I have recently been trying to collect data from the Listening Experience Database (LED) in order to put together a proposal for a conference paper. The LED is a nicely constructed database using linked open data and a structure based on something called the ‘Semantic Web’. Rather than traditional databases that have a hierarchical ‘tree’ structure, the Semantic Web concept is a true ‘network’, where anything can be linked to anything else. The LED, for example, includes links to data on a number of other databases. Have a look at the LED and follow a few links and you will see what this means – a very rich and flexible means of linking data together. Continue reading →
Finding a great dataset is all very well, but the next step is working out how to get the data onto your computer so that you can start playing with it. Datasets come in many forms, and there are different ways of collecting the data. In this article I will use some examples from the list of datasets in this previous article on women composers.
There are three main approaches to collecting data: read it and type it in, download it, or ‘scrape’ it. Continue reading →
There is a lot of interest at the moment in women composers. Until recently, women were a small minority of the composing population, but in working with large datasets, I encounter a surprisingly large number of female names (although it is often frustratingly difficult to find out any details about them). In the nineteenth century, for example, perhaps 1-2% of published music was written by women.1 Whilst that is an embarrassingly small proportion, it still equates to a substantial body of music by many hundreds of women composers – most of whom have since sunk into obscurity. There are of course many more from the twentieth and twenty-first centuries.2 Continue reading →