Zoran Popovic - US grants

Animation is a tremendously powerful medium of expression.
Ideas that would be complex or even impossible to express
with still images or words can easily be conveyed using
animated sequences. Unfortunately, even when the concept to
be conveyed via animation is simple, the process of
producing the animation is inordinately difficult. Indeed,
this is one of the reasons computer animation has become so
dominant - it holds the promise of automating, and thus
significantly simplifying, the production process. Sadly,
the process of transferring ideas into computer animations
is still far from simple. For example, the animated feature
film Toy Story 2 required extremely hard work by as many as
200 highly skilled computer and animation experts for more
than three years.

My career goal is to develop the concepts and algorithms
that will enable effortless, rapid development of realistic
animations that effectively convey the creator's purpose.
This research hopes to create the theoretical and
experimental foundation for building much more powerful
animation tools than exist today. Such tools will sharply
reduce the production time needed by skilled animators and,
more importantly, enable average computer users to express
their ideas through animation. Such tools will also help
educators teach more effectively and allow each one of us to
become creators and directors of personal and fictional
stories. Only when animation production is easier will
animation be as commonplace on the web as images and text
are today. Moreover, intuitive methods for creating
realistic human motion will further enable the creation of
human avatars in tele-presence applications, new
anthropomorphic human-computer interfaces, and realistic
digital actors in feature films and video games. Computer
animation will not achieve its full potential until 1)
animation tools require little or no skill, 2) an animator's
creativity is not stifled by the limitations of the
animation tools, and 3) the animation systems are available
to every person with a computer and a story to tell. I hope
to significantly contribute towards these goals.

At the heart of this research is the observation that the
animator's creativity will not be fully realized until it
becomes easier to produce realistic motion sequences. This
problem is tremendously challenging because the underlying
physical models of motion are difficult to create and
control. I believe that the solution lies in the creation of
highly-flexible realistic motion libraries, together with
tools to modify them intuitively and extensively. The power
of libraries stems from the ability to reuse already
existing high-quality motion, reducing the need for
animation skills. In this paradigm, the process of animation
turns into selecting the specific motion library, and
modifying a set of motion properties that transform the
original motion into a final animation.

In my research, I propose to develop a methodology for
creating and using realistic motion libraries flexible
enough to be used in a wide range of applications. In 1995,
I proposed the use of motion transformation as new way to
create animations - a method fundamentally different from
the traditional way of creating animations from scratch.
This transformation approach is particularly useful for
modifying realistic motion data captured from real-world
actors. Unfortunately, during the transformation process,
much of the realism tends to be lost. Recently, I published
a novel transformation approach which demonstrates that
animations can be intuitively transformed into a wide range
of new sequences without violating the fundamental dynamic
properties of motion. The realism is preserved by
maintaining a model of the dynamic and biomechanic
properties of the animated character.

In the future, I will further develop mathematical models
that can be combined with real-world data to create reusable
motion libraries. Aside from preserving the realism of
motion, the most important requirement for effective motion
libraries is the flexibility with which the libraries can be
adjusted to meet the needs of an animator. I plan to achieve
this flexibility by decomposing a character's motion into a
fundamental component and a style component in a way
that allows us to independently transfer these components to
new characters. For example, the animator can produce a
child's cheerful run sequence by starting with a running
motion library extracted from the captured human run. This
motion can be modified by applying a happy, exuberant motion
style, which is then transferred to an animated character of
a small child. Such decoupling of the fundamental motion,
style, and performing character provides an extremely
powerful and flexible animation paradigm that can produce a
wide range of animations from a very small motion dataset.

The main goal of the educational component of this proposal
is to demonstrate the utility of advanced animation tools in
education and story-telling. I am particularly interested in
introducing these new animation tools to Seattle high-school
students to verify that our research does indeed empower
every story-teller with an accessible medium of expression.
I am also planning a project-oriented animation course
intended for undergraduate and graduate students involved in
animation research, as well as in art and music. Together,
the students will work on using our tools to create an
artistic short film project. This synergistic effort will
expose the artists to the latest animation technology and
provide direct user feedback to students involved in
research. In addition, the created motion libraries as well
as the animation tools, will be freely distributed through
the web and CDs with the hope that they will help animation
become an effective communication medium across educational,
economic and cultural boundaries.

The objective of this project is to capture physical properties of real world phenomenon by analyzing photographs and video. The problem of capturing motion of objects has emerged as a major research area in the fields of computer vision and computer graphics with a wide range of possible applications. This project will attempt to devise new computer vision techniques for capturing and modeling the dynamics and physical characteristics of a number of differing objects in different contexts. If successful, the new techniques will enable more accurate 3D motion estimation from a single video stream and other limited sensor data. Finally, the project will explore potentially valuable application areas for these research products.

EIA-0121326
Borning, Alan
University of Washington

ITR/PE: Interaction and Participation in Integrated Urban Land Use, Transportation, and Environmental Modeling

Patterns of land use and transportation play a critical role in determining the economic vitality, livability, and sustainability of urban areas. Transportation interacts strongly with land use: different kinds of transportation systems induce different patterns of land use, while at the same time, different kinds of land use induce demands for different kinds of transportation systems. Both have significant environmental effects. This integrated research program will support the construction and deployment of sophisticated models of land use, transportation, and environmental impact. The goal is to provide tools for stakeholders, such as urban planners, government staff, and citizens' groups, to help predict future patterns of urban development under different possible scenarios over periods of twenty or more years, allowing them to make more informed choices. Anticipated scientific advances include: in human-computer interaction, more effective ways of understanding the results from and interacting with complex simulations, and ways of linking stakeholder values with design choices in simulations and their interfaces; in graphics, capabilities for producing simulated street-level animations of urban environments from a policy-driven simulation; and in software engineering, new software structures that allow us to design, integrate, and evolve complex and diverse urban submodels.

Proposal #0098005
Popovic, Zoran
U of Washington

We propose a multi-layered approach to capturing and synthesizing realistic human shapes and motions. To capture the static shape of real humans, we will employ 3D scanning techniques including hierarchical light striping, simultaneous multi-striping, and photometric stereo. A feature-tracked motion capture system as well as 3D scanning techniques will generate motion data. The investigators will acquire this motion data at varying resolutions in order to drive the analysis of skeletal motion, body part deformation such as bulging due to flexing a muscle, and secondary motion such as leg vibrations that occur when stomping on the ground.

This wealth of human data will then drive an analysis, modeling, and synthesis stage. The static scan data will be analyzed to construct the space of possible human shapes. This human shape model together with the body part motion capture and full-body motion capture will be used to construct a detailed kinematic
model of the human body. Modeling human shape movement at such different levels of detail will allow control of the human motion on the coarse skeletal level while preserving the fine details such as muscle bulging. Furthermore, this multi-layered approach will enable selective replacement of different layers in the human model structure. For example, it will be possible to map the animated movement onto a different body scan and observe a different surface shape movement and creasing. The detailed kinematic human model will be further extended with a model of human dynamics by taking into account a number of physical properties of the human body such as muscle usage and mass distribution. This additional dynamic information provides a way to preserve the realism of motion even when the structure of motion is significantly modified. In addition, the investigators will extend the skeletal dynamic model with secondary motion simulations constructed to replicate the loose skin and tissue vibrations that occur in high-energy movements.

The investigators will incorporate their work into new curriculum both at their university and in courses being offered to the professional community. This work will be folded into CDROM's that reach a wide audience, including the general public and a broad spectrum of high school students who may be considering careers in information technology. The results of the research will include complex databases of human shape and motion to be distributed to the general research community in order to encourage further research in this area.

This project, enabling next generation research projects in automatic sensing and modeling, aims at acquiring a massive image capture and processing facility. The infrastructure includes specialized digital video cameras and projectors, a high resolution motions apparatus, a three dimensional scanner, a lighting grid, a robot arm, and a video compute cluster. The components form a massive "Digital Eye" that will enable capturing the appearance, shape, motion, and behavior of real objects and scenes at previously unattainable resolutions and frame rates. Interdisciplinary ties and collaborations in Zoology, Mathematics, Statistics, Urban Design and Planning, and Architecture will benefit from the specific projects and applications to be supported by the infrastructure. These projects include:
High Fidelity Modeling of Human Shape and Motion: Digital Humans
Insects in Flight: Biological Motion Analysis, Modeling Insect Flight
Analysis of Physical Processes: Physics Capture
Lightfield Analysis: Plenoptic Imaging and Modeling Research,
Plenoptic Function Capture and Simulating Compound Eyes
3D Object Recognition from Range Data
Urban Simulation and Visualization.
Enabling hand-on experience for students in courses on digital sensing technologies and animation production, the project promotes teaching, training and learning by providing a state-of-the-art facility.

In the past few decades biochemists have discovered that practically all fundamental molecular design problems are about the geometric relationship of complex molecules. Massively parallel and distributed versions of such algorithms have been recently developed in hope of finding such proteins with shear brute force. For example, implementing screensavers that harvest idle CPU time from PC users worldwide to provide sufficient computing power to solve the protein prediction problems. So far, all these attempts have produced very modest improvements. This project takes a radically different approach: the molecular folding problem is cast as a massively distributed 3D puzzle game, and encourages people to work together with computers to find the solutions to current open problems including cures for cancer, AIDS, and discovery of novel biofuels.

The fundamental idea is to employ user-assisted optimization for protein design, and formulate and present it as a competitive game played by thousands of people. The intention in this project is that people will play the game beacause it is fun (it looks like a fun 3d puzzle and not like some biochemistry textbook), it is addicting, it is competitive, it is collaborative (players can work together in groups to solve a problem), has impact (players want to get credit for the drug that cures cancer). The impact of this proposal is in its addressing of a number of fundamental areas: 1) how to best develop games to maximize human ability to discover novel proteins beyond what is currently possible with computation-only approaches; 2) determining the guiding principles of a successful molecular design game; 3) how to best generalize game-development principles to the widest possible range of biochemical problems; 4) revealing what is learned from the way people play the game, and how these strategies could be "distilled" towards developing stronger automated approaches.

This project will develop and evaluate methods by which large numbers of humans, together with computers, can advance the field of synthetic biology by assembling a corpus of creative designs of molecular machines built from DNA segments as well as other molecular structures. Specifically, it will develop a massively-distributed DNA machine construction game that will enable human worldwide collective creativity to be applied to problems ranging from the design of novel self-organizing materials to smart therapeutics that can sense and respond to their environment. The innovative approach is to cast problems of constructing molecular nano-machines with specific functions as a collaborative machine design game governed by the rules of DNA strand interactions.

This approach points to a new paradigm for future science, in which a large group of people together with computers work on difficult creative problems, finding solutions that could not be found by computers alone, or by people alone, or without the massive participation of users. If successful, this approach could change science profoundly, with wide-ranging impact on many disciplines including nanotechnology, biochemistry, medicine, and even social and economic behavior analysis. Although the project specifically focuses on games that use DNA strands as principal building blocks of nano-machines, the potential set of applications is large, and encompasses three of the most significant problems facing humanity today.

The primary goal of the computer game is to develop and focus collective creativity towards a design space of machines governed by DNA molecular mechanisms. It is currently not known whether this form of sophisticated scientific design creativity can be developed rapidly with non-experts. It is also unknown whether this developed creativity can exceed the current capabilities of the scientific community. This project aims to answer a number of fundamental questions: How does one develop computer games to maximize targeted human design creativity? What are the guiding principles of successful molecular design games? How do we generalize game-development principles to the widest possible range of synthetic biology problems? How can we develop a collective creative design process that outperforms any individual creativity? How do we learn from the way people play the game, and distill their strategies towards stronger automated approaches?

The successful outcomes of this project can have a wide ranging impact on health and medicine. One such problem is the design of diagnostic devices and imaging technologies. The game players will work to develop DNA sensors and circuits that can autonomously analyze and interpret the information encoded in a set of molecular disease markers. This approach will enable new devices for multi-analyte testing in low resource settings and will lead to novel medical imaging technologies. Another challenge is design of novel targeted therapeutics, in this case novel RNA-based therapeutics that can autonomously sense and analyze their environment and activate a therapeutic response only where required. A third problem is design of novel materials. This project will develop DNA nanostructures with the potential for the massively parallel self-assembly materials with desired electronic, optical, or chemical properties. These materials will find applications in areas from artificial photosynthesis to biofuels production.

This effort will have positive broader impacts for informal science education. The game will reach out to people of all demographic profiles in hope of educating everyone about key molecular research challenges, empowering them to solve important scientific problems, and engaging them in research and science in general. Hopefully, the best scores in these games turn into seminal discoveries with deep impact on people's lives. Also, undergraduates will be involved directly in game development, and a course centered around prototyping of molecular games will be offered. Furthermore, the research team will work with education scientists to develop a new curriculum about DNA and how nature uses molecular mechanisms to achieve function. The curriculum will be anchored around the DNA Machine game and will be piloted in US high schools.

The objective of this research is to develop algorithms that can make robots and simulated characters move like humans. A range of dynamic locomotion tasks including walking, running, getting up and climbing, as well as task variations such as walking backwards, and concurrent tasks such as holding a cup of water while walking, will be studied. The approach is based on optimal control theory. Human movements will be analyzed, and the performance criteria with respect to which they are optimal will be identified. Algorithms that optimize the same performance criteria will then be developed.

Intellectual merit: Movement analysis will be based on a new mathematical framework where inference of performance criteria from observed movements becomes a convex optimization problem. Control synthesis will exploit new algorithms for real-time optimization which are able to plan long movement sequences involving multiple contact events. These algorithms rely on novel formulations of the physics of contact which are more amenable to numerical optimization, as well as a new physics simulator which exploits advances in parallel processing.

Broader impact: This research will change how robots and simulated characters move. Currently many robotic control systems with the appearance of dynamic movements are controlled in open loop, or are designed to execute one specific task. This work will enable robots to express more natural and versatile movements, as well as make robot programming more automated. The resulting controllers will also serve as models for human motor control.

One of the most exciting potential benefits of big data is that it will enable better decision making in applications such as teaching software, patient treatment plans, and user personalization. In many complex domains involving people, the number of descriptions of a situation and the number of potential decisions that can be made to improve upon the previous situation is enormous. The sheer size of this problem implies that careful selections of additional data must be made to improve decision making. In health, these decisions could involve a course of treatment; in learning these could involve remediations such as additional practice, in personalization it could involve improved choices for consumers. This proposal will develop new algorithms that are especially effective in coming to close to optimal decisions quickly.

This project will aim to develop novel algorithms that specifically address these needs, and thus enable data intensive systems to be efficiently used towards solving the decision making process. Since novel algorithmic methods of this kind are best developed, refined and tested on a real domain, the project will focus on the educational domain of classroom learning activities that optimize learning outcomes. However, the algorithms will be applicable to numerous other decision making domains. Specifically the project will focus on finding algorithmic solutions for:

(1) Large-scale deployment and data collection of lesson enactments in thousands of classrooms worldwide.
(2) Data-driven methods to efficiently determine the likely most important parameters of the classroom decision making process.
(3) The development of novel reinforcement learning algorithms with a focus on efficient use of data to rapidly converge towards beneficial policies.
(4) Using data to determine the key model and decision parameter space bottlenecks, which, if removed, could significantly
improve the outcomes of the Reinforcement Learning process. The efficacy of the optimized policy will be determined by the improvement of classroom learning performance at scale.

The methods that will be developed by this project are general enough to be directly applicable to domains of patient treatment, or any other domain that involves unknown model dynamics and decision space that involves people. The Reinforcement Learning methods will similarly find practical use in situations where a decision policy is deployed in a large number of instances asynchronously and in other high-risk settings where reducing over-exploration as much as possible is of high importance.

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.

With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. David Baker for a project to engage the Foldit community in a collaborative citizen science effort to address the ongoing Ebola outbreak in West Africa. Foldit is a game-based protein design platform that engages gamers drawn from the community at large. This project involves collaboration among the Foldit game-based scientific community, The University of Washington's Institute for Protein Design (IPD), and The University of Washington's Center for Game Science, to improve and tailor the Foldit design platform in order to arrive at proteins that bind and neutralize the Ebola glycoprotein. The project will collectively harness the expertise of computer scientists and protein design experts in collaboration with the constantly growing citizen scientist community to arrive at totally novel functional proteins in an efficient and rapid manner. The proposed research will deliver critical insight into the structure and binding modes of potential anti-Ebola therapeutics; development of these anti-Ebola binders will be extremely valuable both for academic and industrial communities. The work will also significantly expand and broaden the citizen scientist community focused on discovering anti-viral therapeutics for Ebola. In addition to advancing development of anti-Ebola therapeutics, this project will continue to educate the public at large about Ebola, its epidemiology and current research efforts underway to combat this virus.

This project sets out to improve the Foldit game-based scientific environment in the direction of rapid and direct collaboration among the Foldit community, The University of Washington's Institute for Protein Design (IPD) and The University of Washington's Center for Game Science. The project will use Foldit puzzle challenges to facilitate the design of novel anti-viral proteins that bind to the surface glycoproteins of Ebolaviruses, and will experimentally test these binders for in vitro efficacy in collaborator laboratories. The approaches involve (a) customizing the Foldit protein design platform for Ebola studies, (b) engaging the Foldit citizen scientist community with anti-Ebola directed protein design challenges, (c) refining the anti-Ebola Foldit player designs, (d) preparing synthetic genes and manufacturing anti-Ebola proteins with the designed proteins and (e) testing the optimized anti-Ebola proteins prepared, in cell-based studies and animal models. The project will enlist the knowledge of a broad cadre of experts in the design of novel protein binders targeting Ebola and will utilize the Foldit platform to continually educate the public about project progress and more broadly about the virus itself with regular updates and feedback on the work.

The focus for this project is the combination of crowdsourcing and computational biochemistry through the crowdsourcing video game Foldit. Foldit is an online multiplayer game that allows players to compete and collaborate to computationally fold and design proteins. Since its launch in 2008, it has had over 400,000 players, and demonstrated that by leveraging human problem solving and creativity, humans and computers can work together to solve previously unsolvable problems in computational structural biology. Foldit players have contributed toward solutions for two of the "holy grail" problems in computational structural biology: the protein folding problem and the protein design problem (also known as the inverse folding problem). The design of novel synthetic proteins is one of the most important tools in protein engineering, possibly leading us to a better understanding of the processes that underlie life and allowing the discovery of molecules with applications in health, energy, materials, and nanotechnology. As a new approach to addressing the challenges of purely computational approaches in biochemistry, the field of scientific discovery games has recently arisen to crowdsource solutions to computational biochemistry problems, by using human problem solving and creativity from members of the general public. The infrastructure supported by this project will enable new research in crowdsourcing and citizen science. The broader community will benefit from this infrastructure as a platform for crowdsourcing computational biochemistry.

This project aims to enhance the existing infrastructure of the Foldit game, and allow us to recast Foldit to tackle the next big challenges in computational structural biology (including design of enzymes and small molecules), while broadening the community of scientists involved and continuing to engage and educate the public. This project will support equipment and personnel needs for cloud hosting and maintenance, allowing improved robustness and collaborative access to resources, along with continued community outreach. Additionally, support will be provided for developing in-game tools for new applications in protein and small molecule design, new interfaces for scientists, touchscreen and multi-touch support, and a new pipeline for crowdsourced biochemistry challenges. Enhancements made to the game client will be made available via Foldit Standalone -- a non-competitive version of the game that allows researchers to load in and work on their own structures. Educators will be able to use the game as a teaching tool for introducing biochemistry to classrooms, where it is already being used for lectures, lab exercises, homework assignments, and even in textbooks and MOOCs. Foldit has been used by winning student teams in iGEM -- the premiere international design competition for students in synthetic biology. The Foldit website (http://fold.it/) provides access to the online multiplayer game and resources for educators and researchers.

The STEM + Computing Partnership (STEM+C) program seeks to advance multidisciplinary integration of computing in STEM teaching and learning through applied research and development across one or more domains. This project aims at investigating the connections between computational thinking and mathematical and scientific practices as outlined in the Common Core State Standards (CCSS) and the Next Generation Science Standards (NGSS) through large-scale deployment of an online curriculum for development of computational thinking. The computational skills will be delivered to elementary, middle, and high-school students through three separate age- and grade-appropriate game-based computational thinking curriculum using an adaptive educational platform focused on developing higher-order skills that are likely transferable to mathematics and science learning. Two research questions will guide the research and development activities: (1) Can the game-based computational thinking curriculum lead to improvement in students' mathematics and science practices, as well as their computing skills?; and (2) What are the optimal characteristics of curriculum materials and teacher enactment that would lead to greatest outcomes at different grade levels? To answer these questions, the project will: (1) develop computational thinking problem-based learning experiences with a focus on computational thinking embedded in three age-appropriate instructional games for elementary, middle, and high school students; (2) provide scaffolded teacher support for delivering the learning experience that does not require any computational background for teachers; (3) develop valid measures towards assessing acquisition of shared practices; (4) implement the curricula and evaluate their effects through large-scale statewide in three states with over 3,000 teachers and over 90,000 students; (5) connect the computational challenges with mathematics word problem solving and analyze the practice transfer; and (6) iterate the interventions and validation over multiple years with data-driven optimization towards greater transfer outcomes.

The study will adopt the notion of computational thinking as a problem-solving process that is transferable to a wide variety of situations, including practical applications in mathematics and science. Thus, the premise of this effort will be that practices at the core of computational thinking are closely related to the mathematics and science practices identified in the CCSS and NGSS. In order to demonstrate how appropriate skills are developed across grade levels, the project will develop three separate game-based interventions appropriate for elementary, middle, and high school students, including problem-based computational thinking learning experiences that will be embedded in each of these games. To facilitate the successful implementation of the game-based learning technologies and curricula, the project will integrate computational games with the Teacher Co-Pilot, a learning platform that will provide teachers with lesson plans and step-by-step processes for classroom activities, which are directly deployed on student computers. The design of the Teacher Co-Pilot assumes that teachers will learn best by doing the lesson, so the experience will be tailored towards minimum startup time. To address the two research questions, the project will investigate whether a game-based computational thinking curriculum can lead to improvement in selected aspects of students' mathematics and science practices, as well as their computing skills. This will be achieved through the refinement and deployment of the three games at scale and assessing transfer with cognitive interviews of a small sample of students and carefully developed assessments with all the participating students. Likewise, the project will investigate the optimal characteristics of curriculum materials and teacher enactment that would lead to greatest outcomes at different grade levels via integration with the Teacher Co-Pilot platform, through which the project will investigate the most effective minimal scaffolding needed for teachers. This will be accomplished through pilot studies in two iterations with a sample of 40 teachers and focus group interviews to investigate useful and optimal feedback for teachers. The key outcome of this project will be research-informed and field-tested prototypes of game-based curriculum focused on the integration of computational thinking and science and mathematics learning across grade levels, freely available to educators for implementation of adaptation beyond the duration of the project. An advisory board will conduct the formative and summative evaluation of the project.

Project Summary This project aims to leverage the best of both computational and human expertise in neuronal reconstruction towards the goal of accelerating global neuroscience discovery from internationally-sourced imaging data. We propose to create a cloud-based unified platform for converging 3-dimensional images of neurons onto a single analysis platform to (1) train and grow a new expert community of global reconstructors to work across the data from these groups, to (2) generate a community-sourced neuronal reconstruction database of open imaging data that can be incorporated into a 3-dimensional map of neuronal interconnectivity - onto which (3) novel annotations and more complex functional and molecular data can be overlaid. Our approach will evolve with the growing needs of the neuroscience community over time. To do this, in Aim One (Neuronal Reconstruction at Scale), we will test if the newly developed crowd-sourced game-based platform Mozak can develop a collective of new human experts at scale, capable of accelerating the rate of current reconstruction by at least an order of magnitude, at the same time as increasing the robustness, quality and unbiasedness of the final reconstructions. In Aim Two (Robust Multi-Purpose Annotation), we will enhance basic neuronal reconstruction by adding specific semantic annotation? including soma volume and morphological quantification, volumetric analysis, and ongoing features (e.g. dendritic spines, axonal varicosities) requested from the neuroscience community. Experienced and high-ranking members will be given the opportunity to advance through increasingly complex neurons into full arbor brain-wide neuronal projections and multiple clustered groups of neurons in localized circuits. Finally, in Aim 3 (Creation of a Research-Adaptive Data Repository), we aim to develop a database of neuronal images reconstructed using the Mozak interface that will directly serve the general and specific needs of different research groups. Our goal is to make this database dynamically adaptive ? as new research questions will invariably bring new needs for additional annotations and cross-referencing with other data modalities. This highquality unbiased processing repository will also be perfectly suited for training sets for automated algorithms, and the generation of a 3-dimensional maps such as Allen Institute for Brain Science (AIBS) common coordinate framework. We expect that the computational reconstruction methods will further improve with the new large corpus of ?gold standard? reconstructions. Collectively, the completion of these three aims will create an analysis suite as well as an online community of experts capable of performing in depth analysis of large-scale datasets that will significantly accelerate neuroscience research, enhance machine learning for reconstruction analysis, and create a common platform of baseline neuronal morphology data against which aberrantly functioning neurons can be analyzed.