We would like to be able to analyse common fault types for devices that we have seen at our community repair events.
However, the problem field, where the fault information is recorded, is currently free text. To analyse it, the faults need to be ‘coded’ – i.e. classified into common types.
In these sheets, read through the text provided in the problem field, and try to select the appropriate fault type from the dropdown. In addition, if possible, try and categorise the problem into a fault category of either ‘Hardware’ or ‘Software’.
|Category||Publisher||Language||Number of records||Link|
|Tablets||Anstiftung||German||30||Anstiftung – Tablets|
|Laptops||Anstiftung||German||170||Anstiftung – Laptops|
|Desktops||Anstiftung||German||25||Anstiftung – Desktops|
|Tablets||Restart||Mostly English||95||Restart – Tablets|
|Laptops||Restart||Mostly English||262||Restart – Laptops|
|Desktops||Restart||Mostly English||24||Restart – Desktops|
Restart data is since March 2019 – previous data was categorised during and following Open Data Day.
- What are the most common faults in each of the product categories? In the whole cluster of tablets, laptops and desktops?
- How do different faults correspond to repair outcomes?
- Do we see more hardware or software problems? How do they correspond to repair outcomes?
- Do we see a similar split of of fault types between Anstiftung and Restart networks?
Raw data download
The full ORDS dataset is available here, if you would prefer to try and map the free text to fault classifications in a different way. (Note that it includes Restart data from before March 2019, which has already been classified – but is not currently included in the ORDS dataset).
An example R workbook from Open Data Day is available here.