FRICTION

“So you dig into the history of data. You fight metadata friction, the difficulty of recovering contextual knowledge about old records.” – Without historical data there would be no data today.

To begin: Aggregation is the constituting or amounting to a whole; total- bringing things together

Distribution is the opposite of aggregation but put in words: the act of dispersing

The reading entitled A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming is about how Edwards is trying to aggregate the arguments and models of climate change into one source.

The reading offered more or less a timeline of how scientists learnt to understand the environment- and from this then- how different models for data analysis of climate change have progressed and changed and importantly what these changes mean in developing our understanding of information.

Edwards says the Infrastructural Globalism needs socio technical systems that produce knowledge about the whole world. However Global Data needs multiple images to replicate change as one image is not useful in demonstrating a theory as big as Global Warming.

Aggregation is used to analyse information efficiently. Scientific models are the best way, (Edward) to predict the future impacts of changes. Without models, would the data really exist?

Statistics are thus used in models. It is here that frictional data can be observed.

It is data friction because it is close to impossible to term something a ‘fact’ when talking about global warming. There are so many representations of data and models that they over-lap and contradict each other.

Within climate change, there are two sides to the story. Some say it’s happening, some say it’s not. If we were to go back to the original data they used to make these suggestions, there would probably be a debate then about what a certain statistic means – because your views will be based on data you have seen before that, and before that, and before that….etc.

In my own personal views of aggregation, I find it interesting to think how the creation of a hash tag is formed. When I hash tag something on instagram, it’s been done a million times before. To some people, the hash tag has different meanings. For example I hash tagged ‘billgates’ the other day. When I view the global hash tags for Bill Gates, it’s anything from a computer to a dog wearing glasses.

I guess after reading the Edwards article it made me realize that you own interpretation of data is always going to be what you base your future interpretations of data on.