The importance of tools in the data lifecycle
| Published date | 14 August 2017 |
| Date | 14 August 2017 |
| Pages | 235-252 |
| DOI | https://doi.org/10.1108/DLP-11-2016-0042 |
| Author | Malcolm Wolski,Louise Howard,Joanna Richardson |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Records management & preservation,Information repositories |
The importance of tools in the
data lifecycle
Malcolm Wolski,Louise Howard and Joanna Richardson
Department of Information Services, Griffith University, Brisbane, Australia
Abstract
Purpose –This paper aims to outline principal implications for institutions, particularly universities, in
supportingthe increasingly complex tools which are used in the datalifecycle.
Design/methodology/approach –The discussion paper draws upon the experience of authors in this
domain at the institutional,national and international levels.
Findings –Support for research tools by universitieshas high-level implications, rangingfrom financial,
strategic and compliancethrough to capacity, capability and connectivity.The large number of existing tools
highlights the need to evaluate them against standardised checklists to determine suitability and levels of
resources required for support.Librarians and other information professionals need to expand theircurrent
supportfor researchtools beyond the discovery phaseto the entire data lifecycle.
Practical implications –Universities can use this paper to assesstheir maturity in supporting tools in
the data lifecycle. Librarians, in particular, can broaden their knowledge of the various categories of tools
which supportspecific aspects of that lifecycle.
Originality/value –While much attentionis currently being focused on supporting researchers withtheir
data management requirements, there is a general lack of literature on how to support tools as a critical
elementin enhancing research outcomes.
Keywords Academic libraries, Research support, Research lifecycle, Research infrastructure,
Data lifecycle, Tools criteria
Paper type General review
1. Introduction
In writing about economic growth, economistshave long recognised technological advances
as the “key driving force”, with innovationan important pillar (Kim and Nelson, 2000,p.1).
The World Economic Forum (2014) has outlined strategies for fostering innovative-driven
entrepreneurship in Europe. The Obama Administration (White House, 2015, p. 2) updated
its strategy documenton innovation to “to drive economic growth and shared prosperity”.In
the same year, the UK updated its policy on research and development, which has
innovation as one of its cornerstones (UnitedKingdom, Department for Business, Innovation
and Skills, 2015), and the Australian government announcedits national agenda for science
and innovation (Australia, Department of Industry, Innovation and Science, 2015a). The
vision in the Asia-Pacific region is to achieve significant innovative economic growth by
2025 (Asia-PacificEconomic Cooperation, 2015).
If innovation helps in driving higher productivity growth (Jamrisko, 2016), then the
research underpinsthat innovation is critical to its success. As a result of an increased focus,
the research environmentin higher education has changed significantly over the last decade.
The first investment wave saw substantial resources channelled into developing research
infrastructure, e.g. servers,storage and high performance computing (HPC). However, there
has been a growing recognition that “Researchoutputs, whether data, software, methods or
publications,are critical inputs to future research and underpin innovation”(O’Brien,2016).
Tools in data
lifecycle
235
Received 8 November 2016
Accepted 19 December 2016
DigitalLibrary Perspectives
Vol.33 No. 3, 2017
pp. 235-252
© Emerald Publishing Limited
2059-5816
DOI 10.1108/DLP-11-2016-0042
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2059-5816.htm
This paper examines the role of tools as a critical element in enhancing research
outcomes. While much attention is currently being focused on research data management,
less attention is being paid to the implications for institutions, particularly universities, in
supporting the increasinglycomplex tools which are used in the data lifecycle.
2. The importance of tools
Ten years ago, as eResearch was gaining prominence in Australia, tools were being
mentioned as an integral part of the supportpanorama. Denison et al. (2007, p. 2) identified:
[...] a need to access diverse data sources, specialist instruments, software and other analytical
tools, sample populations for surveys and trials, and specialist skills that require high-quality
network access to undertake data- and simulation-intensive research.
Jirotka et al. (2006, p. 253) discussedthe importance of “technologies and tools for supporting
small and large scale research collaborations across time and distance”.Appelbe and
Bannon (2007) outlined the role of tools in establishing –and the subsequent use of –a
national computing grid. For Lawrence (2006, p. 394), “the end user has to have something
that is appropriate to thelevel of that end user and is deployable”.
In more recent years, attention has been focused on managing and leveraging the vast
amounts of data now being generated for research. This has resulted in new methods, e.g.
tools and compute, being developedto manipulate, analyse, process and preserve data.
In Australia, for example, the government’sPublic Data Policy Statement (Australia,
2015) reinforcesnot only the importance of data but also the importance of tools:“[...] where
possible, make data available with free, easy to use, high quality and reliable Application
Programming Interfaces (APIs)”. An even more specific example is the government’sSoil
and Water –Capability Statement (Australia, Department of Industry, Innovation and
Science, 2015b, p. 1): “[...] developing tools for primary producers to integrate and
understand data and informationon soil and water from a variety of sources.”
The Australian Government has also invested in developing online environments,
especially in virtual laboratories,which “draw together research data, models, analysis tools
and workflows to support collaborative research across institutional and disciplinary
boundaries”(Nectar, 2016, p. 2). This is echoed by the European Commission (Andreozzi
et al.,2016) in its discussion of the importance of VirtualResearch Environments (VREs).
In its submission to the European Commission, the High Level Expert Group on
Scientific Data (2010, p. 1) contextualised the importance of tools by emphasising that
“Scientific research is supported by its infrastructures: technical tools and instruments and
socio-economic systems for organising and sharing knowledge”. Within a proposed
knowledge creation cycle for resolving society’s major challenges, Dozier and Gail (2009,
p. 16) discuss the “[...] development of new knowledge types and new tools for acquiring
that knowledge”. Furthermore,Ahmed (2016) notes that “Modern developmental challenges
require powerful research tools, skills and orientation to ensure the production of excellent
research”.Nielsen (2011) has foreshadowed “new tools for collaboration that will enable
discoveries to happenat the speed of Twitter”.
Fiona Tweedie (2016, p. 3) reinforces this new thinking about tools in her statement:
“Effective use of digital tools enables new avenues of research [...]”. In the same vein, a
recent editorial in the eLife journal (Schekman et al., 2015, p. 1) announced the introduction
of a new article type –called Tools and Resources –to highlight new experimental
techniques, data sets, softwaretools and other resources. Crouzier (2016, p. 4) has found that
Open Science will require “Innovative digital tools that facilitate communication,
collaboration,and data analysis”.
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