108 results
A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples
- Gladi Thng, Xueyi Shen, Aleks Stolicyn, Mark J. Adams, Hon Wah Yeung, Venia Batziou, Eleanor L. S. Conole, Colin R. Buchanan, Stephen M. Lawrie, Mark E. Bastin, Andrew M. McIntosh, Ian J. Deary, Elliot M. Tucker-Drob, Simon R. Cox, Keith M. Smith, Liana Romaniuk, Heather C. Whalley
-
- Journal:
- Psychological Medicine , First View
- Published online by Cambridge University Press:
- 18 March 2024, pp. 1-12
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Background
The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.
MethodsWe utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.
ResultsIn UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.
ConclusionOur results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.
Recreating the OSIRIS-REx slingshot manoeuvre from a network of ground-based sensors
- Trent Jansen-Sturgeon, Benjamin A. D. Hartig, Gregory J. Madsen, Philip A. Bland, Eleanor K. Sansom, Hadrien A. R. Devillepoix, Robert M. Howie, Martin Cupák, Martin C. Towner, Morgan A. Cox, Nicole D. Nevill, Zacchary N. P. Hoskins, Geoffrey P. Bonning, Josh Calcino, Jake T. Clark, Bryce M. Henson, Andrew Langendam, Samuel J. Matthews, Terence P. McClafferty, Jennifer T. Mitchell, Craig J. O’Neill, Luke T. Smith, Alastair W. Tait
-
- Journal:
- Publications of the Astronomical Society of Australia / Volume 37 / 2020
- Published online by Cambridge University Press:
- 27 November 2020, e049
-
- Article
-
- You have access Access
- HTML
- Export citation
-
Optical tracking systems typically trade off between astrometric precision and field of view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on 22 Sep 2017. As part of a trajectory designed to get OSIRIS-REx to NEO 101955 Bennu, this flyby event was viewed from 13 remote sensors spread across Australia and New Zealand to promote triangulatable observations. Each observatory in this portable network was constructed to be as lightweight and portable as possible, with hardware based off the successful design of the Desert Fireball Network. Over a 4-h collection window, we gathered 15 439 images of the night sky in the predicted direction of the OSIRIS-REx spacecraft. Using a specially developed streak detection and orbit determination data pipeline, we detected 2 090 line-of-sight observations. Our fitted orbit was determined to be within about 10 km of orbital telemetry along the observed 109 262 km length of OSIRIS-REx trajectory, and thus demonstrating the impressive capability of a networked approach to Space Surveillance and Tracking.
Curcumin improves hippocampal function in healthy older adults: a three month randomised controlled trial
- Andrew Scholey, Katherine Cox, Andrew Pipingas, David White
-
- Journal:
- Proceedings of the Nutrition Society / Volume 79 / Issue OCE2 / 2020
- Published online by Cambridge University Press:
- 10 June 2020, E440
-
- Article
-
- You have access Access
- Export citation
-
The flavonoid curcumin is believed to be responsible for the purported health benefits of turmeric. Like other flavonoids, curcumin affects several systemic and central processes involved in neurocognitive aging. We have previously shown that one month administration of a highly bioavailable curcumin extract (Longvida™) improved working memory and reduced fatigue and workload stress in an older, cognitively intact cohort(1). This study focused on the effects of the same extract, focusing on memory tasks subserved by the hippocampus, one of two areas of the adult brain believed to be capable of adult neurogenesis.
Eighty healthy older participants (aged 50–80 years, mean = 68.1, ± SD 6.34) took part in this double-blind, placebo-controlled, parallel-groups trial. Volunteers were randomised to receive administration of 400 mg daily Longvida™ (containing 80 mg curcumin) or a matching placebo. Assessment took place at baseline and 4 and 12 weeks thereafter. Outcomes included two tasks evaluating memory processes relevant to hippocampal function. These were i) a human analogue of the widely used rodent Morris Water Maze - the virtual Morris Water Maze (vMWM) and ii) a Mnemonic Similarity task evaluating pattern separation. Measures of mood, cardiovascular function and other blood biomarkers were collected, and a subset of the cohort underwent neuroimaging using functional magnetic resonance imaging.
Compared with placebo, there were a number of improvements in the curcumin group. The curcumin group had significantly better performance at 12 weeks on the virtual Morris Water Maze (p = .019). Curcumin was also associated with better performance on a pattern separation task (p = .025). Curcumin was also associated with number of significantly benefits to mood, including, from the Profile of Mood States (POMS), including, at 28 days only, total mood disturbance (p = .006), tension-anxiety (p = .028), confusion-bewilderment (p = .019), anger-Hostility (p = .009). There were also significant benefits to the POMS fatigue scores at both assessments (p ≤ .011). There were no group differences in biomarker levels.
These results confirm that Longvida™ curcumin improves aspects of mood and working memory in a healthy older cohort. The pattern of results is consistent with improvements in hippocampal function and may hold promise for alleviating cognitive decline in populations at risk of pathological cognitive decline.
Cognitive functioning and lifetime major depressive disorder in UK Biobank
- Part of
- Laura de Nooij, Mathew A. Harris, Mark J. Adams, Toni-Kim Clarke, Xueyi Shen, Simon R. Cox, Andrew M. McIntosh, Heather C. Whalley
-
- Journal:
- European Psychiatry / Volume 63 / Issue 1 / 2020
- Published online by Cambridge University Press:
- 21 February 2020, e28
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Background.
Cognitive impairment associated with lifetime major depressive disorder (MDD) is well-supported by meta-analytic studies, but population-based estimates remain scarce. Previous UK Biobank studies have only shown limited evidence of cognitive differences related to probable MDD. Using updated cognitive and clinical assessments in UK Biobank, this study investigated population-level differences in cognitive functioning associated with lifetime MDD.
Methods.Associations between lifetime MDD and cognition (performance on six tasks and general cognitive functioning [g-factor]) were investigated in UK Biobank (N-range 7,457–14,836, age 45–81 years, 52% female), adjusting for demographics, education, and lifestyle. Lifetime MDD classifications were based on the Composite International Diagnostic Interview. Within the lifetime MDD group, we additionally investigated relationships between cognition and (a) recurrence, (b) current symptoms, (c) severity of psychosocial impairment (while symptomatic), and (d) concurrent psychotropic medication use.
Results.Lifetime MDD was robustly associated with a lower g-factor (β = −0.10, PFDR = 4.7 × 10−5), with impairments in attention, processing speed, and executive functioning (β ≥ 0.06). Clinical characteristics revealed differential profiles of cognitive impairment among case individuals; those who reported severe psychosocial impairment and use of psychotropic medication performed worse on cognitive tests. Severe psychosocial impairment and reasoning showed the strongest association (β = −0.18, PFDR = 7.5 × 10−5).
Conclusions.Findings describe small but robust associations between lifetime MDD and lower cognitive performance within a population-based sample. Overall effects were of modest effect size, suggesting limited clinical relevance. However, deficits within specific cognitive domains were more pronounced in relation to clinical characteristics, particularly severe psychosocial impairment.
Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures
- Mathew A. Harris, Xueyi Shen, Simon R. Cox, Jude Gibson, Mark J. Adams, Toni-Kim Clarke, Ian J. Deary, Stephen M. Lawrie, Andrew M. McIntosh, Heather C. Whalley
-
- Journal:
- Psychological Medicine / Volume 50 / Issue 10 / July 2020
- Published online by Cambridge University Press:
- 18 July 2019, pp. 1653-1662
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Background
Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ.
MethodsThe present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants.
ResultsThere was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = −0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons.
ConclusionsOur significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.
In trans variant calling reveals enrichment for compound heterozygous variants in genes involved in neuronal development and growth.
- Allison J. Cox, Fillan Grady, Gabriel Velez, Vinit B. Mahajan, Polly J. Ferguson, Andrew Kitchen, Benjamin W. Darbro, Alexander G. Bassuk
-
- Journal:
- Genetics Research / Volume 101 / 2019
- Published online by Cambridge University Press:
- 13 June 2019, e8
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Compound heterozygotes occur when different variants at the same locus on both maternal and paternal chromosomes produce a recessive trait. Here we present the tool VarCount for the quantification of variants at the individual level. We used VarCount to characterize compound heterozygous coding variants in patients with epileptic encephalopathy and in the 1000 Genomes Project participants. The Epi4k data contains variants identified by whole exome sequencing in patients with either Lennox-Gastaut Syndrome (LGS) or infantile spasms (IS), as well as their parents. We queried the Epi4k dataset (264 trios) and the phased 1000 Genomes Project data (2504 participants) for recessive variants. To assess enrichment, transcript counts were compared between the Epi4k and 1000 Genomes Project participants using minor allele frequency (MAF) cutoffs of 0.5 and 1.0%, and including all ancestries or only probands of European ancestry. In the Epi4k participants, we found enrichment for rare, compound heterozygous variants in six genes, including three involved in neuronal growth and development – PRTG (p = 0.00086, 1% MAF, combined ancestries), TNC (p = 0.022, 1% MAF, combined ancestries) and MACF1 (p = 0.0245, 0.5% MAF, EU ancestry). Due to the total number of transcripts considered in these analyses, the enrichment detected was not significant after correction for multiple testing and higher powered or prospective studies are necessary to validate the candidacy of these genes. However, PRTG, TNC and MACF1 are potential novel recessive epilepsy genes and our results highlight that compound heterozygous variants should be considered in sporadic epilepsy.
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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.
Contents
- Andrew Cox, Eddy Verbaan
-
- Book:
- Exploring Research Data Management
- Published by:
- Facet
- Published online:
- 21 September 2019
- Print publication:
- 11 May 2018, pp v-x
-
- Chapter
- Export citation
Frontmatter
- Andrew Cox, Eddy Verbaan
-
- Book:
- Exploring Research Data Management
- Published by:
- Facet
- Published online:
- 21 September 2019
- Print publication:
- 11 May 2018, pp i-iv
-
- Chapter
- Export citation
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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.
Exploring Research Data Management
- Andrew Cox, Eddy Verbaan
-
- Published by:
- Facet
- Published online:
- 21 September 2019
- Print publication:
- 11 May 2018
-
Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data. Exploring Research Data Management provides an accessible introduction and guide to RDM with engaging tasks for the reader to follow and develop their knowledge. Starting by exploring the world of research and the importance and complexity of data in the research process, the book considers how a multi-professional support service can be created then examines the decisions that need to be made in designing different types of research data service from local policy creation, training, through to creating a data repository.
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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
-
- Chapter
- Export citation
-
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.