Missions in space may come and go, but the National Aeronautics and Space Administration has always stuck to a mission of bringing in data.

(

One of its early achievements in this field was sending a spacecraft close enough to Venus to get accurate readings of its surface and atmosphere. On Dec. 14, 1962, the Mariner 2 spacecraft got within 34,762km (21,600 miles) of the planet. Over a 42-minute period, it was able to pick up many points of data that proved Venus, which had been thought of as Earth's twin, would be uninhabitable, with a surface temperature of 425°C (797°F) and a toxic atmosphere.

This picture (from NASA's site) of the data gathered in that mission is cropped. The paper showing the data that was gathered is actually much longer, as this uncropped version shows.

Back then, the data covered a roll of paper, but the data NASA handles today takes supercomputing power to process. As Nick Skytland wrote in NASA blog post in October:

In the time it took you to read this sentence, NASA gathered approximately 1.73 gigabytes of data from our nearly 100 currently active missions! We do this every hour, every day, every year -- and the collection rate is growing exponentially...
In our current missions, data is transferred with radio frequency, which is relatively slow. In the future, NASA will employ technology such as optical (laser) communication to increase the download and mean a 1000x increase in the volume of data. This is much more then we can handle today and this is what we are starting to prepare for now. We are planning missions today that will easily stream more [than] 24TB's a day. That's roughly 2.4 times the entire Library of Congress -- EVERY DAY. For one mission.

read more at 

Big Data on the Final Frontier

Is your face your calling card?

Many books include pictures of the author on the back cover or inside the jacket. That is one thing I never bother to check when considering whether or not I want to read a book.  I  still don't really think about the author's appearance as I read. And I don't really think about my own as I write. 

I use a quill for my signature picture here, as well as on my other blogs. It also serves as  my profile photo  on Facebook, Google+ and Twitter. I feel it conveys what I am about more accurately -- in terms of my role as writer - than my photo would. Or maybe I'm just camera-shy.

On  the other hand, my actual photo does serve as my profile picture for the UBM boards on which I write. The policy there, as it is for many newspapers, is to require a photo for the writers. Those who comment only and don't blog can get away with using any picture they like for their profile photo or just use the default picture if they don't bother to upload one of their own. 

Once I had my picture posted in that way, I put it in for my LinkedIn profile, as well. It seemed more consistent to have the same picture represent me there. Also the more standard practice on LI is to use an actual photo than a representational picture.  I still can't see attaching a photo to a resume, though anyone who wishes to find my photo simply has to do an online search to find one in a fraction of a second.

While the net dooes tend to attach author faces to content,  I don't believe I am more drawn to articles that feature faces.I must be  in the minority, though, because I'm certain that those who demand faces find that they are effective at drawing more audience interest. 

. What do you think about  the face as calling card?

Inhalers that do more than dispense medication

Louisville, one of IBM's 100 selected selected cities is putting big data to work to track asthma triggers with Asthmapolis. Read about it in

Big Data's Next Target: Asthma

  

Big Data Health Hazards

If anything can go wrong, it will." Murphy's Law (or Sod's Law, as it is known in the UK) applies to big data projects, as well. When those projects concern someone's health, something going wrong in the data can lead to something going very wrong with the patient.

The more one relies on the accuracy of the system, the higher the potential for error. Electronic health records (EHRs) are considered a boon to data aggregation, but they hold a potential downside.  Read more here

Big Data and Cancer Research

That's the subject of my post Big Data Battles Cancer.  For a completely different take on cancer, see Beyond Pink.

Big Data Applied to Health

I've written several pieces on the topic from various angles:

On how cell phone data is used to map the spread of Malaria in order to come up with effective prevention in Africa Analyzing Cellphone Data for the Greater Good

On Retrofit's approach: Data Gets Personal to Fight Obesity

On UPMC $10 million dollar big data plan: Creating Custom-Fit Healthcare

Dial a data scientist

well, not exactly, but you can find one you can hire with Kaggle's new feature. Read more about it in my blog post: 

Top Data Scientists on Tap