For everyone working on data analysis in climatological science, using references is essential. These references, representing some form of truth, is often the target, which models have to reach. Verification (or in non-meteorological science validation) methodologies evaluate the results against the references and dependent on the methodology deliver good results when the model is near to it, matches its variability or is close in other statistical parameters. The power of these references in these analysis and defining our knowledge about the world is immense and so it is essential that it really has something to do with things we see in front of our windows.
Last month Wendy Parker published a paper named “Reanalyses and Observations: What’s the Difference” and looked at the references from a more philosophical point of view. She listed four points, which critically looked at the connection between references and observations and in this post I would like to take a look at them.
There is an elephant in the room, at every conference in nearly every discipline. The elephant is so extraordinary that everyone seems to want to watch and hype it. In all this trouble a lot of common sense seems to get lost and especially the little mice, who are creeping around the corners, overlooked.
The big topic is Big Data, the next big thing that will revolutionise society, at least when you believe the advertisements. The topic grew in the past few years into something really big, especially as the opportunities of this term are regularly demonstrated by social media companies. Funding agencies and governments have seen this and put Big Data at their top of their science agenda. A consequence are masses of scientist, sitting in conference sessions about Big Data and discussions vary between the question on what it is and how it can be used. Nevertheless, there are a lot of traps in this field, who might have serious consequences for science in general. Continue reading
For a few years now, Data Science is a hot topic. Under the theme ‘Big Data’, it got popular and when you believe some media it will solve nearly all problems in the world. But what does it mean to be a data scientist? Is it a jack of all trades or just someone, who know no field really well? As I would myself describe as a data scientist, I would like to write a little bit about how I see this field.
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.
The way scietists should work (Quadt et al 2012)
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
Two years. Two years between the first ideas and the submit of the paper, which has gone on its journey today. Sounds like a long time, but to be honest it is not. To show this I would like to explain in this post a little bit the generalised basic steps of my work towards a paper. I will not take the submitted paper from today as an example, because its creation was quite unusual. Therefore, I will stick with the general approach, which is divided in several phases: Continue reading