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.
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 That this view is a little bit outdated, won’t be anything new.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
When you start a new blog, the most important thing is to think about the scope of it. No, its not my first blog, I had others on different topics in the past. Nevertheless, it is the first, which is connected to my current work.
I am a learned meteorologist, doing something which many would call climate science and in the end my main work can be classified as statistical data science. In this field a huge blogosphere exists, sometimes very political, often as part of a war between different view points. No, my aim is not to take part in this.
I would rather prefer to write on some topics within this field from a view of a simple scientists, to show how the daily work looks like. And yes, sometimes some simple words, spoken in a hurry during discussions, need a longer explanation. When this gets a philosophical touch, then this is more than appreciated. I think it is important that when you work in science you think about the way you work and how we get to our knowledge we call established science. Additionally, new developments like new papers, conferences and other stuff which come along will be shown here, certainly with a different view like it is done at the entity itself.
My aim will be at least one post per month, but I know that this will vary from time to time. Comments are appreciated, but will be controlled before publication: Advertisements and personal attacks will be strictly prevented.
So let’s start the project!