Small words = big ideas

Explaining science is a tricky business. You have to use the right words to be accurate, and you have to assume that your audience may never have heard those words. Recently, I was invited by the nonprofit Science on Tap to talk about simplifying language for an audience at the Pub at Third Place in Seattle. I also will give a talk at the Seattle Aquarium on some of the same ideas.

Maybe you are a newcomer to the idea of the Up Goer language, created by Randall Munroe. But I’ve written about it before, and so you can get some background on that language and why scientists use it by reading an earlier post of mine about Hair-Having animals and a later discussion based on a talk at Town Hall.

What I’d like to share here are some links for exploration on your own of Up Goer, as well as other ways that people have used to try to simplify science communication.

Here is an especially wonderful comment from Chris Rowan’s article linked below:

“Some might not see this as anything more than a gimmick, and argue that the constraints you are forced to work under are too severe; that by replacing jargon with a dense thicket of ‘simple’ words, you are just replacing one sort of linguistic complexity with another.” But, as he says, that’s missing the point.  Rewriting in Up Goer can bring something “quite profound.”

Scientific American magazine article

Text editor to use upgoer yourself

Original cartoon about Saturn V rocket

Blog from Forum on Science Ethics and Policy that sponsors UW contest. This contains excerpts from some of the wonderful entries by contestants who described their research using Up Goer.

Different gizmo for simplifying text

Alternate text editor – By Theo Sanderson who created Upgoer5, Upgoer6

In Theo’s version Six, your text gets analyzed so that you can use more than the 1,000 most common words in English, but the words are sorted by color according to where they fall on the most-common to least-common continuum.


Shallow dive in Seattle big data projects

Seattle has a rich resource of both data-driven research projects and scientists who enjoy designing tools that mine data.

I was asked to talk about that big data heritage here in Seattle as part of a meeting of the Northwest Science Writers Association.

Consider this blog post a cheat sheet for those of you who did not take notes during my presentation or didn’t attend.

1. Big Data is defined as having volume, velocity and variety. If you put sensors on the ocean floor, as researchers are doing at the University of Washington, you will bring back real-time data by the server loads on temperature, salinity, and even on genetic sequences of the microorganisms there. The data will stream at high volume and velocity and with amazing variety. But finding insights from that fire hose of data is not so easy. There is a shortage of the data scientists who know the best way to parse the flood, according to Professor Ed Lazowska, who commented for my recent story.

Lazowska and a team have pioneered the eScience Institute on campus to nurture data science and provide a meeting place, a sort of water cooler, for the best conversations and exchanges among disciplines.

2. Most of us already understand the retail use of big data, because predictive models of our own buying behavior surround us. Apps that help you choose restaurants, airlines, music and other commodities are using models built on the buying habits of thousands of other consumers. The same desire for prediction is driving medical research now. Researchers in Seattle at the Institute for Systems Biology and Fred Hutchinson Cancer Research Center and Allen Institute for Brain Science are all using algorithms to try to understand vast amounts of data about human disease. One wonderful overview of this new way of seeing human disease was just published in Cell by Eric Topol in April.

Among the things Topol envisions in the very near future: “With the power of sequencing, it is anticipated that the molecular basis for most of the 7,000 known Mendelian diseases will be unraveled in the next few years.”

Many human diseases are a complicated mixture of vulnerabilities combined with environment and behavior. Knowing their molecular basis does not mean curing them is easy. But this level of understanding will create new opportunities.

One of the foremost Seattle scientists in genomics is Jay Shendure. You can read why NIH Director Francis Collins praises his newest ambition.

3. For my last comment on this quick overview about Seattle, I want to change the focus of the big data from sensors and molecules to the “big” pool of people that are increasingly seen as a resource for research itself. Patients are key players in new efforts to accelerate medical research by drawing in volunteer patients who free data about themselves. One of the pioneers of this approach is Sage Bionetworks, lead by Stephen Friend and John Wilbanks.  I wrote an earlier post about Friend, when the White House gave him an award. Recently, Forbes wrote about the way drawing on the public may bring faster cures.

Seattle sits at the center of many different strengths in using big data. We have leaders in a variety of sciences, including oceanography and proteomics, but we also have leaders in the creative destruction of the old models for disease discovery. As science journalists, I think you can mine many data projects for stories.

 Photo above is of poster session at the eScience Institute at the University of Washington.




Sharing my microbial fingerprints

Any day  now, I’m going to get lab results I’ve been waiting months to know. They will tell me something about my deep insides.  These will be maps of  thousands and thousands of microbes that live inside my gut and in my house.

But just like some ancient relics pulled from tombs of lost civilizations, nobody knows exactly how to read these maps yet. I’ll get the names of my microbes and the sequences of their DNA, but I won’t know exactly what the maps mean.

At a public meeting in Seattle on Feb. 25, I’m joining scientist Scott Meschke to talk about the human microbiome.

For more than a year, I’ve been part of two different citizen science programs. Both are aimed at understanding more about our health and the myriad ways we are tethered to the health of the microscopic creatures that share our homes and our bodies. One project sampled my body and the other my home.

While much of these projects happens via computers and online interactions, it began in a face-to-face encounter that I had with scientist and author Rob Dunn. I was on a field trip as part of a meeting of the National Association of Science Writers at the University of North Carolina.  During one stop, Dunn pinned me as tightly as an insect sample to styrofoam display while he talked about microbes. Science knows more about exotic environments from mountains to the sea floor, but little about the  “wild life” inside our homes. He launched a project to collect samples from every state and try to find patterns of meaning there. I sent in my samples to The Wild Life of Our Homes. More than one thousand other people joined me.

Wild Life sent detailed instructions for me. They wanted samples of dust from different parts of my house.  They wanted dust that had not been disturbed for a while, so one sample location was the upper sill above a doorway. The other project, Ubiome, asked me to mail in a sample of feces.  The two projects are separate, but both are measuring microbiomes associated with me. Both have promised results soon.

Researchers have found intriguing differences between microbiomes.  To give one example, people with diabetes have different microbial percentages than people who don’t have diabetes.  Obese mice (and perhaps people) have different organisms in their gut than non-obese mice.  A subset of children with autism seem to have differences from children without the diagnosis, and more mouse research suggests that changing the gut may change the behavior of the mice. Lots of speculation, but very little research that would prove causation.

If you will be in Seattle in February, join me and Scott Meschke to talk about microbiomes. What might I learn from mine? What might we all learn collectively from thousands of samples?

If you are new to these ideas, enjoy an  animator’s vision of what it all might mean, courtesy of National Public Radio.

 Image above used with permission of the Pacific Northwest National Labs. Taken by Janine Hutchison. Green is lactobacillus reuteri, purple is collagen microsphere, and brown is intestinal cell.