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I-Corps: Virtual Reality Biofeedback Education Technologies

Award Number: 1938166
Sponsor : Cornell University
Michael Timmons [email protected] (Principal Investigator)

The broader impact/commercial potential of this I-Corps project is to provide a new means for how educational content is delivered in virtual reality (VR) with consistent impact, independent of demographics and environment. This technology offers a higher level of personalized learning that can be administered without requiring accompanying professional development for teachers due to the delivery of lessons in a highly controlled virtual environment. Removing this dependence on additional training facilitates the adoption across schools with limited resources. The increased efficacy in learning and retention of material through VR has made it a technology of interest for teachers and school administrators. This technology can be used across a range of educational subjects, and with the immersive personalized learning experience, offers a high-quality learning solution for educators.

This I-Corps project uses biofeedback to personalize learning in virtual reality (VR). The technology manipulates a user’s environment based on their physiological reactions. This can include the surrounding physical objects, light source, or landscape. The auditory manipulation is a more indirect change where the audio path of a lesson is based on the emotional reaction of a user, such as when someone is distracted or agitated. This emotional reaction is based on tracking numerous physiological inputs over time. The identification process is based on existing research using the same physiological measures to classify users’ emotional states and improved through this project This level of adaptation enables a higher degree of VR program customization and more a meaningful learning experience while using the technology.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

AwardsInventXRMovement ThinkingThe Research University

Data Visualization Literacy: Research and Tools that Advance Public Understanding of Scientific Data

Sponsor: Indiana University

Katy Borner [email protected] (Principal Investigator)
Kylie Peppler (Co-Principal Investigator)
Bryan Kennedy (Co-Principal Investigator)
Stephen Uzzo (Co-Principal Investigator)
Joe Heimlich (Co-Principal Investigator)


As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public’s engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner’s speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.

The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry’s (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy – currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.

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Making it Stick! Mobile Apps to Pedagogically Support Retrieval Practices

Sponsor: SUNY College at Brockport

Osman Yasar [email protected] (Principal Investigator)
Jose Maliekal (Co-Principal Investigator)
Leigh Little (Co-Principal Investigator)
Peter Veronesi (Co-Principal Investigator)


This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students’ motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by employing mobile technology to help up to 530 teachers and more than 9,000 students use proven retrieval experiences to improve STEM learning. The project will design and implement pedagogical and technological retrieval experiences as well as test the hypothesis that these experiences will encourage students to learn, retain, and apply newly acquired scientific knowledge in novel settings. The project will accomplish this goal through the development and use of simple to complex mobile Apps ranging from basic retrieval strategies to interactive problem solving approaches involving interleaved and generative practices. Through an interdisciplinary approach this project will combine STEM content with technology and pedagogy to support more meaningful and in-depth learning. Culturally-oriented, low-threshold technologies along with cognitively effective retrieval practices will be used to increase students’ computational thinking as well as their scientific processing and critical thinking skills associated with careers in the future STEM workforce.

Deductive and inductive reasoning will underpin a mixed-methods research design involving pre-post surveys, rubric-scored annual competitions, classroom observations, reflective journal entries, video recall and face-to-face interviews, activity logs, and classroom artifacts. These measures will capture changes in student and teacher attitudes, beliefs, and classroom instructions brought on by the use of mobile technologies. Data analysis of information from these sources will provide a robust characterization of the validity of research findings inclusive of inter-rater reliability, internal consistency, and testing and retesting of the stability of the study design. Project outcomes will include computational models and patterns common to multiple STEM fields developed through mobile Apps in physics, chemistry, biology, Earth science, and mathematics at different educational levels. A database will be created to maintain and disseminate newly developed mobile Apps. Developed resources and research findings will be shared with Finger Lakes Learning Network of 80 regional school districts as well as STEM practitioners and policymakers elsewhere through conferences and the project?s website.

AwardsInventXRThe Research University

EI: Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data Intensive Science

Sponsor: Rutgers University New Brunswick
Ivan Rodero [email protected] (Principal Investigator)
Manish Parashar (Former Principal Investigator)
Vasant Honavar (Co-Principal Investigator)
Jenni Evans (Co-Principal Investigator)
Grace Agnew (Co-Principal Investigator)
James von Oehsen (Co-Principal Investigator)
Award Number: 1640834


This project develops a virtual data collaboratory that can be accessed by researchers, educators, and entrepreneurs across institutional and geographic boundaries, fostering community engagement and accelerating interdisciplinary research. A federated data system is created, using existing components and building upon existing cyberinfrastructure and resources in New Jersey and Pennsylvania. Seven universities are directly involved (the three Rutgers University campuses, Pennsylvania State University, the University of Pennsylvania, the University of Pittsburgh, Drexel University, Temple University, and the City University of New York); indirectly, other regional schools served by the New Jersey and Pennsylvania high-speed networks also participate. The system has applicability to a several science and engineering domains, such as protein-DNA interaction and smart cities, and is likely to be extensible to other domains. The cyberinfrastructure is to be integrated into both graduate and undergraduate programs across several institutions.

The end product is a fully-developed system for collaborative use by the research and education community. A data management and sharing system is constructed, based largely on commercial off-the-shelf technology. The storage system is based on the Hadoop Distributed File System (HDFS), a Java-based file system providing scalable and reliable data storage, designed to span large clusters of commodity servers. The Fedora and VIVO object-based storage systems are used, enabling linked data approaches. The system will be integrated with existing research data repositories, such as the Ocean Observatories Initiative and Protein Data Bank repositories. Regional high-performance computing and network infrastructure is leveraged, including New Jersey’s Regional Education and Research Network (NJEdge), Pennsylvania’s Keystone Initiative for Network Based Education and Research (KINBER), the Extreme Science and Engineering Discovery Environment (XSEDE) computing capabilities, Open Science Grid, and other NSF Campus Cyberinfrastructure investments. The project also develops a custom site federation and data services layer; the data services layer provides services for data linking, search, and sharing; coupling to computation, analytics, and visualization; mechanisms to attach unique Digital Object Identifiers (DOIs), archive data, and broadly publish to internal and wider audiences; and manage the long-term data lifecycle, ensuring immutable and authentic data and reproducible research.

AwardsNSFThe Research University

Learning From Diverse Populations: A Complexity-Theoretic Perspective

Sponsor: Stanford University
Omer Reingold [email protected] (Principal Investigator)
Award Number: 1908774


Despite the successes of machine learning at complex prediction and classification tasks (such as which add a reader will click? or which word a speaker pronounced?), there is growing evidence that “state-of-the-art” predictors can perform significantly less accurately on minority populations than on the majority population. Indeed, a notable study of three commercial face recognition systems, known as the “Gender Shades” project demonstrated significant performance gaps across different subpopulations at natural classification tasks. Systematic errors on underrepresented subpopulations limit the overall utility of machine-learned prediction systems and may cause material harm to individuals from minority groups. To address accuracy disparity and systematic biases throughout machine learning, the project pursue a principled study of learning in the presence of diverse populations. The project puts high value on education, service to the research community, and wide dissemination of knowledge. The research activities will be accompanied by and integrated with curriculum development, research advising (for students at all levels), service, and outreach to other scientific communities and in popular writing. In addition, in the age of machine-learning and big data, the project’s societal impact is twofold: making sure that algorithms work for everyone but also making sure algorithms uncover all potential talent, which exists in all communities.

The project combines theoretical and empirical investigations to develop algorithmic tools for mitigating systematic bias across subpopulations and to answer basic scientific questions about why discrepancy in accuracy across subpopulations emerges in the first place. Specifically, the project aims to ask and resolve questions that arise in the context of learning from diverse populations along three main axes: (1) Improving predictions for underrepresented populations: Can learning algorithms be developed that provably do not overlook significant subpopulations, (2) Representing individuals to improve the ability to audit and repair models, (3) Understanding the causes for biases in machine common learning models and algorithms.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

AwardsInventXRMovement ThinkingThe Research University

Vine Robots: Achieving Locomotion and Construction by Growth

Sponsor: Stanford University
Allison Okamura [email protected] (Principal Investigator)
Jonathan Fan (Co-Principal Investigator)
Sean Follmer (Co-Principal Investigator)
Award Number: 1637446
Award Number: 1637446


In contrast to legged robots inspired by locomotion in animals, this project explores robotic locomotion inspired by plant growth. Specifically, the project creates the foundation for classes of robotic systems that grow in a manner similar to vines. Within its accessible region, a vine robot provides not only sensing, but also a physical conduit — such as a water hose that grows to a fire, or an oxygen tube that grows to a trapped disaster victim. The project will demonstrate vine-like robots able to configure or weave themselves into three-dimensional objects and structures such as ladders, antennae for communication, and shelters. These novel co-robots aim to improve human safety, health, and well-being at a lower cost than conventional robots achieving similar outcomes. Because of their low cost, vine robots offer exceptional educational opportunities; the project will include creation and testing of inexpensive educational modules for K-12 students.

This work broadens the concept of bio-inspired robots from animals to plants, the concept of locomotion from point-to-point movement to growth. In contrast to traditional terrestrial moving robots that tend to be based on the animal modality of repeated intermittent contacts with a surface, the vine modality begins with a root, harboring power and logic, and extends using growth, increasing permanent contacts throughout the process. This project will demonstrate a soft robot capable of growing over 100 times in length, withstanding being stepped on, extending through gaps a quarter of its height, climbing stairs and vertical walls, and navigating over rough, slippery, sticky and aquatic terrain. The design adopts a bio-inspired strategy of moving material through the core to the tip, allowing the established part of the robotic vine to remain stationary with respect to the environment. A thin-walled tube fills with air as it grows, allowing the vine robot to be initially stored in a small volume at its base, and to extend very large distances when controllably deployed. Mechanical modeling and new design tools will enable the development of task-specific vine robots for search and rescue, reconfigurable communication antennas, and construction. The paradigm of achieving movement and construction through growth will produce new technologies for integrated actuation, sensing, planning, and control; novel principles and software tools for robot design; and humanitarian applications that push the boundaries of collaborative robotics.

AwardsMovement ThinkingUniversity Affairs

The Stanford Data Science Collaboratory

Sponsor: Stanford University
Award Number: 1934578
Awarded Amount to Date: $1,000,000.00
Emmanuel Candes [email protected] (Principal Investigator)
Fiorenza Micheli (Co-Principal Investigator)
Chiara Sabatti (Co-Principal Investigator)
Jurij Leskovec (Co-Principal Investigator)


Data-driven inquiry is key to all aspects of science and discovery, and data-based decisions are becoming integral to society. The challenges and the importance of meeting them are especially critical when the goal is to obtain relevant, valid, reproducible scientific insights. The Stanford Data Science Collaboratory will confront these challenges by creating a community of faculty, postdoctoral scholars, students, and research fellows that leverage data science methods and domain knowledge to tackle pressing problems. In the Collaboratory, data scientists will work closely with scholars from other fields who rely on large, accurate, dependable datasets and data science techniques. The Collaboratory will foster the work of researchers who study the ethical issues related to data collection and use, and will use data to solve societal and scientific problems. A hallmark will be thorough validation of data and a careful statistical calibration of the evidence to avoid misinterpretations that could have adverse consequences. A second major goal of the Collaboratory is the growth of a citizenry literate in data science: universities have an obligation to ensure the next generation understands how to interpret and learn from data, and how to collect and manage it.

The Collaboratory identifies a set of five high-profile, high-impact projects that domain scientists deem important, and where they believe they are unable to make progress without a paradigm shift in the way they approach data sets. The first two concern a sustainable relation between humans and the environment, namely, (1) the problem of managing coral reefs in a changing climate and (2) reducing illegal fishing and forced labor in tuna supply chains. To make progress, the project will leverage new data sources: satellite remote sensing, ground monitoring stations based on soundscape, and genomic measurements that track biodiversity and evolution. The other three projects are about fractures in society and steps towards a sustainable one: (3) How to understand the determinants of poverty in the U.S.; (4) How to detect and track political framing in digital media; and (5) How to develop data science tools that support equitable treatment between individuals. Public data streams (e.g., social media apps, Wikipedia and Wikidata and moderate-resolution satellite imagery) as well as private-sector data (e.g., cell phone records, Facebook activity, internet search queries, drone imagery and fine-resolution satellite data) will inform understanding of the mechanisms causing poverty. To meet the research goals, the Collaboratory will incentivize faculty, students and postdocs to come together to find new data science solutions by supporting collaborative research teams and brainstorming working groups. The Collaboratory will also engage the undergraduate community by providing hands-on guided scientific research experience. To enlarge collaboration beyond Stanford, the Collaboratory will host outside visitors and invite scientists to campus for an annual symposium.

This project is part of the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea activity.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: [email protected]


Stanford University NSF Research Award: RET Site: Teaching Engineering & Design Innovation

Sponsor: Stanford University
Sheri Sheppard [email protected] (Principal Investigator)
Shelley Goldman (Co-Principal Investigator)
Award Number: 1760810


The need for a more robust and diverse engineering pipeline in the U.S. is incontrovertible. Many studies over time indicate that when K-12 students have the opportunity to study engineering, their engagement in and understanding of math and science increases. Science, Technology, Engineering and Math (STEM) teachers want and are required by the Next Generation Science Standards (NGSS) to incorporate engineering concepts and capabilities into their curriculum, regardless of the subjects they teach. Yet, only a fraction have completed engineering coursework. The challenge, then, is to expose STEM teachers to engineering research and its potential applications and provide a process for infusing their curriculum with innovative engineering and design concepts and practices. Stanford’s new Teaching Engineering Design & Innovation (TEDI) program addresses this challenge head on. TEDI will expose 38 teachers and 10,000 students from school districts in the San Francisco Bay Area to the ways science and math content knowledge and Design Thinking (DT) provide solutions to difficult engineering design challenges. TEDI’s primary goal is to promote interest in engineering and proficiency in STEM among Bay Area high school students by having teachers practice engineering and use DT to create innovative and exciting curriculum for their students. The major elements of the TEDI model include 8-week teacher placements into Stanford engineering research laboratories; weekly workshops exposing them to a range of engineering disciplines and DT exercises; paid industry placements for teachers before or after a summer at Stanford; and assistance in developing and disseminating new curriculum for use with their own students and others. At a time when over 60% of all California K-12 students are Hispanic, African American, Native American or Pacific Islander groups historically under-represented in STEM and 62% are eligible for free and reduced-price meals, TEDI educates and supports diverse students from some of California’s most under-resourced schools to enter and thrive in the STEM pipeline, becoming the scientific and technical workforce of tomorrow and contributing to our nation’s economy and global competitiveness.

The RET Site: Teaching Engineering Design & Innovation (TEDI) creates enduring partnerships between San Francisco Bay Area high school STEM teachers, Stanford faculty and graduate students, and industry to focus on critical engineering and design skills that drive U.S. innovation and global competitiveness. TEDI creates a revitalized and retooled teaching “infrastructure” that prepares and invigorates high school educators to effectively guide their students toward engineering- and design-related post-secondary study and careers. TEDI’s primary goal is to promote interest in engineering and proficiency in STEM among high school students in the San Francisco Bay Area. This integrated center of excellence will build capacity in 38 teachers and, through them, more than 10,000 students over three years, introducing them to engineering disciplines, the design thinking process, and the ways science and math curricula relate to and support key engineering concepts. TEDI will provide: 1) 8-week authentic summer research experiences in Stanford engineering labs; 2) Extensive workshops in Design Thinking and exposure to a range of engineering disciplines and applications; 3) Industry placements before or after a summer at Stanford for selected participants; 4) Assistance in developing curriculum that incorporates a design challenge; 5) Assessment of the program’s effectiveness through a rigorous formative and summative study that measures the intermediate- and long-term effects of TEDI on teachers and their students; and 6) Dissemination of new lessons and teaching materials via various community websites, professional development, and teacher attendance at a prestigious national conference.


Broadening Participation in STEM Through Virtual Reality Career Exploration: Introducing Underrepresented Students to High Need STEM Careers: Rowan University

Award Number: 2000865
Investigator(s): Sarah Ferguson [email protected] (Principal Investigator)
Kara Ieva (Co-Principal Investigator)
Christopher Winkler (Co-Principal Investigator)

The project will examine the efficacy of an intervention designed to increase self-efficacy and motivation to pursue STEM careers among rural high school students. Researchers will use a design-based approach to partner with rural school districts to introduce students to high-need STEM career fields using virtual reality technology and career exploration modules. They will also guide students in assessing their potential fit for these fields. The researchers hypothesize that student career decision making self-efficacy and career outcome expectations will statistically improve as a result of the intervention. The project will be implemented in two stages: development of the curriculum modules and then implementation of the intervention with research being conducted concurrently. The project is designed to broaden participation in the STEM workforce for those in rural communities, particularly women and underrepresented groups, by exposing students and school counselors to potential high-need careers.

Guided by social cognitive career theory, the researchers will employ an experimental design with randomization within clusters to measure the effectiveness of the intervention by investigating four research questions: (1) What is the magnitude of the effect of a STEM career exploration intervention using virtual reality technology on student career decision making self-efficacy, career outcome expectations, and knowledge of the targeted careers? (2) How do student inputs of gender, race/ethnicity, and personality impact the effects of participation in the career intervention on outcomes of career decision making self-efficacy, career outcome expectations, and knowledge of the targeted careers? (3) How does student socio-economic status impact the effects of participation in the career intervention on outcomes of career decision making self-efficacy, career outcome expectations, and knowledge of the targeted careers? and (4) How do students characterize the effectiveness of the career exploration intervention in developing their understanding of, and interest in, the target careers? Data will be collected pre- and post-intervention. Additionally, student focus groups will be used to collect data from a representative sample of students from the treatment condition at each school. The project will produce a proof-of-concept for the efficacy of virtual reality technology in rural educational settings.

The project is funded by the EHR Core Research (ECR) program that supports fundamental research focused on STEM learning and learning and learning environments, broadening participation in STEM, and STEM professional workforce development.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

AwardsMovement ThinkingThe Research University

XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement

Sponsor: University of Illinois at Urbana-Champaign
John Towns [email protected] (Principal Investigator)
Kelly Gaither (Co-Principal Investigator)
Philip Blood (Co-Principal Investigator)
Robert Sinkovits (Co-Principal Investigator)
Ralph Roskies (Former Co-Principal Investigator)
Nancy Wilkins-Diehr (Former Co-Principal Investigator)
Award Number: 1548562


This award supports the continuation and evolution of NSF project 1053575 – XSEDE: eXtreme Science and Engineering Discovery Environment. The goal of XSEDE is to accelerate open scientific discovery by enhancing the productivity and capability of researchers, engineers, and scholars, and by broadening their participation in science and engineering. It does so by making advanced computational resources easier to use, integrating existing resources into new, powerful services and building the community of users and providers. XSEDE is a virtual organization that provisions complex distributed infrastructure, support services, and technical expertise. A prominent opportunity for XSEDE is the growing, diverse collection of advanced computing, high-end visualization, data analysis, and other resources and services available to researchers, engineers, and scholars; these resources have the potential to help understand and solve the most important and challenging problems facing the nation and world. The challenge for XSEDE, as a virtual organization, is to organize these disparate resources, creating integrated services and a coordinated environment that serves the end user needs. The challenge also includes fostering awareness of, and training for, full utilization of the capabilities offered by XSEDE and its associated resources, as well as catalyzing workforce developments. All these tasks need to be accomplished in light of evolving user requirements, resources, and NSF strategies.

The XSEDE 2 project will be executed by the principal investigator (PI) and staff of the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign and by the co-PIs and staff of the partner organizations at the Pittsburgh Supercomputing Center (PSC, Carnegie Mellon University and University of Pittsburgh), San Diego Supercomputing Center (SDSC, University of California San Diego), and Texas Advanced Computing Center (TACC, University of Texas at Austin), as well as 15 other partner organizations.

For the next five years, in pursuit of its overall goals of enhancing user productivity and broadening participation of the CDS&E community, XSEDE 2 will provide an adaptive and streamlined framework that anticipates the opportunities afforded by advances in technology, responds to users’ abilities to make effective use of new capabilities, and enables the current and next generation in using these technologies to advance their fields. The three strategic goals remain unchanged from the original XSEDE project:

* To deepen and extend use of the ecosystem of national cyberinfrastructure (CI) by both existing computational researchers and new communities of scientists and students where the use of computation and large-scale data is transforming their respective fields;

* To advance the national CI ecosystem by creating an open and evolving infrastructure, and by enhancing the array of technical expertise and support services offered; and

* To sustain the national CI ecosystem by maintaining a secure, reliable and efficient infrastructure.

XSEDE 2 will reorganize into five goal-driven focus areas that will provide a more agile and responsive program designed to accelerate progress toward the strategic goals:

* The Resource Allocation Service (RAS), led by the National Center for Atmospheric Research (NCAR) and four other partners, will continue to manage the process of receiving, evaluating and awarding proposals for computational resources. In doing so, it will fulfill XSEDE 2’s crucial role of neutral arbiter in allocating resources from the service-provider ecosystem to the research community. RAS will also identify new opportunities for allocation innovations by increased transparency, open reporting of user trends, and adapting the allocation process to new technologies.

* The revised XSEDE Community Infrastructure (XCI) service, led by Cornell University and six other partners, will identify, evaluate, test, and make available new software capabilities. Governance is in place to ensure that these activities are driven by the needs of both users and providers of cyberinfrastructure.

* Community Engagement & Enrichment (CEE), led by the University of Texas and 12 other partners, will build on the XSEDE tradition of outstanding user services, and engage a new generation of diverse computational researchers. In addition to education, training, and outreach activities, CEE will connect to campus HPC communities, to help researchers access both local and national resources.

* The Extended Collaborative Support Service (ECSS), led by the San Diego Supercomputer Center, the Pittsburgh Supercomputing Center and eight other partners, will maximize the effectiveness of HPC resources through its large staff of computational experts who will directly participate in research teams, providing advanced assistance to science projects.

* Finally, XSEDE Operations, led by the University of Tennessee and five other partners, will maintain and evolve an integrated HPC capability of national scale. Operations provides a “one-stop-shop” experience for users across the XSEDE-coordinated HPC ecosystem.

While continuity in providing these services is essential for the large and further-growing user community, XSEDE 2 will also respond to the evolving needs and opportunities of science and technology. To this end, XSEDE 2 will develop novel ways to connect to and collaborate with other national, regional and campus cyberinfrastructure organizations. The project will continue to innovate the use of “e-science portals” (also known as Science Gateways). Science gateways provide interfaces and services that are customized to a domain science and have an increasing role with facilities and research centers, collaborating on large research undertakings (e.g., Advanced LIGO, Polar Geospatial Center). This approach facilitates broad community access to advanced compute and data resources. Science gateways are now serving more than 50% of the user community. XSEDE 2 will also incorporate new methods to serve users interested in cloud computing resources and big-data projects. Furthermore, by analyzing trends in usage and technology, the renewal project will be even better positioned to respond to the evolving needs of its stakeholders and to emerging opportunities in new compute and data resources.

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