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705 results in Computer Hardware, Architecture and Distributed Computing

Page 7 of 36

  • 3 - What Are Research Data?

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 19-32

  • Summary

    Aims

    The aim of this chapter is to explore what research data are. It will help you start to have productive conversations with researchers about their data.

    Research data are important to (some) researchers

    For many researchers in the sciences and social sciences research data are of central importance to their work. Planning the collection of appropriate data is a key part of research design. The term ‘data creation’ may often be a more accurate term than ‘data collection’ or ‘data capture’, which imply that data are something existing before the researcher intervenes to actively construct them. But the language in use varies between fields of study and not all research projects actively create data. Creating data may take up many hours of work, and can be one of the most exciting parts of the research process, where the researcher gets into the laboratory or out into the field in the hope of finding something new. Then, processing the data and analysing them are central to creating new knowledge.

    Skill and innovation in eliciting and then analysing data is central to one's success as a researcher. The researcher's deep relationship to data is strongly linked to their methodological commitments about how they believe science builds knowledge. A common understanding of methods is a central aspect of their subject discipline. Thus they have a strong investment in research data and a concern with their quality.

    Actually, in many fields there is a kudos attached to collecting data oneself. It could be seen as a rite of passage for the novice researcher in some subjects. When talking about their work researchers often talk about ‘my data … my stuff ’. This points to the strong relationship between research, research data and identity. The conversation about research data is a deep one. We have even heard researchers talk about their data as their ‘life's work’. Material they are gathering is part of building a legacy. Imagine the researcher who has pursued their interests over multiple projects throughout a long research career. To them they have an intimate connection to the various datasets that they have accumulated and pored over for many hours. Often it can be this, as much as pragmatic concerns such as fear of being beaten to publication, that inhibits research data sharing.

  • 20 - Conclusion: the Skills and Mindset to Succeed in RDM

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 187-194

  • Summary

    Aims

    The aim of this chapter is to reflect on the kind of knowledge, skills and mindsets needed for work in RDM, and how to plan to develop and demonstrate these, and to discuss some starting points for keeping up to date.

    Working in RDM

    Reflecting on the content of the book, we can begin to say that the knowledge, skills and mindsets that you might have already or will want to develop to work in this field could include the following:

  • • An interest in research and a belief in the benefits to society of research. This implies a willingness to engage with researchers to help them, even if ultimately you may not fully understand what their research is about.

  • • More specifically it would be valuable to have an interest in developments in areas such as data handling, data analysis and data visualisation, and in evaluating digital tools that support these activities.

  • • Curiosity, a thoughtful approach and a willingness to learn new things. You will be the kind of person who wants to go and find out what is going on, be that within your own institution or in others. You are not afraid to deal with complexity or get involved even where professional practices are not well established.

  • • Good influencing skills. Trying to persuade researchers to see the importance and benefits of RDM is a key part of the role. It is a challenging one because of the diversity of research (see Chapter 14).

  • • Leadership skills. In the widest sense you will want to make a difference to your organisation. That implies making some quite courageous and tenacious stands in order to improve the long-term situation (see Chapter 6).

  • • Excellent networking and collaboration skills. Knowing who is who across the organisation and working with others, disregarding boundaries of formal organisational structures and hierarchies, will be a key challenge. Building good contacts outside the organisation is important too. Throughout the book we urge you to get out of your office and talk to people (e.g. Chapters 8, 14).

  • • A service orientation. An interest in developing services to help people do their own job better (Chapter 7).

  • 12 - Practical Data Management

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 107-114

  • Summary

    Aims

    The aim of this chapter is to review the aspects of practical data management that have a bearing on RDM. It will help you think about the day-to-day data management issues researchers face as an important aspect of RDM.

    Introduction

    The issues dealt with in this chapter can be introduced by the story of the poster that was supposedly once seen in a university computer lab. The poster offered a reward to whoever had stolen a laptop. It did not ask for the return of the laptop or even all the data on it, just the files in a single folder called ‘thesis data’. The poster explained that the folder contained the student's only copy of the data for their PhD, and that if it is not recovered the student cannot complete their doctorate. Quite a large reward was offered, but it was negotiable. The student was willing to pay more for the files. The thief was welcome to keep the laptop. No questions would be asked of whoever claimed the reward. We don't know if anyone did claim the reward. Actually we don't know if the whole story is true. It could be. It reflects that if data is core to some research work, the loss of data is a critical area of risk.

    Laptops get stolen or lost all the time. They are notoriously unreliable. Surely people know this and back up their data and document the backups? Actually, no, the evidence seems to be that people are quite bad about managing their ‘stuff’, even critical files: they are not necessarily good at managing risk in an intelligent way. Unexpected eventualities such as fires or natural disasters do happen, which can endanger valuable material such as research data. In a rapidly changing technical environment people can be unclear about the changing nature of the risks they are taking with digital material. For example, cloud storage seems like an easy solution to storing a lot of data. But of course there are security issues. The proliferation of cloud storage platforms itself can lead to losing track of data.

Reliability and Availability Engineering
  • Modeling, Analysis, and Applications
  • Kishor S. Trivedi, Andrea Bobbio
  • Published online:
    30 August 2017
    Print publication:
    03 August 2017
  • Do you need to know what technique to use to evaluate the reliability of an engineered system? This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains, semi-Markov processes, and phase type expansions. Readers will quickly understand the relative pros and cons of each technique, as well as how to combine different models together to address complex, real-world modeling scenarios. Numerous examples, case studies and problems provided throughout help readers put knowledge into practice, and a solutions manual and Powerpoint slides for instructors accompany the book online. This is the ideal self-study guide for students, researchers and practitioners in engineering and computer science.

Page 7 of 36