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Bioinformatics Data Skills : Reproducible and Robust Research with Open Source Tools
Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings.With this practical guide, you'll learn how to use freely available open source tools to extract meaning from large complex biological data sets.At no other point in human history has our ability to understand life's complexities been so dependent on our skills to work with and analyze data.This intermediate-level book teaches the general computational and data skills you need to analyze biological data.If you have experience with a scripting language like Python, you're ready to get started.Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles
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Big Data for the Public Good : Regulating Access to Public Sector Big Data for Research and Innovation
Can researchers and innovators use UK public sector data to produce knowledge that improves policy making, scrutinises government work and promotes the public interest?This book looks at interactions between UK public sector officials and researchers/innovators to shed light on barriers to data access and use.It asks: what are the frameworks that govern access to public sector big datasets for researchers and innovators?How are these frameworks applied in practice? What are the governance solutions for policy makers interested in harnessing the untapped potential of public sector big data to improve their policies and create public benefit?Public sector data is a valuable resource that can help researchers and innovators create knowledge and solutions that benefit society.As public bodies collect increasingly more data about us, UK policy makers try to maximise the use of public sector big data for the benefit of the public.But accessing this data is not easy. There are many legal, technical, and ethical barriers that prevent the use of public sector data for research and innovation.This book is for researchers and innovators who want to understand and overcome the barriers to accessing UK public sector data.It is also for policy makers who are interested in how public sector data can be used to improve decision-making, scrutinise government work, and promote the public interest.
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Applied Mathematics with Open-Source Software : Operational Research Problems with Python and R
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics.The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation.The pairs of chapters in each section demonstrate different families of solution approaches.Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references.This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic. Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
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Managing Cloud Native Data on Kubernetes : Architecting Cloud Native Data Services Using Open Source Technology
Kubernetes has become the primary platform for deploying and managing cloud native applications.But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging.If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help. Using Kubernetes as your platform, you'll discover open source technologies that are designed and built for the cloud.Delve into case studies to avoid the pitfalls others have faced and explore new use cases.Get an insider's view of what's coming from the innovators who are creating next-generation architectures and infrastructure. And you'll learn how to: Manage different data use cases on Kubernetes Reduce costs and simplify application development Leverage data and infrastructure to create new use cases and business models Make data infrastructure choices that are cost-efficient, secure, scalable, and elastic And more
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Is Dewiki a scholarly source for the seminar paper?
No, Dewiki is not a scholarly source for the seminar paper. Dewiki is the German-language version of Wikipedia, which is a collaborative online encyclopedia that can be edited by anyone. While Wikipedia can be a useful starting point for research, it is not considered a scholarly source because the information is not always verified or written by experts in the field. It is important to use scholarly sources such as academic journals, books, and reputable websites for a seminar paper to ensure the accuracy and reliability of the information.
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Does market research hinder innovation in business administration?
Market research does not necessarily hinder innovation in business administration. In fact, it can provide valuable insights into consumer needs and preferences, helping businesses to develop innovative products and services that meet market demands. By understanding market trends and customer behavior, businesses can identify opportunities for innovation and stay ahead of competitors. However, relying too heavily on market research without allowing room for creativity and risk-taking can limit the potential for groundbreaking innovations. It is important for businesses to strike a balance between leveraging market research and fostering a culture of innovation to drive success in business administration.
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How can one access deleted data only with root access?
One can access deleted data with root access by using data recovery tools or software that are specifically designed to retrieve deleted files from a system's storage. Root access allows the user to bypass restrictions and permissions set by the operating system, enabling them to access and recover data that would otherwise be inaccessible. By using root access, one can directly interact with the system's storage and retrieve deleted files before they are overwritten by new data. It is important to note that accessing deleted data with root access should be done carefully to avoid causing further damage to the system.
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Is this a scholarly source that I can use for a university assignment?
To determine if this source is scholarly and suitable for a university assignment, you should consider the author's credentials, the publication or journal in which it appears, and the content of the source itself. Look for information about the author's expertise and qualifications in the field, and check if the publication is peer-reviewed or from a reputable academic source. Additionally, assess the content for evidence of research, citations, and a formal, academic tone. If the source meets these criteria, it is likely to be suitable for a university assignment.
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Open Source Software for Statistical Analysis of Big Data : Emerging Research and Opportunities
With the development of computing technologies in today's modernized world, software packages have become easily accessible.Open source software, specifically, is a popular method for solving certain issues in the field of computer science.One key challenge is analyzing big data due to the high amounts that organizations are processing.Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python.Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.
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Open Source Software for Statistical Analysis of Big Data : Emerging Research and Opportunities
With the development of computing technologies in today's modernized world, software packages have become easily accessible.Open source software, specifically, is a popular method for solving certain issues in the field of computer science.One key challenge is analyzing big data due to the high amounts that organizations are processing.Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python.Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.
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Open Access and the Future of Scholarly Communication : Policy and Infrastructure
It is impossible to imagine the future of academic libraries without an extensive consideration of open access—the removal of price and permission barriers from scholarly research online. As textbook and journal subscription prices continue to rise, improvements in technology make online dissemination of scholarship less expensive, and faculty recognize the practical and philosophical appeal of making their work available to wider audiences.As a consequences, libraries have begun to consider a wide variety of open access “flavors” and business models. These new possibilities have significant impact on both library services and collection policies, and the call for new skills within library staffing.Volume 9 of the series Creating the 21st-Century Academic Library is the first of two addressing the topic of open access in academic libraries and focuses on policy and infrastructure for libraries that wish to provide leadership on their campus in the transition to more open forms of scholarship. Chapters in the book discuss how to make the case for open access on campus, as well as the political and policy implications of libraries that themselves want to become publishing entities.Infrastructure issues are also addressed including metadata standards and research management services.Also considered here is how interlibrary loan, preservation and the library’s role in providing textbooks, support the concept of open access. It is hoped that this volume, and the series in general, will be a valuable and exciting addition to the discussions and planning surrounding the future directions, services, and careers in the 21st-century academic library.
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Scholarly Editing : A Guide to Research
This collection of twenty-six essays, written by acknowledged experts in literary studies, surveys the history of scholarly editing, describes the major research in a variety of disciplines, summarizes the resources available to scholars, and analyzes the issues currently facing textual editors. The book begins with an overview of scholarly editing, followed by four essays on the long tradition of editing the Bible and the Greek and Latin classics.The next cluster of essays proceeds through the major periods of British and American literature, from medieval to modern, further subdividing the Renaissance and the nineteenth century by genre and including a chapter devoted to Shakespeare.Additional essays cover other European literatures: French (Old French and early modern), Italian, medieval Spanish, German, and Russian.The concluding essays discuss representative non-European literatures and the “nonliterary” editing of folk literature in various languages.Each chapter includes a history of scholarly editing in the field, a citation of exemplary editions, and an introduction to a recommended list of further readings.
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Is it generally known that one cannot cite Wikipedia as a scholarly source?
Yes, it is generally known that one cannot cite Wikipedia as a scholarly source. While Wikipedia can be a useful starting point for research and provide a general overview of a topic, it is not considered a reliable or authoritative source for academic work. This is because Wikipedia can be edited by anyone, and its content may not always be accurate or well-researched. Instead, scholars are encouraged to use peer-reviewed sources, such as academic journals and books, for their research and citations.
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What is the best solution for a data repository?
The best solution for a data repository depends on the specific needs and requirements of the organization. However, a cloud-based data repository offers many advantages, including scalability, accessibility, and cost-effectiveness. Cloud-based solutions also provide built-in security measures and the ability to easily integrate with other cloud services. Additionally, using a managed data repository service can offload the burden of maintenance and management, allowing the organization to focus on utilizing the data for their business needs.
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Is the data repository updated monthly or one-time?
The data repository is updated monthly.
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How is the collaboration with the boss?
The collaboration with the boss is excellent. We have a strong working relationship built on trust, open communication, and mutual respect. Our boss values our input and encourages us to share our ideas and feedback. This collaborative environment allows us to work together effectively towards our common goals and achieve success as a team.
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