Unleash your inner (data) scientist : The ability and audacity to scale your science with extensible cyberinfrastructure Nirav Merchant The University of Arizona & iPlant Collaborative

Topic Coverage The “Big Data” and “Data Scientist” wave What is cyberinfrastructure (CI) Delivering pragmatic CI ecosystem What has the community built with our CI Lifecycle of research and innovation Continuing education and learning with CI Future thoughts and challenges

Science Paradigms 1. Thousand years ago: science was empirical describing natural phenomena, observations 2. Last few hundred years: theoretical branch using models, generalizations 3. Last few decades: a computational branch simulating complex phenomena 4. Today: data exploration (eScience) unify theory, experiment, and simulation 3 Based on the transcript of a talk given by the late Jim Gray to the National Research Council – Computer Science and Telecommunication Board in Mountain View, CA, on January 11, 2007

The Fourth Paradigm: Data-Intensive Scientific Discovery Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies. 4

The Discovery Lifecycle 5 The Fourth Paradigm: Data-Intensive Scientific Discovery

Evolution of X-Info The evolution of X-Info and Comp-X for each discipline X e.g. (Bio-Informatics, Computational-Biology) How to codify and represent our knowledge The Generic Problems: 6 How to share it with others Query and Vis tools Building and executing models Integrating data and literature Documenting experiments Curation and long-term preservation Data ingest Managing a petabyte Common schema How to organize it How to reorganize it The Fourth Paradigm: Data-Intensive Scientific Discovery

7 Classic paradigm: You produce data, analyze, interpret (end to end) Conventional paradigm: Consortium/centers produce data and you consume it New Paradigm: Consortium/centers have produced data and creating “cyber infrastructure” to tackle the “grand challenge” Paradigm Shift

8 ∧ big

Real Cost of Sequencing (2011) The real cost of sequencing: higher than you think Genome Biol. 2011; 12(8): 125The real cost of sequencing: higher than you think

Big Data Extracting meaningful results from vast amount of data (linked data) Big data “information assets” demand cost-effective, innovative forms of information processing for enhanced insight and decision making. “Big Data” Is only the Beginning of Extreme Information Management Not NewBig Data Technology, all Is Not New 10 Attributed to Gartner Consulting

A few word about “Big Data” and “Data Science” The 2014 Gartner Technology Hype-Cycle

12 + = Simple Formula for Success

The Reality Excel, R PERL Python ARCGIS Java Ruby Fortran C C# C++ Matlab etc. Excel, R PERL Python ARCGIS Java Ruby Fortran C C# C++ Matlab etc. Amazon Azure Rackspace Campus HPC XSEDE Etc. Amazon Azure Rackspace Campus HPC XSEDE Etc. and lots of glue…..

+ = Simple Formula

The relevance Bioinformatics has become too central to biology to be left to specialist bioinformaticians. Biologists are all bioinformaticians now - Lincoln Stein Dec

iPlant Collaborative: Vision Enable life science researchers and educators to use and extend cyberinfrastructure

The iPlant Collaborative We are a Cyberinfrastructure Platforms, tools, datasets Storage and compute Training and support

The iPlant Collaborative And a virtual organization Developer Expertise Computational Capacity Science Domain Expertise Training Administrative and Organization

Facilitating the 4A’s of “Computational Thinking” approaches for Life Sciences: Abstraction, Automation, Ability and Audacity Allowing researchers and educators to establish and manage data driven collaborations: Supporting distributed teams and virtual organizations (VO) at global scale Making efficient and coordinated use of CI resources from national, regional, institutional and commercial providers: NSF XSEDE, iPlant, campus HPC and high bandwidth connections to commercial cloud providers Adopting best practices from science domains where key CI challenges have been solved: Astronomy, Particle Physics etc. Community driven, self-provisioning, extensible and open source: Development and prioritization driven through community engagement, active engagement with CISE communities iPlant Collaborative: CI for Scalable Science

iPlant Collaborative: Platform Philosophy Strive to provide the CI Lego blocks Danish 'leg godt' - 'play well’ Also translates as 'I put together' in Latin If desired functionality is not available, the community can craft their own by using and extending iPlant CI components (like lego blocks) Through these extensible and customized platforms create a ecosystem of interoperable tools that benefit the broad community (and not few lab groups) Provide the tools to allow community to manage their digital assets (cloud, HPC etc.) Improve Computational Productivity

Who did we build it for ?

iPlant: Platform for Big Data Collaborations

Ready to use Platforms Foundational Capabilities Established CI Components Extensible Services Ease of use iPlant Collaborative: Products

iPlant: Cohesive Platform for Big Data lifecycle

Researchers like to share ! User Statistics ~27000 user accounts 4900 users with data 2600 users (53% of users with data) made at least 1 share 2100 shares per user 42 million files (58% shared) 59 million (1.1 million/month) shares Community Data Statistics 5 million files 55 million (1.0 million/month) shares ~1.1PB of User Managed data Our users consume 5M+ SU annually and more (we graduate them to compete for their own allocations from XSEDE)

How is it being used ? User build their own systems (powered by iPlant components) but managed by them Consume specific components (a la carte, data store, Atmosphere) Directly use applications (DE) Custom design appliances (Atmosphere) Publish their findings (PNAS, Nature) Advocate use Create learning material and courses

Many 1000’s omes project manage their data & analysis Execute large scale workflows (25-50TB data, Million+ CPU hours) Data infrastructure to coordinate digitization efforts for multiple sites Sharing, Visualizing (3D) & Analyzing high resolution microscopy images (40K x 40K) via web browser Learning material, new course work, custom applications iPlant CI: What is the community building ?

And it goes way beyond plants and life science

Partnership with Software Carpentry and Data Carpentry to provide best practices necessary to make efficient use of CI Allowing individual researchers and educators to utilize data and computational infrastructure at scale (and encounter real challenges) Community contributed material (built on iPlant CI) iPlant Collaborative: Training data scientists

Applied Cyberinfrastructure Concepts (ACIC) Semester long project based learning course: introduces fundamental concepts, tools and resources for effectively managing common tasks associated with analyzing large datasets. Graduate + Undergraduate course working on a REAL research workflows where scalability is a bottleneck Provide familiarity with cyberinfrastrucutre (CI) resources available at the University of Arizona campus, iPlant Collaborative, NSF XSEDE centers, Cloud (Future Grid and commercial providers such as Amazon). Learning to apply relevant CI skills (for final project) and developing wiki based documentation of these best practices. Learning how to effectively collaborate in interdisciplinary team settings. Deliver a functional solution to the stakeholder

From research question to reality

Why is it valuable ? Users are able to over come data and computational bottle necks Share data of ANY size with ANYONE Connect data and compute on single platform Manage their data and computations regardless of scale Build their own apps and solutions (create their own community iAnimal, iVirome) Create custom appliances

Even the tech geeks notice

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Luck favors the brave Analysis favors the organized