An NPR investigation finds the death care industry can often be confusing and unhelpful to consumers who must make high-priced decisions at a time of grief and financial stress.
Tagged with “industry” (17)
In his Billy on the Street series, Eichner roams the sidewalks of Manhattan, asking strangers open-ended questions about the entertainment industry. If he likes their answers, he gives them a dollar.
The Talk Show
‘Fresh Out of Prison’, With Special Guest Nilay Patel
Friday, 1 July 2016
Special guest Nilay Patel joins the show. Topics include The Verge and Recode (and the state of the media industry at large), what’s going on with the lack of updates to professional Mac hardware, and, of course, Apple’s purported removal of the headphone jack on the upcoming new iPhones.
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Nilay Patel: “Taking the Headphone Jack Off Phones Is User-Hostile and Stupid”.
Cory Doctorow: “Phones Without Headphone Jacks Are Phones With DRM for Audio”.
Yours truly: “Headphone Jacks Are the New Floppy Drives”.
Jason Snell: “Searching for a Good Reason to Remove the Headphone Jack”.
This episode of The Talk Show was edited by Caleb Sexton.
Twenty years ago the FBI ended their longest-running domestic terrorism investigation with the arrest of the Unabomber, a notorious serial killer obsessed with technology. Between 1978 - 1995, Theodore Kaczynski lived in a remote cabin in rural Montana, from where he planned the downfall of industrial society. A brilliant academic, Kaczynski was motivated by a desire to punish anyone connected with technology.
The Talk Show
‘Anthropomorphic Human Bowel’, With Special Guest Ben Thompson
Wednesday, 10 February 2016
Special guest Ben Thompson returns to the show. Topics include last Sunday’s Super Bowl 50 (and its mostly terrible commercials), Tim Cook’s tweet with a photo he took from the sidelines post-game, Twitter’s algorithmic timeline and the state of today’s Google- and Facebook-dominated online advertising industry, Yahoo’s dismal prospects, and more.
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Tim Cook, Super Bowl photographer.
The New Yorker advertising Josh Topolsky’s “The End of Twitter” article — on Twitter.
Concussions suffered in this year’s Super Bowl.
How we used to finger our friends in college.
Dustin Curtis computed the 2015 revenue and profit per employee for Yahoo, Twitter, MSFT, Google, Facebook, and Apple.
This episode of The Talk Show was edited by Moisés Chiullan.
Jay Allison is one of public radio’s most accomplished producers. Over the last 30 years, he has independently produced hundreds of documentaries and features for radio and television, and has won virtually every major industry award, including six Peabodys. He co-created the acclaimed websites Transom.org and the Public Radio Exchange. He also founded the public radio stations for Martha’s Vineyard, Nantucket, and Cape Cod, where he lives. Allison is currently producing The Moth Radio Hour.
A month is hardly a unit of measurement. It can start on any day of the week and last anywhere from 28 to 31 days. Sometimes a month is four weeks long, sometimes five, sometimes six. You have to buy a new calendar with new dates every single year. It’s a strange design.
North Dakota’s the land of opportunity for people looking for jobs in the oil and gas industry.
The fracking boom has transformed the western part of the state — often overwhelming the small towns that dot the prairie. Todd Melby’s been keeping track of the comings and goings of workers in the oil field.
Recently, he talked to a Razorback who moved there to make ribs for roughnecks, a guy named Oscar Everetts. You can take Oscar out of Arkansas, but you can’t take Arkansas out of…. You know how it goes.
Todd Melby’s series, "Black Gold Boom," is an initiative of Prairie Public and the Association for Independents in Radio.
This week, special guest John Siracusa stops by the program to set us all straight about the Wii U Pro controller, why Nintendo causes Bri and Steve to disagree so often, and whether Crossy Road is on a road to riches or ruin.
Image courtesy of Andy Kirk
Data visualisation has become ever more important as the volume of data is increasing. You see data everywhere, in simple infographics, in the sports reporting and in daily news. We are constantly bombarded with it, and are easily confused by it all. Is it because when we go through our studies as young adults we are only presented with a very small variety of charts? This limitation in our understanding of datasets could be helped by using better visuals.
Andy Kirk, the founder of Visualising Data, is hoping to give scientists a set of tools that will help them to communicate their data in a better way, not only to external audiences, but also to themselves. Being able to visually manipulate data could give scientists a better insight into the stories hidden in their research.
http://www.nature.com/multimedia/podcast/naturejobs/naturejobs-2014-05-01.mp3I caught up with Andy at the British Library during one of his workshops. For Andy, it is to do with “how we give a physical form to the subject data variables. Beyond that, it is also all the other presentational factors.” Here, Andy is referring to the aesthetics to create interactivity, this includes simple things like colour and font type. But some of the more complex issues like arrangement, annotations and architecture are also important to consider. These combined factors “make-or-break the success of a visualisation, particularly when it is a communication device.”
Before you get around to communicating your data however, it will be used during your analysis. “You want to make sense of the data,” says Andy. “To find patterns that you’ve not seen before. You’re moving beyond looking at data, and seeing it.”
The main difference between using data as an analysis tool and a communication tool is that when it is a communication tool you have a different audience. Like a journalist, you now have readers and therefore need to understand your audience. “Characterise the audience” that you’re trying to engage with your data and your science, says Andy. Impact is a big part of the science and academic agenda. So like any other form of science communication, you need to ask yourself: “What do I think they’ll be interested in? What slices of analysis, what slices of a story can I engage with them?” After all, you are the expert. You are the one that completed the research, did the experiments and collected the data.
When analysing the data, you know what you know, and you know what you don’t know. You have the power and capability to “explore data and to tease out new insights, new patterns, new discoveries that,” says Andy, “either confirm what you knew or provide a new enlightenment of a subject.”
To do this, it’s as simple as playing around with different layouts and visualisations. “If we look at visualisation as communication, then things like chart types, this is our visual language. This is the syntax, the verbs that we’ve got to use now to tell stories.” And for many of us, this is made up of a core set of maybe 4 or 5 different types: the bar graph, scatter plot, pie chart, or line graph. What Andy does in his work shops is give scientists a broader vocabulary with which to tell the stories in the data. “There are endless ways we can portray that data.”
Andy thinks that the problem really lies in the way we are taught to analyse data. The visual literacy to read and interpret these graphics doesn’t go beyond those core types mentioned earlier. “We get by, we make sense of a bar chart.” But if scientists decide to go down the more complex visuals route, how can they make sure that they don’t break any bridges between them and their audience? “For a designer, for a creator of these graphics, you need to achieve that through the exponotory features of these graphics,” says Andy. “The labelling, the introductions, the “how to read a graphic” elements.”
“A lot of it is just common sense, caring about the audience: What do I need to give them to learn and read this story that I am portraying?”
A key part of this communication is telling a good story. Story telling in data visualisation comes from threading different elements together into a sequence with a narrative. This is particularly relevant for time-based data.
Andy believes that visualising data as a scientist for analysis comes in two perspectives, the first of which is the Sherlock Holmes perspective: you have a certain hypothesis, and you’ll test it out in the data. You’ll then combine the variables that might lead to a discovery or a confirmation of a hypothesis, or reveal something entirely new. “On the other side, you’ve got this idea of prospecting. You’re going to play with the data, try different combinations of variables. You’re going to almost follow a scent of enquiry and see what clues you will find along the way.” It’s trying to find those unknown unknowns that the biggest challenge, and playing around with the data can help you find them. “Looking at the raw data, you would never find those things. That’s what we’re trying to find with visualisation: seeing the data for the first time.”
There have been many faux-pas when it comes to visualising scientific data, and Andy mentioned several of them in his workshop that morning. But he says one of the biggest ones is when “you’re visualising something that is just inaccessible for a general audience, when it is intended for a general audience.” So even if the subject matter is complex, you still need to find a suitable way to communicate it in the right context.
The second faux-pas is a fundamental misunderstanding of how we perceive fundamental chart types. He uses the simple bar chart as an example:
“The way that we read back a bar chart is by judging the absolute length of the bar that is portrayed. Now if you chop off that bar, and start the baseline at something that isn’t zero, you’re distorting our interpretation of what length that bar actually means.” The other is when visualisation is used as a means of showing off, rather than using it as a tool for understanding.
But in the end, humans aren’t the best at interpreting statistics, so the helping hand that scientists can give their audiences by using visualisation design can be extremely beneficial. “It’s what I describe as the annotation lay: the level of user assistance you need to give your readers in how to read and digest and consume this graphic,” says Andy. On the simplest level, this could be a clear, simple title. Conclusions and take-aways can also be given to help them. It’s almost as if you could recreate that sense of “being stood in front of a big chart on a big display, and you [the scientist] being stood there, pointing out the key things with your hand…. So we have to make sure that if we’re not there to physically point out things, and physically there to coach people through how to read and interpret something, the properties on the chart do that.”
Here are Andy’s top 5 things to think about when putting together your data visuals:
1) What is the intent of the project? Is it to inform, persuade, change behaviour, enlighten or entertain?
2) When exploring data, get a sense of physicality. Get a sense of range and variable types, as this is linked to the architecture of the data.
3) What is the story? What is the narrative and what questions do we want our audience to ask and answer when consuming the graphic? That is a journalistic sensibility, as there are endless ways in which the data can be sliced and diced as science has big data sets and a variety of variables, you need to be able to find the focus.
4) Chart types: “The chart types are the way of delivering the story. The correct deployment of a chart type will deliver the stories, the questions we’ve already identified.”
5) Presentation layers: key design choices.
So you want to be a data scientist?
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