Software and Education at NSF/ACI


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A talk about NSF/ACI programs and example projects in cyberinfrastructure software and education at the CyberGIS'14 conference

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Software and Education at NSF/ACI

  1. 1. Software and Education at NSF/ACI Daniel S. Katz Program Director, CISE/ACI
  2. 2. Big Science and Infrastructure • Hurricanes affect humans • Multi-physics: atmosphere, ocean, coast, vegetation, soil – Sensors and data as inputs • Humans: what have they built, where are they, what will they do – Data and models as inputs • Infrastructure: – Urgent/scheduled processing, workflows – Software applications, workflows – Networks – Decision-support systems, visualization – Data storage, interoperability
  3. 3. Long-tail Science and Infrastructure • Exploding data volumes & powerful simulation methods mean that more researchers need advanced infrastructure • Such “long-tail” researchers cannot afford expensive expertise and unique infrastructure • Challenge: Outsource and/or automate time-consuming common processes – Tools, e.g., Globus Online and data management • Note: much LHC data is moved by Globus GridFTP, e.g., May/ June 2012, >20 PB, >20M files – Gateways, e.g., nanoHUB, CIPRES, access to scientific simulation software NSF grant size, 2007. (“Dark data in the long tail of science”, B. Heidorn)
  4. 4. Cyberinfrastructure (e-Research) • “Cyberinfrastructure consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible.” -- Craig Stewart • Infrastructure elements: – parts of an infrastructure, – developed by individuals and groups, – international, – developed for a purpose, – used by a community
  5. 5. Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21) • Cross-NSF portfolio of activities to provide integrated cyber resources that will enable new multidisciplinary research opportunities in all science and engineering fields by leveraging ongoing investments and using common approaches and components ( • ACCI task force reports ( – Campus Bridging, Cyberlearning & Workforce Development, Data & Visualization, Grand Challenges, HPC, Software for Science & Engineering – Included recommendation for NSF-wide CDS&E program • Vision and Strategy Reports – ACI - – Software - – Data - • Implementation – Implementation of Software Vision
  6. 6. Infrastructure Role & Lifecycle Create and maintain a software ecosystem providing new capabilities that advance and accelerate scientific inquiry at unprecedented complexity and scale Support the foundational research necessary to continue to efficiently advance scientific software Enable transformative, interdisciplinary, collaborative, science and engineering research and education through the use of advanced software and services Transform practice through new policies for software addressing challenges of academic culture, open dissemination and use, reproducibility and trust, curation, sustainability, governance, citation, stewardship, and attribution of software authorship Develop a next generation diverse workforce of scientists and engineers equipped with essential skills to use and develop software, with software and services used in both the research and education process
  7. 7. Learning and Workforce Development CI-focused Workforce as Cyberinfrastructure Cyber Scientists to develop new capabilities CI-enabled Area Scientists to exploit new capabilities CI-skilled Professional Staff to support new capabilities CI
  8. 8. Example: Interactions in Understanding the Universe (I2U2) • An "educational virtual organization" to strengthen education and outreach activities of scientific experiments at U.S. universities and laboratories (CMS, Cosmic Rays, LIGO) • Creates and maintains infrastructure and common fabric to develop hands-on laboratory course content and provide interactive learning experience bringing tangible aspects of experiments into an accessible "virtual laboratory” – e-Labs: for classrooms, using web tools – i-Labs: for museums, using physical interactive interfaces • Collaboration of scientists, computer scientists and educators to grow and sustain scientific workforce, and to promote public appreciation of and support for the complex collaborations of our national scientific programs •
  9. 9. Example: Data Science for the Social Good (DSSG) • Eric & Wendy Schmidt Data Science for Social Good fellowship is a University of Chicago summer program for aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact • 48 undergrad and grad students come to Chicago, work in small-teams with governments and non-profits, led by full-time mentors, to tackle real-world problems in education, health, energy, transportation, etc. •
  10. 10. Common Elements of Examples • Bring together students and educators/teachers/mentors • Use/build tools (preexisting for U2I2, some preexisting, some novel for DSSG) • Have impact for students, and ideally for science/society
  11. 11. What’s needed • Education • Training • Materials • Tools • Funding/Organization • Inspiration/Motivation
  12. 12. Curricula Activities (CS) • Parallel Computing – TCPP model curriculum • – Others also exploring this space • E.g., new curriculum at CMU - – Intel Academic Community • • Distributed Computing – NSF Workshop: Designing Tools and Curricula for Undergraduate Courses in Distributed Systems • Parallel and Distributed Computing – ACM/IEEE-CS Computer Science Curricula 2013 • Common elements that can apply outside CS – Concerned faculty come together – Work through needed changes (not just additions) – Work with early adopters – Update and expand
  13. 13. HPC University (HPCU) • A virtual organization • Goal: to provide cohesive, persistent, and sustainable on-line environment to share educational and training materials for continuum of HPC environments from desktops to the highest-end facilities • Resources to guide researchers, educators and students to – Choose successful paths for HPC learning and workforce development – Contribute high-quality and pedagogically effective materials that allow individuals at all levels and in all fields of study to advance scientific discovery • Actively seeks participation from all parts of HPC community to: – Assess the learning and workforce development needs and requirements of the community – Catalog, disseminate and promote peer-reviewed and persistent HPC resources – Develop new content to fill the gaps to address community needs – Broaden access by a larger and more diverse community via a variety of delivery methods – Pursue other activities as needed to address community needs •
  14. 14. Repository: National Science Digital Library •
  15. 15. The role of training • There’s a lot of computer/computing training available • Synchronous and asynchronous • Often led by centers, and universities with computational research programs (XSEDE, DOE, etc.) • Also supported by organizations such as Shodor Foundation, • Some aimed at users, others at educators (both K-12 and university) • Some is fairly specific computational science (e.g., molecular dynamics), but other is fairly general (parallel programming) • OSG offers training for grid computing, but mostly for actually running or using OSG or HTDC systems (Condor) - https:// – E.g. security for users, security for admins – Also see, and U Wisconsin and U Nebraska classes and curricula based on their experience with and collaboration with OSG
  16. 16. Software Carpentry • Helps researchers be more productive by teaching them basic computing skills • Runs boot camps at dozens of sites around the world, and also provides open access material online for self-paced instruction – Introduction to Unix shell; introduce pipes, loops, history, and the idea of scripting – Introduction to Python, to building components that can be used in pipelines, and to when and why to break code into reusable functions. – Version control for file sharing, collaboration, and reproducibility. – Testing (both the mechanics and the use of tests to define problems more precisely). – An introduction to either databases or NumPy, depending on the audience
  17. 17. Software Carpentry Model • Good expansion model – Initial instructors still are active, but new instructors come from those trained who want to share what they have learned, using the material provided • Material is all open for use and improvement • In some sense, Software Carpentry has become the coordinators between those who want to learn and those who want to teach •
  18. 18. Argonne Training Program on Extreme-Scale Computing (ATPESC) • Two-week program for computational scientists • Provides intensive hands-on training on the key skills, approaches, and tools to design, implement, and execute computational science and engineering applications on current high-end computing systems and the leadership-class computing systems of the future • As a bridge to that future, this program fills the gap that exists in the training computational scientists typically receive through formal education or other shorter courses.
  19. 19. How can NSF help (if you are in the US) • Research Experiences for Undergraduates (REU) • Division of Undergraduate Education (EHR/DUE) • Cyberlearning • NSF Research Traineeship (NRT) • Support of workshops • Support undergraduate travel to conferences
  20. 20. Research Experiences for Undergraduates - Supplements • Goals: expand student participation in all kinds of research; attract diversified pool of talented students into careers in science and engineering; help ensure that they receive the best education possible • REU supplements typically provides support for 1-2 undergrads to participate in research, can be more for large projects • Mentoring is important; project should develop students' research skills, involve them in the culture of research in the discipline, and connect their research experience with their overall course of study • Support for undergrads involved in carrying out research should be included as part of the research proposal rather than as a post-award supplement, unless it was not foreseeable at the time of the original proposal • REU supplement requests are handled by the NSF program officer for the underlying research grant
  21. 21. Research Experiences for Undergraduates - Sites • REU Sites host a summer cohort of undergraduates for a structured research-learning experience – Vision: Extend research participation to students who would otherwise lack such opportunities • At least 50% from institutions other than the host • At least 50% from schools with limited STEM research opportunities • Outreach to underrepresented groups is a plus – Implementation: Create empowering cohort experience that promotes STEM engagement • Coherent intellectual focus to research topics • Research mentoring and support • Professional development, grad school prep • Cohort building, networking opportunities, social events
  22. 22. Education and Human Resources / Undergraduate Education (DUE) • DUE goals include: – Support Curriculum Development • Stimulate and support research on learning. • Promote development of exemplary materials and strategies for education. • Support model assessment programs and practices. • Effect broad dissemination of effective pedagogy and materials. • Enable long-term sustainability of effective activities. – Prepare the Workforce • Promote technological, quantitative, and scientific literacy. • Support an increase in diversity, size, and quality of the next generation of STEM professionals who enter the workforce with two- or four-year degrees or who continue their studies in graduate and professional schools. • Invest in the nation's future K-12 teacher workforce. • Fund research to evaluate and improve workforce initiatives. •
  23. 23. Cyberlearning and Future Learning Technologies NSF 14-526 • Vision: – New technologies will transform learning opportunities, interests, and outcomes in all phases of life, making it possible for learning to be tailored to individuals and groups – Best technological genres and socio-technical systems designed for these purposes will be informed by how people learn – Can make progress in understanding learning, moving toward predictive computational models of individual and group learning • Aims: – Learning how to design and effectively use the learning technologies of the future (Future Learning Technologies) – Understanding processes involved in learning when learners can have experiences that only technology allows (Cyberlearning) • Every project addresses and connects 3 thrusts: – Innovation – Advancing understanding of how people learn in technology-rich learning environments – Promoting generalizability and transferability of new genres
  24. 24. NSF Research Traineeship (NRT) NSF14-548 • To develop bold, new, potentially transformative, and scalable models for STEM graduate training • Ensure that graduate students develop the skills, knowledge, and competencies needed to pursue a range of STEM careers • 1 initial priority research theme - Data-Enabled Science and Engineering (DESE) - but other crosscutting, interdisciplinary themes are also allowed, aligned with national research priorities • Emphasizes the development of competencies for both research and research-related careers • Creation of sustainable programmatic capacity at institutions is an expected outcome. • Replaces IGERT
  25. 25. Other events • Grace Hopper Celebration of Women in Computing – World’s largest gathering of technical women in computing – Place where technical women gather to network, find or be mentors, create collaborative proposals, and increase the visibility of women’s contributions to computing • Tapia Celebration of Diversity in Computing – Brings together undergraduate and graduate students, faculty, researchers, and professionals in computing from all backgrounds and ethnicities • Both aim to promote their attendees work and increase networking & mentoring • Both can be inspirational for undergraduates!!
  26. 26. Learning and Workforce Development CI-focused Workforce as Cyberinfrastructure Cyber Scientists to develop new capabilities CI-enabled Area Scientists to exploit new capabilities CI-skilled Professional Staff to support new capabilities CI
  27. 27. Conclusions • Lots of demand for trained staff and users – Those who have the need are trying to provide training to fill that need (pull) • Seems to be lots of demand for educated developers, staff, users – In general, the burden for filling this need seems to be on the traditional academic system (push) – Software Carpentry as an exception? • In CS, lots of people teaching – Starting to share experiences and lessons learned – Moving towards some consensus on what to teach or how to teach it? • Cyberinfrastructure is a common point where big science, long-tail science, and education meet; it has a dual role – Used to train/educate – Needs to be refreshed by trained/educated developers • Lots of opportunities exist! – Form a community – Decide what needs to be done – Find the right opportunity