Traditionally within the different disciplines of earth science the scientists are divided into two groups: modelers and observationalists. In this view the modellers are those who do theory, possibly with pen and paper alone, and the observationalist go into the field and get dirty hands. That this view is a little bit outdated, won’t be anything new. In my opinion, it really started with the establishment of remote sensing that this division reunited (Yes, reunite, because in the old days, there were a lot of scientists who did everything). As I am a learned meteorologist, from my view it is quite common that this division is not really existent anymore. Both types of scientists sit in front of their computer, both are programming and both have to write papers with a lot of mathematical equations. In other fields, the division might be still more obvious (e.g. Geology), but for many its only the type of data someone is working with, which classify someone as observationalist or modeller. Continue reading
In philosophy, several great minds have addressed the way scientist should work to gain their knowledge. Among others Bacon (1620) and Popper (1934) showed different ways to gain information and how it can be evaluated to become science. During my PhD I developed a relatively simple and general working scheme for scientists, which was published in Quadt et al (2012). The paper analysed the way how this general scientific working scheme could be represented by scientific publications.
While the traditional journal paper, which exists since the Philosophical Transactions of the Royal Society, edited by Henry Oldenburg in 1665, covers the whole scientific process, new forms have emerged in the last decade. Data papers (Pfeiffenberger & Carlson, 2011), a short journal article focussing on the experimental design and present the data from the experiment, filled a gap and should simplify the use of data. Another process is the publication of data and metadata at a data centre itself, without an accompanying journal article.
This type of publication was part of my project at that time. A general question therein was how such a publication can be made comparable to the other types. The comparison showed that it is quite comparable, but that one important element is missing: peer review. Continue reading
In the last year during a larger meeting I had made a comment, which let a lot of attendees shake their head and others just smile. The statement was:
“Observations represent the truth, models the state of our understanding.”
Like I have said before, on the first sight it is of cause rubbish that observations have anything to do with the truth. Indeed, truth is a great word with many different meanings and implications. In the context above “truth” (which anyhow should always set between quotation marks) describes the possible best estimation of the real world by the current available technology in real case situations. When I personally write things up, I usually use a measurement operator to make this clear that observations are never able to describe the full reality. How much effort observers might put at it (and they usually do an amazing job), the real physical state of a physical system can only be approximated. Continue reading
In my last post I showed that observations are models as well. But when this is the case, why do we distinguish between these two kinds of data the way we do? Why is everyone so keen on observations, when they are just another model output?
The reason can be found usually in their different structure. The amount of modelling, which is applied to an observation to still be called observation should usually be very basic. Coming from the atmospheric sciences myself, the border between the two worlds can often be drawn in the type of the data. Generally the observations in that field are point data, often in situ data, which are irregular in time and space. In contrast to this, model data is usually very regular and sometimes high-dimensional.
Doing statistics between the two worlds of observations and model results lead often to the assumption that both are completely different things. There are the observations, where real people moved into the field, drilled, dug and measured and delivered the pure truth of the world we want to describe. In contrast to this, the clean laboratory of a computer, which takes all our knowledge and creates a virtual world. This world need not necessary have something to do with its real counterpart, but at least it delivers us nice information and visualisation. But this contrast between the dirty observations and the clean models is usually only something, which exists in our heads, in reality they are much more connected to each other.