Posted on by Renee Furner

Lifestream, Diigo: A critical reflection on Big Data: Considering APIs, researchers and tools as data makers | Vis | First Monday

“This paper looks at how data is ‘made’, by whom and how. Rather than assuming data already exists ‘out there’, waiting to simply be recovered and turned into findings, the paper examines how data is co–produced through dynamic research intersections. A particular focus is the intersections between the application programming interface (API), the researcher collecting the data as well as the tools used to process it. In light of this, this paper offers three new ways to define and think about Big Data and proposes a series of practical suggestions for making data.”
from Diigo http://ift.tt/2aFY3FC
via IFTTT

A couple of points from this paper seem relevant this week.

  1. The tools we use when researching ‘limit the possibilities of the data that can be seen and made. Tools then take on a kind of data-making agency.’ I wonder what the influence of the Tweet Archivist API is on my sensemaking of our data.
  2. Data are always selected in particular ways’ some data are made more visible than others and the most visible doesn’t necessarily align with or take into account what was most valued by and meaningful to users. ‘It is important to remember that what you see is framed by what you are able to see or indeed want to see from within a specific ideological framework.’ What did we value most in our tweetorial (obviously different things for different folks)? We still need to construct research questions that focus on those things most important to us, even if the data are less readily available.
  3. ‘Visibility can be instrumentalised in different ways, depending on the interests of those seeking to make something visible. Visibility can be useful as a means of control, it can be commercially exploited, or it can be sold to others who can exploit it in turn.’ How are we exploiting visibility in education?
  4. The monetisation – or making valuable in other ways – of data makes the data itself unreliable. Helen suggests this in her blog post, where she muses that perhaps if she’d known what aspects of our behaviour in the tweetorial were being analysed, she would have ‘gamed it’.