Research And Development Data Needs

This report contains the proceedings of a one-day workshop organized by the National Research Council's Board on Science, Technology, and Economic Policy (STEP), in conjunction with a study by a panel of the NRC Committee on National Statistics (CNSTAT). This combined activity was commissioned by the Science Resources Statistics Division (SRS) of the National Science Foundation (NSF) to recommend improvements in the Foundation's portfolio of surveys of research and development spending by the federal government, state governments, private industry, the nation's universities and colleges, and other nonprofit institutions.

This report contains the proceedings of a one-day workshop organized by the National Research Council's Board on Science, Technology, and Economic Policy (STEP), in conjunction with a study by a panel of the NRC Committee on National Statistics (CNSTAT). This combined activity was commissioned by the Science Resources Statistics Division (SRS) of the National Science Foundation (NSF) to recommend improvements in the Foundation's portfolio of surveys of research and development spending by the federal government, state governments, private industry, the nation's universities and colleges, and other nonprofit institutions.

Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University

This symposium, which was held on March 10-11, 2003, at UNESCO headquarters in Paris, brought together policy experts and managers from the government and academic sectors in both developed and developing countries to (1) describe the role, value, and limits that the public domain and open access to digital data and information have in the context of international research; (2) identify and analyze the various legal, economic, and technological pressures on the public domain in digital data and information, and their potential effects on international research; and (3) review the existing and proposed approaches for preserving and promoting the public domain and open access to scientific and technical data and information on a global basis, with particular attention to the needs of developing countries.

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) to "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. 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.

The internationally recognised methodology for collecting and using R&D statistics, the OECD's Frascati Manual is an essential tool for statisticians and science and innovation policy makers worldwide. It includes definitions of basic concepts, data collection guidelines, and classifications ...

This report improves the evidence base on the role of Data Driven Innovation for promoting growth and well-being, and provide policy guidance on how to maximise the benefits of DDI and mitigate the associated economic and societal risks.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.