.
• So we need to start capturing structure through storytelling. That is, allow people to work in a natural way and pick up the structure from what they do.
• You can create a virtuous circle between metadata, which a user would normally not bother filling in or fill in incorrectly, and user interface. Arrange the UI so that the metadata populates the UI. The user wants that UI to make their life easier, and they start to notice that getting the metadata right is necessary.
• (Lab) instruments as bloggers. Have them push results straight into the blog. Take the human out of the loop (I might qualify, as I think was intended: where they add no value).

Cameron has had early access to Google Wave, it seems, and is impressed with the potential. He had some cool examples of Wave "robots" going off and filling in extra information, drawing graphs, creating links, tidying up references.

He was less impressed by the Google client: too much stuff going on in a single UI, too non-standard. He also expressed concern about users understanding the model. It's the eternal problem with geeks developing stuff for "normal people" again. We're geeks because we do abstract with ease.

There was a lot that I recognise, and some new thoughts as well. I'm all about computational analysis in engineering decision making rather than lab-based science, so there are some differences, but there's a lot of overlap too. In theory we should have an easier time here since so much of our "stuff" comes in the form of information artefacts, and our "experiments" take place in silicon. There are instruments, and the process of getting data from them into our analysis is currently extremely painful, but most of that is "upstream" of our analysis.

I liked the picture of an lab process generating artefacts and narrative, and then a separate process of arranging these things into a different narrative for publication. The story we tell when we want to explain our results to others isn't the story of the trials and tribulations of getting to those results, its a story that gets the reader as smoothly as possible from their current knowledge to where we want them to be. That's true of engineering analysis as of science. And in both cases, we'd be in a much better place if, given that latter story, we could easily investigate the artefacts referred to: look at them, do our own visualisations or further processing, and follow the trail back to see exactly how they were generated.

What is uncertainty?

A couple of recent activities have forced me to explore my discomfort with with the way the word uncertainty is used in flood risk management. One of these activities was a refinement and extension of the wiki on methods for handling uncertainty in flood risk management that I have been involved in putting together as part of the Flood Risk Management Research Consortium. The other is an ongoing attempt to write a paper, which also deals with, among other things, uncertainty. I find writing incredibly hard, not least because I can't stop thinking about things like this.

I have assumed, since I first thought about it, that certain and its various cognates (certainty, uncertain, uncertainty, ...) referred originally to a state of mind, and that through a process of metonymic transfer they had come to be used to refer to the object of that state of mind: the thing about or because of which one was, or was not, certain. A trawl through the OED, to which the University subscribes, presumably hoping that doing so will result in a net productivity gain among its employees, suggests that this is not the case.

For all of these words, the first definition (and I presume order is significant) given by OED is related to something being "determined, fixed, settled". For certainty, the mental state which I assumed was the primary meaning appears only in the fourth, "the quality or fact of being (objectively) certain", and fifth, "the quality or state of being subjectively certain; assurance, confidence; absence of doubt or hesitation" definitions.

The etymology given for certain makes things clearer

a. OF. certain (= Pr. certan, Sp. and It. certano), repr. late L. or Romanic type certan-us, certan-o, f. cert-us determined, settled, sure, orig. pa. pple. of cern-ere to decide, determine, etc. The sense-development had taken place already with L. certus.

(OED)

So the root meaning of certain would appear to be "having been decided/determined", and the metonymic transfer, if anything, proceeded in the opposite direction from that which I had supposed, from the fact of my having decided something to my state of mind having done so.

In launching myself into the OED, I was looking for justification for using uncertainty in a constrained sense to refer only to a state of mind. I didn't find it, which leaves my original problem unsolved. To design software which supports a more explicit treatment of uncertainty in flood risk management requires that we address all of the many aspects of uncertainty, and these go far beyond the numerical-computational treatment provided by the tools of quantitative uncertainty analysis. To do so, or even to explain the need to do so, we must be able to distinguish these many aspects in both thought and language, in turn requiring the use of a vocabulary which recognises these distinctions. Using uncertainty to refer indisciminately to both the object of a state of mind and that state itself renders clarity of thought, and with it communication, impossible.

As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.

(Albert Einstein)

True objective certainty is only possible in the context of formal systems such as mathematics and logic. Everything else, including the results of modern science, is subjective. The use of certitude, according to the OED, has since Hobbes been increasingly restricted to "the quality of being subjectively certain" [my emphasis], and is thus the opposite of doubt. Science allows us to develop justified certitude, which might tend towards objective certainty but cannot ever get us there. Flood risk management draws heavily on modern-scientific methods and results; this injects a certain amount of "objectivity" into the process, but in the end most decisions still have much more to do with certitude, subjective certainty, than with objective certainty.

Perhaps, then, I can use doubt and certitude to refer to the states of mind of participants in the decision-making process and uncertainty, in the manner familiar to most of my potential readers, to refer to the objects which, by their uncertainty, inspire doubt.

“Languages and metamodels” session at MODSIM05

I guess I should have mentioned this here a long time ago; my eye wandered from a lot of balls over the last few months. Things can only get better …

I am organising a special session at MODSIM05 entitled, "Languages and metamodels for model composition." The abstract (find it under "Special Sessions" in the menu at the left of the page; the fixed masked URI stops easy linking grumble grumble) reads,

A lot is being invested at present in the production of facilities to support the composition of model components into integrated models. Any such framework embeds an ontology of models, or metamodel; this is a fundamental prerequisite of interoperability. This metamodel finds expression in the form of a composition language, be it as an API in a general purpose programming language, an application of XML, or language built on a logic-based formalism such as RDF. This combination of language and metamodel acts as a "strong pair of glasses", bringing certain things into sharp focus and obfuscating others, making some constructs easy to express, others intractably difficult. Papers are invited discussing composition languages and the metamodels they embed and express. Topics of interest include, but are not limited to: description of the metamodel embedded, by design or otherwise, in a particular composition language; the power of particular metamodel in terms of the range of model types and model structures which can be described and the parsimony of description; analysis of the strengths and weaknesses of the chosen foundation layers (programming language, XML, RDF, etc) and the influence of these; comparisons of two or more languages or metamodels; the relationship between the metamodel and the modelling domain ontology; reports on difficulties encountered in model linking resulting from metamodel or ontology mismatch and possible approaches to resolving these.

Getting any fish to bite with this bait is a long shot, perhaps, and getting longer if I don't get more specific invitations out. If anyone has any ideas about people who might have something to contribute, even if they don't know it, please let them, or me, know. The abstract should be interpreted broadly; if in doubt, err on the side of submitting an abstract (deadline March 18) or drop me an email.

Artificial Intelligence? Pah.

Long ago Dad introduced me to a quote, which he believes to have been first uttered by a head of the AI group at the University of Edinburgh.

Artificial it may be, Intelligence it most certainly isn't. [Source unknown]

I have in the past found Google to be quite effective at locating sources of quotes for me. In this case it is of no assistance whatsoever. If anyone has any leads on this, I'd love to know. If not, you heard it here first.

Spread your wings and fly, little meme.

Exploratory Modelling

An interesting over coffee conversation just had. The context was how one might go about constructing models of geomorphological systems, and how the qualitative descriptions of geomorphologists might be tested and informed by interactive development of quantitative expressions of those descriptions.

Grep tells me that I didn't use the phrase exploratory modelling in my PhD thesis, which I thought I had. Let's post it here.

Google tells me that it's not new. Which is great; people to talk to. Why didn't I run this search before? Zarine Kemp looks to be doing some interesting work, and has some publications I need to follow up.