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705 results in Computer Hardware, Architecture and Distributed Computing

Page 6 of 36

Computer Programming with C++
  • Kunal Pimparkhede
  • Published online:
    28 May 2018
    Print publication:
    18 October 2016
  • This textbook provides in-depth coverage of the fundamentals of the C and C++ programming languages and the object-oriented programming paradigm. It follows an example-driven approach to facilitate understanding of theoretical concepts. Essential concepts, including functions, arrays, pointers and inheritance, are explained, while complex topics, such as dynamic memory allocation, object slicing, vtables, and upcasting and downcasting, are examined in detail. Concepts are explained with the help of line diagrams, student-teacher conversations and flow charts, while other useful features, such as quiz questions and points to remember, are included. Solved examples, review questions and useful case studies are interspersed throughout the text, and explanations of the logic used to implement particular functionality is also provided. This book will be useful for undergraduate students of computer science and engineering, and information technology.

  • 4 - Case Study of RDM in an Environmental Engineering Science Project

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 33-40

  • Summary

    Aims

    The aim of the chapter is to give you a deeper insight into the nature of the issues around RDM by exploring a particular case study in some depth.

    The project

    In this chapter we will look at a case study of a particular research project. The focus is on the different types of data that are collected, created and reused. We will also use it to consider the challenges that the complexity of the research project present to the management of active data and their long-term preservation. The chapter gives you direct access to a researcher speaking about their work in their own words.

    This case study consists of an interview with Steve Banwart, Professor of Environmental Engineering Science at the University of Sheffield. He is the leader of a large-scale project that is funded by the European Union. The project looks at how soil – one of our planet's essential natural resources – is produced and how it degrades. The aim is to quantify the impacts of environmental change on key functions of the soil and capture this in predictive models that can be used in decision making.

    The project is international in scope. Soil Transformations in European Catchments (SoilTrec) brings together a network of over 30 research field sites. Professor Banwart explains that ‘it has 16 different institutions, as partners located in 3 different continents.’ The partner institutions are primarily in Europe, but also in the USA and in China.

    Working with geographically dispersed teams that each collect data that need to be understood and ultimately combined with the data that other teams collect is quite a challenge in itself. But to add to the complexity of the project, these teams are also multidisciplinary and include, for example, engineers, chemists, biologists and physicists. It is therefore not surprising that the researchers working on the project handle a wide variety of data, sometimes in large quantities. This project handles data that are generated principally through observations and experiments in the field, ‘digging holes in the ground and studying dirt, if you will’, as well as laboratory experimentation. Data are also generated through computer modelling. And, Professor Banwart adds, ‘we also use existing remote earth observation data, primarily satellite data and geographical information systems held by government agencies like the European Soil Bureau.’

  • 5 - RDM: Drivers and Barriers

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 41-56

  • Summary

    Aims

    The aim of this chapter is to explore the forces that have led to RDM becoming important now, but also to explore why this has not led to change smoothly across every institution and for every researcher. It will prompt you to start to think about how these forces play out across particular institutions, such as one that you work for now or want to work for.

    Introduction

    This century has seen a gathering momentum behind the idea of research data management and open data.

    Data sharing in the sciences has been common practice for many years. It is well established in such fields as meteorology, astronomy and genomics, for example. Data archives for particular fields of research have existed for half a century in many countries. For example, in the UK there has been a repository for social science-related datasets for decades, funded by the main government funder of social science research. Therefore there have also been for a while policies that mandate the deposit of material. Yet the extension of these ideas across the gamut of research is relatively new. Unravelling exactly how this has happened would probably take a book in itself, but it is instructive to explore some of the forces at work, because they pull in somewhat different directions and are still working themselves through. Some are pragmatic, some are ideological. How these arguments play out today at an institutional level will reflect the complexity of the underlying forces. Having a handle on these drivers is essential to positioning your own work effectively, in what is inevitably a rather politicised landscape.

    To summarise what follows, much of the increasing stress on RDM can be traced back to the impact of digital technologies on how science is done, and particularly on the amount of data being generated in research and the potential to share it, because of the easy mobility of digital data. In addition, in some subjects there has also been a ‘crisis of reproducibility’: a loss of confidence in the integrity of scientific practice, resulting in a call for greater transparency. There is also a somewhat broader movement to reform research practice, often under the umbrella term ‘open science’.

  • 6 - RDM as a Wicked Challenge

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 57-66

  • Summary

    Aims

    The aim of this chapter is to explore the nature of research data as a management and leadership issue, in particular in the light of the distinction between ‘tame’ and ‘wicked’ challenges.

    Types of problem

    Management in any context involves tackling problems. However, it may be useful to distinguish different types of problem, because they demand a slightly different managerial approach and skillset.

    In everyday management there are plenty of ‘tame’ problems. These are known issues that we have well-trodden ways of dealing with. For example, we need to change our policy to respond to a new government initiative or law. We review the change, look at our existing procedures, talk to different stakeholders and try and devise a new policy. Then we publicise the change and offer training. After a while we evaluate how well the change has been put into practice. The management problem is simply to manage the resources available to carry through a fairly familiar set of steps.

    At the other end of the spectrum of complexity and uncertainty are what are sometimes called ‘wicked’ problems or challenges. These are far less familiar and understood; they are so entangled with multiple issues it is hard to know even where to start to address them. We may not be quite sure what sort of outcome we really want. Perhaps there is no ‘solution’ to them, only ways of coping. It follows that the approach to management and leadership in this kind of context requires us to operate differently from addressing the tame problem.

    Exploring further

    We find this idea of different types of problem an immensely interesting one. Reflect on an area of professional or personal life and see if you can identify a tame and a more wicked type of issue. Recognising the difference, do you approach the issues differently?

    The wicked challenge concept

    The concept of a wicked problem was originally defined in urban planning in the 1970s by Rittel and Webber (1973). An analogous concept is the ‘social mess’, a term coined by Horn and Weber (2007). Thinking about applying the concept to RDM, Cox, Pinfield and Smith (2016) suggested that we could synthesise such work to produce a list of features of a wicked challenge.

  • 17 - Evaluation of RDS

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 159-172

  • Summary

    Aims

    The purpose of this chapter is to consider how to evaluate RDS and RDM.

    Introduction

    Evaluation is a controversial topic. On the one hand, it feels obvious that without clearly defined objectives it is impossible to say whether a set of activities are worthwhile. And if we do not collect some data about how we are performing against those objectives we do not know how well we are performing. Tools like SMART objectives reflect this thinking and encourage us to measure whether objectives have been met. At the same time, it may be difficult to define purposes, especially where complex, intangible values are being pursued. In such a context defining our purposes and defining valid measures, i.e. ones that actually measure what we want to achieve, are hard. Capturing data about our achievements, especially quantitative data, is likely to be problematic. At some point more complex measures break down because they are hard to collect and understand.

    In the library world, for example, there is pressure to demonstrate the value of the library to student learning. But learning is such a complex construct that it is hard to see how it could ever easily be measured, in a totally valid way. Tools such as the LibQual survey, while widely used in libraries to measure service performance, only compare satisfaction against expectation, not learning itself. Other types of measure, such as the number of resources or even downloads, do not directly link to learning, only the levels of activity.

    In addition, the question of evaluation has resonances with the debate around the new public management and neo-liberalisation (see Chapter 5). For many, the way that academia is increasingly run like a private organisation with crude quantitative measures of performance – ‘key performance indicators’ – erodes the university's true purpose to promote learning and research in their widest sense.

    Nevertheless, it would be odd not to write about the evaluation of RDS/RDM here, even though the literature on the topic is actually surprisingly sparse. From a management perspective it makes sense to collect data about performance, even if it is purely for internal consumption within the RDS team.

  • 16 - Infrastructure for Research Data Storage and Preservation

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 147-158

  • Summary

    Aims

    The aim of this chapter is to outline the infrastructure required for RDM, particularly a repository for sharing data. This is as much about processes and policies as technologies.

    Technical infrastructure

    Developing and maintaining a technical infrastructure to support research data management is not the goal of institutional RDM, but it is a significant means to achieve the kind of behavioural change that is likely to be the real objective. It does this by enabling researchers to plan their data management from the start of a project, to look after their primary research data whilst they are working on a project, and to support the long-term preservation and sharing of those data. Each stage of this cycle may have its dedicated supporting technical infrastructure, for example:

  • • an online data management planning tool

  • • a means for the secure storage of primary research data whilst the project is ongoing, giving access to those who need it, thus facilitating the sharing of data amongst a project team

  • • and finally, a facility to store data for long-term preservation and, where possible, to share those data with the wider community.

    • Again, although repository (including preservation and sharing) systems have often been the focus of research data management projects, it is important to realise that they really are only the end point, where a selection of the primary research data produced by a research project will find a permanent home, for example at the point of publication or when the project is wrapped up. This chapter focuses on this end point: a repository for long-term preservation and data sharing.

    The repository

    A repository is likely to have three components:

  • a catalogue

  • physical storage

  • file format preservation.

    • The catalogue and storage functionality are part and parcel of any repository solution, yet in some circumstances an institution may only need a catalogue of assets, and may not prioritise preservation. In some cases it may be worth investing in separate software that manages long-term preservation of the data files by monitoring the file formats that are in use, assessing their obsolescence and facilitating their migration to other file formats when needed.

  • 7 - 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 67-74

  • Summary

    Aims

    The aim of this chapter is to explore the potential elements of a research data service(s) (RDS). It also sets this in a wider context of shifts in how research is supported as a whole in HE institutions.

    Research data services (RDS)

    Looking across the HE sector there seems to be a pattern towards developing services in areas where researchers need particular guidance and support. Following this trend, a central RDS may be created to take some of the burden of effort off researchers, apply special expertise and benefit from the economies to be gained of performing roles centrally. Such services can be understood as consisting of the following five components:

  • 1 institutional policy

  • 2 developing a clear and agreed mission

  • 3 support, advice and training

  • 4 infrastructure

  • 5 evaluation strategy.

    • First, the task of defining an institutional policy on research data, backed by a business case, is likely to define the context within which the RDS can be built. A second component of an RDS is about developing a clear and agreed mission. A central aspect of this could be user requirements gathering at an initial stage. Specific services that can be developed are usefully grouped under the support, advice and training component and, as another component, infrastructure.

    Support, advice and training

  • • Advocacy work – increasing understanding amongst the academic community and university leadership of the complex issues around RDM. This includes promoting awareness of policy. This is an important enough aspect of advocacy to be given special emphasis.

  • • Advice – from tailored one-to-one support through to generic FAQs and web pages. Supporting data management planning (DMP) is one specific aspect of an advisory service, but is important enough to often be seen as a service in itself. Hence in this book there is a chapter on data management planning.

  • • Training – this could be anything from one-to-one training to workshops, short courses, webinars and online tutorials.

    • Infrastructure

  • • Managing active data – this includes data storage, back-ups and data security.

  • • Appraisal and description of data for deposit, including data cataloguing. The catalogue may or may not be linked to a repository with access to data. It might be simply a listing of contact details and descriptions of datasets.

  • • Data sharing and long-term preservation.

  • 15 - Training Researchers and Data Literacy

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 139-146

  • Summary

    Aims

    The aim of this chapter is to work through the decisions that need to be made to set up a programme of training about research data management.

    Introduction

    Central to any RDS will be the function of training members of the organisation in RDM. The chapter is organised around a series of interrelated decisions involved in designing such a training programme.

    One of the outcomes of your talking to researchers and your more systematic user requirements gathering (Chapter 9) will be to have a feel for what the needs are around the institution. But determining audiences, content and channels for training is in itself quite challenging.

    Exploring further

    Think about some of the more engaging CPD or training courses you have been on. What made the best events stand out? It could be:

  • • the immediate usefulness of what you learned

  • • the hands-on time

  • • what you learned from other people on the course, as opposed to the trainers

  • • the time you were given to talk through how the challenge affected you

  • • the warmth and helpfulness of the trainers

  • • something else.

    • Think about how you can manage the learning situation to recreate such a good learning experience for anyone who comes on one of your courses.

    Step 1: Who is the training for?

    At some level it may be that every researcher in the institution needs some training in basic awareness of institutional policy and guidelines and to draw attention to critical areas of risk. But attempting to develop training that feels relevant to everyone, in every discipline and level of experience, is challenging.

    It is probable that people in different meta-disciplines will have widely differing views on what RDM is about. That does not mean that they should be taught separately but it will take extra effort to ensure that the material feels relevant to everyone. An obvious compromise is to organise training by meta-discipline (e.g. for humanities scholars separate from engineers). Training customised to each department or subject discipline may not be scalable.

    Actually it could be argued that those with similar methodologies might be best brought together, regardless of their field.

  • 14 - Advocacy for Data Management and Sharing

  • Andrew Cox, Eddy Verbaan
  • Book:
    Exploring Research Data Management
    Published by:
    Facet
    Published online:
    21 September 2019
    Print publication:
    11 May 2018, pp 125-138

  • Summary

    Aims

    The aim of this chapter is to prompt you to think about strategies to influence researchers to see the importance of RDM to themselves.

    Introduction

    Many international and national bodies governing research have expressed their commitment to the ideal of open data. For all the qualifications, this is the ideal put forward by the UK Concordat on Open Research Data discussed in Chapter 5, for example. Yet data could be shared in a number of ways, many of which may be more familiar to researchers than open unrestricted sharing in a data repository, for example:

  • • with collaborators in a research group

  • • with collaborators beyond the institution in a particular project

  • • by request with peers

  • • within a particular community of researchers

  • • as supplementary information or otherwise linked to a publication.

    • There are some compelling reasons why most research data could be potentially shared in these different ways and much of it openly. This section considers some of those reasons.

    You will recall from Chapter 4 that the soil scientist professor Steve Banwart said a lot about sharing within a specific project. Other RDMRose interviewees talked about data sharing:

    Richard Rowe is a senior lecturer in the Department of Psychology at the University of Sheffield.

    As a researcher, in some ways, it feels a bit strange to collect data and then just have anyone analyse it, but I think, as far as I understand it, there's ample time to do the analyses that you promised to do before everyone else gets hold of it. And as I say, a lot of the data that I work with is publicly available, so that's sort of always there for anybody to look at. That causes some problems in that you can end up with the same people doing the same work and duplicating efforts. The worst case of this I know of is one that I was involved in. [He describes the setting up of a project to reanalyse an existing dataset]. Then three weeks before we started, we just happened to turn on the news and see that someone had reported and done exactly the same analyses, and it was on the news, with exactly the same dataset.

  • 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.

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