April 2016

MAY 2016

THE FEDERAL BIG DATA

RESEARCH AND DEVELOPMENT

STRATEGIC PLAN

THE NETWORKING AND INFORMATION 

TECHNOLOGY RESEARCH AND 

DEVELOPMENT PROGRAM 
About this Document

This report was developed by the Big Data Senior Steering Group (SSG). The Big Data SSG reports to the 

Subcommittee on Networking and Information Technology Research and Development (NITRD). The 

report is published by the Executive Office of the President, National Science and Technology Council.

About the Subcommittee on Networking and Information Technology Research and 

Development

The Subcommittee on Networking and Information Technology Research and Development is a body 

under the Committee on Technology (CoT) of the National Science and Technology Council (NSTC). The 

NITRD Subcommittee coordinates multiagency research and development programs to help assure 

continued U.S. leadership in networking and information technology, satisfy the needs of the Federal 

Government for advanced networking and information technology, and accelerate development and 

deployment of advanced networking and information technology. It also implements relevant provisions 

of the High-Performance Computing Act of 1991 (P.L. 102-194), as amended by the Next Generation 

Internet Research Act of 1998 (P. L. 105-305), and the America Creating Opportunities to Meaningfully 

Promote Excellence in Technology, Education and Science (COMPETES) Act of 2007 (P.L. 110-69). For 

more information, see www.nitrd.gov.

About the NITRD Big Data Senior Steering Group

The Big Data SSG was formed in 2011 to identify current Big Data research and development activities 

across the Federal Government, offer opportunities for coordination, and identify what the goal of a 

national initiative in this area would look like. Subsequently, in March 2012, The White House Big Data 

R&D Initiative was launched and the Big Data SSG continues to facilitate and further the goals of the 

Initiative.

Acknowledgments

This document was developed through the contributions of the NITRD Big Data SSG members and staff. 

A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson 

(NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith 

Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE 

Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF).

Copyright Information

This is a work of the U.S. Government and is in the public domain. It may be freely distributed, copied, 

and translated; acknowledgment of publication by the National Coordination Office for Networking and 

Information Technology Research and Development (NITRD/NCO) is appreciated. Any translation should 

include a disclaimer that the accuracy of the translation is the responsibility of the translator and not the 

NITRD/NCO. It is requested that a copy of any translation be sent to the NITRD/NCO.
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

Subcommittee

on

Networking and Information Technology Research and Development

James Kurose, Co-Chair, NSF

Keith Marzullo, Co-Chair, NCO

NITRD Member Agencies

Department of Commerce

National Institute of Standards and Technology (NIST)

National Oceanic and Atmospheric Administration (NOAA)

Department of Defense

Defense Advanced Research Projects Agency (DARPA)

National Security Agency (NSA)

Office of the Secretary of Defense (OSD)

Service Research Organizations (Air Force, Army, Navy)

Department of Energy

National Nuclear Security Administration (NNSA)

Office of Electricity Delivery and Energy Reliability (OE)

Office of Science (SC)

Department of Health and Human Services

Agency for Healthcare Research and Quality (AHRQ)

National Institutes of Health (NIH)

Office of the National Coordinator for Health Information Technology (ONC)

Independent Agencies

Department of Homeland Security (DHS)

Environmental Protection Agency (EPA)

National Aeronautics and Space Administration (NASA)

National Archives and Records Administration (NARA)

National Reconnaissance Office (NRO)

National Science Foundation (NSF)

Office of Management and Budget (OMB)

Office of Science and Technology Policy (OSTP)

National Coordination Office for 

Networking and Information Technology Research and Development (NITRD/NCO)
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

Big Data Senior Steering Group 

Co-Chairs

Chaitanya Baru

Senior Advisor for Data Science

National Science Foundation

Allen Dearry

Associate Director for Research Coordination, 

Planning, and Translation

National Institute of Environmental Health Sciences 

(NIEHS)

National Institutes of Health (NIH)

Staff

Wendy Wigen

Technical Coordinator, Big Data Senior Steering 

Group

National Coordination Office for Networking and 

Information Technology Research and Development

Members

Marc Allen

Deputy Associate Administrator for Research

Science Mission Directorate

National Aeronautical and Space Administration 

(NASA)

Laura Biven

Senior Science and Technology Advisor

Office of the Deputy Director for Science Programs

Department of Energy (DOE)

Robert J. Bonneau

Office of the Assistant Secretary of Defense for 

Research and Engineering

Office of the Secretary of Defense

Stephen Dennis

Program Manager

Science and Technology Directorate

Department of Homeland Security (DHS)

Alan Hall

CLASS Operations Manager/ Enterprise Architect

National Oceanic and Atmospheric Administration 

(NOAA)

Thuc Hoang

Program Manager

Office of Advanced Simulation and Computing

National Nuclear Security Administration (NNSA)

Department of Energy (DOE)

Suzanne Iacono

Acting Office Head

Office of Integrative Activities (OIA)

National Science Foundation (NSF)

James Keiser

Technical Director, Laboratory for Analytic Sciences

National Security Agency (NSA)

John Launchbury 

Office Director

Information Innovation Office

Defense Advanced Research Projects Agency 

(DARPA)

James St. Pierre

Deputy Director

Information Technology Laboratory

National Institute of Standards and Technology 

(NIST)

James Szykman

Environmental Engineer

Environmental Sciences Division

Environmental Protection Agency (EPA)

Participants

Sky Bristol

Applied Earth Systems Informatics Research 

Manager

United States Geological Survey (USGS)

Department of the Interior (DOI)

Mark Peterson

Division Chief for Data and Analytics

United States Agency for International Development 

(USAID) 
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

Contents

Executive Summary ...............................................................................................................................................1

Introduction...........................................................................................................................................................4

Strategies...............................................................................................................................................................6

Strategy 1: Create next-generation capabilities by leveraging emerging Big Data foundations, techniques, 

and technologies.............................................................................................................................6

Scale Up to Keep Pace with the Size, Speed, and Complexity of Data .................................................................6

Develop New Methods to Enable Future Big Data Capabilities...........................................................................8

Strategy 2: Support R&D to explore and understand trustworthiness of data and resulting knowledge, to 

make better decisions, enable breakthrough discoveries, and take confident action.................... 11

Understand the Trustworthiness of Data and Validity of Knowledge................................................................11

Design Tools to Support Data-Driven Decision-Making .....................................................................................14

Strategy 3: Build and enhance research cyberinfrastructure that enables Big Data innovation in support of 

agency missions ............................................................................................................................ 16

Strengthen the National Data Infrastructure .....................................................................................................16

Empower Advanced Scientific Cyberinfrastructure for Big Data .......................................................................17

Address Community Needs with Flexible and Diverse Infrastructure Resources..............................................19

Strategy 4: Increase the value of data through policies that promote sharing and management of data ........ 20

Develop Best Practices for Metadata to Increase Data Transparency and Utility .............................................20

Provide Efficient, Sustainable, and Secure Access to Data Assets .....................................................................22

Strategy 5: Understand Big Data collection, sharing, and use with regard to privacy, security, and ethics...... 24

Provide Equitable Privacy Protections ...............................................................................................................24

Enable a Secure Big Data Cyberspace ................................................................................................................26

Understand Ethics for Sound Data Governance.................................................................................................27

Strategy 6: Improve the national landscape for Big Data education and training to fulfill increasing demand 

for both deep analytical talent and analytical capacity for the broader workforce........................ 29

Continue Growing the Cadre of Data Scientists.................................................................................................29

Expand the Community of Data-Empowered Domain Experts..........................................................................30

Broaden the Data-Capable Workforce...............................................................................................................32

Improve the Public’s Data Literacy.....................................................................................................................32

Strategy 7: Create and enhance connections in the national Big Data innovation ecosystem ......................... 34

Encourage Cross-Sector, Cross-Agency Big Data Collaborations .......................................................................34

Promote Policies and Frameworks for Faster Responses and Measurable Impacts..........................................34

Acronyms............................................................................................................................................................. 36

References........................................................................................................................................................... 37
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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Executive Summary

A national Big Data1innovation ecosystem is essential to enabling knowledge discovery from and 

confident action informed by the vast resource of new and diverse datasets that are rapidly becoming 

available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all 

Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable 

everything from predicting the future behavior of complex systems to precise medical treatments, smart 

energy usage, and focused educational curricula. Government agency research and public-private 

partnerships, together with the education and training of future data scientists, will enable applications 

that directly benefit society and the economy of the Nation.

To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a

“Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to 

the President for Science and Technology and Director of the Office of Science and Technology Policy, 

stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, 

environmental and biomedical research, education, and national security.”

The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and 

excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies 

as they develop and expand their individual mission-driven programs and investments related to Big 

Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared 

vision:

We envision a Big Data innovation ecosystem in which the ability to analyze, extract 

information from, and make decisions and discoveries based upon large, diverse, and realtime datasets enables new capabilities for Federal agencies and the Nation at large; 

accelerates the process of scientific discovery and innovation; leads to new fields of research 

and new areas of inquiry that would otherwise be impossible; educates the next generation 

of 21st century scientists and engineers; and promotes new economic growth.3

The Plan is built around seven strategies that represent key areas of importance for Big Data research 

and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can 

be addressed by the missions and research funding of NITRD agencies. These include advancing human 

understanding in all branches of science, medicine, and security; ensuring the Nation’s continued 

leadership in research and development; and enhancing the Nation’s ability to address pressing societal 

and environmental issues facing the Nation and the world through research and development.

Strategy 1: Create next-generation capabilities by leveraging emerging Big Data foundations, 

techniques, and technologies. Continued, increasing investments in the next generation of large-scale 

data collection, management, and analysis will allow agencies to adapt to and manage the everincreasing scales of data being generated, and leverage the data to create fundamentally new services 

and capabilities. Advances in computing and data analytics will provide new abstractions to deal with 

complex data, and simplify programming of scalable and parallel systems while achieving maximal 

performance. Fundamental advances in computer science, machine learning, and statistics will enable 

future data-analytics systems that are flexible, responsive, and predictive. Innovations in deep learning 

will be needed to create knowledge bases of interconnected information from unstructured data. 

Research into social computing such as crowdsourcing, citizen science, and collective distributed tasks 

will help develop techniques to enable humans to mediate tasks that may be beyond the scope of 
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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computers. New techniques and methods for interacting with and visualizing data will enhance the 

“human-data” interface.

Strategy 2: Support R&D to explore and understand trustworthiness of data and resulting knowledge,

to make better decisions, enable breakthrough discoveries, and take confident action. To ensure the 

trustworthiness of information and knowledge derived from Big Data, appropriate methods and 

quantification approaches are needed to capture uncertainty in data as well as to ensure reproducibility 

and replicability of results. This is especially important when data is repurposed for a use different than 

the one for which the data was originally collected, and when data is integrated from multiple, 

heterogeneous sources of different quality. Techniques and tools are needed to promote transparency 

in data-driven decision making, including tools that provide detailed audits of the decision-making 

process to show, for example, the steps that led to a specific action. Research is needed on metadata 

frameworks to support trustworthiness of data, including recording the context and semantics of the 

data, which may evolve over time. Interpreting the results from analyses to decide upon appropriate 

courses of action may require human involvement. Interdisciplinary research is needed in the use of 

machine learning in data-driven decision making and discovery systems to examine how data can be 

used to best support and enhance human judgment. 

Strategy 3: Build and enhance research cyberinfrastructure that enables Big Data innovation in 

support of agency missions. Investment in advanced research cyberinfrastructure is essential in order to 

keep pace with the growth in data, stay globally competitive in cutting-edge scientific research, and 

fulfill agency missions. A coordinated national strategy is needed to identify the needs and requirements 

for secure, advanced cyberinfrastructure to support handling and analyzing the vast amounts of data, 

including large numbers of real-time data streams from the Internet of Things (IoT), available for 

applications in commerce, science, defense, and other areas with Federal agency involvement—all while 

preserving and protecting individual privacy. Shared benchmarks, standards, and metrics will be 

essential for a well-functioning cyberinfrastructure ecosystem. Participatory design is necessary to 

optimize the usefulness and minimize the consequences of the infrastructure for all stakeholders. 

Education and training to build human capacity is also critical: users must be properly educated and 

trained to fully utilize the tools available to them. 

Strategy 4: Increase the value of data through policies that promote sharing and management of 

data. More data must be made available and accessible on a sustained basis to maximize value and 

impact. The scale and heterogeneity of Big Data present significant challenges in data sharing.

Encouraging data sharing, including sharing of source data, interfaces, metadata, and standards, and 

encouraging interoperability of associated infrastructure, improves the accessibility and value of existing 

data, and enhances the ability to perform new analyses on combined datasets. Building upon the 

current state of best practices and standards for data sharing, as well as developing new technologies to 

improve discoverability, usability, and transferability for data sharing, will enable more effective use of 

resources for future development. Research is necessary at the “human-data” interface to support the 

development of flexible, efficient, and usable data interfaces to fit the specific needs of different user 

groups. Federal agencies that provide R&D funding can assist through policies to incentivize the Big Data 

and data science research communities to provide comprehensive documentation on their analysis 

workflows and related data, driven by metadata standards and annotation systems. Such efforts will 

encourage greater data reuse and provide a greater return on research investments. 

Strategy 5: Understand Big Data collection, sharing, and use with regard to privacy, security, and

ethics. Privacy, security, and ethical concerns are key considerations in the Big Data innovation 

ecosystem. Privacy concerns affect how information is viewed and managed by data collectors and data 

providers; security concerns about personal information demand attention to data protection; and 
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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ethical concerns about the possibilities of data analyses leading to discriminatory practices have 

reignited civil rights debates. Research in Big Data is necessary to understand and address the variety of 

needs and demands of different application domains to achieve practical solutions to challenges in data 

privacy, security, and ethics. New policy solutions may be necessary to protect privacy and clarify data 

ownership. Techniques and tools are needed to help assess data security, and to secure data, in the 

highly distributed networks that are becoming increasingly common in Big Data application scenarios. 

The ability to perform comprehensive evaluations of data lifecycles is necessary to determine the longterm risk of retaining, or removing datasets. Additionally, the Nation must promote ethics in Big Data by 

ensuring that technologies do not propagate errors or disadvantage certain groups, either explicitly or 

implicitly. Efforts to explore ethics-sensitive Big Data research would enable stakeholders to better 

consider values and societal ethics of Big Data innovation alongside utility, risk, and cost.

Strategy 6: Improve the national landscape for Big Data education and training to fulfill increasing 

demand for both deep analytical talent and analytical capacity for the broader workforce. A 

comprehensive education strategy is essential to meet increasing workforce demands in Big Data and 

ensure that the United States remains economically competitive. Efforts are needed to determine the 

core educational requirements of data scientists, and investments are needed to support the next 

generation of data scientists and increase the number of data-science faculty and researchers. As 

scientific research becomes richer in data, domain scientists need access to opportunities to further 

their data-science skills, including projects that foster collaborations with data scientists, data-science 

short courses, and initiatives to supplement training through seed grants, professional-development 

stipends, and fellowships. In addition, employees and managers in all sectors need access to training 

“boot camps,” professional-development workshops, and certificate programs to learn the relevance of 

Big Data to their organizations. More university courses on foundational topics and other short-term 

training modules are also necessary to help transform the broader workforce into data-enabled citizens. 

Data-science training should extend to all people through online courses, citizen-science projects, and K12 education. Research in data-science education should explore the notion of data literacy, curricular 

models for providing data literacy, and the data-science skills to be taught at various grade levels.

Strategy 7: Create and enhance connections in the national Big Data innovation ecosystem. Persistent 

mechanisms should be established to increase the ability of agencies to partner in Big Data R&D both by

removing the bureaucratic hurdles for technology and data sharing and by building sustainable 

programs. One such possible mechanism is the creation of cross-agency development sandboxes or 

testbeds to help agencies collaborate on new technologies and convert R&D output into innovative and

useful capabilities. Another is the development of policies to allow for rapid and dynamic sharing of data 

across agency boundaries in response to urgent priorities, such as national disasters. A third is the 

formation of Big Data “benchmarking centers” that focus on grand challenge applications and help 

determine the datasets, analysis tools, and interoperability requirements necessary in achieving key 

national priority goals. And, finally, a national Big Data innovation ecosystem needs a strong community 

of practitioners across Federal agencies to facilitate rapid innovation, ensure long-term propagation of 

ideas, and provide maximal return on research investments.
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Introduction

The Federal Big Data Research and Development Strategic Plan (Plan) defines a set of interrelated 

strategies for Federal agencies that conduct or sponsor R&D in data sciences, data-intensive 

applications, and large-scale data management and analysis. These strategies support a national Big 

Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions 

and discoveries based on large, diverse, and real-time datasets enables new capabilities for both Federal 

agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to 

new fields of research and new areas of inquiry that would otherwise be impossible; educates the next 

generation of 21st century scientists and engineers; and promotes new economic growth. 

In March 2012, the Obama Administration announced the Big Data Research and Development 

Initiative4to leverage the fast-growing volumes of digital data to help solve some of the Nation’s most 

pressing challenges. The Initiative calls for increasing government support and R&D investment to 

accelerate the Federal agencies’ ability to draw insights from large and complex collections of digital 

data. To augment Federal agency activities, the Administration reached out to other Big Data 

stakeholders in private industry, academia, state and local governments, and nonprofits and 

foundations to collaborate on new Big Data innovation projects. In November 2013, dozens of public 

and private organizations gathered at an event, “Data to Knowledge to Action,”5sponsored by the White 

House’s OSTP and the NITRD Program. Together, public and private partners announced an inspiring 

array of new projects that address such national priorities as economic development, healthcare, energy

sustainability, public safety, and national security. 

In 2014, the NITRD Big Data Senior Steering Group (SSG) initiated a process to summarize findings and 

produce a coordinated Big Data R&D agenda. Through a series of internal workshops, NITRD agency 

representatives examined a range of game-changing ideas with the potential to drive Big Data

innovations. The Big Data SSG then synthesized the body of ideas and information into a cross-agency 

framework. Public comment on this framework was solicited in a Request for Information and a 

workshop was convened at Georgetown University to engage non-government Big Data experts and 

stakeholders. This document is the result of these efforts.

A primary objective of this document is to outline the key Big Data R&D strategies necessary to keep the 

Nation competitive in data science and innovation and to prepare for the data-intensive challenges of 

tomorrow. As a strategic plan, this document provides guidance for Federal agencies and policymakers 

in determining how to direct limited resources into activities that have the greatest potential to 

generate the greatest impact. The Plan profiles R&D areas that span multiple disciplines, surfacing 

intersections of common interest that could stimulate collaboration among researchers and technical 

experts in government, private industry, and academia. The Plan also offers ideas for decision makers to 

consider when deliberating about investments in Big Data in their respective domains. Additionally, this 

Plan is the Big Data SSG’s response to Recommendation 11c of the 2015 review of NITRD by the 

President’s Council of Advisors on Science and Technology (PCAST)6to “coordinate a process to publish 

and publicly discuss periodically a research and coordination plan for its area of interest.”

The Plan is built around the following seven strategies that represent key areas of importance for Big 

Data research and development (R&D):

 Strategy 1: Create next-generation capabilities by leveraging emerging Big Data foundations, 

techniques, and technologies.
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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 Strategy 2: Support R&D to explore and understand trustworthiness of data and resulting 

knowledge, to make better decisions, enable breakthrough discoveries, and take confident action.

 Strategy 3: Build and enhance research cyberinfrastructure that enables Big Data innovation in 

support of agency missions. 

 Strategy 4: Increase the value of data through policies that promote sharing and management of 

data.

 Strategy 5: Understand Big Data collection, sharing, and use with regard to privacy, security, and 

ethics.

 Strategy 6: Improve the national landscape for Big Data education and training to fulfill increasing 

demand for both deep analytical talent and analytical capacity for the broader workforce.

 Strategy 7: Create and enhance connections in the national Big Data innovation ecosystem.
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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Strategies

Strategy 1: Create next-generation capabilities by leveraging emerging Big Data 

foundations, techniques, and technologies

As Big Data technologies mature, society will increasingly rely on data-driven science to lead to new 

discoveries and data-driven decision making as the basis of confident action. To address new challenges 

in Big Data, there should be continuous and increasing investments in research on technologies for 

large-scale data collection, management, analysis, and the conversion of data-to-knowledge-to-action;

and on the privacy, security, and ethical issues of Big Data. In the past, Federal investments in 

foundational research in computer science—encompassing topics ranging from computer architecture 

and networking technologies to algorithms, data management, artificial intelligence, machine learning, 

and development and deployment of advanced cyberinfrastructure—have served as major drivers of the

Nation’s successes in scientific discovery, Internet commerce, and national security. R&D investments by 

NITRD agencies resulted in the creation of the Internet that in turn enables today’s generation of Big 

Data. NITRD agency-funded research in algorithms, such as PageRank7 and FastBit,

8

resulted in the 

creation of robust indexing and search engine capabilities. Most recently, the discovery of the Higgs 

boson was enabled by the development of algorithms to identify complex signals from petabytes of 

data.9

Scale Up to Keep Pace with the Size, Speed, and Complexity of Data

Big Data encompasses a range of data scenarios—from large and rapid data streams to highly 

distributed and heterogeneous data-collection networks. Big Data contexts may require highperformance and complex processing of data, and very large warehouses and archives for data storage.

As highlighted by the National Strategic Computing Initiative,

10 there is a need to scale up computing 

systems to deal with the sizes, rates, and extreme syntactic (format) as well as semantic (meaning) 

heterogeneity of such data. Further, for human users, the overall system must provide highly 

interactive, easy-to-use interfaces to allow human users to be “in the loop” to control the system, as 

well as to use the information and knowledge products generated by it.

Many NITRD agencies are tasked with the development and maintenance of major scientific 

experiments, observations, and simulations that generate unprecedented volumes of data. Scientists 

increasingly want to integrate these datasets to facilitate discovery. Dedicated networks are needed to 

transport large volumes of scientific data generated at experimental facilities (such as the Large Hadron 

Collider at CERN or the new Linac Coherent Light Source at SLAC National Accelerator Laboratory) to 

distant and, in some cases, distributed computing resources for analysis. Data-management bottlenecks 

can occur at almost every stage of the scientific workflow including capturing data from an experimental 

or computing facility, transporting it for further analysis, and analyzing and visualizing the data, as well 

as finding appropriate environments for sharing data.

A range of computer system architectures are required to serve the wide range of applications 

requirements—from tightly interconnected systems to more loosely coupled, distributed systems. Large 

system configurations, high-speed network interconnects, deep memory hierarchies, and high 

performance storage systems will be required in order to process large-scale and high-speed data

interactively. These systems must be resilient and autonomic to deal with hardware and software faults 

and failures. New abstractions will be necessary to simplify the challenges of programming such systems
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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and exploiting parallelism for scheduling computation, communication, and output for interactive as 

well as batch-oriented Big Data applications. The proper set of abstractions must be provided to enable 

applications to specify their resource requirements and execute efficiently in an environment with 

shared resources.

Naive scaling of current tools and techniques will not be sufficient as Big Data applications confront and 

supersede hard limits in, for example, input/output data rates from computing systems, or the amount 

of data that a human can perceive or understand, even using visualization. Biases may need to be 

introduced for the sake of tractability, requiring fundamentally new techniques and understanding.

Along with convergence in architectural approaches, there are opportunities for coordination and 

collaborations between computational science (the Third Paradigm) and data science (the Fourth 

Paradigm). In many scenarios, complex computational models are validated via evaluation-driven 

research programs where Big Data is collected and analytics are measured in experimental settings. 

Conversely, many Big Data problems lead, eventually, to the creation and execution of computational 

models. Many techniques, tools, and approaches can be shared between both communities, especially if 

investments in measurement science yield metrics and evaluation frameworks that are generalizable 

across the many challenges that exist between computational and data science. What emerges is a 

fundamentally new workflow for scientific discovery where simulation and experimental data are 

inextricably linked. Advances in computation and data analysis need to be coordinated.11

Big Data application scenarios are typically characterized by large-scale system configurations—for 

example, within a datacenter, across widely distributed datacenters, or across the IoT. With rapid 

changes in hardware and software technologies, and evolving applications needs and requirements, the 

notion of Software Defined Environments (SDE) or Software Defined Infrastructure (SDI) becomes 

important within the context of such large configurations. Different types of applications, or different 

phases within a given application, may require different configurations among various system 

components. In many cases, Big Data applications may execute within a cloud environment where the 

cloud provider provides a generic system that can be customized for a particular computation using SDE. 

In other scenarios, when the cost of moving data is too high and latencies become a major roadblock, 

future infrastructures may help move the computation to the data. National platforms, such as the 

Global Environment for Network Innovations (GENI)12 must continue to provide the large-scale 

experimental testbeds to carry out such research. In addition, public-private, national-state level 

partnerships, such as US Ignite,13 are necessary to foster novel applications and digital experiences.

Big Data applications must deal with data from multiple sources that may be heterogeneous in a variety 

of ways, such as the syntax and semantics of the data, the quality of the data, and the policy regime 

under which the data was produced and by which it can be used. Core technical infrastructure to enable 

representation of semantic information is a key next-generation capability for Big Data.

A core capability for enabling such applications is a semantic information infrastructure to enable easier 

discovery of relevant data and integration across related datasets. Scalable approaches are needed to 

tackle the full scope of this problem. Techniques employed can range from automated machine-learning 

algorithms to human-in-the-loop approaches, including crowdsourcing methods. One of the critical 

technology components is named entity identification, to assist in transforming unstructured data to 

structured data. New directions for research are opening up in this area as well as in data quality, which 

is being explored by a number of agencies.
FEDERAL BIG DATA RESEARCH AND DEVELOPMENT STRATEGIC PLAN

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Image courtesy of the Homeland Security Advanced 

Research Projects Agency.

DHS Homeland Security Advanced Research Projects Agency (HSARPA) 

CONNECTED, PROTECTED, AND FULLY AWARE

Thick smoke, scorching heat, and blaring alarm 

bells fill the building. Over 60 pounds of protective 

gear and countless hours of training help support 

our Nation’s firefighters, but timely and accurate 

situational awareness or “scene size-up” remains a 

challenge on every call. 

The Department of Homeland Security (DHS) 

operates the National Fire Incident Report System 

(NFIRS) to gather and analyze information on the 

Nation's fires. Despite today’s education, research, 

and training efforts, fires kill over 3,000 people and 

injure over 17,000 people nationwide each year. 

Annual property loss due to fire approaches $12 

billion. Most of these fatalities, injuries, and losses 

are preventable. 

In partnership with the Federal Emergency Management Agency (FEMA) and the US Fire 

Administration (USFA), HSARPA developed an analytical prototype and worked with four regional 

fire departments to explore ~225 million NFIRS incidents at the national, state, and regional levels. 

Big Data technologies such as geospatial and graph analytics were used to identify trends and 

patterns about incident types, equipment failures, and firefighter casualties, delivering new insights 

on how to improve training and reduce losses.

The IoT is a rapidly emerging source of Big Data. Estimates are that by 2018 over half of Internet traffic 

will originate not from computers but from devices.

14 The devices in this category include sensors of all 

types, mobile phones, and other consumer and industrial electronic devices. Increasingly, instrumented 

systems or environments are becoming the norm in science and engineering scenarios. Federal agencyfunded scientific research is pioneering new approaches, such as the National Ecological Observatory 

Network (NEON),

15 that are deploying large numbers of heterogeneous sensors and sensor networks 

that will collect large amounts of heterogeneous data. New tools are needed to unify and organize this 

information into human- and machine-readable summaries in a timely fashion. Many of these 

environments are characterized as cyber-physical systems because they seamlessly integrate 

computational algorithms and physical components. New technologies for handling the breadth and 

scope of data from IoT will be essential for many future Big Data applications.

Develop New Methods to Enable Future Big Data Capabilities

Cutting-edge data management, querying, and analysis techniques in computer science must be linked 

with fundamental approaches in statistics and machine learning to create data systems that are flexible, 

responsive, and predictive. Computer-science techniques need to incorporate more statistical 

approaches, while statistical techniques need to develop approaches for trading off statistical power 

and computational complexity. This merging of computer science and statistical techniques will usher in

a “Smart Data” era with enormous opportunities for new applications. However, the scalability of 

statistical methods also poses a major challenge. When data becomes big, the possible number of 

simultaneous hypotheses, as well as data points, can be on the order of millions.

16 Robust statistical 

algorithms may not run within an acceptable time frame, forcing users to rely on less sophisticated and 

more error-prone algorithms. Integration of statistical inference principles as part of Big Data will be 

essential to resolve these challenges.
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Illustration courtesy of the Defense Advanced Research Projects Agency.

A suite of tools and best practices is also needed for real-time statistical inference using data streams.

These tools and best practices should process data-quality information, be scalable, and be able to 

support a broad range of application areas, such as security, bioinformatics, consumer behavior, climate, 

civic infrastructure, and demographics.

A large class of new Big Data applications is required to deal with diverse sources and forms of data, 

ranging from highly structured to unstructured data. Data-driven model development is a key approach 

to extracting structure and meaning from Big Data. Machine-learning techniques are essential to this 

endeavor. Research is needed in deep learning methods that can add identification and predictive 

power to data and algorithms. The structure of the human brain, based on studies from the BRAIN 

Initiative,

17 may itself provide new insights and inspiration for a new generation of neural network 

algorithms and computing architectures, and lead to research in areas such as neuromorphic computing.

DOD Defense Advanced Research Projects Agency (DARPA) 

MAKING SENSE OUT OF THE COMPLEX

Some of the systems that 

matter most to us are very 

complicated. Ecosystems, 

the brain, social systems, and 

the economy have many 

parts and processes. These 

processes, however, are 

often studied piecewise, and 

the literature and data on 

them can be fragmented, 

distributed, and inconsistent.

Although the collection of 

Big Data is increasingly 

automated, the creation of 

big mechanisms (that is, the 

full explanations of

complicated systems), 

remains a human endeavor.

DARPA’s Big Mechanism 

program aims to speed up the scientific research process by enabling machines to read, synthesize, 

and reason about complicated systems. The goal of the program is to develop technologies for a 

new kind of science in which research is integrated immediately into causal, explanatory models of 

unprecedented completeness and consistency.

The first challenge taken on by Big Mechanism researchers is cancer biology. The program is 

working on machine reading of scientific papers to identify molecular interactions in signaling 

pathways, modeling languages to integrate fragments of knowledge into large models, and 

algorithms to identify possible drug targets that show promise. Tools developed by the Big 

Mechanism program may enable a new kind of scholarship, in which scientists model and 

understand entire systems, not just system components.

While automated techniques can greatly improve productivity, humans continue to perform some tasks 

better, such as in data identification, curation, and categorization. Systems for human-aided 

computation can range from the computational tools that put scientists and experts “in the loop” to 

expansive platforms for crowdsourcing and citizen science. These “social computing” systems employ 

various methods of engagement, including social media, peer production, crowdsourcing, and collective 

distributed tasks. In many citizen-science projects, such as Galaxy Zoo18 and Foldit,19 and collaboration 

experiments like the DARPA Network Challenge, human volunteers provided insights and discoveries 
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Image courtesy of Los Alamos National Laboratory.

that expert analysis missed. The 2013 PCAST review of NITRD identified social computing as an area ripe 

for further attention and investment, particularly noting the potential of mobilizing citizens to address 

national priorities in health, public safety, and science.

20 Incorporating human workers (volunteer or 

paid) into data-processing workflows could augment analysis capabilities and benefit many applications.

DOE Advanced Scientific Computing Research program (ASCR) 

INTERACTIVE EXPLORATION OF ENORMOUS DATA

“A picture is worth a thousand words,” but 

how do you picture a billion data values 

computed by a simulation? The idea is 

daunting. Nevertheless, the In Situ Big Data 

Visualization of Scientific Climate Data project,

funded by DOE ASCR, aims to understand the 

impact of climate change on the Nation’s 

power infrastructure (e.g., power plants). To 

achieve this ambitious goal, scientists need the 

next generation of climate simulations and 

exascale supercomputers. 

Current practices for analyzing simulation data 

rely on moving the data from the supercomputer that runs simulations to another computing 

environment for manipulation and analysis. In the era of exascale computing, such practices will 

not be feasible because of the vastly larger datasets involved and the very limited bandwidth and 

storage for moving and saving data. This project will use “in situ” analysis, which will allow scientists 

to process the data before it is moved, thereby transporting and storing only the data that provides

new insights and discoveries. 

The In Situ Big Data Visualization of Scientific Climate Data project brings together an in situ 

workflow for data reduction and a visualization tool called ParaView. The combination will allow 

researchers to interactively explore simulations and extract meaningful information from datasets 

that would otherwise be inscrutable. These new tools and techniques will help scientists visualize, 

analyze, and understand the potential impacts of climate on our energy production and power 

infrastructure over long periods of time and for specific regions. In addition, the tools and 

techniques are transferable to other science disciplines that face similar challenges with their data.

Given the complexity of Big Data, there are key roles for metadata, uncertainty information, and quality

visualizations to play in understanding its significance. Further research and innovation is needed to 

detect both the expected and unexpected; provide timely, defensible, and understandable assessments; 

and communicate those insights effectively. This will require multi-user, multi-stakeholder engagements 

that are equipped with the necessary collaborative environments and tools.

Many Big Data analyses begin with discovering correlations among factors, which can provide important 

insights into underlying phenomena. Research and advances in measurement science are needed in 

hypothesis generation, causal inference, and other fundamental statistical methods using Big Data. 

Progress in these areas will allow researchers to obtain better insights and recognize spurious 

correlations, which might lead to incorrect conclusions. Robust techniques are also needed for 

representing and processing data quality information.
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Strategy 2: Support R&D to explore and understand trustworthiness of data and 

resulting knowledge, to make better decisions, enable breakthrough discoveries, 

and take confident action

Traditional statistical approaches are used to handle “designed” datasets derived from controlled 

experiments or surveys. Big Data is often comprised of such designed data, but also may include data 

collected “opportunistically,” or collected for one purpose and reused for another. The data may have 

been processed through a series of incremental analyses, each for a different purpose. These 

characteristics make it challenging to provide a holistic view of the data and the uncertainty in the 

underlying information.

This major challenge in today’s Big Data landscape is due, in part, from the rise in the sharing and 

availability of data facilitated by the Internet and promoted by several U.S. Government policies. There 

is also a growing recognition among data scientists that access to relevant data is essential for building 

on previous results. The consequence is that data users are increasingly removed from the generators or 

collectors of the data they use. As the expectation for data access grows, data users will need to 

understand and assess how data can be used and whether the source is trustworthy. However, deriving 

accurate knowledge from data puts burdens on both the data disseminator and the data user. Data that 

is appropriately documented, formatted, and accompanied by complete and meaningful metadata 

facilitates confident use. Robust measures are needed to quantify uncertainty and capture context to 

ensure reproducibility of results; this will give decision makers the ability to validate the trustworthiness 

of the data and the products of analyses. Decision makers will require tools for parsing the relevant 

knowledge applicable to decisions, converting knowledge into possible action, and understanding the 

implications and impact of those actions.

Understand the Trustworthiness of Data and Validity of Knowledge 

In the Information Quality Act of 2001, the White House Office of Management and Budget (OMB) 

recognized the importance of the quality of data disseminated by Federal agencies.21 In general, 

determining overall “trustworthiness” of data is a challenging task—the definition of the term may vary 

depending upon the application scenario and use-case. In one framework employed in the social 

sciences, 22 trustworthiness is delineated into the truthfulness (i.e., internal validity or credibility), 

applicability (i.e., external validity or transferability), consistency (i.e., reliability or dependability), and 

neutrality (i.e., objectivity or confirmability) of the information. Inferences based on data would need to 

account for which of the characteristics are satisfied by the data.

The scale, heterogeneity, and rapidly changing nature of Big Data further complicate the notions of 

trustworthiness and inference. Traditional tests for validity, soundness, and significance may need to be 

modified and adapted. Quantities such as accuracy, error, precision, anomalies, authenticity, and 

uncertainty may need to be measured and tracked to enable robust data-driven decision making. 

Software may be required to track the propagation of these quantities through multi-step and iterative 

processing pipelines, complex transformations, and integration across heterogeneous data sources.

Understanding data trustworthiness is essential in order to derive accurate inferences from Big Data.

Research is needed to develop robust statistical techniques that use a wide diversity of data inputs. 

Interdisciplinary research is also needed to create state-of-the-art techniques that combine heuristics 

(trial and error) and statistics. A key differentiator of Big Data is the ex post facto discovery of uses for 

previously collected data. Another is the combining of independently collected datasets, each of which 
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may fulfill different assumptions. Interpreting the output quality of statistical tests on this type of data 

depends upon the specific type of statistical test being performed, i.e., the nature of the question being 

asked, which may vary by application. Traditional statistical tests may be insufficient. New, innovative

techniques may be needed for Big Data.

In data science, replicability is the ability to rerun the exact data experiment—with the same data 

inputs, parameter settings, and computations—to produce exactly the same result. Reproducibility is 

the ability to use different data, techniques, and/or equipment to confirm the same result as previously 

obtained. Both are fundamental to the validations of results and conclusions drawn from data. NITRD 

agencies are interested in replicability and reproducibility for science R&D applications as well as for 

decision-making applications. However, both are challenging notions to implement in a Big Data 

computing environment, where datasets can be extremely large and constantly evolving. Both a

common framework and a common understanding of these concepts are needed to help improve the 

trustworthiness of data and computed results.

NIST Information Technology Laboratory (ITL) and Materials Measurement Laboratory (MML) 

BATTLING VISION LOSS IN AMERICA

Slowly going blind is the challenge faced by 

over 7 million Americans with age-related 

macular degeneration (AMD). AMD is the 

leading cause of blindness in adults, but 

effective treatments, such as cell-based 

therapies, could offer a way to replace 

damaged eye tissue with healthy tissue and 

save the Nation $30 billion annually in lost 

GDP. However, to ensure the effectiveness of 

cell-based therapies, decisions on whether to 

implant tissues into the patient must be made 

based on trustworthy tissue images. Big Image 

Data, a joint ITL and MML project, addresses the need for high quality imaging measurements with 

a goal of achieving 10 times the quality of current medical image interpretation. This improvement 

will enable individuals with AMD to be diagnosed and to receive cell-based therapies with lower 

risks of adverse events. 

At the National Institute of Standards and Technology (NIST), the long-term goal is to provide 

validated methods for automated manufacturing and product release tests that will speed and 

improve the decision-making process. For example, the technologies developed within NIST can be 

reused in other fields such as materials reliability to promote trustworthy measurements.

Data processing is typically performed via analysis pipelines. Comprehensive tools and best practices are 

needed to ensure that existing analysis pipelines can persist into the future, and that the results can be 

replicated at some future point in time. The use of open-source software, data, and Application 

Programming Interfaces (APIs) can be key enablers in this regard. This requires contextual information,

well-defined metadata frameworks, and the ability to instantiate a past processing environment in the 

future. Tools such as the Code, Data, and Environment (CDE) package are able to overcome technical 

barriers to reproducibility by converting all software dependencies to a code that can be executed on 

Linux computers, other than the original Linux system. Containers, virtual machines, and packaging 

systems like CDE are useful tools for enabling replicability in Big Data, but these systems will need to 

evolve as technologies and analysis capabilities progress.

Researchers can be incentivized to adopt good practices by requiring reproducible research strategies as 

part of their research activity. This ensures the reliability of their analyses and allows other researchers 
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Image courtesy of the Human Immunology Project Consortium.

to derive further value from past datasets and analyses. For example, the NIH’s “Principles and 

Guidelines for Reporting Preclinical Research”23 is a prime example of guidance that agencies can 

provide to improve reproducibility within a specific domain.

In addition to creating better metadata standards for future data collection, some Federal agencies must 

also maintain legacy scientific data whose use is limited because it lacks metadata. Agencies such as 

NASA are investing in technology for “data archeology” that strives to automate the generation of 

metadata from data content. 

Learning and using proper methods and protocols for data collection, analysis, and interpretation are an 

essential starting point for data trustworthiness. This requires a well-educated workforce that can stay 

current through on-going training programs as techniques and tools evolve. This is particularly

important in the Big Data world, where data and information may have diverse origins and 

unpredictable use. Education and training in the authoring and use of metadata and, as mentioned in 

the next section, adoption of strong metadata standards, will be essential. 

NIH Center for Expanded Data Annotation and Retrieval (CEDAR) 

ESTABLISHING BETTER DATA FOR BETTER SCIENCE

Imagine a library with an incomplete 

catalog that made it impossible to 

know where a resource might be 

located, what language it is written in, 

or whether it is a video or a document. 

Similar to a library’s indexing methods, 

metadata is needed to index large 

numbers of experimental datasets so 

that they can be retrieved, reused, and 

properly attributed. The challenge is 

that creating accurate and adequate 

metadata is a tedious process.

The Center for Expanded Data Annotation and Retrieval (CEDAR) at Stanford University, funded 

through the NIH, wants to help investigators in the sciences achieve the promise of Big Data by 

making the process of metadata creation as painless as possible. CEDAR’s goal is to create a unified 

framework that all scientific disciplines can use to create consistent, easily searchable metadata 

that allows researchers to locate the datasets that they need, consolidate datasets in one location, 

integrate multiple datasets, and reproduce the results. For example, CEDAR projects like the 

Human Immunology Project Consortium (HIPC) will enable widespread, free sharing of immunology 

data. This knowledge base will serve as a foundation for the future study of a variety of 

inflammatory diseases as well as immune-mediated diseases, such as allergy, asthma, transplant 

rejection, and autoimmune diseases.

Significant efforts are needed to curate datasets—to record the context as well as semantics associated 

with the data, and with the analyses performed on the data. Effective and proper reuse of data 

demands that the data context be properly registered and that data semantics be extracted and 

represented. While automation will be essential to accomplish this for Big Data, tasks related to 

curation, context, and semantics will also require a human-in-the-loop approach. Tools and ecosystems

are needed to assist in this task, such as entity identification that utilizes global persistent identifiers and 

the use of domain ontologies for knowledge representation. Research in metadata modeling, 

automated metadata generation and registration, semantic technologies, ontologies, linked data, data 

provenance, and data citation will be important.
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Some metadata may change or evolve over time, depending on how the data is used, the presence of 

new datasets, etc. Ontologies may also evolve with the addition of new data and information. While 

well-defined metadata frameworks are essential for capturing this information, research will also need 

to take into account the evolution of such information over time. Tools may be needed to bridge data 

collections by retrofitting and migrating older metadata into schemas compatible with current and 

future collection efforts.

Design Tools to Support Data-Driven Decision-Making

From algorithmic stock trading to managing the smart electric grid, systems used today are increasingly 

automated. However, the vast majority of data-driven decision-making systems still require human 

intervention. In almost all complex decisions, humans must interpret the information generated from 

algorithms, determine the validity of the information in the given context, take policy considerations 

into account, and then decide upon an appropriate course of action. Research is needed on how 

technology can best augment human judgment in such data-driven decision-making scenarios, in order 

to better inform their choices as well as increase the speed at which trustworthy and confident decisions 

and actions can be taken.

The next generation of data-driven decision-making systems must also be adaptive and have the ability 

to integrate analysis of real-time data flows with historical data. Human-mediated decision making will 

remain essential in these complex, multifaceted situations, where information may be derived from 

multiple heterogeneous sources. User-friendly interfaces to these complex decision support systems 

and environments will be needed.

Another key requirement is to establish the provenance of data-driven decisions, with tools to make the 

decision-making process reproducible, traceable, and transparent. In order to build trust in data-driven 

systems, the tools must clearly and succinctly enumerate the steps by which conclusions were reached.

Machine-learning approaches, including deep learning systems, are also needed to build better datadriven models that can be used to reliably augment human decision making. Artificial Intelligence (AI)

research will have profound impacts on decision-making systems. Question and answer systems that 

automate answers to human-posed questions (e.g., IBM’s Watson), and systems that reason (e.g.,

DARPA’s Big Mechanism program) are examples of how AI can augment human capabilities. These 

systems are a resource for human decision makers, who can now leverage a much broader knowledge 

base as input while minimizing human biases in their output. Big Data SSG agencies recognize that 

research into the foundational areas of AI is critical to creating systems that make Big Data more 

actionable.

Given the complexity of information that will be available in the future, decision-support systems must 

also be capable of functioning in a collaborative manner with multiple agents to satisfy multiple 

objectives. Humans are often asked to make decisions that satisfy a disparate and potentially divergent 

set of end goals. Intelligent agents must be capable of operating under similar circumstances. A 

decision-making system must be able to either reconcile these criteria within a recommended course of 

action or present clear options and consequences that allow a human to understand and decide, while 

also taking into account high-level policy considerations.
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National Science Foundation (NSF) and the Treasury Office of Financial Research (OFR) 

SHINE A LIGHT INTO DARK CORNERS

The 2007-2009 financial crisis made clear that 

our understanding of the financial system was 

deficient in many respects. Market participants 

and regulators underestimated how 

disruptions could emerge and spread quickly 

across interconnected companies and markets, 

with severe consequences for the economy. As 

a result, in 2010, Congress created the Office 

of Financial Research (OFR) to serve the needs 

of the Financial Stability Oversight Council and

its member agencies. OFR’s mission is to shine 

a light in the dark corners of the financial 

system to see where risks are going, assess 

how much of a threat they might pose, and 

provide policymakers with the information, the 

policy tools, and the analysis to mitigate them. 

The OFR is a virtual research-and-data 

community that uses a collaborative approach 

to expand its capacity to meet urgent needs 

and complement the work of others in the financial sector. To accomplish its goals, the OFR has 

partnered with the National Science Foundation to support Big Data financial-stability research, 

policymaking, and decision-making. This program involves collaboration by computer scientists, 

statisticians, economists, social scientists, and financial experts in using Big Data tools and 

techniques to identify and assess risks to the financial stability of the United States. The research 

will help support a more transparent, efficient, and stable financial system.

To ensure trustworthy, robust conclusions and decisions, it is necessary to fully understand the human 

as well as the technical components of a data-driven decision-making system. Research in augmenting 

human decision making with data-driven decisions is inherently socio-technical and interdisciplinary in 

nature. Understanding the nature of human behavior and cognitive biases when faced with different 

kinds of information is as critical as the validity of the information itself. Counterintuitively, presenting 

more data to a human can sometimes lead to a greater chance of erroneous conclusions. For example, 

studies have demonstrated that when presented with gender information, clinicians tend to underdiagnose heart disease in women, as compared to men exhibiting the same symptoms.24

It is likely that an increasing number of decisions in the future will be mediated through Big Data 

knowledge processes. Decisions made by people in all walks of life will be informed by knowledge 

derived from Big Data. Clear communication of how that knowledge was created, and the set of robust 

conclusions that could be drawn from that knowledge, is valuable to experts and non-experts alike.

Increased data literacy of Big Data technologies is critical to increasing the adoption of such tools and 

strengthening the entire “data-to-knowledge-to-action” process. 
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Strategy 3: Build and enhance research cyberinfrastructure that enables Big Data 

innovation in support of agency missions

State-of-the-art cyberinfrastructure is necessary if Federal agencies are to take advantage of the 

opportunities that Big Data offers. Innovative advanced cyberinfrastructure will need to combine the 

powers of Big Data and large-scale computing into a coherent capability for data analysis;25 address the 

challenges of data transport at all scales, from on the chip to across the globe;26 and satisfy the growing 

need for new environments for data sharing and analytics.27 Federal agencies have had a long history of 

supporting research on leading-edge infrastructure from the development of ARPANET in 1969, to the 

NSFNET and DOE ESnet programs, to the current NSF Global Environment for Network Innovations 

(GENI) program in computer networking and Extreme Digital (XD) program in high-performance 

computing.

Advancements in research cyberinfrastructure must serve a wide range of Big Data application needs 

and requirements. At the high end of computing and data, the National Strategic Computing Initiative 

(NSCI) provides guidance for “accelerating delivery of a capable exascale computing system that 

integrates hardware and software capability to deliver approximately 100 times the performance of 

current 10 petaflop systems across a range of applications representing government needs,” with 

“increasing coherence between the technology base used for modeling and simulation and that used for 

data analytic computing.” The NSCI is a “whole-of-government effort designed to create a cohesive, 

multi-agency strategic vision and Federal investment strategy, executed in collaboration with industry 

and academia, to maximize the benefits of [high-performance computing] (HPC) for the United States.”28

In other areas of Big Data, the applications may require very different types of cyberinfrastructure. 

Examples include highly networked systems, such as the IoT, that may not require exascale computing, 

but may pose challenges to operating in a highly distributed, parallel system on data stored across deep 

storage/memory hierarchies, or large memory systems for in-memory operations on extremely large 

data structure, such as complex graphs. In concert with the NSCI and other related initiatives and 

activities, a coordinated national strategy is needed to identify the needs and requirements for secure, 

advanced cyberinfrastructure to support handling and analyzing large amounts of data. Design 

considerations must include the full range of data scenarios, from large-scale warehoused historical data 

to data from multiple, concurrent, real-time data streams. Regardless of the type of application, stateof-the-art cyberinfrastructure is essential in a data-driven world, for maintaining global competitiveness 

in cutting-edge scientific research, promoting a vibrant data-driven industry sector, and fulfilling the

public mission of government agencies.

Strengthen the National Data Infrastructure

Datasets themselves constitute essential infrastructure for Big Data. A key aspect of the Big Data 

strategy is enabling access to open data, supporting sustained access, and providing controlled access to 

protected data. In the 1970s, research infrastructures like ARPANET and NSFNET led to the creation of 

the Internet. In the 1980s, a concerted effort in high-performance computing led to the creation of 

supercomputer centers at multiple NITRD agencies and research institutions. Today, the need is to 

significantly enhance national data infrastructure to exploit the full power of Big Data. While there are 

community-based efforts, such as the Research Data Alliance (RDA) and the National Data System (NDS),

that focus on issues related to creating a national (and international) capability, the Big Data SSG is
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interested in a coordinated plan for a national data infrastructure that can serve the needs of a wide 

range of stakeholders.

There is a need to standardize access to data resources within and across agencies. The collaborative 

development of standards and metrics for the entire data-driven cyberinfrastructure pipeline, including 

the hardware, analytics, data resources, and interfaces with which they interact, will be critical for a 

well-functioning cyberinfrastructure ecosystem. Several agencies have highlighted the importance of 

developing data and metadata standards in order to improve interoperability among data resources 

across organizations (e.g., the NIST ISO/IEC JTC 1 Study Group on Big Data,29 USGS Modular Science 

Framework30). An open systems approach and a federated implementation would greatly enhance the 

ability to share and combine datasets within and among agencies and with the public.31 Data curation

and data elements registration, development of standards, and data sharing and data integration 

approaches, should involve all relevant stakeholders. The approach should be federated, modular, and 

extensible in order to allow for agency-specific additions and enhancements. Community-based 

organizations such as the RDA are engaged in grassroots activities to further the development and 

adoption of such standards. New standards are also needed for measuring the performance and 

effectiveness, including price/performance, of Big Data systems. These standards should reflect end-toend performance for realistic application scenarios, necessarily combining data-intensive and computeintensive aspects. Initiatives under way include the NSF’s Benchmarks of Realistic Scientific Application 

Performance of Large-Scale Computing Systems (BRAP) program32 and the community effort to develop 

the Big Data Top 100 List.33 Along with performance, the new metrics should incorporate 

price/performance and energy performance.

A multiplicity of solutions such as shared repositories, federated and virtual approaches, and 

discoverability systems are needed to share data across disciplines and among agencies. In any field, 

discovery is enabled by the availability of cyberinfrastructure oriented to Big Data. All Federal agencies 

with annual research budgets greater than $100 million have developed public access plans to increase 

access to the results of Federally funded scientific research, including data. DARPA supports the DARPA 

Open Catalog, which contains a curated list of DARPA-sponsored software and peer-reviewed 

publications. Many agencies support community data repositories across a wide range of science and 

engineering disciplines. In many fields, including those represented by the Precision Medicine Initiative 

and the Materials Genome Initiative, a transition is taking place from the generation of small disparate 

datasets (the so-called “long-tail data”) to the bundling and integration of these data to enable easier 

discovery, access, and analysis. Programs such as the NIH BD2K and NSF’s Building Community and 

Capacity in Data-Intensive Research in Education (BCC-EHR)34 are vital for ensuring that all communities 

have access to new resources and analytical techniques to advance their fundamental research.

With advances in simulation methodologies and computing power, the validity of results from 

simulations could equal those from instrumentation. There will be increasing opportunities for 

integrating simulation data with observational and experimental data, resulting in accelerated progress 

of data-intensive computational research. The Big Data SSG recognizes the opportunity for a strategic 

effort in this area for systems and standards that enable easy sharing and use of research, government, 

and other open data. 

Empower Advanced Scientific Cyberinfrastructure for Big Data

Investment in shared leadership high-performance computing (HPC) resources have been made by Big 

Data SSG agencies such as NSF and DOE, which have traditionally focused on modeling and simulation 

applications. Increasingly, however, agencies are also sponsoring high-end HPC systems that provide 

processing capabilities for data analytics, and are pushing the development of computer system 
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Image courtesy of the Department of Energy’s Office of Science.

architectures that can efficiently support high-performance computing for both memory and computeintensive codes and efficient data access and processing. Among the goals of the NSCI is to promote the 

development of exascale systems that are also capable of performing data analytics operations 

efficiently.

There is growing recognition that there are significant data issues common to different disciplines and 

application areas, even while some issues are specific to a discipline or application area. Some aspects of 

cyberinfrastructure for Big Data may focus on specific application domains, while others are common 

and shared across multiple research domains. Investments in both categories are critical for supporting 

the diversity of Big Data innovation. The former is important so that domains with specific and difficult 

Big Data challenges can be well supported with resources optimized tor those applications; and the 

latter so that a shared infrastructure can offer access to resources that an individual community alone

would not be able to build and sustain.

DOE Office of Science (SC) 

SUPPORTING COLLABORATIVE SCIENCE AROUND THE GLOBE

Science is global. 

Facilities like the 

Large Hadron 

Collider, the 

Advanced Light 

Source, and the Joint 

Genome Institute 

create multi-terabyte 

to multi-petabyte 

scale datasets that 

need to be 

disseminated and 

analyzed by scientists 

and computing 

resources around the world. Enabling this experimentation and collaboration requires extremely 

fast networking speeds. The DOE Office of Science’s Energy Sciences Network (ESnet) seeks to 

ensure that scientific progress is unconstrained by the location of experimental instruments, 

people, computational resources, or the size of the data. ESnet can move data at 100 gigabits per 

second and is engineered to be an international resource for collaborative data-intensive science.

ESnet is helping researchers with real-time feedback on experiments using DOE Office of Science 

light sources. For example, a detector at a light source facility can now capture data from a sample, 

automatically send it to a supercomputing center to be processed and visualized, and then allow 

the scientist to access the data from a web portal in near real time. Using ESnet, groups of 

scientists can get feedback on whether they have taken the right sample or if they have calibrated 

the experiment appropriately, thereby greatly reducing the time it takes to discover new 

phenomena.

There is a significant opportunity for coupling advanced cyberinfrastructure with physical data sources—

whether a telescope, an MRI machine, or instrumentation at border crossings. Such a linked system 

could provide targeted, efficient, and effective data services and efficient pathways to appropriate 

computing resources. When developing this type of cyberinfrastructure, it is important to have a

comprehensive understanding of the instrumentation that generates the data, have an understanding of 

processing and computational objectives, and collaborate with the data collection groups and end users 

of the data.
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Address Community Needs with Flexible and Diverse Infrastructure Resources

The process of designing and developing cyberinfrastructure benefits greatly when stakeholders and the 

intended user-base are included. Community involvement is essential when a project requires the 

design of hardware that is specific to handling a particular type of data or dataset. Early stakeholder 

involvement optimizes the usefulness of the resulting infrastructure and minimizes unwanted 

consequences.

Robust cyberinfrastructure for Big Data will require real-world deployment of systems using state-ofthe-art as well as emerging Big Data technologies. However, enterprise-level systems are costly and 

require consistent management and maintenance that may be beyond the scope of universities or midsized enterprises to support for testing the performance of Big Data techniques. To allow researchers to 

test the deployment of infrastructure that meets the current and anticipated needs of Big Data 

communities, there is a need to invest in cyberinfrastructure pilot programs and testbeds for specific 

research communities and applications that use and analyze data. Such pilot programs would provide 

researchers with platforms for testing new techniques at scale, across a variety of application domains.

Pilot programs may include multi-site and/or multi-year projects to test collaborative infrastructure

adequately. Big Data SSG agencies may need to develop new funding models or partnerships to ensure 

support and resources for researchers participating in pilot programs. Also, a robust transition-topractice pipeline is an integral part of the research process to implement effective pilots.

Cloud computing provides multiple choices for implementation, including use of private clouds and 

public clouds. Big Data SSG agencies are already exploring multiple implementation models that are 

appropriate to their respective missions and organizational objectives. One example is the NIH 

Commons,35 which is a shared and interoperable computing environment intended to take advantage of 

both private and public cloud computing platforms with HPC resources. Public clouds are increasingly 

used as a computing platform by biomedical researchers because they afford a high degree of scalability 

and flexibility in both the cost and configuration of compute services. The NIH Commons framework 

focuses on interoperability between resources through common container frameworks and open APIs.

The development of common tools and standards will be a critical component of a national data 

cyberinfrastructure. The Big Data software ecosystem is currently dominated by open-source software 

packages. Agencies such as the NSA have also made some of their Big Data software open source (e.g.,

Apache AccumuloTM and Apache NiFiTM). Big Data SSG agencies have a commitment to support opensource software development that both reduces costs and produces innovative, high-quality software. 
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Strategy 4: Increase the value of data through policies that promote sharing and 

management of data

Big Data SSG agencies are dedicated to promoting a culture of data sharing and open data for 

government. Such a culture requires data stakeholders to play an active role in data stewardship—to 

mitigate losses of data and the subsequent losses of opportunities for machine learning, inference, and 

longitudinal studies. New policies, standards, and environments for sharing data and tools can facilitate 

analyses across datasets that would otherwise be impossible, thereby making new areas of research 

accessible. Such transformations have already occurred in fields such as biology and cosmology and are 

emerging in the areas of materials science and climate.

Similar to the “open-source software” movement, a robust “open-source data” movement can foster a 

sustainable ecosystem for discoverability, accessibility and sharing of data. Although large amounts of 

data are currently being generated and collected, much of it remains “dark” or inaccessible. The scale 

and heterogeneity of Big Data present significant new challenges that also need to be addressed.

Furthermore, policies and protocols for data sharing must be created to support sustainable sharing 

across sectors.36 “Data sharing” includes the curation and sharing of data itself but, equally importantly, 

the metadata and the APIs to the data.

Indeed, the Federal government has already been investing in data-sharing practices. There have been 

longstanding efforts to help catalog high-value datasets generated by agencies as well as for datasets 

generated within specific domains. For example, in 2009 the Chief Information Officer of the United 

States launched Data.gov to increase public access to all high value, machine-readable datasets created 

or collected by the Executive Branch of the Federal government. As a complement to Data.gov, 

independent agencies and interagency collaborations have also generated inventories of domainspecific datasets that use customized metadata frameworks to enable better search and discovery. For 

example, USAID has opened a publicly accessible Development Data Library to share data from its 

international development projects. The NIH National Library of Medicine catalogs over 60 different NIH 

Data Sharing Repositories on topics from cancer imaging to nanomaterials to drug addiction. The U.S.

Group on Earth Observations (USGEO) has been working with the international Group on Earth 

Observations (GEO) to share high-quality data about the Earth. These datasets include information 

about weather systems, crops, ecosystems, etc., and come from a diverse array of scientific instruments 

worldwide. In addition, Federal agencies support international community-based data sharing efforts 

and organizations such as the Committee on Data for Science and Technology (CODATA) and the 

Research Data Alliance (RDA). 

Develop Best Practices for Metadata to Increase Data Transparency and Utility

As part of their responses to the White House memos Transparency and Open Government and Open 

Data Policy – Managing Information as an Asset, all Federal agencies are examining their policies 

concerning data sharing and management.

37

The establishment of standards and requirements entails the development of metadata communities 

with opportunities to share best practices, build consensus on metadata systems, and provide a network 

for scholarly communication. Federal agencies support many metadata efforts in research sciences, for 

example, NIH’s Request for Information (RFI) for Metadata Standards and NSF’s Metadata for Longstanding Large-scale Social Science Surveys program. The DOE Office of Science currently runs an 
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initiative to guide principal investigators to incorporate data management in their evaluations. Big Data 

SSG agencies are examining when to mandate metadata and curation components in funded research.

Efficient and usable data interfaces are needed in the form of meta-models, metadata standards, data 

APIs, data type registries, persistent identifier systems, or container frameworks. These interfaces must 

be available for general use and tailored for domain-specific use. While data communities’ needs will 

not be the same or use the same data model, it is likely that diverse communities will have Big Data 

needs and concepts that overlap. Data interfaces need to be developed that allow a wide array of 

communities to access data and analytical tools, but can also be flexibly modified to fit specific needs. A 

capability is needed in which any data stored in any location across various domain boundaries can be 

identified and retrieved, along with any relatable data, in response to a data request. These interfaces

can provide improved capabilities to discover, search, access, consume, and deliver machine-actionable 

analytics insights through “mash-ups” of disparate data.

Research on effective mechanisms for data sharing should build upon existing best practices. Large-scale 

datasets are currently shared among communities in domains such as astronomy, genomics, physics, 

healthcare, and energy. Depending upon the size of the dataset and the nature of the data, many 

communities have already gained experience and know what processes work well and where additional 

or improved hardware and software resources are needed. Research to examine the resources and 

technologies various communities employ for data sharing will enable more effective use of resources 

for future development.

Work still remains to develop metadata and descriptive frameworks, as well as ontologies and 

taxonomies, for a wide variety of datasets from many domains. Support is needed for the development 

of datasets that are properly curated and include machine-readable metadata ontologies. Science 

funding agencies will encourage scientists and data collectors receiving Federal funding and at 

intramural programs to plan for the reuse of scientific data through, for example, appropriate 

documentation and metadata. Additionally, funding agencies will encourage coordination within the 

research communities of specific disciplines to collaborate and develop metadata frameworks for their 

respective domains. Ultimately, increased metadata curation will provide a greater return on research 

investments, as previously collected data will be able to serve new purposes.

Best practices for data-sharing platforms today include not only retaining and sharing data assets 

themselves, but also sharing the context of data collection, generation, and analysis. This process 

includes metadata strategies, but may also include the actual gathering techniques, analytics codes, 

processes, and workflows used to collect and analyze the data. In addition, the development of 

workflow tools that automate the capture of data provenance would be helpful. The desire to retain 

such useful information must be considered along with its feasibility; research is needed to help 

determine the balance between these two priorities. Further research is needed to determine how best 

to include and share contextual information when sharing datasets.

Contextual information for a dataset includes information about the processes and workflows involved 

in the creation of that dataset. A standardized language system, or ontology, for describing data 

processes is essential to enable better sharing of data. Building upon current standardization efforts, a 

consistent annotation system for workflows and the development of workflow registries and 

repositories will help communities efficiently share information about analysis processes. Related efforts 

have been underway over the past decade, for example, in the field of process automation for business 

process modeling and annotation.

Recording metadata and contextual information extends further to representing semantic information 

associated with data. One approach is to use top-down methods for developing controlled vocabularies, 
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domain ontologies, and “top-level” ontologies. Another is a data-driven approach, employing natural 

language processing and other data-mining approaches to extract semantic information such as named 

entities and common relationships. Big Data SSG agencies are engaged in a number of efforts to develop

domain ontologies for specific science areas. Research is needed to develop best practices for data and 

process annotation for different application domains, data types, and data-science scenarios. 

Provide Efficient, Sustainable, and Secure Access to Data Assets

Agencies need to address the issue of data archiving by investing in research to help determine the 

“value” of datasets in different contexts, including supporting tools. While it is challenging to assign 

future value to a dataset, it is nonetheless important to develop methodologies for performing 

systematic cost-benefit analyses and for determining relative value among datasets. As the number of 

data resources increases, this task becomes more challenging. The notion of data value becomes more 

challenging as one searches data inventories broadly (across many resources) and deeply (finding the 

right data within a resource). Research is needed into intelligent, scalable, robust search methods that 

can keep pace with the growing library of datasets. Additionally, tools to automate the data lifecycle are 

needed, including tools that can aid agencies with strategic decisions about when and how datasets 

should be retained, archived, or deleted. The data storage, archival, and deaccessioning issues are also 

closely linked to advances in corresponding storage technologies. New high-density, low-cost storage 

solutions could make it easier in the future to store and retrieve vast amounts of data.

The United States Agency for International Development (USAID) 

A BOTTOM-UP APPROACH TO FARM DATA

Small farmers can suffer because they lack the 

data to make crucial and timely decisions 

about their trade. The USAID Global 

Development Lab is using data systems to help 

farmers get the data they need.

In Senegal, a locally run farm database project 

has reached more than 25,000 small farmers. 

Data sharing and open discussion allow 

farmers to negotiate prices for supplies, 

compare farming techniques, and develop best 

practices. For example, farmers started

measuring the spacing of plants in their fields. 

By sharing this information, they identified the density that produced the highest yield and 

adjusted sowing practices accordingly.

38

In Kosovo, distributing milk quality results via text message has helped over 2,035 small-scale dairy 

farmers by allowing buyers to set a transparent base price and offer premiums based on 

measurable milk quality. To date, more than 70% of the farmers have improved their milk by one or 

more quality grades, making the food safer and increasing revenues by 17%.

The sharing, use, and analysis of Big Data will not be limited to our immediate future. The full potential 

of an individual dataset may not be realized until, for example, a scientist in the distant future combines 

that data with another dataset that has just been collected. The Big Data ecosystem must be designed 

with long-term sustainability of data access in mind. How will data collected this year be available in two 

years, or five years? Will a tool developed today be adaptable to even larger or more complex datasets

than those it was originally designed to analyze? Sustainability encompasses the development of 

scalable cyberinfrastructure to enable continued, uninterrupted access to data systems, even as new 

technologies evolve.
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Image courtesy of the Group on Earth Observations.

As emphasized in the next section, while data sharing within and across public and private sectors is a 

critical aspect of the Nation’s collective Big Data future, issues of privacy and security are paramount in 

all sharing platforms. These issues must be addressed to ensure controlled and proper dissemination of 

data in order to engender trust among the various stakeholders and in the data. Defining data privacy 

and security measures will be critical elements in the advancement of Big Data sharing as well. Trust in 

the privacy and security measures for sensitive datasets must be integral elements of the design of any 

data-sharing technology, and is part of a much larger series of concerns around Big Data ethics and 

societal implications. Conversely, a lack of privacy and security strategies will hinder the creation or 

collection of data as well as reduce the potential for data sharing by degrading trust. The conclusions 

that can be drawn from Big Data are not limited to those gleaned by the data’s original collector, but can 

be based on reuse and repurposing of datasets, and use of the data in combination with other datasets 

and for different end purposes. Thus, ensuring trust in the original datasets and derived products is 

essential.

The Global Earth Observation System of Systems (GEOSS) 

A WORLD WITHOUT DATA BORDERS

Envision a world where more people will be fed, 

more resources will be protected, more diseases 

will be mitigated or even prevented, and more 

lives will be saved from environmental disasters.

The international Group on Earth Observations 

(GEO) is a voluntary partnership of 96 

governments and 87 participating organizations 

working to develop the Global Earth Observation 

System of Systems (GEOSS).

39 GEOSS links 

international earth observation resources in the 

areas of agriculture, biodiversity, climate, 

disasters, ecosystems, energy, health, water, and 

weather. The goal is to provide the right Earth 

observation information, in the right format, to 

the right people, at the right time, to make the 

right decisions. This requires Big Data analytics, infrastructure, and principles around open data 

exchange. For example, early agreement on a set of data sharing and data management principles 

has allowed the development of open-source software to collect worldwide data on health 

epidemics, assess the state of global water resources, produce fire-potential maps from weather 

data, and calculate and communicate earthquake risk.

Thanks in part to GEO’s policy advocacy and technical coordination, initiatives on data sharing have 

been announced in Brazil, China, Japan, South Africa, Spain, the United Kingdom, the United States, 

and through organizations such as the European Space Agency, the European meteorological 

satellite agency, and the International Council on Science. 
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Strategy 5: Understand Big Data collection, sharing, and use with regard to 

privacy, security, and ethics

Emerging Big Data technologies hold great promise for society, but also present new challenges to the 

ethical use of data, analyses, and results, and to the privacy and security of data. The solutions and 

approaches needed to address these challenges require deep attention and will have a major impact on 

the ability to access, share, and use Big Data. 

Many large datasets originate in industry, government, and educational institutions, and are not readily 

accessible due to concerns about privacy or loss of competitive advantage. Privacy concerns about 

altering the balance of power between data collectors (often industry or government) and data 

providers (often individuals) challenge our ideas about public information and how such information

should be equitably managed and used. Security concerns around intimate personal details of 

individuals have introduced new notions of harm to privacy and autonomy, and led to a demand for 

protections to safeguard the populace. Ethical concerns about Big Data leading to discriminatory 

practices and outcomes have also sparked renewed discussion on how to best enforce, for example, 

long-standing civil rights protections, particularly in housing, employment, and credit.40 To address these 

concerns, some “rules of the road” are needed for data governance. Efforts to continue examining the 

implications and consequences of Big Data collection, sharing, and use are also needed.

Cybersecurity research in Big Data is supported by a variety of efforts within the Federal Government. 

These include the NSF’s Secure and Trustworthy Cyberspace (SaTC) program41 and the activities and 

investments of NITRD agencies that align with the Federal Cybersecurity Research and Development 

Strategic Plan: Ensuring Prosperity and National Security.

42 The Cybersecurity R&D Strategic Plan

provides a framework for Federal R&D activities with regard to enhancing defenses against cyber 

threats, increasing resiliency should a threat occur, and providing ways of staying ahead of new attacks.

Consequently, the intention of this section is not to provide an exhaustive list of areas for Big Data 

privacy and security research. Instead, it provides a set of priorities for the equitable consideration of

privacy and security protections as they concern Big Data collection, sharing, and use.

Privacy research in Big Data is a national imperative. Federal agencies working under the NITRD Program 

are engaged in a separate, but related effort to develop a strategic plan for privacy research.

43 The effort 

reflects the importance of distinguishing the needs of privacy research from those of security research, 

and recognition that a solution in one does not necessarily protect the other. For example, poor 

cybersecurity is a clear threat to privacy, e.g., a hacker could breach a retailer database and gain access 

to private information. However, even with strong cybersecurity controls in place, privacy violations 

could still occur. For example, a user authorized to use a security camera system could spy on and 

breach the privacy rights of a co-worker.

Provide Equitable Privacy Protections

A major issue arising from Big Data is the so-called mosaic effect—as more data is made discoverable 

and integrated with data from other sources, there is an increased threat that seemingly disparate 

threads of information could be pieced together to expose private information in unanticipated ways. A 

growing number of studies have demonstrated the ability to identify or re-identify anonymized 

individuals by aggregating information from multiple publicly released datasets. To further examine this 

mosaic effect requires an understanding of how data can be combined that, in turn, requires systems to 

model the content and context of multiple datasets and how they will be used. While these are difficult 

technical challenges, especially when dealing with large multimedia data such as combinations that 
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include images, videos, location data, audio, text, or graphic objects, the benefit of achieving technical 

solutions is that the mosaic effect could be mediated with Big Data. Limits could be specified and 

implemented on the combinations of data that can be accessed by certain users or classes of users. 

Research into automated content discovery can be important to enhance issues of privacy and 

confidentiality. 

Many Big Data applications entail integration of data from multiple sources, each created under 

different policy regimes. The data may be associated with individual policies, protections, and rules of 

use that need to be enforced even when the data is integrated. For example, a health insurance 

company may partner with a social media company to combine datasets, but the two sets of data were

created under very different rules of use and regulations. Handling the merger of such previously 

separate datasets while remaining sensitive to access and privacy needs is a critical research objective.

When dealing with dynamic environments, where sources, users, applications, and data usage are 

continuously changing, the ability to design and evolve policies automatically and in real time will be 

essential to ensuring that data is readily available for use while guaranteeing data confidentiality.

Research should provide technological and policy solutions for new environments and tools for 

managing policies dynamically; this is to ensure that the privacy of a particular dataset’s contents is 

protected as the context surrounding it varies.

In current practice, “notice and consent” is the most widely used strategy for protecting consumer 

privacy. When an app is downloaded or a new online account is made, a privacy policy is displayed for 

the user to accept. One challenge to this paradigm is that it places the burden of privacy protection on 

the individual to read and understand any legal, privacy, or ethical implications. This burden is made 

untenable in the Big Data era where the data may be used in new, unanticipated ways. Also, data 

collected during transactions, or from rapidly changing sources such as social media, cannot adhere to 

the “notice and consent” paradigm. New conceptualizations of data privacy and protections that 

complement, supplement, or replace some of the traditional methods of privacy are needed. For 

example in February 2012, the Administration issued a report setting forth a Consumer Privacy Bill of 

Rights (CPBR).44 The CPBR addresses commercial (but not public sector) uses of personal data and is a 

strong statement of American privacy values.45 Research and insights into current and novel 

implementations of CPBR in the context of Big Data could provide a continuing framework for resolving 

privacy concerns.

Privacy issues with automated decisions can occur in “precision” initiatives—those that aim to provide 

customized individual interventions, such as precision medicine or precision education. The data that is

collected in precision initiatives enable applications and decisions that can help an individual student to 

learn or an individual patient to recover more quickly. However, the data is sensitive because it can be 

falsified, stolen, or misused, which can directly impact a specific individual. There is support for new 

legislation in this area, for example, to protect student data privacy, while still allowing students to reap 

the advantages of a precision education.46 Technological resources, such as de-identification or contentand role-based access control, have also been proposed to protect sensitive information. Research into 

the implications of using Big Data for precision initiatives will be important to understand the trade-offs, 

if any, involved in data collection. Also, research into the effectiveness of policy versus technological 

interventions to protect privacy will be instructive for supporting future data-driven decisions.

The quality of data-driven decisions is directly related to the quality of the original data. Therefore, 

some relatively new forms of data, such as data from social media, need to be evaluated for systematic 

sources of error. For example, recent studies have shown that people give false answers, especially in 

social networks, when they are unsure if their privacy is preserved. The decisions that are then made 

from this social media data must be evaluated in context. Research is recommended on the limitations 
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Image of Louis Brandeis courtesy of the Library 

of Congress.

of new data forms, on data quality, and the subsequent downstream decisions and strategies that come 

from that data.

DOD Defense Advanced Research Projects Agency (DARPA) 

GIVING PRIVACY BY DEFAULT 

The right to privacy, coined by Louis Brandeis in 

1890, was a reaction to the ability of the new 

“instantaneous camera” to record personal 

information in new ways. It seemed there was a 

tension between access to information and 

protecting privacy. Now, in the digital 

information age the same tension is arising due 

to the ease of gaining access to personal 

information online. The difference between 

Brandeis’ time and today is that privacypreserving technologies now have the potential 

to keep pace with the accelerating speed at 

which users can access data.

DARPA’s Brandeis program aims to break the 

tension between maintaining privacy and being 

able to tap into the huge value of data.

Specifically, Brandeis will develop tools and 

techniques that enable us to build systems in 

which private data may be used only for its 

intended purpose and no other. The potential 

for impact is dramatic. Assured data privacy can 

open the doors to personal medicine, effective 

smart cities, detailed global data, and finegrained Internet awareness. Without strong privacy controls, every one of these possibilities would 

face systematic opposition. 

Privacy-enhancing technologies that enable computation on data in such a way that an individual’s 

contribution cannot be revealed might be one approach to eliminating the need for users to consent to 

data use. A range of technologies aims to preserve privacy based on cryptographic techniques such as 

identity‐based encryption, attribute‐based encryption, homomorphic encryption, “zero‐knowledge” 

systems, secure multiparty computation, oblivious RAM, multi-input functional encryption, and others.

Research support must continue in these areas and will need to address issues of scalability, as well as 

lack of adoption of these technologies within enterprise-scale applications.

Enable a Secure Big Data Cyberspace

In the cyberspace context of Big Data, data may flow among many different data systems and subsystems. The overall security of the entire network of systems is critical as well as the security of the 

individual system components. Trustworthy Cyberspace: Strategic Plan for the Federal Cybersecurity 

Research and Development Program discussed the idea of tailored trustworthy systems, where the 

system recognizes the user’s context and evolves as the context evolves.47 However, organizations 

would have to design methods that monitor the data security of a network of changing trustworthy 

systems to ensure that the combined system is still secure. For example, if a trusted credit card system is 

linked to a trusted report-generation tool, then there should be a reliable way to ensure that the 

combined system is still equally secure from cyber-attacks. Research is needed to provide the tools for 

monitoring cybersecurity as data passes through a large number of potentially changing sub-systems.
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There is a reciprocal opportunity for Big Data technologies to contribute to research in cybersecurity. 

Research is needed to enable the use of security-related data (e.g., network traffic, detailed device 

internal state information) to enhance the security of information and networking technologies.

Understand Ethics for Sound Data Governance

In the past, agencies have benefitted by supporting research into the Ethical, Legal, and Social 

Implications (ELSI) of transformative areas, such as genetic engineering or nanotechnology. The NIH has 

a 20-year research program in the Ethical, Legal, and Social Implications of genomics within the National 

Human Genome Research Institute48 and the cross-agency National Nanotechnology Initiative has 

sponsored two nanotechnology Centers of Excellence that focus specifically on ELSI issues, with support 

from DOE, NASA, NIH, NIST, USGS, and other agencies.49 The benefits of these and other programs have 

been to connect science to the rest of society by identifying best practices and uncovering valuesensitive consequences associated with specific research. These same benefits should be reflected in Big 

Data research through the creation of an ELSI research agenda for Big Data.

Data-driven decisions are becoming more common in the workplace as data is used for activities such as 

performance tracking, workplace evaluation, operations, benefits eligibility determination, employment, 

and prescribing. Disputes over employer-collected data have become part of the bargaining process 

between worker unions and some major corporations, specifically on questions of whether disciplinary 

actions should be taken based solely on data, or if data can be collected without employee knowledge.

Large-scale use of automated decision-making tools, such as scores and algorithms in Big Data systems, 

must be investigated specifically to understand the impact that such tools have on the private as well as 

public sectors.50 As human experts are partially or completely replaced by automated decision-making 

algorithms, it will be important to support research on the quality of data-driven decisions in the 

workplace. This research will address how these automated decision systems affect issues such as the 

flexibility and reliability of organizational processes, the nature of workers’ subject matter knowledge 

and expertise, organizational memory and learning, job characteristics, employment opportunities, 

workforce re-training, and human auditing or appeal. In addition, research is needed into how these 

factors are balanced with elements like reduced costs or increased quality of products or services.

Big Data ethics research will help determine the guidelines and policies for working with Big Data. Data 

governance requires balancing different types of risk relationships, i.e., Big Data might increase privacy 

risks in some domains but reduce security risks in others (e.g., national security). Ethical models for 

these different types of risks are necessary to identify suitable trade-offs. In particular, all Big Data users

must pay careful attention that Big Data does not consistently propagate errors or systematically 

disadvantage certain groups, whether inadvertently or intentionally. Individuals and groups can also be 

unknowingly put at risk from voluntary data collection. For example, family history data for one member 

of a family can be sufficient to infer disease susceptibility of other members of the same family or even 

ethnic group. Currently, the 2008 Genetic Information Nondiscrimination Act (GINA) provides Federal

protection from genetic discrimination in health insurance and employment. Other forms of Big Data 

may need similar protections around appropriate use and warrant detailed investigation to inform the 

public as well as policymakers.
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NSF Directorate for Computer and Information Science and Engineering (CISE) 

BUILDING AN ETHICAL ROADMAP

Big Data is big not only because of the size of the 

data but also the number of people involved and 

affected by the data. New opportunities for 

education and healthcare, as well as disruptive 

business paradigms, can raise major ethical concerns 

for schools and students, hospitals and patients, 

businesses and workers, and the government and 

citizens.

The Council for Big Data, Ethics, and Society51 was 

developed, with funding from NSF CISE, to provide 

critical social and cultural perspectives on Big Data 

initiatives. The Council brings together researchers 

from diverse disciplines to examine the issues and 

help develop mutually agreed-upon frameworks to 

help researchers, practitioners, and the public understand the social, ethical, legal, and policy 

issues surrounding the Big Data phenomenon. To date, the Council has released reports on data 

ethics, data management, and the history and development of ethics codes in related fields.

The Council provides the structure necessary to coordinate scholars and develop social and ethical 

research initiatives in Big Data. The Council’s work will lead to new research projects and help 

shape future research agendas in Big Data to benefit society and science.

Who is the “owner” of a piece of data is often a difficult legal question to answer. Multiple stakeholders 

could be associated with any data item, each with different (possibly conflicting) objectives.

Furthermore, not all stakeholders may be known to each other. For example, a dataset user (and thus a 

stakeholder for that data) may not be aware that a law-enforcement agency is also using the same data.

Solutions need to be investigated to eliminate conflicts and balance data utility with data ethics. Efforts 

to explore ethics-sensitive research, i.e. research that is explicitly attentive to the values, needs, and 

goals of multiple stakeholder groups, would enable stakeholders to dynamically consider common 

elements like data utility, risk, and cost, alongside values and societal ethics. This interplay of utility and 

ethics connects science to the larger society in ways that represent and empower our national ideals.

In general, solutions that address the complex issues of Big Data privacy, security, and ethics may not 

result in a “one size fits all” model. Because different domains may possess different definitions of data 

utility, multiple dimensions may need to be tailored for different application domains to achieve 

practical solutions. Therefore, identifying and responding to the needs of specific communities will be 

critical in resolving the toughest Big Data privacy, security, and ethical concerns.
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Strategy 6: Improve the national landscape for Big Data education and training to 

fulfill increasing demand for both deep analytical talent and analytical capacity for 

the broader workforce

Big Data and the field of data science are flourishing. Many surveys and studies have identified a 

shortage in the number of people with relevant Big Data skill sets and the need for more training and 

formal education programs in data science. To meet the demand, educational institutions are working to 

establish programs at all levels.

The need is for both domain experts who are trained in data science and “core” data scientists who 

focus on data science as their primary field of expertise. Individuals educated in data science at the 

undergraduate and graduate levels are vital to meeting needs across all sectors—industry, government, 

and academia. The core specialists in Big Data include data science researchers, information scientists, 

statisticians, computer scientists, database and software programmers, curators, librarians, and 

archivists with specialization in data science. The National Science Board emphasized the importance of 

these experts to the successful management and analyses of digital data collections.52 Research 

investments are needed to expand the current pipeline of support to the field of data science and 

provide direction on the necessary competencies for the development and training of future core data 

scientists. The National Science Foundation Research Traineeship program, for example, has a priority 

interdisciplinary research theme on Data-Enabled Science and Engineering (DESE).

The challenge of producing a properly trained workforce with Big Data skills is enormous. Integrating 

data science—the field of study focused on data foundations, tools, and techniques—throughout our 

Nation’s education system is essential to developing a workforce that can address our national priorities 

across domains and sectors. Additionally, as more industries take advantage of Big Data to accelerate 

discovery and develop new products and services, it is in our national interest to meet the increased 

workforce demand to ensure that the United States remains economically competitive.

To build the human capacity for Big Data research, development, and implementation and expand the 

current data science landscape requires a multi-faceted approach. Education and training strategies 

could include the core technical facets of data science that originate from multiple traditional domains, 

such as statistics, machine learning, data mining, visualization, and ethics. Such strategies could target 

the training of new data scientists, and also increase the data science competencies and skillsets of 

current professionals.

Continue Growing the Cadre of Data Scientists

There has been a groundswell of new Data Science programs offered at institutions that teach the 

necessary skills for dealing with Big Data. Many of these programs are at the Master’s level, but the 

number of programs at the undergraduate and Ph.D. levels is increasing. A core data science curriculum 

includes course material from computer science, statistics, ethics, social science, and policy. In addition 

to Data Science programs at undergraduate and graduate levels, programs are emerging to train 

students from other disciplines in the basics of data science. These disciplines cover the full range from 

science, engineering, biomedicine, clinical medicine, business, social science, humanities, law, and the 

arts. Consensus about the program content is beginning to emerge. Big Data SSG agencies can play a 

significant role in helping define the needs and requirements for these programs. In conjunction with 

the National Research Council’s Committee on Applied and Theoretical Statistics (CATS), NSF brought 
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together the community at a workshop entitled “Training Students to Extract Value from Big Data” to 

discuss how to educate and train students in order to increase the cadre of data scientists.

Growing and sustaining R&D investments in Big Data helps advance the field of data science and support 

the training for the next generation of core data scientists. Funding in the form of competitive grants to 

academic institutions will help train faculty, post-doctoral scholars, graduate students, and 

undergraduates in cutting edge data science technology. In particular, funding that allows more 

graduate students to engage in data science research at academic institutions will enable both the 

research needed to advance the field and the training needed to grow a cohort of core data scientists.

As the field of Big Data continues to grow and evolve, coordination will be necessary to assess 

educational needs. The education and training community will help determine how to structure the data 

science curricula and how to reallocate educational resources to meet the demand for specific skillsets.

At this early stage, maintaining agility and flexibility in undergraduate as well as graduate curricula and 

programs is necessary to ensure that cutting-edge concepts and techniques are being incorporated.

As data-science programs grow, it is essential that the pedagogy encourage the participation of women, 

underrepresented minorities, and persons with disabilities. These populations are significantly 

underrepresented in computer science; therefore, data science education should proactively develop 

and evolve to incorporate broad participation. For example, curricula that emphasize the role of Big 

Data for “social good” in both formal and informal education settings have demonstrated success at 

engaging greater proportions of women and underrepresented minorities.53 Also, curricula that actively 

foster collaboration have proved successful at increasing productivity, retention, and success of women 

and underrepresented minority scientists in multiple Science, Technology, Engineering, and 

Mathematics (STEM) fields.

As data-science curricula expand and diversify content, universities and colleges should be prepared to 

accommodate additional students by increasing the number of advanced degree programs in data 

science. To date, there are approximately 60 institutions that offer a Master’s degree program in data 

science, but more programs and teachers are needed.

54 Agencies can also institute awards that honor 

data scientists who exemplify the role of teacher-scholars through outstanding research, excellent 

education, and the integration of education and research.

Expand the Community of Data-Empowered Domain Experts

Domain researchers, in fields outside of computer science and statistics, would benefit from financial

support to supplement their expertise with discipline-specific data science training. Such researchers

come from many different data-enabled fields, from science and engineering, to social science, 

humanities, and law. Educating these researchers to utilize Big Data provides them the opportunity to 

enhance the work within their own disciplines. A 2014 National Academies of Science report explains 

that there exists an expertise gap between domain and data scientists. Domain scientists lack knowledge 

about the technologies that are available and relevant to their work while data scientists have not 

considered additional domains where their work is relevant.55

To bridge this gap between domain and data scientists and support the utilization of increasingly datarich research, support can be expanded for projects that require interdisciplinary teams to devise 

solutions. These collaborations will enable domain scientists to access resources and learn the tools to 

incorporate large datasets within their research. Additionally, agencies could encourage universities to 

incorporate Big Data challenges posed by industry experts into a practicum to complement the data 

science curriculum. One model of this approach is the NSF-sponsored eScience Institute at the 

University of Washington. The eScience Institute currently supports collaborative challenges posed by 
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industry experts by offering training, curricula, fellowships, and seminars to answer real-world data 

problems. Fostering an environment for multi-sector partnerships to grow and thrive will accelerate 

innovation and expand the impact of Federal investments.

NIH Big Data to Knowledge (BD2K) Initiative 

EXPANDING DATA SCIENCE CAPABILITY ACROSS BIOMEDICINE 

A current bottleneck in the development of health 

knowledge is the ability of the workforce to 

efficiently manage and fully analyze large amounts 

of complex data. A data-savvy workforce is 

necessary for advancing biomedical science, 

improving health knowledge, and developing 

successful strategies to combat disease.

Big Data to Knowledge (BD2K)56 is a trans-NIH 

initiative established to enable biomedical 

research as a digital research enterprise, to 

facilitate discovery and support new knowledge, 

and to foster data science skills in the biomedical 

workforce. Biomedical data science pre-doctoral students, postdocs, and researchers are being 

trained to develop and use new tools and methods. 

An essential element of the Initiative is the multi-institutional BD2K Centers consortium. Each

Center addresses distinct biomedical research challenges, but they all include workforce training 

and collaboration components. Together with the other BD2K training activities, these training 

strategies are essential to accelerate advancements in health and well-being in an era where 

biomedical research is increasingly analytical and more data rich than any time in prior history.

Domain science educators often have limited flexibility to enhance their academic curriculum with the 

full data science core curriculum. In these cases, domain science students can benefit from a general 

data science foundation delivered through data science short-term courses, or modules. These courses 

can be developed to incorporate real-world application of theory to improve course engagement and 

relevancy. For example, scientists and students in specialized data science courses could work as a class 

to develop algorithms to address research questions in a particular domain, for example energy grid 

demands, water usage in agriculture, or human microbiome populations.

Domain scientists would also benefit from initiatives to supplement their training with data science 

through seed grants, professional development stipends, internships, fellowships, and summer research 

experiences. NSF recently announced an NSF Research Traineeship (NRT) program designed to 

encourage the development and implementation of transformative and scalable models for STEM 

graduate education training. The NRT program includes a Traineeship Track dedicated to effective 

training of STEM graduate students in high-priority interdisciplinary research areas, through the use of a 

comprehensive traineeship model that is innovative, evidence-based, scalable, and aligned with 

changing workforce and research needs. Additionally, NSF is catalyzing the growth of data science 

infrastructure and data scientists by leveraging existing programs to incorporate data science training 

into their solicitations.57

The NIH is implementing strategies to train domain researchers to engage with Big Data. In May 2015, 

NIH announced the first round of Big Data to Knowledge (BD2K) Institutional Training Grant awards, 

which provide undergraduate and graduate students with integrated training in computer science, 

informatics, statistics, mathematics, and biomedical science. The NIH BD2K program will also offer 

awards to support an effective and diverse biomedical data science workforce by supporting educational 

resource development, training opportunities, and public engagement projects.
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Image courtesy of the National Aeronautics and Space 

Administration.

Broaden the Data-Capable Workforce

Many of the skills that are essential for working with Big Data include basic competency in data 

preparation, simple data visualization, basic descriptive statistics, and data characterization. Indeed, 

these essential skills, which sometimes occupy 70-80% of the time involved in analyzing Big Data, are 

often overlooked in data analysis. There is a demand for workforce trained to collect, record, extract, 

clean, and annotate data.58 Community colleges, two-year colleges, certificate programs, and intensive 

workshops can provide opportunities for students and professionals to acquire the necessary data 

preparation and analysis skills for a small investment of their time and money. Such basic education and 

training initiatives are especially pertinent to workforce professionals and managers interested in 

expanding their professional skills. This level of data science training could also be a component of 

programs for Veterans returning to the workforce and as re-entry opportunities for the unemployed.

Community colleges and 2-year academic institutions could address the need for knowledge workers in 

Big Data by developing data science tracks to prepare their graduates for either job readiness or 

advancement to a bachelor’s degree in data science. These institutions could also support cross training, 

recycling skills, and re-entry programs to expand the workforce base in Big Data.

National Aeronautics and Space Administration (NASA) 

WE ARE ALL DATA SCIENTISTS

Is there life beyond this planet? Can students

experience what it is like to be an astronaut on 

the Moon? What is the air quality and water 

temperature like around the world? Today’s 

technologies allow citizens to explore and 

contribute to these types of questions once 

reserved for scientists.

NASA has a long-standing involvement with

citizen science. Pioneering projects like 

SETI@Home and GalaxyZoo gave millions of 

participants the ability to help search for 

extraterrestrial life and classify heavenly 

objects. Current NASA hack-a-thons and 

challenges invite citizens to explore audio files 

to help re-live past space missions or create space games to mimic working on different planets. In 

2014, a group of citizen scientists successfully established communication with an inactive 35-yearold NASA spacecraft in an attempt to renew its scientific mission.

NASA and the National Science Foundation (NSF), with support from NOAA and the Department of 

State, jointly fund the Global Learning and Observations to Benefit the Environment (GLOBE) 

Program that connects schools, students, and teachers worldwide in measuring data about the 

Earth, such as air quality, water temperature, and freshwater animals. Using a GLOBE visualization 

tool, they can map, graph, filter, and export data that has been measured across GLOBE protocols

since 1995. Another initiative, known as My NASA DATA, provides schools with satellite data and 

lesson plans to engage students in atmospheric science and introduce them to data analysis.

Improve the Public’s Data Literacy

A data-driven world requires a citizenry that is data literate. This includes the ability to read, correctly 

interpret, and communicate information from data, as well as create data and knowledge derived from 

other data. Data science education at the K-12 levels can assist in providing nationwide data literacy, 

while also preparing students for more advanced data science concepts and course work after high 

school. There are numerous opportunities for the data science research community to engage with K-12 
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education, and Big Data SSG agencies should encourage more of them. Access should be provided to 

data science educational resources, such as textbooks, online courses, practicums, challenges, and 

citizen science projects, in order to help increase data literacy. A college education should not be a 

prerequisite to acquiring data literacy.

To date, very few public high schools offer data science coursework. Continued research in data science 

education is necessary to explore the basics of data literacy and should incorporate rigorous 

assessments and evaluations. What curricular models help build effective data literacy? Which data 

science skills should be taught at the various grade levels? Additionally, as the K-12 education system 

begins to expand its data science curriculum, professional development opportunities for teachers will 

be needed that are short in duration, offer data science kits, and enable easy integration into existing 

curricula. The data science curricula should also make use of open-source data science tools in order to 

accommodate the varied technological infrastructure in public schools.

For example, one high school data science course is currently being piloted in the Los Angeles Unified 

School District. In 2014, the district offered their first Introducing Data Science (IDS) course in ten high 

schools to address the data science talent shortage. The IDS course was designed to provide a 

meaningful and alternative pathway for math requirements while providing a strategy to prepare 

students for new job skills. The course is easily incorporated into the Common Core standards and has 

approval from the University of California system to count as a math course and statistics course for preadmission requirements.
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Strategy 7: Create and enhance connections in the national Big Data innovation 

ecosystem

To enable sustained engagements at the Federal level, involving tangible, measurable goals, the NITRD 

Big Data SSG is seeking ways to lower communications barriers and create agile collaboration 

mechanisms. There is strong interest both in removing bureaucratic hurdles to enable technology and 

data sharing and in building lasting programs with sustainable funding models, involving interagency 

collaborations as well as collaborations with industry.

Encourage Cross-Sector, Cross-Agency Big Data Collaborations

Big Data SSG agencies support collaborative activities across all sectors—government, industry, not-forprofits, academia, and the general public. In 2013, the Big Data SSG organized a White House-sponsored 

“Data to Knowledge to Action” event, which launched over 30 new multi-sector collaborations involving 

some 90 partners across the public and private sectors. Additional collaborative activities that have 

grown out of the Big Data R&D Initiative include: the release of an NSF and NIH joint solicitation, “Core 

Techniques and Technologies for Big Data;” NASA, NSF, and DOE working with Topcoder to run a series 

of ideation challenges around data fusion; NASA working with Amazon.com to provide access to earth 

science data with the NASA Earth Exchange; NOAA working with a consortium consisting of Amazon, 

IBM, and the Open Cloud Consortium to release all NOAA data for public access; and the NSF Innovation 

Transition (lnTrans) awards that facilitate transition of research results to practice, with industry 

sponsorship.

In 2015, NSF launched the Big Data Regional Innovation Hubs program (BD Hubs) to foster regional, 

cross-sector collaborations and multi-sector projects to foster innovation with Big Data. As a 

complement to the institutional gateways, the regional hubs provide the ability to engage with local or 

regional stakeholders, e.g., city, county, and state governments, as well as permit a focus on regional 

issues. These collaborative activities and partnerships play a critical role in building and sustaining a 

successful national Big Data innovation ecosystem.

The Big Data SSG also identified the need for “development testbeds” or “sandboxes” to enable 

conversion of agency-funded R&D results into innovative production capabilities, as well as for engaging 

in proofs of concept with both open source and proprietary commercial off-the-shelf solutions. Such a

program would enable the sharing of experiences, results, and capabilities among agencies, shorten the 

development phase of a project, and allow agencies to assimilate and integrate new results and 

solutions quickly. Industry engagement in the program would demonstrate broader utility, foster better 

interoperability, and potentially provide long-term sustainability of solutions. Pilots and testbed 

infrastructure could be shared among agencies, thereby helping to maximize investments and share the 

benefits of projects and technologies that would otherwise remain isolated within a particular agency.

Promote Policies and Frameworks for Faster Responses and Measurable Impacts

Agile mechanisms should be developed to enable rapid and dynamic coalescing of stakeholders—across 

agencies and with industry—in response to urgent priorities. For example, after the Deep Water Horizon 

oil spill event, NOAA, USGS, EPA, DHS, DOE, and others collaborated to bring together timely 

information from diverse sources, from satellite imagery to sampling sites, in order to inform a rapid 

response to the crisis. In the future, policies must be put in place so that agencies with relevant data and 

resources for addressing the problem can be quickly assembled, together with industry, academia, and 
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non-profit partners, where needed, to leverage a data inventory and common knowledge base that cuts 

across agency operations, along with open source as well as proprietary tools for analysis. Processes 

should be put in place so that useful products generated by such rapid response teams are adopted and 

sustained when operations return to normal.

Materials Genome Initiative (MGI) 

MATERIALS FOR THE FUTURE

Materials make a difference. Human health 

and welfare, clean energy, and national 

security are all pressing needs of our time, but 

all have underlying challenges whose solutions 

require advanced materials. For example, 

packaging that keeps food fresher and more 

nutritious, new lightweight materials for 

vehicles that significantly improve fuel 

efficiency, or solar cells as cheap as paint 

would be game changing. Yet, it can take 20 

years or more and millions of dollars to move a 

new material from discovery to the market.

The Materials Genome Initiative (MGI) is a 

multi-agency initiative designed to create a new era of advanced materials. The aim is to use Big 

Data techniques and multiple agencies’ research capacity to discover, develop, manufacture, and 

deploy advanced materials twice as fast as possible today and at a fraction of the cost. Since the 

launch of MGI in 2011, the Federal government has invested over $250 million in new R&D and 

innovation infrastructure to anchor the use of advanced materials in existing and emerging 

industrial sectors in the United States. Nine Federal agencies are cooperatively sponsoring 

workshops and joint meetings of principal investigators to enhance research coordination.

Currently, Big Data is enabling advanced applications that are, in many cases, outpacing the policy 

regimes under which they operate. Agencies foresee the need for a venue that includes industry for 

discussing policies governing Big Data use. The policies must keep pace with technological innovation 

and the new applications made possible by the technology. 

Support is needed for metrics and benchmarks to assess trends in technology and outcomes of policies. 

Both system- and application-level benchmarks and analysis can be used to help determine where

improvements are called for, improve performance, establish interoperability requirements, and 

determine the types of computing systems required. For example, they can help analyze response times 

under real-world conditions and help create a community of practice around a range of Big Data 

performance metrics. For example, in the past, metrics and benchmarks were successfully created to 

study network interoperability.

And, finally, to increase the impact of Big Data research investments, the research enterprise should be

linked with end users early in the project lifecycle. Such connections can help speed the transition of 

technology from the lab to operations, while maintaining a strong foundation of exploratory, curiositydriven research. Practitioner communities need support as they progress through a series of 

engagements with Big Data researchers and participate in Big Data challenge competitions. Each step 

requires tangible, measurable targets in order to create a process for achieving significant results on 

grand challenge problems through the use of Big Data.
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Acronyms

AI – Artificial Intelligence

API – Application Programming Interface

ASCR – DOE’s Advanced Scientific Computing Research Program

BD2K – NIH’s Big Data to Knowledge Initiative

BRAP – NSF’s Benchmarks of Realistic Scientific Application Performance of Large-Scale Computing 

Systems Program

DARPA – Defense Advanced Research Projects Agency

DHS – Department of Homeland Security

DOE – Department of Energy

ESnet – DOE’s Energy Sciences Network

GEM – Global Earthquake Model

GENI – NSF’s Global Environment for Networking Innovations Project

GEO – International Group on Earth Observations or NSF’s Geosciences Directorate

GEOSS – Global Earth Observation System of Systems

GINA – Genetic Information Nondiscrimination Act

GLOBE – Global Learning and Observations to Benefit the Environment Program

IoT – Internet of Things

ITL –NIST’s Information and Technology Laboratory 

MGI – Materials Genome Initiative

NASA – National Aeronautics and Space Administration

NIH – National Institutes of Health

NIST – National Institute of Standards and Technology

NITRD – Networking and Information Technology Research and Development Program

NOAA – National Oceanic and Atmospheric Administration

NRT – NSF’s Research Traineeship Program 

NSF – National Science Foundation

NSTC – National Science and Technology Council

OFR – Office of Financial Research

OMB – Office of Management and Budget

PCAST – President’s Council of Advisors on Science and Technology

RFI – Request for Information

SaTC – NSF’s Secure and Trustworthy Cyberspace Program

STEM – Science, Technology, Engineering, and Mathematics

USAID – U.S. Agency for International Development

USGEO – U.S. Group on Earth Observations

USGS – U.S. Geological Survey
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