Template matching data tables

With all images scanned and sorted in my COBECORE project the next step involves the transcription of the images into meaningful, machine readable, data. Due to the complexity of the data, such as various handwriting styles in faded or runny ink, automating this process is very difficult. We will therefore aim to crowdsource the transcription of the data. Yet, large tables are difficult to transcribe as the location within a table is of importance, and not only the values. As such, mistakes are more easily made when transcribing tables as a whole.

Why I code

MODIS hdf data extraction in R (part 2)

In a previous blog post I describe how to subset MODIS hdf data. However, this was a rather simple example. Today, a graduate student emailed me to help her out with a subsetting problem she had when running the code, or better the lack of an option to extract a region of interest (rather than point data) in the previous example I gave.

The Pareto-Kanban squeeze

In business management the Pareto principle, or the law of the vital few, translates into the notion that 20% of the clients bring in 80% of the sales. I argue that in science the same principle applies. Here, focussing 20% of your time on necessary projects will translate in the bulk of your output.


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