The Federal Big Data Research and Development Strategic Plan

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). A national Big Data1 innovation 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 real-time 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. 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

The Federal Big Data Research and Development Strategic Plan

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). A national Big Data1 innovation 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 real-time 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. 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

Brainhack: a collaborative workshop for the open neuroscience community

International audienceBrainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science

DolphinNext: a distributed data processing platform for high throughput genomics

BACKGROUND: The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. RESULTS: To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. CONCLUSION: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results

Using Data in Undergraduate Science Classrooms

Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm

Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices

Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visu‐ alizations. This research investigates the use of co‐located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non‐Shared) and how being placed at various position arrangements (Corner‐to‐Corner, Face‐to‐Face, and Side‐by‐Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 partici‐ pants divided into 6 groups (2 modes×3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side‐by‐Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them