Big data, #crowdsourcing and machine learning tackle Parkinson’s

http://successfulworkplace.com/2013/07/31/big-data-crowdsourcing-and-machine-learning-tackle-parkinsons/

Parkinson’s is a very tough disease to fight. People suffering from the disease often have significant tremors that keep them from being able to create accurate records of their daily challenges. Without this information, doctors are unable to fine tune drug dosages and other treatment regimens that can significantly improve the lives of sufferers.

It was a perfect catch-22 situation until recently, when the Michael J. Fox Foundation announced that LIONsolver, a company specializing in machine learning software, was able to differentiate Parkinson’s patients from healthy individuals and to also show the trend in symptoms of the disease over time.

Crowdsourcing Big Data analysis

To set up the competition, the Foundation worked with Kaggle, an organization that specializes in crowdsourced big data analysis competitions. The use of crowdsourcing as a way to get to the heart of very difficult Big Data problems works by allowing people the world over from a myriad of backgrounds and with diverse experiences to devote time on personally chosen challenges where they can bring the most value. It’s a genius idea for bringing some of the scarcest resources together with the most intractable problems.

Machine learning a Big Data solution

To create a solution using machine learning, the winning group from LIONsolver had to consult with doctors to first figure out what symptoms look like in data form. From that information, they were able to create a training set of data that represented known disease symptoms. Using that training set combined with data streamed from mobile apps worn by patients, LIONsolver’s software was able to learn any individual patient’s particular patterns and provide doctors with highly accurate information that is crucial to appropriate treatment.

Drake Pruitt, CEO of LIONsolver, explains the long-term benefit of this discovery this way:

We see this opportunity as part of an overall trend in healthcare toward applying forecasting and prediction to health record and wellness data, in order to help doctors and their patients achieve healthier lives with manageable healthcare costs. In short: More and more mobile devices are linking with monitoring services to analyze a growing amount of data. This analysis will provide a unique opportunity to take better care of patients, and to teach patients to take better care of themselves.

The takeaways

One of the biggest takeaways from this story is the evidence that Big Data isn’t just hype. Enormous opportunities exist for passive data collection through smartphones and other sensors. We’ve reached a point where the cost of data collection is significantly lower and in this case, was essentially an app running on a common device.

A second takeaway is the power of crowdsourced solutions in areas where resources like data scientists are hard to find and hire through conventional means.

The third significant lesson is the value of machine learning alongside Big Data. Classic data mining skills aren’t effective for every problem and machines learning can be used to find patterns in data that humans can’t. Machines don’t feel the same incentives that scientists feel to prove their own theories and for this reason alone, can be far more effective.

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The Serval Project: Reclaiming Your Phone #publicprize

http://www.huffingtonpost.com/paul-gardnerstephen/the-serval-project-reclai_b_3677089.html

I still remember when I heard the first news about the earthquake in Haiti in early 2010. I knew that when a society loses the ability to communicate for more than a few days, lawlessness rapidly develops. People realize that no one can call the police, and even if they could, the police aren’t going to show up.

Haiti was already a stretched society before the earthquake, and it was apparent to me that the massive loss of life and government capacity, combined with damaged communications infrastructure, was likely to result in chaos. Sadly this hunch proved right, with stories of militia road blocks, rape gangs and other atrocities emerging as the situation in Haiti degenerated.

I still recall the emotional rawness that resulted, and the overwhelming feeling that something needed to change. People living in disaster zones, in areas of conflict and unrest, and those simply too far from help needed to keep connected with one another, and so in some way reduce their suffering.

The then-recent proliferation of mobile phone ownership in poorer countries was not lost on me, and with my digital communications background I began to think of ways to restore, or better, to sustain mobile telecommunications in these difficult situations.

My search for solutions led me to consider air-droppable self-erecting mobile phone towers, balloon-lofted repeaters and other ideas before I finally realized that there was no need to deploy any additional equipment: the mobile phones themselves had the ability to form local networks without relying on any cellular infrastructure.

It was a watershed moment as I realized that I could make an infrastructure-independent mobile mesh telephony system. I spent the next five months working on a prototype using secondhand Google G1 Android phones, and we unveiled the system to the world in July 2010 under an open-source license – just six months after the earthquake in Haiti. Making it open-source was non-negotiable, to ensure that the technology would be available for those who need it most.

In the process we had to create a number of innovations to make sure that it would be useful during disasters, when support from local carriers and delivery of new equipment is not possible. We needed a solution that would allow people to use their existing phone numbers on the mesh, so that they could still contact one another. We also needed a way to allow the software to be copied from phone to phone in the field, so that if even just one phone had our software, it could be used to equip all other phones in the affected area, without relying on an Internet connection or anything else. It was also important to encrypt communications so that adversaries could not listen in to or tamper with people’s exchanges.

Turning the prototype into a secure, scalable and robust solution is an ongoing endeavor. This is partly because it is a lot of work to replicate the major functions of a modern cellular network without relying on the cellular network. There are also roadblocks that have either been accidentally or purposely left in the path of anyone who tries to make mobile phones communicate directly with one another. Our dedication to keeping the technology free and open has also limited the sources of funding available to us.

While these roadblocks slow us down, so far we have always been able to find a way to engineer around them. In this particular instance, we have come up with our last-resort “MacGyver solution,” where we use things like duct-tape or a pair of socks to acoustically couple two mobile phones together and combine them with our software to create an imperfect, but surprisingly effective, connection between a mesh and the cellular network that can be built in five minutes, using only the kinds of materials that people are likely to be able to find in a disaster or crisis zone. We have already called New Zealand from our Australian lab using this approach, and we have plans for additional testing.

When we heard about The Tech Challenge for Atrocity Prevention, hosted by Humanity United and the United States Agency for International Development (USAID), it struck a real chord, and it was tremendously encouraging for us when we heard that we had won first place in the Communications category. The prize has already elevated our visibility and resulted in a number of conversations with organizations focused on humanitarian communications. As a non-profit, we are dependent on donations and philanthropic support to continue our work, and this challenge has not only provided us with prize money to help our efforts, but also momentum to take us to the next step with the launch of our crowd-funding campaign .
This blog post is part of a series produced by The Huffington Post, Humanity United and the United States Agency for International Development (USAID), in recognition of the Tech Challenge for Atrocity Prevention. To see all the other posts in the series, click here. For more information about The Tech Challenge for Atrocity Prevention, click here

NYU Builds Data-Sharing Network For Scientists–But Is It Legal?

http://www.fastcolabs.com/3014757/nyu-builds-data-sharing-network-for-scientists-but-is-it-legal

If data-sharing is going to catch on, then science, technology and medical publishing will need to embrace the modern mantra of Open Everything–whether it’s technically within the boundaries of the law or not. Databrary, a video data-sharing site being developed at NYU, shows the enormous potential of allowing people to see behind the scenes of scientific studies.

You pay for science. Your tax dollars fund the national agencies that finance research. Yet you can’t see most results of the science your dollars support–from cancer treatments to robotics–without paying the price.

Journals like Science and Nature will charge you $20 or more for access to a few-page-long report. Open Science advocates like NYU developmental psychologist Karen Adolph believe scientific information should be free, like books in a library, and she’s determined to do something about it.

Labs tend to be secretive places, where researchers guard their data from competition, and seldom share full methods openly. Scientists often publish selectively–sharing their successes whilehiding null results, which is a common publication bias. Scientific elitism like this is worse than unfair: It’s dangerous.

Adolph says science needs to be open, and her project Databrary, a sharing platform announced by NYU this month, may free scientists to discover more powerful findings, faster–if it doesn’t get hamstrung by antiquated privacy laws first.

Here’s Why You Should Care About Open Data

Adolph is bothered by another layer of opacity in science: Researchers don’t often share the raw data on which their published papers’ results are based, making them hard to reproduce. Opening up data and methods is what she hopes her new online library will do–allowing researchers to share, browse, tag, critique, and reanalyze video clips across labs.

If you see a doctor who prescribes you a pill–say, Abilify, the #2 top-selling drug last year, a mood-stabilizer–and you want to read about what the chemical does to your brain–not some slick ad-copy written by the company that sells the dope, but the peer-reviewed scientific evidence itself, you’re out of luck: That’ll cost you $71, plus tax.

Your health is at stake here–not to mention your hunger for information. Shouldn’t that science be everyone’s right to read, seeing as your tax dollars funded the work? And shouldn’t the raw data and procedures the company used to prove the drug’s effects be open to the scientific community at large, to reinterpret and replicate? The same patient one psychiatrist calls “cured” another might call “sedated”: Why should we take labs at their word?

If the government gets on board with transparent science, data sharing could become the new normal, by law–leveling the playing field for universities, hospitals, and companies alike. Free online journals like Public Library of Science (PLoS) are increasingly attracting the work of top scientists; The Public Access Policy of the National Institutes of Health requires all NIH-funded research since 2008 to be made public a year after publication on PubMed Central, the free online database. But Adolph believes we need much more.

How Government Is Teaming Up With Scientists To Set Data Free

 

Open-source science has been Adolph’s priority since the ’90s, when she worked on software for video labeling and data visualization. DataVyu (formerly OpenSHAPA) is mostly used by developmental psychologists like Adolph who study how infants learn–but in principle it can be used by anybody analyzing video, for whatever purpose.

“Everything we’re doing is open,” says Adolph. “Every line of code is on GitHub… All our administrative documents, operating procedures, everything is up there with all its bells and whistles, all its pimples and blemishes… So we are really moving forward with the intent that it’s all just open: open Science, open sharing, open source.”

 

When the Obama administration decided they wanted to invest public money in data-sharing, to make scientific research faster and more efficient, they approached Adolph for her expertise in open science. Adolph organized a conference in 2011 of 35 behavioral researchers, computer scientists, and library scientists to come up with a way to share video data, while protecting subject privacy. She invited representatives from every federal agency she knew.

“There’s a whole list of worries people have, and many of them got raised at that workshop,” Adolph says. “Will I get credited? What if I’m not done using my data?… What if people find things wrong in my data, and I’m sort of outed? But the people at the NIH and NSF kept telling us: It’s going to happen. So the choice is: Researchers can figure out how to do it, or it will happen through the government. But one way or another, people are going to have to figure out how to share data.”

 

The result of the two-day National Science Foundation workshop was a team headed by Adolph, along with Rick Gilmore, an associate professor at Penn State who studies vision and brain development, and David Millman, NYU’s Director of Digital Library Technology Services.

The NSF and NIH awarded Adolph grants for the project:$2,443,500 and $786,677, for the first year of a five-year grant. These federal funds are more transparent than much of the science process, because they’re tax dollars: We can see where our money goes–why not what comes out of it?

YouTube For Scientists: The Rawest Data Sharing

 

“Raw data” may bring to mind a spreadsheet of rows and columns–but that’s not the only kind Adolph wants. Sure, Databrary can deal with “flat” numbers, but what she’s after first is data in its rawest form: videos, labeled with nothing but the age and sex of subjects.

Data in a spreadsheet, she explains, is only useful insofar as you know what the labels mean, and are interested in the same question as the person who built the database. But video shows reality and lets you ask whatever question you want.

Film has been standard in developmental science for a century. From early pioneers like Yale’s Arnold Gesell, who tracked babies from womb to walking, and Myrtle McGraw’s 1938 video Growth: A Study of Johnny & Jimmy, to MIT’s Deb Roy, who filmed the first 90,000 hours of his son’s life to analyze how he learned language, studies of kids have begun with movies. Since babies can’t talk, Adolph explains, studying them is kind of like studying animals: You have to infer what they’re attending to, thinking, or feeling from what they do: what they look at, who they move toward or away from. “Looking time,” as a result, is one of the main variables in developmental psych: How long did a baby look at “Display A” versus “Display B,” at his mother versus an experimenter, for example. Videos are used to study how children learn walking and coordination (Adolph’s lab’s topic), as well as language, social attachment, and self-control. In the “marshmallow test,” one famous example, videos showed that a kid’s ability to withhold eating a treat often predicts academic success later in high school and college.

I’ll Name Your Data However I Want To

 

Databrary’s name is intentionally broad, to include not just video, but all kinds of data streams, from physiological measurements like brain scans or blood tests, to spreadsheets or questionnaire data–IQ, personality or mental health check-lists to diagnose psych patients, for example–or even transcripts of talk or text from media. Data-sharing efforts for brain-scans, like OpenfMRI,HumanConnectome.org, and Neuroshare have influenced the design of Databrary.

“What I’m saying is: By opening the data up, and allowing transparency, the field can police itself,” Adolph says. “We’ll have a better basis for deciding what’s good science. So we as the builders, or the developers of this repository, aren’t going to decide what’s good science. We’re just going to open up the science and allow the community to decide where the promising areas of growth really are.”

 

Databrary videos are meant to be categorized democratically–“bottom-up” rather than “top-down.” They’re defined by users rather than the librarians or video creators.

The only mandatory labels for a video will be the age and sex of people in it, plus links to any papers published on the data. The “meta-data” attached to the video will define what it is “about”–i.e., what information different scientists have dug out of it. If Adolph posts videos of children crawling and walking, tagged for “falls,” a language scientist might tag the same video every time the baby speaks, or one interested in social bonding might tag it for the moments when the kid approaches his mom. And pretty soon, a single video will sprout a forest of papers around it, covering a whole range of behavioral research.

Databrary may become a “YouTube for Scientists” of many kinds. Neuroscientists sometime use video to record the positioning of brain-scanning equipment. Doctors use it to record patients with movement disorders, before and after a surgery or drug intervention, or children with developmental problems. Animal researchers use video to record procedures like surgeries on rodent or monkey brains, so that other scientists can see exactly what part of the brain they tracked. Education researchers routinely use video to study classroom lessons, too, finding patterns in teacher and patient behavior. With this new tool and the proper permissions from participants, all this video could become open for critique, reanalysis, and to inspire questions in young scientists.

Data-Sharing: The Times Are A-Changing

 

Open data is the convention in a few sciences already, because of shared technology and cost. Astronomers, for example, pool data from a small number of powerful, expensive telescopes worldwide. Particle physicists also share data, as well as earth scientists. Genomics has had an open-data policy from the beginning: Whenever a species’ genome is sequenced, it’s required by law to be shared in GenBank, an open repository.

“[In science], just like in any other industry, cultures can change,” Adolph says. “So that’s part of what we’re trying to do, is to be part of this new wave–changing the culture of behavioral science to make it more open, in the way that [other sciences] have moved. I think there’s still plenty of room to compete, even if we share…You may even get more citations by opening up your lab rather than by keeping it closed.”

 

The Clinical Problem: Trading Privacy For Transparency?

Transparency in science is trickiest with medical research. This makes it harder for Databrary to help the very people who have the most to gain from open data: sick people.

Privacy is an issue in all of the videos, since subjects are identifiable by face and voice. Databrary videos won’t be public, but shared with a group of authorized researchers who have signed agreements with Databrary, to keep confidential the identities of the people on the videos. People in the videos must give written permission for their videos to be shared. Kids’ videos can be shared by caregivers, but medical records are a different story–more strictly regulated by the government.

In building the community of potential Databrary contributors, Adolph and her collaborators contacted around 120 behavioral scientists. Of those, only a few declined to participate. Some were clinical researchers who study children with developmental disorders like autism, so-called “protected populations.” Government regulations, called HIPAA (Health Information Portability and Accountability Act) particularly restrict sharing hospital records and other forms of private health information: psychiatric diagnoses, sexual histories, or medical illnesses, for example.

“The irony is: If you’re a mother, and you have an autistic child, [you’re] the most eager to get that data shared and let people figure it out–similar to a cancer cure,” says Lisa Steiger, NYU’s Community Engagement and Outreach officer for Databrary. “[Mothers of disabled kids] are the most eager to have their data shared and reused and analyzed more deeply. And yet they’re the ones who are the least likely–[because] it’s the most challenging to have their [HIPPA protected] data shared. We don’t know that it’s impossible. We just have to figure out how to navigate that.”

 

Privacy laws for current health care has not kept up with modern technology. Patients still need to be protected, of course, and Databrary wants to find a way to protect privacy while enabling data sharing.

“In this digital age, everything’s changing,” Adolph says. “One of the last things to change has to be people’s comfort at having certain kind of private data shared. Institutional review boards [the bodies at universities, colleges, and hospitals that decide if a study can be performed]–they’re lagging behind. They’re from the days long before YouTube and Instagram.”

“We’re in a time now where I have to remind my teenage daughter every day: You better be careful what you text and what images you post of yourself, because those are digital files now, out in the wild. People are much more comfortable sharing videos and pictures of themselves. Most basic research is pretty harmless.”

There’s an irony of a government that requires extreme privacy protections in hospitals, while spying on its own citizens through the NSA. In any case, videos can be authorized to play for other parties.

“Obviously, the restrictions were put in place with good intent,” says Dylan Simon, the software developer of Databrary. “These are vulnerable populations, and you don’t want people taking advantage of them. But I don’t think they were put in place with the current technological landscape, where there are cameras everywhere, in mind. They were put in place so that dangerous people couldn’t find [patients’] addresses and go and stalk them and take advantage of them, not so that researchers couldn’t do science.”

 

First #OpenSource Airplane Could Cost Just $15,000

http://www.wired.com/autopia/2013/07/open-source-airplane-design/

There’s an open source airplane being developed in Canada, and now its designers are looking to double down on the digital trends, turning to crowdsourced funding to finish the project. The goal of Maker Plane is to develop a small, two-seat airplane that qualifies as a light sport aircraft and is affordable, safe, and easy to fly. But unlike other home-built aircraft, where companies or individuals charge for their plans or kits, Maker Plane will give its design away for free.

The group behind the project consists of pilots and engineers who are designing the airplane, allowing it to be built using the kind of personal manufacturing equipment somebody in the maker community might already have at home or can easily purchase. The idea of a home-built airplane is nothing new. It dates back to the earliest days of flight, after Orville and Wilbur made and flew their own airplanes (and engine), the homemade plane movement — literally — took off.

Today, the home-built movement continues, and this week tens of thousands of pilots and fans of home-built airplanes are descending on the annual Airventure in Oshkosh, Wisconsin.

In the spirit of the open source and maker movements, the Maker Plane group is including components from many designers and builders outside their circle. As they focus on the design of the airplane (fuselage, wings, etc.), the Maker Plane team helps connect those interested in building their own with other open source components such as an air data computer and radios. They even show you where you can get plans to build your own traffic and collision avoidance system.

A look at the wing design on the Maker Plane. Image: Maker Plane

The structural parts of the airplane, including the fuselage, will be built from composites. There are many home-built composite airplanes already taking to the skies, so the techniques are well proven. Smaller pieces such as knobs and handles will be made using 3-D printing. And after a year and a half of design, the Maker Plane team has started to build the first prototype. That’s why they’re turning tocrowdsourced funding to help the project along.

The basic specifications of the airplane follow the guidelines of the light sport aircraft regulations. The aviation industry and the Federal Aviation Administration created the LSA category to encourage more people to fly. The airplanes are limited to two seats, a maximum weight of 1,320 pounds, and a top speed of 120 knots (138 mph). Maker Plane says they expect their design will fall within these requirements and have a range of 400 miles. More ambitious: They hope the cost to build the airplane will be under $15,000, including the engine.

The aviation world is filled with optimistic ideas that don’t always get off the ground, but the Maker Plane is the first attempt at sourcing the entire airplane from the open source community, which should help keep costs down, assuming you have the skills to build the various components. And if they succeed, Maker Plane hopes to fly the first prototype in 2015.

List of News Clips from the Results of NASA’s Asteroid Initiative RFI

NASA asks for help lassoing an asteroid, gets flooded with replies – Christian Science Monitor

http://www.csmonitor.com/Science/2013/0729/NASA-asks-for-help-lassoing-an-asteroid-gets-flooded-with-replies

 

NASA receives hundreds of responses to asteroid mission planning – CBS News

http://www.cbsnews.com/8301-205_162-57596003/nasa-receives-hundreds-of-responses-to-asteroid-mission-planning/

 

NASA Gets 402 Ideas for Dealing With Asteroids – Discovery News

http://news.discovery.com/space/asteroids-meteors-meteorites/nasa-gets-402-asteroid-mission-ideas-130726.htm

 

NASA’s Asteroid Capture Mission Flooded With Ideas From Private Companies, Non-Profits – Huffington Post

http://www.huffingtonpost.com/2013/07/28/nasa-asteroid-capture-mission-ideas_n_3663966.html?ir=Science

 

How to capture an asteroid: NASA weighs ideas – MSN

http://news.msn.com/science-technology/how-to-capture-an-asteroid-nasa-weighs-ideas

 

NASA receives ideas on how to deal with asteroids on collision course with Earth – Hindustan Times

http://www.hindustantimes.com/HTNext/LifeAndUniverse/NASA-receives-ideas-on-how-to-deal-with-asteroids-on-collision-course-with-Earth/Article1-1100208.aspx

 

NASA Sees Enthusiastic Response to Asteroid Call for Ideas – SpaceRef

http://www.spaceref.com/news/viewsr.html?utm_campaign=&utm_medium=srs.gs-twitter&pid=44414&utm_content=api&utm_source=direct-srs.gs

 

Dramatic Changes to Google Lunar X Prize Cash Prizes Under Consideration

http://spaceref.biz/2013/07/dramatic-changes-to-google-lunar-x-prize-cash-prizes-under-consideration.html

The plans laid out in this draft document embody a radical departure from the current approach to awarding prizes i.e. one winner, one big prize with several smaller runner-up prizes. Now, multiple teams will be able to get even smaller cash prizes for efforts already completed or near completion – but far short of actually sending a mission to land on the Moon.

If approved, this approach would help inject some much needed cash into the coffers of several competitors. No word yet on whether this plan will be formally adopted or when it will be adopted but a quick turn around time for comments suggests that there is an interest in getting these new rules in place soon.

Editor’s note: This document has been widely circulated among several hundred people inside and outside of the Google Lunar X Prize community for several weeks. No markings were placed on this document to note that it is either confidential or proprietary. Indeed, the cover memo encouraged its wider distribution for review and comment.

Google Lunar X Prize Milestone Prizes Guidelines Draft v0.3 July 10, 2013

Download full document

1 Overview

1.1 Scope

This guidelines document describes a number of milestone prizes that have been established within the framework of the Google Lunar XPRIZE (GLXP). It will form the basis for a requirements document for the Milestone Prizes upon review and decision by the GLXP Judging Panel.

1.2 Objectives

The objectives of the Milestone Prizes are the following:

– Helping teams get past difficult technical milestones on their way to winning the GLXP Grand and Second Place prizes

– Strengthening teams’ business plans by bringing forward some of the prize money for teams that retire key risks

– Provide major public relations opportunities to strengthen awareness of the team and Prize as a whole

The Milestone Prizes have been defined to reward teams for verifiable technical steps, most of which they would anyway need to accomplish whilst preparing or executing their GLXP missions, thus requiring minimal additional work on the part of the teams.

The subsystem designs developed and verified for the Terrestrial Milestone Prizes (see below) are also expected to be useful for future space missions after the GLXP.

1.3 Types of Milestone Prizes

The following Milestone Prizes are available:

– Camera Milestone Prize – $750,000 per team for up to 4 teams

– Mobility Milestone Prize – $750,000 per team for up to 4 teams

– Launch Milestone Prize – $7,000,000 purse split (using a % of launch cost formula with a cap) between teams making the earliest successful launches

– Lunar Arrival Milestone Prize – $1,000,000 for first team to reach a specified distance from the moon

The first two prizes are for technical verification of the respective subsystems (camera or mobility) needed to complete the GLXP mission requirements. These two types of Milestone Prizes will be available prior to launch of the team’s GLXP mission and shall be collectively referred to as the “Terrestrial Milestone Prizes”. There is no obligation to award a specific number of Terrestrial Milestone Prizes in either category (camera or mobility).

The latter two prizes shall be collectively referred to as the “In-Space Milestone Prizes”. The Camera Milestone Prize also includes a complete simulation of the GLXP Mooncasts (both “Arrival” and “Mission Complete”) in addition to the technical verification of the camera system’s design.

1.4 Milestone Prizes Schedule and Validity

The Terrestrial Milestone Prizes will consist of two rounds as follows:

– Terrestrial Milestone Definition Round: August 1st – December 31st, 2013

– Terrestrial Milestone Accomplishment Round: January 1st – June 30th, 2014

Extension of the Milestone Definition Round or Milestone Accomplishment Round is at the discretion of the Judging Panel. The In-Space Milestone Prizes will be available during any GLXP mission that is on track for completion by the end of December 2015 (or “Termination Date”).

1.5 Who Can Participate?

The Milestone Prizes are open to all registered and eligible GLXP teams. Refer to MTA Section 3.2 for more information about eligibility.

Each registered and eligible GLXP team can compete in one or both of the Terrestrial Milestone Prizes – Camera and Mobility.

All teams become automatically eligible for the In-Space Milestone Prizes upon XPRIZE’s acceptance of the corresponding team’s Notification of Launch Attempt.

Download full document

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Could We Stop An Asteroid? Feat. Bill Nye #NASAasteroid

Could we stop an asteroid on a collision course for Earth?
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