Meeting Curation Challenges 171
The International Journal of Digital Curation
Issue 1, Volume 3 | 2008
Meeting Curation Challenges in a Neuroimaging Group
Angus Whyte
Digital Curation Centre,
University of Edinburgh
Dominic Job, Stephen Giles, Stephen Lawrie,
Division of Psychiatry, School of Molecular and Clinical Medicine,
University of Edinburgh
July 2008
Summary
The SCARP project is a series of short studies with two aims; firstly to discover more about
disciplinary approaches and attitudes to digital curation through ‘immersion’ in selected cases;
secondly to apply known good practice, and where possible, identify new lessons from practice in
the selected discipline areas. The study summarised here is of the Neuroimaging Group in the
University of Edinburgh’s Division of Psychiatry, which plays a leading role in eScience
collaborations to improve the infrastructure for neuroimaging data integration and reuse. The Group
also aims to address growing data storage and curation needs, given the capabilities afforded by new
infrastructure.
The study briefly reviews the policy context and current challenges to data integration and sharing
in the neuroimaging field. It then describes how curation and preservation risks and opportunities
for change were identified throughout the curation lifecycle; and their context appreciated through
field study in the research site. The results are consistent with studies of neuroimaging
eInfrastructure that emphasise the role of local data sharing and reuse practices. These sustain
mutual awareness of datasets and experimental protocols through sharing peer to peer, and among
senior researchers and students, enabling continuity in research and flexibility in project work. This
“human infrastructure” is taken into account in considering next steps for curation and preservation
of the Group’s datasets and a phased approach to supporting data documentation.
The International Journal of Digital Curation is an international journal committed to scholarly excellence and
dedicated to the advancement of digital curation across a wide range of sectors. ISSN: 1746-8256 The IJDC is
published by UKOLN at the University of Bath and is a publication of the Digital Curation Centre.

172 Meeting Curation Challenges
Introduction: SCARP Themes and Approach
Given the increasing importance attached to curating and preserving digital
research data for informed reuse, further study is needed of researchers’ practices and
how these vary across disciplines (Borgman, 2007). A recent Research Information
Network report makes broad disciplinary comparisons and concludes:
“In developing their policies, research funders and institutions
need to take full account of the different kinds and categories of
data that researchers create and collect in the course of their
research, and of the significant variations in researchers’
attitudes, behaviours and needs in different disciplines, sub-
disciplines and subject areas...” (Research Information Network
[RIN], 2008).
The SCARP case studies, funded by the JISC, contribute to this area with a focus
on a range of disciplines including medical and social sciences; and on four themes:
Policy drivers, enablers and barriers: organisational and institutional factors
including different skill levels, preservation policies and arrangements,
willingness to use these, and relationships to incentives and reward structures.
Stewardship practices: how the research process and methods relate to the
primary data created and external sources, how these are reused and linked to
publications, attitudes to doing this, the usefulness of prior data, and the
sustainability of collected digital information.
Tools and infrastructure: tools and facilities used to collect, deposit, find, cite,
discuss and annotate the data, and to ensure persistence and preservation.
Preserving context: how communities of practice and their knowledge bases can
be characterised, and how lineage and provenance is or may be documented.
The study aimed to be “immersive”, using a qualitative approach combining
ethnographic field study in the research context with “appreciative intervention” to
facilitate change, drawing on action research traditions (e.g. Karasti, 2007). Field study
data was gathered using 20 semi-structured interviews with a cross-section of Group
members, and by observing meetings over five months. In parallel, a data preservation
risk assessment was facilitated using the DRAMBORA approach (Digital Curation
Centre [DCC] & DigitalPreservationEurope [DPE], 2007) and the Digital Curation
Lifecycle (DCC, 2008), leading to recommendations for new measures to address
risks. The broader lessons are summarised in Conclusions to this article, which begins
with an overview of neuroimaging in psychiatry. Then challenges and risks identified
in the Neuroimaging Group study are described, with mitigation steps acknowledging
the role of ‘human infrastructure’ in sharing knowledge between researchers of
different skill levels and specialisms.
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Neuroimaging and Psychiatry
Neuroimaging in psychiatry focuses on finding neurobiological explanations of
psychiatric disorder (Lawrie, Weinberger, & Johnstone, 2005). The rationale is that
imaging techniques can depict differences at one point in time between groups of
patient and control brains, or sometimes changes over time in brains, which may then
be correlated with a range of measures of behavioural, social and clinical phenomena.
The SCARP study introduced here (Whyte, in press) took place against a
background of medical research funders’ interests in improving data curation and
sharing. The Medical Research Council and Wellcome Trust, major UK funders of
neuroimaging research and of psychiatry, both of which are relative UK research
strengths, recently published policies on documentation and sharing of medical
research outputs (Medical Research Council [MRC], 2007). These establish principles
for grant holders and roles of data creators and custodians; to curate datasets
throughout their lifecycle, make them available with few restrictions, and with
sufficient information for informed reuse. Custodians are called on to provide
transparent access policies, while complying with the research ethics approval process,
which places limits on the kinds of data that may be gathered, their processing and
retention. An important factor in studies involving (psychiatric) patients is that any risk
of the loss of medical confidentiality must be minimised (MRC, 2007).
The MRC also funds eScience projects in the UK to permit data sharing by
providing an infrastructure to integrate neuroimaging datasets. While various imaging
techniques have been used in psychiatric research, MRI (Magnetic Resonance
Imaging) has become predominant. MRI has provided a means to investigate brain
structure without surgical or even X-Ray exposure and, with the introduction of
“functional” MRI, to couple that with studies of brain processes (Pekar, 2006). A
structural MRI image highlights the spatial distribution of brain tissue components,
enabling structure to be mapped against standard templates and potentially tracked
through repeated scans. Three-dimensional images of the brain are “reconstructed”
from individual “slices” of the head, captured digitally from scanners that subject the
research participant to intense magnetic pulses. Functional (fMRI) studies measure the
flow and oxygenation level of blood in the brain, which change in response to task
“stimuli” participants/ subjects are asked to respond to inside the scanner. fMRI
scanning sacrifices some spatial image resolution for the added dimension of time,
building up a movie-like sequence (Pekar, 2006).
The Neuroimaging Group in Edinburgh University’s Division of Psychiatry
researches major psychiatric disorders, and is particularly known for schizophrenia
research. Neuroimaging studies typically follow a case-control design; subject groups
with a positive diagnosis are compared with groups at high risk, plus healthy controls
(Lawrie et al., 2005). The Group has unusually large and rich datasets. For example the
longitudinal Edinburgh High Risk Study (Johnstone, Russell, Harrison, & Lawrie,
2003) includes social and economic classification data, information on family history
and life events, and on alcohol and drug use for over 200 subjects. Clinical and
behavioural data includes diagnoses and case history, psychiatric assessment,
performance in IQ and other cognitive tests. The majority of participants were seen on
several occasions over up to ten years. Subjects in this and other studies have also
given genetic data to illuminate the heritable characteristics of psychiatric disorders.
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Challenges in Data Integration: Wider and Deeper Studies
Neuroimaging researchers are increasingly seeking to integrate datasets from
different centres through collaboration in multi-centre studies, to improve the
statistical power and reliability of research findings from larger study populations than
single centres could feasibly recruit. Integrated datasets provide a wider range of
clinical, behavioural and demographic data to identify and correlate variables. Dataset
integration is a prime target of eScience projects such as the UK-based Neurogrid and
NeuroPsygrid and U.S.-based BIRN (Biomedical Informatics Research Network). The
cost efficiencies of multi-centre studies are a further incentive: the possibilities of
retrospective meta-analysis underpinning work on effective data mining (Keator,
Gadde, Grethe, Taylor, & Potkin, 2006; Ure et al., 2007).
A number of factors however confound image and other data integration: scanners
vary in magnetic field and image intensity, centres may recruit from markedly
different populations, and adopt any of a number of different scales to measure (for
example) psychotic symptoms. Also there is wide variation in image analysis tools -
hence projects increasingly focus on standardised tools to harmonise methods,
normalise scanner output, coordinate quality assurance, and bridge symptom scales
(Ure et al., 2007).
Data Sharing Resources and Risks
There are obstacles to sharing neuroimaging data apart from the barriers to
integration, including concerns about disclosure of confidential data. A key issue for
Gardner et al. (2003) is that neuroimaging data reuse is relatively straightforward, but
susceptible to misinterpretation with insufficient representation of the original
experimental context. As a result;
“…the scope of shareable data may legitimately vary depending
upon the standards and practices of different fields or techniques,
and may thus include or exclude any or all of ‘raw’, partially
processed, processed or selected datasets. Ideally shareable data
should be defined as the combined experimental data and
descriptive metadata needed to evaluate and/or extend the results
of a study” (Gardner et al., 2003, p.291).
This indicates the early stage of standards for experimental context metadata,
dataset structure and content (Gardner et al., 2003) reflecting the rapid pace of change
in this field. Neuroimaging laboratories tend not to have invested in database
technologies, and according to Geddes et al. (2006) data curation in neuroimaging
research tends to be poor. Large-scale curation and publication of datasets have
however been embarked upon by U.S. and international collaborations, including
fBIRN (Keator et al., 2006). Some databases provide canonical reference data: web-
based brain atlases and coordinate systems, and statistics representing norms of brain
structure or function. Other databases provide primary data or derived results from
studies to support meta-analysis (Toga, 2001). While the UK currently lacks
established data centres to support domain archiving, the MRC-funded e-Science
projects are developing services intended to be sustainable (although it was not the
study’s remit to assess that). The MRC is also establishing a data support service, and
supporting the Mental Health Research Network’s Cohort Dataset Directory (Mental
Health Research Network [MHRN], 2007).
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The need to safeguard patient confidentiality is paramount in arrangements for
data sharing. Research councils provide specific guidelines on the levels of
anonymisation required by Research Ethics Committees. However neuroimaging
raises particular concerns regarding image identifiability. While personally identifying
metadata are easily removed, three-dimensional reconstructions of the head are
potentially recognisable from photographic databases of known individuals, including
by automatic facial identification techniques (Kulynych, 2007). Levels of access are
therefore highly variable, for example PsyGrid limits it to approved collaborators using
a role-based model (Ainsworth et al., 2007).
Access limitations are characteristic of medical domains, for example, Lowrance
(2006) notes “open access” may refer to data that is open to application for access.
Determining which applications are legitimate may involve various considerations
including confirmation of professional competence, and screening of the scientific
merit of proposed collaborations. One of the challenges for medical e-infrastructure is
to manage the range of access rights needed; Lowrance (2006) identifies
confidentiality and anonymisation as one of the “issue clusters” most in need of
attention for data sharing in medical research.
Challenges and Risks from the Lab Perspective
The SCARP research site was a single neuroimaging centre in contrast to recent
studies of neuroimaging, which adopt eScience collaborations as their research site
(Ure et al., 2007; Lee, Dourish, & Mark, 2006). The latter’s study of fBIRN however
concludes that viewing collaborations as virtual organisations or “disembodied”
infrastructure disregards the local alignments needed to make them work, i.e. they can
better be understood as ways to blend local concerns, organizational relationships and
arrangements, including those for access to data. So, although locally focused, the
SCARP study spanned Neuroimaging Group researchers’ activities in both local and
wider collaborations, including Neurogrid and NeuroPsygrid.
At the study’s outset, interviews with a cross-section of the group’s researchers
identified that curation was seen in terms of managing the groups ever increasing
needs for secure storage, and integrating local datasets. The study then facilitated a
preservation risk assessment to understand the background to these issues, and as a
means to identify a ‘way forward’ to address curation and risks to data preservation.
Assessing Curation and Preservation Risks
Risk assessment used the DRAMBORA methodology (DCC/DPE, 2007).
Although intended for larger and formally established data archives or institutional
repositories, it was used prospectively here to consider the range of activities that a
data archive could entail, given that the UK has no established archiving service in this
domain. The DRAMBORA approach has three main stages (DCC/DPE, 2007): firstly
the organizational context is characterized in terms of formal mandates and objectives,
policy influences and community best practices. This stage identified a draft statement
of curation and preservation objectives. The activities currently undertaken to pursue
the latter were also identified with the digital assets considered of value.
The second stage involved standard risk assessment steps, identifying risks with
the relevant activities and digital assets, then assessing the probability and impact of
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176 Meeting Curation Challenges
each risk. Interviews and a questionnaire for researchers were used here. Probability
was assessed as the likelihood of the risk event in a given period. Impact was rated in
terms of loss of dataset usability and value. The third stage identified how risks are
mitigated and possible additional measures.
Neuroimaging Group’s main digital assets are its datasets, valued for their large
number of subjects, time span, and range of associated data. Methods are also valued
as they provide new ways to analyse this data. Other assets comprise the local
technology infrastructure, where high performance parallel computing enables
analyses that would otherwise be infeasible. The systems administration role manages
many of these assets. As one would expect of an active research group, much curation
and preservation activity is embedded in other research roles, particularly Principal
Investigators who as custodians are responsible for clinical data management and
security.
Despite these differences from an established data archive it was helpful to use the
OAIS functional model (Consultative Committee for Space Data Systems [CCSDS],
2002) to map current activities to the seven main functions in that model as a basis for
envisaging more formalised procedures. The scope of current activities and relevant
risks were identified from interviews and from risks previously identified by
repositories using the DRAMBORA process, and the candidate list rated for possibility
and impact. This provided a risk register, reusable by the Group to monitor risks
periodically. The outcomes were then elaborated as recommendations for changes to
the Group’s data policy to support data documentation and preservation, to which we
return below.
New Challenges to Preservation from Innovations in Integration
The Curation Lifecycle Model (DCC, 2008) was used to consider new measures
to mitigate risks to data, highlighting the curation steps where a fresh focus would be
advantageous. Much of the Group’s current work on infrastructure targets the data
integration issues identified earlier. It has also widened the range of data it collects and
developed new forms of image analysis.
The lifecycle model helps to draw attention to the effect of measures emerging
from the eScience infrastructure for dataset integration, i.e. that these address risks in
some steps, while adding to curation requirements in others. In Neurogrid the Group’s
“skull stripping” scripts automatically remove the identifiable faces and ears, enabling
images to be shared with collaborators, and the project also provides scripts as grid
services, enabling remote access to analyses. Neuroimaging Group also contributes to
scanner inhomogeneity correction, enabling data to be pooled from different MRI
scanners. Also, in NeuroPsyGrid, ontologies of psychosis symptoms are being
developed to bridge the assessment scales used in different centres and over different
periods of data collection.
These developments each add value to datasets by enabling new, shared and more
reliable analyses. Each also however implies new provenance metadata requirements;
e.g. details of who stripped which images and when, details of who used what grid
services, and of which assessment scales were originally used by which centre and for
what purpose. Provenance metadata are needed to ensure the accuracy, reproducibility
and reusability of results (MacKenzie-Graham, Van Horn, Woods, Crawford, & Toga,
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2008) and is broader than that needed and currently gathered for solely local use. Also,
since these innovations entail new forms of derived image data, storage requirements
are greater. The Group uses a variety of image analysis software packages and also
developed its own techniques to automate identification of anatomical changes. This
has increased not only the number of ways any one scan can be processed but also the
capacity to process large numbers of scans simultaneously; again adding to storage
demands. Indeed, storage is already at a premium given the increasing proportion of
studies that use functional imaging, which produces large volumes of images.
The curation lifecycle steps where these measures address significant challenges
are the later ones; dataset access and use, and the transforming of datasets for new
purposes. Storage requirements continue to grow as each of the earlier steps need
additional measures (see Figure 1 and further details in DCC, 2008). The
Neuroimaging Group and other labs hold most of their data locally and in server file
store rather than databases integrating image and associated clinical data. Decisions on
dataset appraisal are complicated by the additional value that new techniques and data
(for example genetic data) provide for retrospective analysis of old datasets. These
largely remain in online storage, for which the Group has developed innovative backup
solutions to minimise file recovery time and the possibility of data loss.
Figure 1. Measures to improve digital curation across the lifecycle.
The areas this study found most in need of additional resources were in
preservation planning and action, i.e. to assign metadata, and to appraise datasets and
migrate them accordingly to cheaper storage or disposal. As a first step the Group is
defining a locally relevant “core schema” and standard set of study-related files; taking
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data integration
addresses risks
here…
…adding to
local data
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needs here

178 Meeting Curation Challenges
account of the neuroimaging community’s development of various metadata schema
for provenance and study context. Progress towards more comprehensive data
publication requires investment in an evolutionary approach to move from inter-
personal sharing of data documentation, using the core schema as a basis for more
structured collaboration support (see also Treloar & Harbroe-Ree, 2008). Additionally,
to address risks identified at the “create/ receive” step of the lifecycle, quality
assurance is being strengthened and locally standardised.
Human Infrastructure: Curation as Learning
While there was support among the Group for measures to standardise
documentation, they reported very low incidence of major data loss and the identified
risks were mainly seen as having a low probability. The most valued resource for
resolving any issues of understanding unfamiliar datasets was not a set of formal
procedures but rather the Group’s informal weekly research meetings. Observations of
these meetings during the study sought to understand their role in research practice as a
form of “human infrastructure”, the importance of which has been acknowledged in
US studies, including in the neuroimaging domain (Lee et al., 2006). These drew on
Star and Ruhleder (1996)’s influential analysis of infrastructure, emphasising that this
is learned as a part of membership, and both shapes, and is shaped by, the conventions
of a community of practice.
Group meetings involve senior and junior researchers in an informal form of peer
review, in which data and interim results are presented, carefully and constructively
critiqued, and problems addressed through “heedful interaction” (Weick & Roberts,
1993). Group meetings aid mutual awareness of ongoing work, continuity of research
strands, and enable senior researchers to recommend areas of collaboration. They
complement the close inter-personal interaction between clinical and imaging
researchers (largely from engineering and neuroscience backgrounds) and help junior
researchers learn the field’s interdisciplinary terminology. Their learning process is
highly participatory, requiring students to contribute skills to others’ projects, and to
reuse datasets so they may gain sufficient experience to acquire their own data. The
interdependencies set up a “chain of learning” from newcomers to experienced
researchers, which involves sharing experimental protocols and notes, but is
nevertheless under strain as the Group expands. Data documentation directly benefits
the learning process for new researchers, as well as contributing to the continuity and
replicability of research. On that basis, postgraduate learning in this interdisciplinary
domain may have a key role in curation.
Conclusions
Multi-centre neuroimaging collaborations target the need for data integration and
foster innovation in image analysis. This in turn adds to the need to record information
about the context of studies and track the provenance of data that have been integrated
from disparate sources and analysed by multiple people and/or centres. Innovations in
analysis also place new demands on archiving, increasing the demand for online
storage by making analysis of more images practicable, which in turn increases storage
requirements for secondary data. New analysis techniques also highlight the need for
active appraisal of datasets. They make retrospective analysis of neuroimaging datasets
increasingly fruitful, while the timespan of longitudinal studies lengthens with the
maturity of the field, and datasets are sustained through successive projects and
custodians.
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Standardisation in neuroimaging methods and data documentation is driven by the
need for higher reliability in studies that also require larger-scale collaboration and
hence wider trading of methods and data. The study demonstrates the need for a
nuanced view of “enablers and barriers” to data sharing, curation, preservation and
reuse. For example, the lack of standardisation in neuroimaging methods is a barrier to
data sharing. However it also means that to learn methods and perform studies, lab
researchers must share access to, and descriptions of, their data with others who have
differing skills levels or specialities. Junior researchers learn by participating in
colleagues’ studies, directly benefit from sharing experimental protocols, and could
play an active role in building study documentation to serve research group needs.
The study illustrates that neuroimaging in the psychiatry domain involves
continuous care of large and dynamic datasets. However investment in data
documentation and development of integrated data management facilities at the lab
level is required to mitigate preservation risks. Initial steps are being taken to identify a
core metadata schema and group collaboration support technologies appropriate to a
shift from inter-personal and study-level sharing of documentation to Group-level and
wider data publishing.
Acknowledgements
The study would not have been possible without the generosity and patience of
colleagues in the Neuroimaging Group, Division of Psychiatry, University of
Edinburgh. SCARP is funded by the JISC.
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