TCC Executive Summary 2017

BD2K: Big Data to Knowledge

TCC Executive Summary 2017

Table of Contents

About the BD2K Training Coordinating Center

1

Scope of the TCC

1

Educational Resource Discovery Index (ERuDIte)

2

BD2K Center Interactions and Training Activities

7

Big Data U Website

7

BD2K Guide to the Fundaments of Data Science

8

Data Science RoAD-Trip

8

Big Data Mini Movie(s)

9

Data Science Innovation Lab

9

Imagining Tomorrow’s University

10

The Stevens Neuroimaging and Informatics Institute

11

Information Sciences Institute (ISI)

11

USC School of Cinematic Arts

11

Georgetown University

11

BD2K TCC Team

12

About the BD2K Training Coordinating Center

The National Institutes of Health’s (NIH) Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) exists to equip current and future generations of biomedical researchers with tools to navigate the rapidly evolving and increasingly complex ecosystem of data science. As ever-larger quantities of data are obtained and generated by diverse domains ranging from genomics to health informatics to environmental research, powerful new methods have emerged for confronting critical research questions through the exploration, preparation, analysis, visualization and interpretation of Big Data. The TCC is a multifaceted program with key projects including a personalized online educational platform, science rotations, indexing of BD2K Big Data resources, evaluations, community outreach, and coordinated training with BD2K partners through the BD2K Centers-Coordination Center (BD2KCCC). These projects are building a diverse network of data scientists and researchers with the ability to prepare, analyze, and interpret Big Data and are training new generations of scientists to use data accurately, efficiently, and effectively.

Training Coordination

Indexing Training Materials

▪▪ U54, R25, T15/32, K01 ▪▪ Program Interactions ▪▪ Working Groups –– Core Competencies –– Resource Discovery –– Diversity –– Career Paths

▪▪ “ERuDIte” ▪▪ “Knowledge map” ▪▪ Personalized Training ▪▪ Curriculum construction ▪▪ Training “Workflows”

Webpage

Data Science RoAD-Trip

▪▪ BigDataU.org –– Innovation Lab –– RoAD-Trip –– TCC News –– Training Events –– Data Sci Seminars Scoping Workshop

▪▪ Matching young biomedical researchers with senior quantitative scientists ▪▪ Fund two-week intensive residencies

Innovation Lab

▪▪ Craft the theme for the Innovation Lab

▪▪ 30 participants and 6 mentors

▪▪ In close association with NIH and NSF Program Officials

▪▪ Background in biomedical or quantitative

Scope of the TCC

Public Outreach ▪▪ Facebook ▪▪ Twitter ▪▪ Public Calendar ▪▪ Mailing Lists ▪▪ USC School of Cinematic Arts National Science Foundation ▪▪ Innovation Lab Travel support for quantitative scientists –– Mathematicians –– Statisticians –– Computer Science

Career Paths ▪▪ University of Illinois, Urbana-Champaign ▪▪ Bring university leadership together to discuss the shifting notion of data science in higher education International Collaborataions ▪▪ On training, held at USC, including –– TCC –– Elixir –– H3Africa –– NIH and NSF –– Others

U24 Grant

Educational Resource Discovery Index (ERuDIte) ERuDIte is the Educational Resource Discovery Index technology which powers the BD2K Training Coordinating Center (TCC) Web Portal. ERuDIte not only serves as a resource collector and aggregator but also as system powered

by Machine Learning, Information Retrieval, and Natural Language Processing that intelligently organizes resources to provide a dynamic and personalized curriculum for biomedical researchers interested in learning about Data Science (http:// www.bigdatau.org/explore_erudite). In the context of this document, biomedical researchers are the intended audience of ERuDIte and the TCC Web Portal, and consequently they will be addressed as users or learners.

As an important research initiative in itself, ERuDIte aims to:

Training Coordinating Center 1 U24 ES026465-01 Big Data U: Empowering Modern Biomedicine via Personalized Training Van Horn, John Darrell jvanhorn@usc.edu University Of Southern California bigdatau.org

• Systematically identify, store and synthesize large volumes of relevant educational resources in a scalable fashion • Maintain a detailed schema that aligns with other resource collection initiatives (e.g. the EU-based ELIXIR) to promote the sharing of training-related metadata • Serve high-quality, up-to-date educational content to the biomedical community (and research community at large) which not only teaches Data Science concepts but also supports the practical application of such concepts into specific analysis tasks • Aid learners in navigating the vast number of resources pertaining to Data Science through semi-automatic tagging and prerequisite identification • Provide an individualized learning path through recommendations tailored to learners’ interests, experience, and progress over time ERuDIte provides the most relevant and up-to-date Data Science educational resources to its users. ERuDIte provides navigable knowledge maps of educational concepts and resources. It employs the notion of Concept Maps - a graph of concepts (tags) within Data Science and their relations to each other. These hierarchical concepts are used to annotate the training resources, and facilitate search and browsing. This enables ERuDIte to form and provide Prerequisites Maps – that is training resource dependency graphs which gives learners a sense of what concepts and knowledge must be mastered before learning others. To do this, ERuDIte provides a unified schema and index of resources which enables streamlined data collection, storage, and faceted search of educational resources. In turn, this permits collaboration with learning resource developers to facilitate addition of new resources or new collections of resources. ERuDIte incorporates learner feedback by allowing learners to rate, review, and comment on learning resources, as well as allows learners to tag resources on their own, thereby enriching ERuDIte’s reach. ERuDIte provides personalized curricula to learners, keeping a personal learning history for its users, and makes recommendations on new resources based on those similar learners liked (collaborative recommendation). In other words, ERuDIte recommends resources based on learner’s history and needs. Finally, ERuDIte evolves over time by automatically searching for new learning resources, using topic modeling methods to categorize new learning resources, thereby, growing the ERuDIte Knowledge Map to allow for new topics in the future.

To accomplish these objectives, ERuDIte has multiple components responsible for the resource to ERuDIte to TCC Web Portal pipeline. These include: • Resource Identification Component: collects links to relevant resources and gathers any available data for the resources • Resource Integration Component: unifies data from heterogeneous resources and conforms them to a standard schema

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• Resource Database: stores resource data, making it available for the Resource Organization Engine, Curation Interface, TCC Web Portal, and Resource Personalization Engine • Resource Organization Engine: automatically assigns tags, identifies prerequisites, and evaluates resource depth and uses curator data from the Curation Interface and user data TCC User Database to improve its algorithms • Curation Interface: tool for curators to validate organization data and assess resource quality • TCC Web Portal: presents ERuDIte data and collects learner activity and progress.

• TCC User Database: stores user data, including (but not limited to) learner profile data and usage activity, and informs the Resource Personalization Engine and the Resource Organization Engine • Resource Personalization Engine: synthesizes user activity, resource tags and prerequisites, and resource similarity measurements to recommend resources to learners

The Current Contents of ERuDIte As of February 12, 2017, the ERuDIte database contains over 8,625 individual educational resources.

This includes of 255 from the NIH BD2K initiative alone with more being added on a regular basis.

Also included are the complete educational content available through the ELIXIR Training eScience Support System (TeSS) project (https://tess.elixireurope.org/) based in Europe. Other resources have been indexed from major sources such as Carnegie Mellon University, Videolectures.net, the Josef Stefan Institute, Microsoft Research, Stanford University, YouTube, the Max Planck Institute, MIT, UC Berkeley, University College London, edX, Coursera, and Udacity. These include video lectures and print content covering themes such as machine learning, knowledge representation, natural language processing, artificial intelligence, image data analysis, supervised/unsupervised learning, graph-based analytics, information retrieval, kernel methods, topics in bioinformatics, and a variety of MOOC-based course content. All in all, this content is presented by 8317 distinct instructors and from across 8573 resource providers.

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The The Current Current Contents of ERuDIte

ERuDIte Statistics

View the the current current contnets contents of the ERuDIte database by selecting the fields below. As of View February 2017, 2017, the the ERuDIte ERuDIte database contains over 8,625 individual educational resources.

February This content content includes includes video video lectures and print content presented by 8317 distinct instructors This and from from across across 8573 8573 resource resource providers throughout the world. and

Click below for details Most Common Fields Providers

Resources

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Total: 8,624

Top Ten Providers

114 Videolectures.net 92 Carnegie Mellon University 84 JoĹžef Stefan Institute

Machine Learning

82 Microsoft Research

2680 Resources

64 Stanford University 52 UC Berkeley 46 Cornell University 44 Max Planck Institute 44 University College London 35 MIT

Knowledge Representation 1559 Resources

Top ten providers Natural Language Processing 682 Resources

Artificial Intelligence 620 Resources

Data Science Process 522 Resources

Computer Science 471 Resources

Big Data 440 Resources

Unsupervised Learning 385 Resources

Graph Analytics 365 Resources

Information Retrieval 327 Resources

ERuDIte Resource Schema Overview The schema of the Resource Database captures the details of each Data Science Learning Resource domain, providing a template of the data that the Resource Identification Component used to describe each resource and to enable faceted search (or elastic search). In developing the schema, we collected and reviewed existing standards: Dublin Core, Learning Resource Metadata Initiative (LRMI), IEEE’s Learning Object Metadata (LOM), eXchanging Course Related Information (XCRI), XML Schema Definition (XSD), Metadata for Learning Opportunities (MLO), and Schema.org. Furthermore, we collaborated with BioSchemas, a European initiative that includes eight organizations that focuses on the development of a cross- institution metadata standard for educational resources, to design and implement a metadata standard that optimizes data exchange across many resource collection and development initiatives. This collaboration resulted in a standard that builds heavily on the one established by Schema.org, a common standard used by many different educational content providers, including Coursera, Udacity, EdX, and other major MOOC platforms. ERuDIte also includes into our schema video resources from YouTube and Vimeo as well as content from the EU-based ELIXIR project.

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Total Providers 1593

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Cornell University

Providers

126 Resources

Total: 8,573

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The Resource Organization Engine The Resource Organization Engine utilizes algorithms from Machine Learning, Information Retrieval and Natural Language Processing to automatically provide initial tag assignments, prerequisites, and depth levels. To ensure that the organizational data is correct, the Curation Interface introduces human validation to the ERuDIte system. Validation not only guarantees that the resource organizational data on the TCC Web Portal are correct but also provides ground truth data that will improve the algorithms used by the Resource Organization Engine. Furthermore, curators give the final signoff on whether or not a resource should appear on the TCC website, providing feedback to the Resource Identification Component on where it should (or should not) look for content.

The ERuDIte Knowledge Map The ERuDIte Knowledge Map provides an interactive exploration through a hierarchy of concepts, allowing learners to see parent-child and sibling relationships between concepts. The ontology powering the ERuDIte Knowledge Map consists of seven semi-orthogonal dimensions and was constructed with automatic resource classification in mind. In fact, the Resource Organization Engine uses the ERuDIte Knowledge Map’s vocabulary and relationships to perform automated resource tagging, and the Resource Personalization Engine utilizes the tags for recommendation. As a result, learners can combine a resource’s tags with the concepts in the ERuDIte Knowledge Map to understand how a resource compares to other resources with similar or different tag assignments and how it fits into the Data Science field or their current knowledge.

In addition, the concepts of the ERuDIte Knowledge Map form the facets for faceted search (using tools such as ElasticSearch), allowing learners to explore search results based on broader and narrower concepts in each knowledge dimension. The second layer of nodes in the ERuDIte Knowledge Map represents each of the broader dimensions, which are: • Data Science Process - the steps in a data science project • Datatype – the types of data addressed • Field – the field of study • Programming Tool – the programming tool used or taught • Level of Focus – the depth of pedagogical coverage • Presentation Format – how a resource is presented

Prerequisite Mapping The Prerequisites Map provides a stricter relationship between concepts and between resources. Specifically, the Prerequisites Map illustrates the depends-on relationship identified by the Resource Organization Engine, giving learners clear paths from learning the fundamentals of Data Science to applying technical skills and concepts to a practical research task or project. Like the concepts of the ERuDIte Knowledge Map, Prerequisites, both at the concept and resource level, influence recommendations made to the learner.

Resource Personalization Engine Importantly, user are able to gather selected educational and training resources into collections, or “educational plans”, along any theme that is of interest to them. Users can create as many plans as they desire, color code their contents, organize them, and share them with colleagues, associates, and other ERuDIte users.

Moreover, ERuDIte is able to leverage this information to make recommendations via it’s the Resource Personalization Engine. This makes recommendations of additional resources to individual users based on: • Resource tags or text similarity • Other learners’ resource navigation patterns • Resources that similar learners found useful (collaborative filtering) • Resources that a learner has viewed, completed, rated, or reviewed • The prerequisites of a resource for which that a learner has conveyed interest Thus, users can explore the expanse of ERuDIte resources while receiving recommendations on other content which might be relevant to them. Through this process, users are able to maximize their personalized biomedical data science training and education.

Interactive ERuDIte Knowledge Map

Big Data U Website http://bigdatau.org/ With research-related Big Data expanding so rapidly, novel tools and techniques arise from myriad sources spanning multiple disciplines and domains, and information regarding the proper implementation of these tools is spread across hundreds of educational, research and training program websites. The need for centralized knowledge resources is clear. The Big Data U Knowledge Map and Educational Resource Discovery Index (ERuDIte) address this need by automating the process of identifying, aggregating, indexing, modeling, and incorporating online resources into a comprehensive, personalized learning platform. Leveraging extensive experience and infrastructure, and built with reference to existing platforms (e.g. ELIXIR-TeSS), these resources draw upon knowledge from higher education, cognitive science, curriculum development and program evaluation to create a comprehensive “go to� Big Data training resource that allows users to easily identify and engage with didactically significant resources aligned with their experience, preparation, specific scientific domain, and personal learning needs.

BD2K Center Interactions and Training Activities The TCC collaborates with many centers to help facilitate training activities. One example of this is the collaboration between the TCC and the BD2K Centers-Coordination Center (BD2KCCC) with bioCADDIE to link instances of data discovery with educational discovery and vice versa. In addition, the TCC holds meetings (virtual or inperson) with each BD2K Training grantee and BD2K Centers of Excellence. To help facilitate collaborations and involvement, the TCC holds monthly training working group conference calls. These working groups include topics such as Core Competencies, Resource Discovery, Diversity, and Career paths.

BD2K Guide to the Fundaments of Data Science Working with the BD2K Centers-Coordination Center (BD2KCCC) and the NIH Office of Data Science, we are spearheading a virtual lecture series. Beginning in September 2016, the Series will consist of regularly scheduled weekly webinar courses covering the fundamentals of data science including data management, representation, computation, and data modeling and inference. The Series will provide essential training suitable for individuals at all levels of the biomedical community. All video presentations from the Series will be streamed for live viewing, recorded, and posted online for future viewing and reference. These videos will also be indexed as part of ERuDIte and shared/mirrored with the BD2KCCC and with other BD2K resources.

Data Science RoAD-Trip The TCC will coordinate and fund a two-week intensive residency, Data Science Rotations for Advancing Discovery (Data Science RoAD-Trip), in which selected junior investigators “take to the road� and collaborate with prospective senior data science mentors at one of many eligible research universities across the United States, working together to model, analyze, and extract new knowledge from large scale datasets.

Data Science Innovation Lab 2017 microbiome

2016 mHealth

The Innovation Lab series is a 5-day residential workshop supported by the National Institutes of Health and the National Science Foundation, and facilitated by Knowinnovation (http:// knowinnovation.com). The primary goal is to foster new interdisciplinary collaborations that will generate creative strategies for addressing challenges associated with the visualization, modeling, and analysis of large datasets generated by biomedical research. The first workshop of the series was held from June 14-19, 2016 at Lake Arrowhead Conference Center in California, which brought together 29 junior faculties and 5 senior mentors with expertise in mathematical, statistical, and biomedical fields to discuss topics of growing interest in the field of Mobile Health (mHealth) Informatics, such as the effective use of wearables or ambient sensors. The second workshop of this series, to be held from June 19-23, 2017 at the Wylie Inn and Conference Center, California will be using a similar approach to invite quantitative and biomedical researchers to address the challenges in our understanding of the human microbiome by utilizing their fresh perspective, aided by real-time mentoring.

Big Data Mini Movie(s) One aspect of the community outreach project is the Big Data mini movie(s) project. These movies, produced in collaboration with the USC School of Cinematic Arts, focus on what Big Data is and address questions like, How big is “big”? The movies showcase key examples and explore big data’s implications for the future of biomedical research, including the ways new techniques will contribute to the personalization of medicine.

Imagining Tomorrow’s University: Rethinking Scholarship, Education, and Institutions for an Open, Networked Era March 8 (evening) - March 9 (day), 2017 Big 10 Conference Center, Rosemont, IL In the 21st Century, research is increasingly data- and computation-driven. Partly due to this trend, researchers, funders, and the larger community today emphasize openness and reproducibility.

“Imagining Tomorrow’s University” is an NIH BD2K and NSF-funded one-day, invitation-only workshop where researchers who practice open science and key university administrators will come together to start a new dialog.

The research world has changed, and the university needs to change too—but how? How should it adapt its structure, mission, infrastructure, education, recruitment plans, etc.? Do we need new educational programs? New disciplines or new departments? How can universities recognize the value in new types of research outputs, such as software and data? Does research staffing need to change? Do research data engineers or research software engineers have a place? What are different measures of success for faculty active in open science/open research?

The crucial question is: how do universities make themselves competitive to attract the best students, staff, and faculty in this new world? The workshop participants are summoned to reimagine scholarship, education, and institutions for an open, networked era. As a result, university leaders will be awakened to new opportunities to create value and serve society.

The output of this workshop is envisioned to be a set of principles for how universities can thrive in the new world. http://www.ncsa.illinois.edu/Conferences/ImagineU/

UPCOMING An NIH BD2K and NSF-funded oneday, invitation-only workshop

March 8-9

Imagining Tomorrow’s University Rosemont, IL

The Stevens Neuroimaging and Informatics Institute The Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) which includes The Laboratory of Neuro Imaging (LONI), The Imaging Genetics Center (IGC), and The Center for Image Acquisition (CIA) is dedicated to understanding the structure and function of the human brain in all its staggering complexity—using imaging, genetics, behavioral and other measures to study the brain’s structure, function, development and change in both health and disease. During three decades of global advances in neuroimaging, the institute has amassed the world’s largest repository of images of healthy and diseased brains, as well as medical and genetic data from diverse populations around the globe. LONI is a leader in the design, development and wide dissemination of advanced informatics and software tools for the comprehensive mapping of brain structure and function across space, time, and scale.

Information Sciences Institute (ISI) The ISI is a world leader in research and development of advanced information processing, computer and communications technologies. A unit of the University of Southern California’s highly ranked Viterbi School of Engineering, ISI is one of the nation’s largest, most successful university-affiliated computer research institutes. The Institute attracts nearly $60 million annually for basic and applied research from federal agencies and the private sector. The work at ISI ranges from theoretical basic research, such as core engineering and computer science discovery, to applied research and development, such as design and modeling of innovative prototypes and devices. For the TCC, members of ISI provide essential skill in information technology, data management, and information retrieval and modeling.

USC School of Cinematic Arts Since its founding, the School of Cinematic Arts has had a profound impact on feature and independent film, television, film studies, animation, documentaries, commercials, and most recently, interactive media. With the school’s launch, the University of Southern California became the first university in the country to offer a Bachelor of Arts degree in film. The school continues to build upon its legacy by redefining its curriculum to fit the changing spectrum of media arts and technology and strives to lead the way in every aspect of motion picture and television production, education and scholarly study. Working with TCC team members based at USC Cinema, we are working to produce a number of informational videos which describe Big Data and underscore its importance for the future of biomedicine.

Georgetown University Established in 1789, Georgetown University is one of the world’s leading academic and research institutions - a vibrant community of exceptional students, faculty, alumni and professionals dedicated to real-world applications of research, scholarship, faith and service. Georgetown has grown to be a major international research university with eight schools, an affiliated hospital, and many highly ranked academic programs. Georgetown continues to move forward as one of the world’s leading universities, building upon its distinctive history, unique values and commitment to justice and the common good.

Members of the TCC team based at Georgetown provide unique leadership and expertise in the monitoring and assessment of Big Data educational programs.

John Darrell Van Horn, M.Eng., Ph.D. PrincipaI Investigator, USC INI

BD2K TCC Team USC Stevens Neuroimaging and Informatics Institute (INI)

Jeana Kamdar, MPH Project Manager, USC INI

USC Information Sciences Institute (ISI) Rochelle Tractenberg, Ph.D. Co-Investigator, GU

USC School of Cinematic Arts (Cinema) Georgetown University (GU)

Jose-Luis Ambite, Ph.D. Co-Investigator, USC ISI

Sumiko Abe, Ph.D. Research Programmer, USC INI Krithika Ramaswamy, M.Sc. Computer Science, Web Developer, USC INI

Avnish Bhattrai, M.S., M.Sc. Project Assistant USC INI

Caroline O’Driscoll, M.S. Medical Illustrator, USC INI

Gully Burns, D.Phil Data Scientist USC ISI

XiaoXiao Lei, M.S., Research Programmer, USC INI Crystal Stewart, MA Training Coordinator, USC INI

Michael Taylor, BA USC Cinema

Carmen Tan, BS Web Developer, USC INI

John Berardo, MFA USC Cinema

Lily Fierro, BS Data Analyst, USC ISI

Jonathan Gordon, Ph.D. Postdoctoral Researcher, USC ISI

Shobhit Agarwal, M.Sc. Computer Science, Web Developer, USC INI

Kristina Lerman, Ph.D. CoInvestigator, USC ISI

USC Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California 2025 Zonal Avenue Los Angeles, CA 90033 Phone: (323) 44-BRAIN (442-7246)

For more information visit our websites

USC Cinematic Arts

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http://bigdatau.org

USC Laboratory of Neuro Imaging (LONI) http://loni.usc.edu/

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https://www. georgetown.edu/

Funded by National Institutes of Health

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http://www.isi.edu/home

Georgetown University

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USC Information Sciences Institute

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https://cinema.usc.edu/

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