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772 results in Computer Hardware, Architecture and Distributed Computing

Page 8 of 39

  • 18 - Ethics and Research Data Services

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 173-178

  • Summary

    Aims

    The aim of this chapter is to consider the ethical dimensions of work in RDM.

    An ethical service

    Like any area of professional work, RDM has its ethical basis and its own ethical dilemmas and challenges.

    Many who work in the area of RDM are driven by a strong belief in the benefits of data sharing (or even open data) and its ability to improve science through replicability or transparency and the possibility of new research. Others are motivated by a simpler (but also ethical) desire to do good by providing an excellent service to research communities or by contributing to learning. Others have more pragmatic, less ethically based motives.

    Whatever the basis for our belief in the importance of RDM, we should always ask questions about the demands being made on researchers by institutions and governments, and our own role in potentially enforcing these. Open data sounds like an inherently good thing. But there is the potential for it to be used as a means to disadvantage particular types of research, such as qualitative research, where gaining consent for re-use may in some cases affect participation rates negatively. It also has the potential to be used by senior academics to appropriate the work of more junior colleagues. This reflects the fact that any agenda can and will be used politically within a nexus of power in an organisation. More generally there may be systematic connections between the agenda to share data and control over research. Such issues of power relate to local situations within departments; equally they exist in the imbalance between Western institutions and in developing countries. Open access, especially gold open access − where the publisher makes the final published version openly available on their web site − seems to actually disadvantage developing countries. We should always be asking critical questions about equity and justice within digital scholarship.

    Exploring further

    What is the moral purpose of RDM for you? Do you see any tensions with other values that you hold? For example, do you see any conflicts between best practices in managing data and the need for researchers to have autonomy and freedom to do research as they believe is best? Are there potential conflicts around beliefs about who owns data?

  • 19 - A Day in the Life Working in an RDS

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 179-186

  • Summary

    Aims

    The aim of this chapter is to give you a more vivid sense of what working in RDM is like through a sketch of what this looks like on a day-to-day basis for someone whose main role is RDM.

    RDM in practice

    There are many roles in a university that engage with research data management. Research administrators prepare grant applications or manage post-award administration that involves RDM. IT professionals manage the hardware and software that our RDM infrastructure uses. A librarian might help a researcher identify suitable datasets for re-use. But there are only a handful of roles where RDM is a substantial part of a job description. Examples are research support librarians, research data management advisors, repository managers, and research data coordinators. What this might be like will vary a lot between institutions, depending on their level of commitment to RDM.

    In this chapter we explore the daily experience of someone in the role of Research Data Manager. Everyday work with research data management in this role is likely to include three areas:

  • strategic development

  • advocacy, training and support

  • repository and infrastructure management.

    • Strategic development

    Strategic work is the future-oriented aspect of the job. It involves such things as policy development, including keeping the RDM policy up to date, and plans for future service development. It also involves regularly evaluating where you are, for example using a maturity framework or through measuring compliance with funder requirements, or monitoring restrictions to openness of data (See Chapter 17). For example, the Concordat on Open Data (discussed in Chapter 5) implies that there needs to be some form of continuous oversight of progress.

    Many universities have set up research data management steering groups that bring together stakeholders from throughout the university to give direction to and oversee the establishment and smooth working of research data management services. This has often been in response to funder requirements and other government policy. In this process it is vital that all stakeholders are involved, including academics from all faculties. As we have seen, buy-in from influencers is important but not guaranteed – especially in the arts and humanities, where the perceived absence of data gives rise to the feeling that research data management does not apply.

  • 13 - Data Management Planning

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 115-124

  • Summary

    Aims

    The aim of the chapter is to explain data management planning and to explore how best to support it.

    The data management plan

    Increasingly funders are requiring that any proposal for a project must include a detailed explanation of how research data will be managed during the project, including such aspects as how much data will be collected, in which formats, how participant confidentiality will be protected and which parts of the data will be shared at project close. This is often called a data management plan (DMP), though different funders give it different names.

    The requirement to write a DMP may be one of the first times a researcher may encounter the idea of RDM. For many researchers this requirement may feel like an unwelcome extra hurdle in the long and arduous task of writing a research proposal. It is unclear if a bad DMP will really affect the likelihood of being funded. Because it deals with seemingly minute aspects of data management it may not feel very important, in the context of the wider ambitions of a project – although, as we saw in Chapter 4, data management could also be recognised as a cornerstone of a successful project. A researcher may well feel it is almost impossible to anticipate exactly how much data they might have in megabytes or what preservation formats they might choose. They are likely to see it as quite a technical document that needs input from the computing service, around what data storage facilities exist. But they are quite likely also to be unclear about what the funder is really looking for in writing such a document.

    Because of the detail and unfamiliarity of the DMP, having to write one frequently leads researchers, right at the last minute before a proposal submission deadline, to seek help from the RDS. It will probably be one of the main enquiries from staff that the RDS receives. Working out how best to support researchers to write their DMP is a key task for the RDS. It is also a critical opportunity for the RDS, because it is one of the main occasions that researchers might actively seek help.

  • 10 - Institutional Policy and the Business Case for Research Data Services

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 95-100

  • Summary

    Aims

    The aim of this chapter is to discuss aspects of developing and using an RDM policy.

    Writing a policy

    A policy is a written statement of general principles that guides activities and decision making on a particular issue across an organisation. Going through the process of bringing together key stakeholders (or their representatives) to agree on a common stance lays the essential foundation for a co-ordinated response to an issue. The very process of agreeing a policy helps solidify the issue, co-ordinate views on how it should be understood and clarify the stance of the institution.

    Having the written policy in place motivates and aligns action, and reduces misunderstanding. At its heart is a definition of roles and responsibilities for key stakeholders. It is the mandate for any services the institution offers.

    Developing a policy

    An internal policy discussion to make a policy establishes that RDM is a recognised concern for the institution. Indeed, simply establishing that there is a need for a policy may be the first battle. As we saw in Chapter 6, some responses to a wicked challenge could be that ‘there is no issue’ or that ‘it is adequately covered by existing policy or good practice’. Bringing key stakeholders together at a high level enables the institution to understand the full ramifications of the issue.

    Because RDM touches on researchers at different levels and a number of professional support services there is a particular need to clarify roles.

    It might be that writing the policy is the first action of an institution to respond to RDM; but it might be that pilot services need to be developed to prove the nature of the need.

    Researching the policy is likely to involve data gathering in six areas:

  • 1 The wider trends in the sector around RDM, e.g. why it has become a key issue.

  • 2 More specifically, the wider policy context: what are the key current policies from governments, relevant funders and so on, with which local policy must align? What are the relevant legal reference points, e.g. in relation to data protection or copyright?

  • 3 The state of play within the institution, to scope out the nature of the challenge, and to identify existing activities and resources.

  • 11 - Support and Advice for RDM

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 101-106

  • Summary

    Aims

    The aim of this chapter is to consider the requirements for an effective support and advice service for RDM, especially designing its web presence.

    Offering support and advice

    A core aspect of an RDS will be to provide information, support and advice to researchers in a timely and reliable manner. Providing a good support and advice service is challenging for a number of reasons:

  • Researchers’ issues are inevitably diverse, reflecting the wide range of research practices in different fields.

  • Their issues are likely to be quite technical and couched in very specific forms related to a specific research project. In offering advice RDS staff are unlikely to fully understand the research objective or the wider norms of the research culture within which the research is being conducted.

  • The timing of when issues are likely to be raised is unpredictable. A project idea can be developed at any time, so requests for information, while they will reflect cycles of activity for research, cannot be easily predicted. A request is quite likely to be perceived by the researcher as urgent.

  • Researchers will not necessarily label the issue they have as ‘research data management’, so what they ask and who they ask for help is again unpredictable. An advice service has to be both visible and able to explain clearly the scope of the RDS. Equally it's probable that the service may be asked questions somewhat beyond its remit, which it may seek to answer or refer to others. For example, researchers might well ask about relevant standards or anonymisation techniques.

  • Drawing the line between information and advocacy may also be problematic.

    • A big part of the advice service will probably be around data management planning. The topic is important enough to have a chapter of its own (Chapter 13).

    As in any form of advice service, there are some general principles it makes sense to observe. For example, the American Reference and User Services Association (RUSA, 2013; 2017) identify the following five factors as key to a successful reference service:

  • Visibility/approachability – the service needs to be visible to its potential users and offer an approachable appearance.

  • Interest – in responding to initial enquiries there is a need to project a sense of interest in questions asked.

  • 2 - The Social Worlds of Research

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 11-18

  • Summary

    Aims

    The aim of this chapter is to prompt you to reflect on the nature of research. By definition, if we are going to support RDM, we need to have some understanding of the intellectual and social organisation of research.

    Introduction

    If you come from an academic library background you may well have been attracted to the profession by an enthusiasm for information literacy. Libraries have made huge strides in the last few years towards making a very strong contribution to teaching in universities. Now we seem to be seeing a turn towards more support to research. Something of the same trend seems to be happening in IT services. In this context it is useful to reflect more on the current research landscape. You may work in research administration, in which case much of this will be familiar, but it is worth stepping back and reflecting on one's assumptions about research.

    Exploring further

    Jot down some keywords that describe ‘research’ as an idea. Then do some work trying to think how these might link through to RDM.

    The research landscape

    Research is a central activity for many universities. It is a key source of revenue: a multi-billion dollar business. Ideologically it is core to many university missions: particularly in ‘research-intensive’, elite institutions it is really what defines their special status.

    Some key features of research you might have thought of earlier are:

  • • Funding – the competitive struggle to gain funding for research is central to many researchers’ lives. Gaining a grant means having the resources to do bigger scale work and come up with more significant findings. Thus the positon of funders on RDM is critical. Nevertheless, it should be remembered that much research is still unfunded, or perhaps more accurately funded by institutions themselves through the time they give academic staff to do research.

  • • Projects – a lot of research, similarly to professional support work, is organised in projects. This colours a lot of research-related behaviour, e.g. it shows up in how people store their personal files. Thus, they are fixed-funded for a limited time period with fairly clear deliverables. This has consequences for RDM in terms of what happens when the project finishes. At project end there may be no resources for doing work on sharing data.

  • 9 - Requirements Gathering for a Research Data Service

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 85-94

  • Summary

    Aims

    The aim of this chapter is to explore how to identify the requirements for an RDS in an institution. It will help you think through what you would want to learn about user needs from an RDS and how to gather such information.

    Finding out more about an institution

    Throughout the book so far we have prompted you to talk to researchers about research and research data as a way of starting to build your own deeper understanding of the issues. This is solid groundwork for developing an intuition for what types of service are needed.

    At some point you may well want to gather evidence more systematically as a foundation for making decisions about services. Of course, one cannot simply ask about what services people want. It is hard for ‘users’ to imagine what services there could be. We will be thinking more about what they need, based on an examination of existing practice. But in any case systematic data will be invaluable for working out the appropriate service for the needs of your institution. Systematic evidence will certainly help you in influencing others about the direction the service should take. As it will be an inherently collaborative challenge for a number of professional services working with researchers themselves, you will need an evidence base to persuade others what the service should look like.

    There are a number of other advantages to undertaking such a study. It might seem that senior research leaders in the institution will already know the situation in their faculty or department. Actually, they are probably as uncertain about current data practices as anyone else. They will probably be extremely interested in the results of any study. They can help themselves to identify potential problem areas. Undertaking the formal task of having a project to study user needs will help assemble stakeholders and get them working together. It will identify problem areas but it will also help you identify pathfinders and examples of good practice.

    An evidence-gathering process itself will also be a means to inform users, alerting them to the existence of an institutional issue they may not have thought about. As the first step in a change management process an evidence-gathering exercise could itself have a powerful effect.

  • 1 - Introducing Research 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 1-10

  • Summary

    Aims

    The aims of this chapter are to:

  • • introduce the topic of research data management (RDM) and what it means in practice

  • • explain the thinking behind the book, so you can use it effectively.

    • A thought experiment

    Imagine going to a busy researcher's office:

  • • What would you expect to see?

  • And if you asked them about their research history:

  • • What would their story be like?

    • And if you asked them specifically about the ‘data’ that they collect as part of their research:

  • • What types of data would they say they have?

  • • How much data would they have?

  • • How would they store and back up their data?

  • • Who would they say owns the data?

  • • Would they say they share the data with others, or not?

    • Let's offer an answer based on the answers of one of the authors of this book (himself a researcher). Andrew says:

    Well, I am embarrassed to say my office is pretty untidy: a table strewn with papers; three bookshelves packed with books, reports, print-outs – a lot relating to research, but also teaching. A filing cabinet, which if you unlock it, is jam-packed with various papers, including some things like hand-drawn maps of an area of Sheffield; a stack of completed questionnaires; a roll of flipchart paper covered with Post-it notes, from a data collection workshop last year. All that is stuff I have gathered for my research. There are quite a few folders of interview transcripts as well. Some go way back! Also in there are some old-looking memory sticks. I wonder what's on them myself!

    Of course where I work most of the time is here at the computer. Again, it's going to be hard for me to summarise what is on the computer. Here is a secure network drive where I keep a lot of project work – or used to – alongside files relating to teaching. The university also has a secure Google drive service. There is also a research data server. I guess I basically keep material in folders by project. But quite often it's a bit more complicated than that. For example, I might re-use material across a number of projects.

  • 8 - Staffing a Research Data Service

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 75-84

  • Summary

    Aims

    The aim of this chapter is to help you think through the staffing issues around supporting research data management and for a research data service.

    New activities and roles

    A greater focus on RDM implies some new activities, such as preparing data to be shared or providing training. Or it could imply a redistribution of activities, e.g. from the researcher to someone in an RDS. It may create wholly new roles, especially in the support area or in running a repository. Logically, this creation or redistribution of work can be met in a number of ways:

  • 1 Research teams themselves may take on new tasks, be that the principal investigator or research assistants, e.g. taking on primary responsibility for data management in a project or documenting data at the end of the project.

  • 2 Existing local support staff might take on a role; for example, if an academic department has an IT specialist or someone who helps write project proposals, they might take on some roles associated with RDM. They might offer day-to-day support around storing active data or help write a data management plan.

  • 3 Existing central support staff could add a new role – be they in the library, IT, records management, research administration, staff development, or in a number of other departments. Such new roles could involve anything from adding some slides about RDM to a briefing on information literacy through to running a data repository. This could be on the basis of their existing knowledge and skills or by them having some retraining and upskilling. It might be that a few people have their job significantly changed, or that tasks are widely distributed across a large number of staff in different teams.

  • 4 The organisation could employ new staff to take on the role; perhaps even in a new organisational structure. For example, a new coordinator might be appointed to ensure that all the professional services supporting RDM are moving in the same direction.

    • Logically there are three other possibilities:

  • 1 Some new activities created in the context of RDM are met collaboratively across a number of institutions. For example, it could be that a local cross-institutional network could take on training of researchers in RDM.

  • 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.

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