Archives for posts with tag: UCAR

I came across a very interesting white paper (to be published by the OGC in the current EarthCube development) by Ben Domenico today that does not use the term reproducible research, but pretty much talks about it in the realm of using the web for interactive scientific publications. It has some good arguments and many links to other resources – one of them the SOS, but also ERDDAP or Dryad, to name just two.

I think there are some great opportunities here to use sos4R in a web environment that I’d really like to explore, i.e. integration with RServe and/or WPS. More on that in the future!

Read the full paper:

Abstract: Imagine a scientific environment in which authors create online publications that allow readers to access, analyze, and display the data and processes discussed in the publication.  Rudimentary examples of such documents can already be cobbled together using existing technological tools in conjunction with the appropriate interface standards. Working together, the science, technology and publishing communities can build on these foundations to develop sophisticated cyber- and organizational infrastructure that will revolutionize how scientists and science educators interact with one another and with the general public.   The idea is simple: the reader of a publication will have access not only to the datasets under discussion but also to the processes used by the author to carry out the analysis and display of those datasets. The reader will be able to repeat the experiment as it is published, or perform related experiments by using different datasets or different processes.

One motivation for the SOS4R plugin is reproducible research – and what data could be more appropriate than the question of climate change? I am certainly not saying that all problems (“Climategate“) could be helped with making analysis simpler just because data is easily accessible… but hopefully some!

My two use cases (excerpt from application) that I will base one temperature data are:

First, a researcher wants to use temporally ordered univariate data to create a forecast based on an ARIMA model. She uses the package forecast to achieve her goal.

Second, a point pattern analysis shall be performed. The user wants to analyse covariate effects of spatially distributed data. She uses the package spatstat. An analysis from the workshop paper “Analysing spatial point patterns in R” by Adrian Baddeley is carried out.

Natually, I am not a climate researcher, but nevertheless I’d like to see whether I can make my own temperature model of the earth based on publically available data and Open Source software. Read the rest of this entry »