Massive ensemble paper background: Massive ensembles: How to make use of simple models?

The new paper on the LIG sea-level investigation with massive ensembles analyses simple models. In this post I want to talk a bit about their importance and how they can be used in scientific research.

Simple models are models with reduced complexity. In contrast to complex models their physics is simplified, they are more specified for a specific problem and their results are not necessarily directly comparable to the real world. They can have a smaller, easier to maintain code base, but also a simple model can grow in lines of codes fast. A simple model is defined depends on the processes it includes, not the mass of coding lines. Continue reading


Are data scientists “research parasites”?

Last week the two scientists Dan L. Longo and Jeffrey M. Drazen, both from medicine, published an editorial, which was also picked up by some news outlets. In this they talk about an emerging class of scientists, who are not anymore involved in the basic data collection, but reinterpret the results of original studies by their own methodologies and by combining several studies into a new dataset. They state that some scientists name this kind of researchers “research parasites” and claim that they are a problem for science overall. The authors prefer a system, where the scientists work directly together and publish their studies as coauthors.

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Big Data – More risks than chances?

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