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Machine Learning for Planetary Science

Machine Learning for Planetary Science
A Book

by Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner

  • Publisher : Elsevier
  • Release : 2021-03-01
  • Pages : 400
  • ISBN : 0128187220
  • Language : En, Es, Fr & De
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Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
A Book

by Anonim

  • Publisher : Academic Press
  • Release : 2020-09-25
  • Pages : 316
  • ISBN : 0128216840
  • Language : En, Es, Fr & De
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Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Deep learning for the Earth Sciences

Deep learning for the Earth Sciences
A comprehensive approach to remote sensing, climate science and geosciences

by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein

  • Publisher : John Wiley & Sons
  • Release : 2021-09-27
  • Pages : 432
  • ISBN : 1119646146
  • Language : En, Es, Fr & De
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Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices in the field Deep learning is a fundamental technique in modern artificial intelligence and is being applied to disciplines across the scientific spectrum. Earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferate broad spread. Deep Learning for the Earth Sciences delivers a perspective and unique treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described within in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
A Book

by Enrico Camporeale,Simon Wing,Jay Johnson

  • Publisher : Unknown Publisher
  • Release : 2018-06
  • Pages : 400
  • ISBN : 9780128117880
  • Language : En, Es, Fr & De
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Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB(R) scripts that allow for replication of results in the book, also familiarizing readers with algorithms

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
A Book

by Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser

  • Publisher : CRC Press
  • Release : 2017-08-01
  • Pages : 208
  • ISBN : 1315354462
  • Language : En, Es, Fr & De
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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Astrogeoinformatics

by Petr Skoda,Fathalrahman Adam

  • Publisher : Elsevier
  • Release : 2020-04-10
  • Pages : 472
  • ISBN : 0128191554
  • Language : En, Es, Fr & De
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Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences
Neural Networks and Kernels

by William W. Hsieh

  • Publisher : Cambridge University Press
  • Release : 2009-07-30
  • Pages : 349
  • ISBN : 0521791928
  • Language : En, Es, Fr & De
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A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
A Book

by Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser

  • Publisher : CRC Press
  • Release : 2017-08-01
  • Pages : 208
  • ISBN : 1498703887
  • Language : En, Es, Fr & De
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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

The Sun, Interplanetary Medium, Earth's Magnetosphere and Planetary Sciences

The Sun, Interplanetary Medium, Earth's Magnetosphere and Planetary Sciences
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 233
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Machine Learning on Mars

Machine Learning on Mars
A New Lens on Data from Planetary Exploration Missions

by Hannah Rae Kerner

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 269
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

Machine Learning for Decision Makers

Machine Learning for Decision Makers
Cognitive Computing Fundamentals for Better Decision Making

by Patanjali Kashyap

  • Publisher : Apress
  • Release : 2018-01-04
  • Pages : 355
  • ISBN : 1484229886
  • Language : En, Es, Fr & De
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Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Overcoming Data Scarcity in Earth Science

Overcoming Data Scarcity in Earth Science
A Book

by Angela Gorgoglione,Alberto Castro Casales,Christian Chreties Ceriani,Lorena Etcheverry Venturini

  • Publisher : MDPI
  • Release : 2020-05-22
  • Pages : 94
  • ISBN : 3039282107
  • Language : En, Es, Fr & De
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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

Machine Learning

Machine Learning
A Book

by Yagang Zhang

  • Publisher : BoD – Books on Demand
  • Release : 2010-02-01
  • Pages : 446
  • ISBN : 9533070331
  • Language : En, Es, Fr & De
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Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.

Machine Learning, Optimization, and Data Science

Machine Learning, Optimization, and Data Science
6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II

by Giuseppe Nicosia

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030645800
  • Language : En, Es, Fr & De
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Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
A Book

by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava

  • Publisher : CRC Press
  • Release : 2012-03-29
  • Pages : 744
  • ISBN : 1439841748
  • Language : En, Es, Fr & De
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Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Discovery Science

Discovery Science
18th International Conference, DS 2015, Banff, AB, Canada, October 4-6, 2015. Proceedings

by Nathalie Japkowicz,Stan Matwin

  • Publisher : Springer
  • Release : 2015-10-04
  • Pages : 342
  • ISBN : 3319242822
  • Language : En, Es, Fr & De
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This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions. The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.

An Astrobiology Strategy for the Search for Life in the Universe

An Astrobiology Strategy for the Search for Life in the Universe
A Book

by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Space Studies Board,Committee on Astrobiology Science Strategy for the Search for Life in the Universe

  • Publisher : National Academies Press
  • Release : 2019-04-20
  • Pages : 188
  • ISBN : 0309484162
  • Language : En, Es, Fr & De
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Astrobiology is the study of the origin, evolution, distribution, and future of life in the universe. It is an inherently interdisciplinary field that encompasses astronomy, biology, geology, heliophysics, and planetary science, including complementary laboratory activities and field studies conducted in a wide range of terrestrial environments. Combining inherent scientific interest and public appeal, the search for life in the solar system and beyond provides a scientific rationale for many current and future activities carried out by the National Aeronautics and Science Administration (NASA) and other national and international agencies and organizations. Requested by NASA, this study offers a science strategy for astrobiology that outlines key scientific questions, identifies the most promising research in the field, and indicates the extent to which the mission priorities in existing decadal surveys address the search for life's origin, evolution, distribution, and future in the universe. This report makes recommendations for advancing the research, obtaining the measurements, and realizing NASA's goal to search for signs of life in the universe.

Space Science and Public Engagement

Space Science and Public Engagement
21st Century Perspectives and Opportunities

by Amy Paige Kaminski

  • Publisher : Elsevier
  • Release : 2021-06-15
  • Pages : 298
  • ISBN : 0128173912
  • Language : En, Es, Fr & De
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Space Science and Public Engagement: 21st Century Perspectives and Opportunities critically examines the many dimensions of public engagement with space science by exploring case studies that show a spectrum of public engagement formats, ranging from the space science community's efforts to communicate developments to the public, to citizenry attempting to engage with space science issues. It addresses why public engagement is important to space science experts, what approaches they take, how public engagement varies locally, nationally and internationally, and what roles "non-experts" have played in shaping space science. Space scientists, outreach specialists in various scientific disciplines, policymakers and citizens interested in space science will find great insights in this book that will help inform their future engagement strategies. Critically examines how expert organizations and the space science community have sought to bring space science to the public Examines how the public has responded, and in some cases self-organized, to opportunities to contribute to space science Outlines future engagement interests and possibilities

Algorithmic Learning Theory

Algorithmic Learning Theory
26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings

by Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles

  • Publisher : Springer
  • Release : 2015-10-04
  • Pages : 395
  • ISBN : 3319244868
  • Language : En, Es, Fr & De
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This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Advances in Earth Science

Advances in Earth Science
From Earthquakes to Global Warming

by J. M. T. Thompson

  • Publisher : Imperial College Press
  • Release : 2007
  • Pages : 314
  • ISBN : 1860948715
  • Language : En, Es, Fr & De
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Advances in Earth Science outlines the latest developments and new research directions currently being made world-wide in the earth sciences. It contains invited and refereed articles by leading younger researchers on their cutting-edge research, but aimed at a general scientific audience. This exciting volume explains how powerful methodologies such as satellite remote sensing and supercomputing simulations are now profoundly changing research in the earth sciences; how the earth system is increasingly being viewed in a holistic way, linking the atmosphere, ocean and solid earth; and how the societal impact of the research in the earth sciences has never been more important. Published by Imperial College Press in collaboration with the Royal Society of London, the book features many articles originating from invited papers published in the Philosophical Transactions of the Royal Society. Eleven of the distinguished contributors hold prestigious Royal Society Research Fellowships.