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Sapping Attention: The history of looking at data visualizations
The standards for data visualization being good change seem to change over time. Preferred color schemes, preferred geometries, and standards about the use of things like ideograms change over time. But, although styles change, the justifications for styles are frequently cast in terms of science or objective rules.
So you might think that I'm just saying: the style changes, but the science of perception remains the same. That's kind of true: but what's interesting about thinking historically about data visualization is that the science itself changes over time, so that both what's stylistically desirable and what a visualization's audience has the cognitive capacity to apprehend changes over time. Studies of perception can tap into psychological constants, but they also invariable hit on cultural conditioning.
data  dataviz  graphicdesign  interactiondesign  communication  psychology  history  perception  share 
14 hours ago by mayonissen
Collaboration Overload Is a Symptom of a Deeper Organizational Problem
Meetings, emails, IMs and other workplace interactions don’t just happen; they are a by-product the company’s organization. They reflect attempts by managers and employees to get work done within the confines of prescribed structures, processes, and norms. In our experience, unhealthy collaboration most often stems from two underlying organizational maladies: organizational complexity and a “collaboration for collaboration’s sake” culture.
change  collaboration  management  communication  organization 
yesterday by devin
Practical advice for analysis of large, complex data sets
By Patrick Riley: “For a number of years, I led the data science team for Google Search logs.”

I liked this — guiding principles, and a few pieces of tangible advice, for running data analyses and modeling projects. My favorite at the moment:

> Separate Validation, Description, and Evaluation
> I think about about exploratory data analysis as having 3 interrelated stages:
> 1. Validation or Initial Data Analysis: Do I believe data is self-consistent, that the data was collected correctly, and that data represents what I think it does? This often goes under the name of “sanity checking”. For example, if manual testing of a feature was done, can I look at the logs of that manual testing? For a feature launched on mobile devices, do my logs claim the feature exists on desktops?
> 2. Description: What’s the objective interpretation of this data? For example, “Users do fewer queries with 7 words in them?”, “The time page load to click (given there was a click) is larger by 1%”, and “A smaller percentage of users go to the next page of results.”
> 3. Evaluation: Given the description, does the data tell us that something good is happening for the user, for Google, for the world? For example, “Users find results faster” or “The quality of the clicks is higher.”
> By separating these phases, you can more easily reach agreement with others. Description should be things that everyone can agree on from the data. Evaluation is likely to have much more debate because you imbuing meaning and value to the data. If you do not separate Description and Evaluation, you are much more likely to only see the interpretation of the data that you are hoping to see. Further, Evaluation tends to be much harder because establishing the normative value of a metric, typically through rigorous comparisons with other features and metrics, takes significant investment.
> These stages do not progress linearly. As you explore the data, you may jump back and forth between the stages, but at any time you should be clear what stage you are in.
data-science  communication 
2 days ago by DGrady
Scientists, Stop Thinking Explaining Science Will Fix Things. It Won’t.
If you consider yourself to have even a passing familiarity with science, you likely find yourself in a state of disbelief as the president of the United States calls climate scientists “hoaxsters” and pushes conspiracy theories about vaccines. The Trump administration seems practically allergic to evidence. And it’s not just Trump—plenty of people across the political spectrum hold bizarre and inaccurate ideas about science, from climate change and vaccines to guns and genetically modified organisms.

If you are a scientist, this disregard for evidence probably drives you crazy. So what do you do about it?

It seems many scientists would take matters into their own hands by learning how to better communicate their subject to the masses. I’ve taught science communication at Columbia University and New York University, and I’ve run an international network of workshops for scientists and writers for nearly a decade. I’ve always had a handful of intrepid graduate students, but now, fueled by the Trump administration’s Etch A Sketch relationship to facts, record numbers of scientists are setting aside the pipette for the pen. Across the country, science communication and advocacy groups report upticks in interest. Many scientists hope that by doing a better job of explaining science, they can move the needle toward scientific consensus on politically charged issues. As recent studies from Michigan State University found, scientists’ top reason for engaging the public is to inform and defend science from misinformation.
science  communication  science.for.the.public 
2 days ago by verstehen

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