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Technological advances and the rise of collaborative, interdisciplinary approaches have changed the practice of research. The 21st century researcher not only faces the challenge of managing increasingly complex datasets, but also new data sharing requirements from funders and journals. Success in today’s research enterprise requires an understanding of how to work effectively with data, yet most researchers have never had any formal training in data management. Libraries have begun developing services and programs to help researchers meet the demands of the data-driven research enterprise, giving librarians exciting new opportunities to use their expertise and skills. The Medical Library Association Guide to Data Management for Librarians highlights the many ways that librarians are addressing researchers’ changing needs at a variety of institutions, including academic, hospital, and government libraries. Each chapter ends with “pearls of wisdom,” a bulleted list of 5-10 takeaway messages from the chapter that will help readers quickly put the ideas from the chapter into practice. From theoretical foundations to practical applications, this book provides a background for librarians who are new to data management as well as new ideas and approaches for experienced data librarians.
This book constitutes the refereed proceedings of the 8th International Conference on Software Business, ICSOB 2017, held in Essen, Germany, in June 2017. The 11 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 30 submissions. They were organized in topical sections named: software startups and platform governance; software business development; software ecosystems and App stores.
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
ArcheoLogica Data wants to reach an Italian and international audience of scholars, professionals, students, and, more generally, early-career archaeologists, and it accepts contributions written both in Italian and English. ArcheoLogica Data proposes to indissolubly associate data and interpretation. It embraces that global idea of ​​archaeological data that integrates all the discipline declinations without any thematic or chronological constraints. Data is at the centre, and around lies everything that can stem from it: interpretations, hypotheses, reconstructions, applications, theoretical and methodological reflections, critical ideas, constructive discussions.
The volume LNCS 12393 constitutes the papers of the 22nd International Conference Big Data Analytics and Knowledge Discovery which will be held online in September 2020. The 15 full papers presented together with 14 short papers plus 1 position paper in this volume were carefully reviewed and selected from a total of 77 submissions. This volume offers a wide range to following subjects on theoretical and practical aspects of big data analytics and knowledge discovery as a new generation of big data repository, data pre-processing, data mining, text mining, sequences, graph mining, and parallel processing.
Perhaps the most popular of all Institute products, selected Working Papers are now available for the first time in a print format. These papers contain the preliminary results of ongoing Institute research. The book is divided into four sections: Trade and the Global Economy, Outsourcing, Asia, and the Middle East. Included in the book are papers by Edwin M. Truman, Morris Goldstein, Gary Clyde Hufbauer, Nicholas R. Lardy, Catherine L. Mann, and Marcus Noland. Volume I contains papers from 2005. Future volumes will be published on a semi-regular schedule as material is available.
"Report of the Dominion fishery commission on the fisheries of the province of Ontario, 1893", issued as vol. 26, no. 7, supplement.
The pandemic wave of Covid 19 made many organizations in different sectors rethink their business strategy. The pandemic wave soon became an endemic and the organizations adapted themselves to the new ways of doing business. Endemic situation indicates that the Covid virus is here to stay but with limited impact. The book provides deeper research on how organizations adapted themselves to the post Covid situation and how they look at the future. The book covers studies from the areas of marketing, finance, human resource, operations, healthcare and education.
A selection of 50 papers presented at CAA2016. Papers are grouped under the following headings: Ontologies and Standards; Field and Laboratory Data Recording and Analysis; Archaeological Information Systems; GIS and Spatial Analysis; 3D and Visualisation; Complex Systems Simulation; Teaching Archaeology in the Digital Age.
An insider’s guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data. Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever. Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required. The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes. With contemporary case studies from a range of institutions and disciplines, tips for best practice, study aids and links to key resources, this book is a must-read for all new entrants to the field, library and information students and working professionals. Key topics covered include: • the evolution of data libraries and data archives • handling data compared to other forms of information • managing and curating data to ensure effective use and longevity • how to incorporate data literacy into mainstream library instruction and information literacy training • how to develop an effective institutional research data management (RDM) policy and infrastructure • how to support and review a data management plan (DMP) for a project, a key requirement for most research funders • approaches for developing, managing and promoting data repositories • handling and sharing confidential or sensitive data • supporting open scholarship and open science, ensuring data are discoverable, accessible, intelligible and assessable. This title is for the practising data librarian, possibly new in their post with little experience of providing data support. It is also for managers and policy-makers, public service librarians, research data management coordinators and data support staff. It will also appeal to students and lecturers in iSchools and other library and information degree programmes where academic research support is taught.