| NSF Org: |
DRL Division of Research on Learning in Formal and Informal Settings (DRL) |
| Recipient: |
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| Initial Amendment Date: | August 26, 2015 |
| Latest Amendment Date: | August 26, 2015 |
| Award Number: | 1551063 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Finbarr Sloane
DRL Division of Research on Learning in Formal and Informal Settings (DRL) EDU Directorate for STEM Education |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
185 Stevens Way SEATTLE WA US 98195-2350 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | ECR-EDU Core Research |
| Primary Program Source: |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
This award is supported by the EHR Core Research (ECR) program. The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education,learning, and participation.
Neuroscience is arguably one of the most important sciences in terms of potential breakthroughs in the next decade. Through a neuroscience game, the PI expects that people will learn many aspects of science that are directly and indirectly related to the game. There is strong evidence of this collateral learning process in the Foldit community where many people learned more about proteins and shared this knowledge with hundreds of their team members. Foldit is an online puzzle game about protein folding.
The PI will build a virtual gaming environment around neuron reconstruction that carefully scaffolds instruction along with social support and a reward system for novice players. This would allow motivated players to contribute to neuroscience directly by performing neuron reconstructions, independently verifying others' results, iteratively testing interfaces, visualizations and reconstruction tools, as well as collectively developing a corpus of knowledge around the activity of neuronal reconstruction that can be studied and absorbed back into automated methods. These results will be fed into a new database created in a subsequent larger project - to classify neurons.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Since Mozak launched in late 2016, the novice players — numbering roughly 200 a day — and Allen Institute neuroscientists have been able to reconstruct neurons 3.6 times faster than previous methods. The game provides a framework to greatly increase the number of people who can tackle this core task in neuroscience.
The players have also outperformed computers at tracing the complicated shapes of neurons. With minimal oversight, they can produce reconstructions that are 70 to 90 percent complete, compared to roughly 10 to 20 percent for the most effective computer-generated reconstructions.
Mozak has also enabled Allen Institute researchers to shift away from tools that require complicated training and extensive expert input, without sacrificing quality.
New players can also get real-time feedback from expert neuroscientists, a unique feature that allows Mozak players to acquire world-class expertise much faster. Mozak also requires general consensus among multiple players about a neuron’s shape, which allows for unprecedented levels of accuracy. By providing reconstructions that are confirmed not just by one or two scientists but by a collection of trained players working independently, Mozak in effect can provide neuroscience with the first validated, gold standard reconstructions.
In the future, Mozak players may provide data that enables artificial intelligence and computer-vision tools to become smarter and more effective at reconstructing neurons on a much larger scale.
Last Modified: 01/03/2018
Modified by: Zoran Popovic
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