Open Data Day is an annual celebration of open data all over the world. Groups from around the world create local events on the day where they will use open data in their communities. It is an opportunity to show the benefits of open data and encourage the adoption of open data policies in government, business and civil society.
All outputs are open for everyone to use and re-use.
This year, we’re celebrating the day by inviting you to join our investigation into the environmental impacts of our devices, which you can learn more about here:
We need your help to track down life cycle assessment (or LCA) reports that companies and researchers sometimes publish on the products we buy. These reports estimate the environmental impacts each device has at every stage of its life, from the materials that go into it, to manufacturing, transport, use and end of life. This includes the amount of CO2 produced by each device.
This data can help us quantify the benefits of repairing things and we think it should be open to everyone. But it’s often hard to find. Some companies hide it in obscure corners of their websites or only make it available in hard-to-process formats. Many don’t even publish it at all.
Can you help us track some down?
In particular, we’re trying to find CO2 data on the following types of devices:
Home office devices, e.g.: printers & scanners (we already have lots of data for computers, smartphones and tables)
Audio/video equipment, e.g.: cameras and DSLRs, headphones, hi-fis/speakers, home assistant devices (Alexa/Siri etc.)
Small household appliances, e.g.: fans, irons, lamps, power tools, sewing machines, vacuum cleaners, IoT devices
Hair & Beauty items, e.g. hair dryers, hair straighteners/curlers, electric razors
If you want to join the hunt for data, simply post below letting us know which kind(s) of devices you’d like to investigate. And feel free to ask questions if you’re not sure about something
I don’t understand what the link to the “spot the smartphone fault” is designed to do.
It starts with a failed repair!
When I have tried the fix another item, the translation is terrible to English.
What is the object?, try and diagnose the repair strategy?
You don’t seem to end up with a repaired item, only another step, OR this is technical repair!
It sounds like you’re talking about MobiFix:ORA, the latest quest on the Data Workbench, is that right?
The idea of that quest is simply to classify the smartphone faults seen at repair events. Because much of the repair data is entered as free text, the only way to analyse it at scale is to work through each record and categorise it by hand. If enough people go through enough records, we can then determine which faults were most common across all the repair attempts. In turn, that helps us understand how these devices can be made to last and be repaired more easily.
For the translation, we’re using Google Translate to automatically convert text in other languages to English. It’s not quite there for some languages, so bear with it
Worked it out at last and completed all requests.
From the text given, work out what is the correct category that this fix/non-fix be allocated to!!
Do not diagnose it yourself, only translate the text into the given categories.
I notice that some options are NOT given.
Replace external cable (USB etc).
SIM Card fault/ configuration, not the Socket.
External Equipment (keyboard/ monitor etc) faulty, not the device.
Ah, just noticed this, thank you for your efforts!
The reason for that is that those faults lie with peripheral items while the task is specifically to determine the type of fault affecting the smartphone device itself, so in these cases “Other” is an acceptable opinion. Keyboards, monitors and other peripherals have their own category - sometimes repairs do get miscategorised.
These tasks deliberately include a few vague fault types that act as buckets to hold a variety of issues that are often quite fuzzy in nature. The “problem” text doesn’t always give enough clues to the nature of the fault and the tasks are wanting us to identify, if we can, the nature of the fault as gleaned from the text, not to try and guess at what the problem might have been.
The vague, catch-all fault types such as “Other” and “Performance” can be useful to us though, we can scan through them ourselves looking for issues such as USB cables and peripherals if we need to, and we can also identify miscategorisation.
Here are some examples:
a Dell monitor [PDF]
An Apple iPhone [PDF]
A Lenovo laptop [PDF]
Can you help us track some down?
