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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.

On January 30-31, 2019 the Board on Mathematical Sciences and Analytics, in collaboration with the Board on Energy and Environmental Systems and the Computer Science and Telecommunications Board, convened a workshop in Washington, D.C. to explore the frontiers of mathematics and data science needs for sustainable urban communities. The workshop strengthened the emerging interdisciplinary network of practitioners, business leaders, government officials, nonprofit stakeholders, academics, and policy makers using data, modeling, and simulation for urban and community sustainability, and addressed common challenges that the community faces. Presentations highlighted urban sustainability research efforts and programs under way, including research into air quality, water management, waste disposal, and social equity and discussed promising urban sustainability research questions that improved use of big data, modeling, and simulation can help address. This publication summarizes the presentation and discussion of the workshop.

Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests.

The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11–12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

The Board on Science Education and the Board on Mathematical Sciences and Analytics of the National Academies of Sciences, Engineering, and Medicine convened the Workshop on Increasing Student Success in Developmental Mathematics on March 18-19, 2019. The Workshop explored how to best support all students in postsecondary mathematics, with particular attention to students who are unsuccessful in developmental mathematics and with an eye toward issues of access to promising reforms and equitable learning environments.

The two-day workshop was designed to bring together a variety of stakeholders, including experts who have developed and/or implemented new initiatives to improve the mathematics education experience for students. The overarching goal of the workshop was to take stock of the mathematics education community''s progress in this domain. Participants examined the data on students who are well-served by new reform structures in developmental mathematics and discussed various cohorts of students who are not currently well served - those who even with access to reforms do not succeed and those who do not have access to a reform due to differential access constraints. Throughout the workshop, participants also explored promising approaches to bolstering student outcomes in mathematics, focusing especially on research and data that demonstrate the success of these approaches; deliberated and discussed barriers and opportunities for effectively serving all students; and outlined some key directions of inquiry intended to address the prevailing research and data needs in the field. This publication summarizes the presentations and discussion of the workshop.

Biomedical research results in the collection and storage of increasingly large and complex data sets. Preserving those data so that they are discoverable, accessible, and interpretable accelerates scientific discovery and improves health outcomes, but requires that researchers, data curators, and data archivists consider the long-term disposition of data and the costs of preserving, archiving, and promoting access to them.

Life Cycle Decisions for Biomedical Data examines and assesses approaches and considerations for forecasting costs for preserving, archiving, and promoting access to biomedical research data. This report provides a comprehensive conceptual framework for cost-effective decision making that encourages data accessibility and reuse for researchers, data managers, data archivists, data scientists, and institutions that support platforms that enable biomedical research data preservation, discoverability, and use.

Biomedical research data sets are becoming larger and more complex, and computing capabilities are expanding to enable transformative scientific results. The National Institutes of Health''s (NIH''s) National Library of Medicine (NLM) has the unique role of ensuring that biomedical research data are findable, accessible, interoperable, and reusable in an ethical manner. Tools that forecast the costs of long-term data preservation could be useful as the cost to curate and manage these data in meaningful ways continues to increase, as could stewardship to assess and maintain data that have future value.

The National Academies of Sciences, Engineering, and Medicine convened a workshop on July 11-12, 2019 to gather insight and information in order to develop and demonstrate a framework for forecasting long-term costs for preserving, archiving, and accessing biomedical data. Presenters and attendees discussed tools and practices that NLM could use to help researchers and funders better integrate risk management practices and considerations into data preservation, archiving, and accessing decisions; methods to encourage NIH-funded researchers to consider, update, and track lifetime data; and burdens on the academic researchers and industry staff to implement these tools, methods, and practices. This publication summarizes the presentations and discussion of the workshop.

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine''s Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

The needs and demands placed on science to address a range of urgent problems are growing. The world is faced with complex, interrelated challenges in which the way forward lies hidden or dispersed across disciplines and organizations. For centuries, scientific research has progressed through iteration of a workflow built on experimentation or observation and analysis of the resulting data. While computers and automation technologies have played a central role in research workflows for decades to acquire, process, and analyze data, these same computing and automation technologies can now also control the acquisition of data, for example, through the design of new experiments or decision making about new observations.

The term automated research workflow (ARW) describes scientific research processes that are emerging across a variety of disciplines and fields. ARWs integrate computation, laboratory automation, and tools from artificial intelligence in the performance of tasks that make up the research process, such as designing experiments, observations, and simulations; collecting and analyzing data; and learning from the results to inform further experiments, observations, and simulations. The common goal of researchers implementing ARWs is to accelerate scientific knowledge generation, potentially by orders of magnitude, while achieving greater control and reproducibility in the scientific process.

Automated Research Workflows for Accelerated Discovery: Closing the Knowledge Discovery Loop examines current efforts to develop advanced and automated workflows to accelerate research progress, including wider use of artificial intelligence. This report identifies research needs and priorities in the use of advanced and automated workflows for scientific research. Automated Research Workflows for Accelerated Discovery is intended to create awareness, momentum, and synergies to realize the potential of ARWs in scholarly discovery.

The Earth system - the atmospheric, hydrologic, geologic, and biologic cycles that circulate energy, water, nutrients, and other trace substances - is a large, complex, multiscale system in space and time that involves human and natural system interactions. Machine learning (ML) and artificial intelligence (AI) offer opportunities to understand and predict this system. Researchers are actively exploring ways to use ML/AI approaches to advance scientific discovery, speed computation, and link scientific communities.

To address the challenges and opportunities around using ML/AI to advance Earth system science, the National Academies convened a workshop in February 2022 that brought together Earth system experts, ML/AI researchers, social and behavioral scientists, ethicists, and decision makers to discuss approaches to improving understanding, analysis, modeling, and prediction. Participants also explored educational pathways, responsible and ethical use of these technologies, and opportunities to foster partnerships and knowledge exchange. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

The assessment of risk is complex and often controversial. It is derived from the existence of a hazard, and it is characterized by the uncertainty of possible undesirable events and their outcomes. Few outcomes are as undesirable as nuclear war and nuclear terrorism. Over the decades, much has been written about particular situations, policies, and weapons that might affect the risks of nuclear war and nuclear terrorism. The nature of the concerns and the risk analysis methods used to evaluate them have evolved considerably over time.

At the request of the Department of Defense, Risk Analysis Methods for Nuclear War and Nuclear Terrorism discusses risks, explores the risk assessment literature, highlights the strengths and weaknesses of risk assessment approaches, and discusses some publicly available assumptions that underpin U.S. security strategies, all in the context of nuclear war and nuclear terrorism.

As part of its responsibilities under the Clean Air Act, the U.S. Environmental Protection Agency sets National Ambient Air Quality Standards (NAAQS) for the air pollutants carbon monoxide, lead, oxides of nitrogen, particulate matter, ozone, and sulfur dioxide. EPA uses a "weight of evidence approach" to evaluate evidence from scientific studies and describe the causal relationships between these "criteria pollutants" and any adverse impacts on human health and on public welfare - including impacts on wildlife, water, forests, agriculture, and climate. The evaluation, called an Integrated Science Assessment, is used to inform standards setting associated with the criteria pollutants.

This report, produced at the request of EPA, describes EPAs and several other frameworks for inferring causality of health or welfare effects and the characteristics of evidence useful for forming a causal determination. The report concludes that EPAs causal framework is effective, reliable, and scientifically defensible, provided that key scientific questions are identified and a range of necessary expertise is engaged. More transparency in how EPA integrates evidence could improve confidence in their determinations, and more guidance is needed in the framework on how evidence should be examined for vulnerable groups (e.g., human sub-populations) and sensitive ecosystems or species.

Across multiple domains of science, engineering, and medicine, excitement is growing about the potential of digital twins to transform scientific research, industrial practices, and many aspects of daily life. A digital twin couples computational models with a physical counterpart to create a system that is dynamically updated through bidirectional data flows as conditions change. Going beyond traditional simulation and modeling, digital twins could enable improved medical decision-making at the individual patient level, predictions of future weather and climate conditions over longer timescales, and safer, more efficient engineering processes. However, many challenges remain before these applications can be realized.

This report identifies the foundational research and resources needed to support the development of digital twin technologies. The report presents critical future research priorities and an interdisciplinary research agenda for the field, including how federal agencies and researchers across domains can best collaborate.

The National Academies Board on Mathematical Sciences and Analytics and Board on Infrastructure and the Constructed Environment convened a 3-day public workshop on July 13, 20, and 27, 2022, to explore state-of-the-art analytical tools that could advance urban sustainability through improved prioritization of public works projects. Invited speakers included people working in urban sustainability, city planning, local public and private infrastructure, asset management, and infrastructure investment; city officials and utility officials; and statisticians, data scientists, mathematicians, economists, computer scientists, and artificial intelligence/machine learning experts. Presentations and workshop discussions provided insights into new research areas that have the potential to advance urban sustainability in public works planning, as well as the barriers to their adoption. This publication summarizes the presentation and discussion of the workshop.

Artificial intelligence (AI) has the potential to aid new mathematical discoveries. Particularly as the amount of data available grows beyond what any person can study, AI can be useful in its power to identify patterns in data and refine relationships between properties. Sponsored by the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine Board on Mathematical Sciences and Analytics convened a 3-day public virtual workshop on June 12-14, 2023, to bring together stakeholders to discuss the state of the art and current challenges and opportunities to advance research in using AI for mathematical reasoning. This publication summarizes the presentations and discussion of the workshop.

Decades of research have shown that disadvantaged communities exist at the intersection of high levels of hazard exposure and poverty. Geospatial environmental justice (EJ) tools, such as the White House Council on Environmental Quality-developed Climate and Economic Justice Screening Tool (CEJST), are designed to integrate different kinds of health, social, environmental, and economic data to identify disadvantaged communities and to aid policy and investment decisions that address the pervasive, persistent, and largely unaddressed problems associated with environmental disparities in the United States.

Constructing Valid Geospatial Tools for Environmental Justice evaluates several EJ tools, including CEJST, and provides a conceptual framework and data strategy recommendations for developing the composite indicators that are the heart of geospatial EJ tools. An EJ tool that is transparent, legitimate, and has the trust of its users and the communities it represents is based on a structured iterative process that includes: a clear statement of tool objectives and definitions for the concepts being measured; the selection and integration of data and indicators; and assessment of robustness of the selected data and integration processes. Decisions regarding the tool should be iteratively informed by meaningful community engagement, validation to ensure tool results reflect real-world experiences, and careful and thorough documentation of all decision and data processes.

The Committee on Risk Analysis Methods for Nuclear War and Nuclear Terrorism was established and managed by the National Academies of Sciences, Medicine, and Engineering in response to a congressional mandate to independently explore U.S. government methods for assessing nuclear war and nuclear terrorism risks and how those assessments are used to develop strategy and policy. This publication is the public, abbreviated version of the classified report.

The United States and the world face serious threats to nuclear stability and peace, now and in the coming decades. Within the nuclear arena, U.S. policy makers will need to make strategic decisions related to nuclear risks to assist with long-term planning as well as responding in real time to unanticipated events. The occurrence of unanticipated nuclear events is expected to increase as more countries develop, expand, or field nuclear energy capability; more countries consider development of nuclear weapon capability and new nuclear weapon states emerge; and nuclear weapon states expand their nuclear arsenals.

At the request of the Department of Defense, the National Academies of Sciences, Engineering, and Medicine established and managed the Committee on Risk Analysis Methods for Nuclear War and Nuclear Terrorism to explore U.S. government methods for assessing nuclear war and nuclear terrorism risks and how those assessments are used to develop strategy and policy. This publication is the unclassified Phase 2 version of the final classified report. Risk Analysis Methods for Nuclear War and Nuclear Terrorism: Phase II builds on an earlier Phase I unclassified report. This book expands upon the use of analytical methods to assess the risks of nuclear terrorism and nuclear war and the role such approaches may play in U.S. security strategy.

Additive manufacturing (AM), the process in which a three-dimensional (3D) object is built by adding subsequent layers of materials, enables novel material compositions and shapes, often without the need for specialized tooling. On March 11-13, 2024, the Board on Mathematical Sciences and Analytics of the National Academies held a workshop on Statistical and Data-Driven Methods for Additive Manufacturing. The workshop brought together researchers from different AM communities, statisticians, data scientists, and AI/machine learning (ML) experts to examine approaches that enhance dimensional accuracy and dimensional stability; recent advances and future directions in statistics, data analytics, AI, and ML; and the issues associated with a rapid advance of AM material qualification and part certification.