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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
    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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  • Open Source Software for Statistical Analysis of Big Data : Emerging Research and Opportunities
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    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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  • 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.

  • 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.

  • 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.

  • 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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  • Open Source Software for Statistical Analysis of Big Data : Emerging Research and Opportunities
    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
    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
    Scholarly Editing : A Guide to Research

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  • Gender Differences in Technology and Innovation Management : Insights from Experimental Research
    Gender Differences in Technology and Innovation Management : Insights from Experimental Research

    Even though the number of working women has steadily increased over the last few years, women are still significantly under-represented in STEM activities (i.e. mathematics, informatics, science and technology). In order to eliminate this under-representation, numerous education policies and corporate initiatives, particularly in the recent past, have been aimed at increasing women's enthusiasm for STEM activities and professions.According to the latest surveys, however, it is clear that these efforts have not yet led to the desired success.Compared to their male counterparts, women continue to do fewer STEM activities. One possible reason for this is that relatively little is yet known about the concrete impact of the above education policies on working with innovation and technology: What are the gender differences between women and men?Is it enough to recognize these differences, or should these differences ideally not only be recognized, but also treated appropriately or even encouraged? This anthology deals with current topics in technology and innovation management against the background of these and other gender-relevant aspects.Empirical analyses and experiments in collaboration with companies from various sectors provide a sound scientific basis on which new results and findings are presented: How do women and men deal with creativity and competition?How are technologies applied and how can differences in access to technology be deduced? Answers to these and other questions help decision-makers in politics and business to proactively use the differences between women and men to motivate women to work in the STEM field and to strengthen them by acknowledging existing differences.

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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.

  • Is the data repository updated monthly or one-time?

    The data repository is updated monthly.

  • Is Wikipedia a reliable source for research?

    Wikipedia can be a useful starting point for research, as it provides a broad overview of a topic and can lead to other reputable sources. However, because it is a user-generated platform, the information on Wikipedia can be edited by anyone, which means it may not always be accurate or reliable. It is important to verify the information found on Wikipedia with other credible sources before using it in academic or professional research.

  • What is the difference between the Data Dictionary and the Repository?

    The Data Dictionary is a centralized repository of information about data such as data definitions, data relationships, data usage, and data constraints. It provides a comprehensive view of the data within an organization. On the other hand, the Repository is a broader term that refers to a centralized location for storing and managing various types of data, including data models, metadata, code, and other artifacts related to software development. While the Data Dictionary focuses specifically on data-related information, the Repository encompasses a wider range of information and artifacts related to software development and data management.

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