More interesting stuff from Jon Udell, this time taking some climate data for his area, using the ManyEyes platform and trying to see what has been happening in New Hampshire in the last century.
The experiment is non-conclusive, but there is an excellent debate in the comment threads, about the problems with amateurs getting their hands on the data – and the hash they can make of things because they are not experts.
Says one commenter (Brendan Lane Larson, Meteorologist, Weather Informaticist and Member of the American Meteorological Society)
Your vague â€œweâ€ combined with the demonstration of the Many Eyes site trivializes the process of evidence exploration and collaborative interpretation (community of practice? peer review?) with an American 1960s hippy-like grandiose dream of democratization of visualized data that doesnâ€™t need to be democratized in the first place. Did you read the web page at the URI that Bob Drake posted in comments herein? Do you really think that a collective vague â€œweâ€ is going to take the time to read and understand (or have enough background to understand) the processes presented on that page such as â€œhomogenization algorithmsâ€ and what these algorithms mean generally and specifically?
To which Udell replies:
I really do think that the gap between what science does and what the media says (and what most people understand) about what science does can be significantly narrowed by making the data behind the science, and the interpretation of that data, and the conversations about the interpretations, a lot more accessible.
To turn the question around, do you think we can, as a democratic society, make the kinds of policy decisions we need to make â€” on a range of issues â€” without narrowing that gap?
There is much to be said about this … but Larson’s comment “Do you really think that a collective vague â€œweâ€ is going to take the time to read and understand (or have enough background to understand) the … XYZ…” is the same question that has been asked countless times, about all sorts of open approaches (from making software, to encyclopaedia, to news commentary). And the answer in general is “yes.” That is, not every member of the vague “we” will take the time, but very often with issues of enough importance, many of the members of the vague “we” can and do take the time to understand, and might just do a better job of demonstrating, interpreting or contextualizing data in ways that other members of the vague “we” can connect with and understand.
The other side of the coin of course, is that along with the good amateur stuff there is always much dross – data folk are legitimately worried about an uneducated public getting their hands on data and making all sorts of errors with it – which of course is not a good thing. But, I would argue, the potential gains from an open approach to data outweigh the potential problems.
UDATE: good addition to the discussion from Mike Caulfield.
data folk are legitimately worried about an uneducated public getting their hands on data and making all sorts of errors with it – which of course is not a good thing
I’ve only dealt with a few small repositories of data (some for bioinformatics, some for computer security). The data itself is useful, but the metadata behind it at least as important: How was the data collected? What does it include? How do we pare out non-representative data? How do we quantify and eliminate noise from the dataset?
Udell neatly avoids the hard question in your original post:
Your vague â€œweâ€ combined with the demonstration of the Many Eyes site trivializes the process of evidence exploration and collaborative interpretation (community of practice? peer review?)
How do the people using the data know that they are interpreting it properly? How do they know what visualizations make sense? How do they tell the difference between random correlation and actual causation?
Any idiot can throw data at a visualization tool and produce meaningless pictures. The trick lies in encoding the appropriate metadata in the original dataset, making tools aware of it, and ensuring that the tool can provide appropriate commentary to prevent naive users from falling into common traps.
Comments are now closed.