OER Visualisation Project: What I know about #UKOER records on Jorum and OER Phase 1 & 2 [day 18]

*viewed doesn’t necessarily viewed by a human. There are multitude of bots and automated code that might visit the jorum site which could increase a records view count. For example the process of me getting this data scraping each record page generated almost 10,000 ‘views’.
Most of the numbers above come from two spreadsheets: CETIS PROD Spreadsheet; and jorumUKOERReconciled – Issue 2.  I’ve mentioned both of these spreasheets before (day 8 | day 16), but you might like to File > Make a copy of these to play with the data yourself and see some of the formulas used. An additional note on the resource view counts. These were collected by fetching the each resource page on Jorum using Google Refine and scraping the data (more details on using Refine to extract data from day 11.
[The additional processes were to extract a Jorum url by using the expression filter(value.split("|"),v,contains(v,"jorum.ac.uk"))[0]) on the identifier column, fetching a new column based on the new url and then extracting a count using toNumber(replace(filter(cells["jorumpage"].value.parseHtml().select("p.ds-paragraph"),v,contains(v.htmlText(),"viewed"))[0].htmlText(),/\D/, ''))]
So I now have a decent amount of data (some of which might be dropped), next to communicate …


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