A critical analysis of lifecycle models of the research process and research data management
| Date | 19 March 2018 |
| Pages | 142-157 |
| Published date | 19 March 2018 |
| DOI | https://doi.org/10.1108/AJIM-11-2017-0251 |
| Author | Andrew Martin Cox,Winnie Wan Ting Tam |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
A critical analysis of lifecycle
models of the research process
and research data management
Andrew Martin Cox
Information School, University of Sheffield, Sheffield, UK, and
Winnie Wan Ting Tam
Centre for Information Management, School of Business and Economics,
Loughborough University, Loughborough, UK
Abstract
Purpose –Visualisations of research and research-related activities including research data management
(RDM) as a lifecycle have proliferated in the last decade. The purpose of this paper is to offer a systematic
analysis and critique of such models.
Design/methodology/approach –A framework for analysis synthesised from the literature presented and
applied to nine examples.
Findings –The strengths of the lifecycle representation are to clarify stages in research and to capture key
features of project-based research. Nevertheless, their weakness is that they typically mask various aspects of
the complexity of research, constructing it as highly purposive, serial, uni-directional and occurring in a
somewhat closed system. Other types of models such as spiral of knowledge creation or the data journey
reveal other stories about research. It is suggested that we need to develop other metaphors and
visualisations around research.
Research limitations/implications –The paper explores the strengths and weaknesses of the popular
lifecycle model for research and RDM, and also considers alternative ways of representing them.
Practical implications –Librarians use lifecycle models to explain service offerings to users so the
analysis will help them identify clearly the best type of representation for particular cases. The critique
offered by the paper also reveals that because researchers do not necessarily identify with a lifecycle
representation, alternative ways of representing research need to be developed.
Originality/value –The paper offersa systematic analysis of visualisationsof research and RDM current in
the Libraryand Information Studiesliterature revealingthe strengths and weaknessesof the lifecycle metaphor.
Keywords Metaphor, Research, Lifecycle, Research data management, Research process, Visualization
Paper type Research paper
Introduction
In the last decade Library and Information Studies (LIS) has shown a growing interest in the
detail of the research process, in part due to the creation of a new depth of research support
services in libraries (Corrall, 2014). The academic library’s role has been turned “insideout”from
mainly providing the user community with access to literature, to stewarding the knowledge
being created within the institution and making it discoverable to the wider world (Dempsey
et al., 2014). This includes an increasing investment in helping local researchers to manage data
within the research process, stewarding different versions and types of outputs, including data,
and also supporting and measuring all sorts of dissemination and impact beyond academia.
Thus, libraries are seeking to offer services across the course of research as a whole, and as a
consequence the black box of the research process has been opened. In this context, a number of
commentators and practitioners have commented on the proliferation of lifecycle models/
visualisations in the research data management (RDM) and research support area (Wilson, 2014;
L’Hours, 2014; Carlson, 2014). Ball (2012) reviews nine such data lifecycle models; the Committee
on Earth Observation Satellites Working Group on Information Systems and Services (2011)
review 44 models. There is a significant amount of variation in their purpose and assumptions,
but we can learn a lot about how research is conceptualised from examining them.
Aslib Journal of Information
Management
Vol. 70 No. 2, 2018
pp. 142-157
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-11-2017-0251
Received 8 November 2017
Revised 10 January 2018
24 February 2018
Accepted 5 March 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
142
AJIM
70,2
The lifecyclehas long been a favourite model in LIS (Ma and Wang,2010a, b); in particular,
it is a core concept in records and archive management (Williams, 2006). The appeal is the
temporaldimension the metaphor adds to ourunderstanding of the differingactivities in view.
The metaphor seems to be particularly appealing in the research area because it fits into
thinking about designing systems workflows, be those administrative or IT based. Yet, the
term lifecycle isalso ambiguous implying the modelof a birth to death journey or, somewhat
in contrast, the pattern of birth and reproduction where the cycle is endlessly repeated or,
indeed, enters a progressive upward cycle. A number of authors, notably Carlson (2014),
Waddington et al. (2012) and Wissik and Ďurčo (2015) have begun to reflecton these models.
But there remain questions about what different types of model there are; what uses they
have; and what assumptions are made in them. Given the way that the lifecycle is becoming
virtually the default way of representing the research process, it is important at a theoretical
level to question whether this adequately conceptualises research. At a practical level, given
their increasing use by practitioners to conceptualise and explain services, it is important to
weigh up whether they capture how researchers themselves view the research process.
In this context, the aim of this paper is to explore the strengths and weaknesses of
representing research and research data-related activities in a lifecycle model, through a
systematic comparison of some that have been published recently in the research data
space. It does this by examining the literature to produce an analytic framework;
undertaking a systematic analysis of a selection of nine models using the framework; and on
this basis reaching some conclusions about the strengths and limits of this type of model/
visualisation. Having probed the typical limits of the lifecycle approach, thought can be
given to the benefits of radically different representations such as the knowledge spiral or
data journey which offer more critical perspectives.
The lifecycle
A number of authors have already attempted to differentiate different types of lifecycle.
According to Ma and Wang (2010a, b), the lifecycle of information can be understood from two
perspectives. The perspective of value focusses on how the worth of information changes,
usually deteriorates, over time. The other more common perspective is of management. The
authors identify six types of management-based lifecycle models. The commonest is what they
call the chain model. It is a simple chain of steps. The second type, the matrix, expands the chain
model by giving more detail on each of the stages in the chain. The third type is the circular
model, with the crucial difference being that the end of the chain links back to the beginning thus
restarting the cycle. The spiral extends the circular model by illustrating how each cycle is not
just a repetition of previous cycles but builds and strengthens, as things such as audiences or the
nature and structure of information change. An integrated model combines two of the other
types as a way to indicate the complexity of processes or to bring in external factors. The final
model is the wave, which is able to express an ongoing activity but an overall decline in the value
of data. This set of categories itself reveals the complexity of the concept.
Whereas Ma and Wang (2010a, b) emphasise the different visual structure of the models,
Carlson (2014) working more directly in the research service area emphasises differing
underlying audiences. He suggests that lifecycle models in the data service context can be
divided into three types:
(1) individual based, which are for a particular project giving detail on how it unfolds;
(2) organisation based, which are for showing how services fit to different stages of
research or how researchers should access different services at different stages in
their work; and
(3) community based, which are for a particular academic community or discipline
(including professionals that support them) to define existing or good practice.
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Research
process and
research data
management
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