Author: angus.whyte

More than spilled milk- CREAM at the Research Data Spring workshop

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image of spilled milkThe Jisc Research Data Spring workshop at the Warwick Conference Centre in Coventry had some welcome moments of blue sky before the mid-December dull grey set in. These included a breakout session from one of the projects [1];  Collaboration for Research Enhancement by Active Metadata (CREAM). Their breakout session explored the active use of metadata in the arts and sciences, a theme the project members have been exploring for some time [2].

The workshop was titled ‘Observations on Commonalities of Process’, and led by Iris Garrelfs and Graham Klyne’s two-handed presentation on key parallels between the arts and sciences. Iris Garrelfs spoke as a PhD student and artist who works “on the cusp of music, art and sociology” [3]. Graham Klyne of Nine by Nine [4], spoke as an ex University of Oxford bioinformatician, and contributor to many semantic web standards.

This seemed unaccustomedly philosophical territory for a Jisc programme workshop, in my experience anyway. And, despite any seasonal temptation, nobody made any rubbish puns about C.P Snow, or made too much of his big theme. It was the rifts between the ‘two cultures’ of the sciences and humanities, which the chemist-turned-novelist famously wrote about [5] and are still with us to today.  Much of the research-driven impetus behind Research Data Management has come from the STEM disciplines. Perhaps understandably, given the impact of the EPSRC’s data policy on UK institutions, this has antagonised many humanities researchers who would rather deal with policy directions couched in their own terms.  So I guess if Snow were still around he would have approved of this session.

Rather than becoming bogged down in the differences of terminology and epistemology, the session brought fresh thinking on common methods and tools for dealing with arts and humanities metadata. The four discussion themes were:

  •      planning and agility
  •      decision-making
  •      workflow and lifecycle
  •      dissemination

Each theme was introduced by Iris and Graham, based on the project’s effort to develop a model for ‘active metadata’. They included reflections on the research processes followed by artists at University of the Arts London, and by chemists and geoscientists in Southampton and Edinburgh. So there were many contributions from collaborators Athanasios Velios, Simon Coles, and others outside the project. 

1.     Planning and agility

Some of the fresh thinking mentioned earlier is in the shape of Iris Garrelf’s Procedural Blending model. This is an abstract framework for describing creative processes, set out in her PhD thesis [6], and based on her work in sound art.

If I picked up Iris’s quick introduction to the model correctly the gist of it is that creative processes do not follow a stepwise linear process from input to output, but blend parallel strands of action (or ways of framing a problem), that become joined together at key points in the research process. The question is, how can this be recorded in useful ways?

Provenance metadata is part of the answer for CREAM. Reflecting on his involvement in the W3C PROV collaboration, Graham Klyne’s take on this standard for provenance metadata [7] was that it offers a very useful structure for encoding process, but it is not forthcoming about describing its less mechanical aspects. The Procedural Blending model, he said, has offered a fascinating counterbalance to PROV. It may offer the provenance standards a broader framework for these less tangible aspects of data management. Of course provenance is a retrospective record of action, and research planning and workflow design are prospective. Addressing the tacit and intangible seems key to working out how to apply the provenance metadata emerging from a project as a resource for planning-in-action.

At first arts-science differences were most evident when the project began working out how to take that forward, but then parallels became clear. These include several aspects of the trade-off between planning and agility in research.

  • Amendments and changes in process. The project has considered research around chemical reactions, responses to planning that research, and the role of improvisation. At one extreme improvisation can be thought of as ‘developing a plan in the moment’. At the other it can refer to points where a researcher is responding to observations and adapting (say) a spreadsheet to record experiment outcomes.
  • Re-framing. Iris pointed out that artists are used to taking conceptual and physical objects and turning them on their head to look at from a different perspective, whether literally or metaphorically. Science aims to nail down processes in a more definitive and reproducible way. But as Graham and others commented, the way that science research is reported suggests design that is more planned than it is in practice. So CREAM has become focused on the messy aspects of research design; not just when the milk gets spilled, as it were, but acting on the smell of it; those points when arbitrary choices are made, or the data that researchers are faced with suggest a new line of investigation. Here it is detailed knowledge of background that makes for the ability to make decisions about what line to take.

These points resonated with challenges to reproducibility that the RDM community is trying to address. Simon Coles mentioned for example that a minority, perhaps 20%, of chemical syntheses are reproducible, because of tacit knowledge and arbitrary decisions that do not get recorded.

Neil Jefferies made a further connection with the under-reporting of negative results, and the idea that capturing this vast body of knowledge of ‘what didn’t work’ could save time by identifying what won’t work in future. Simon Coles pointed out that accounting for negative results that don’t go according to plan is a very different thing from accounting for agility in planning. And Southampton ex-colleague Mathew Addis pointed out that the desire for this level of accounting varies by discipline, but isn’t restricted to academia. Chemists try to record everything, and so does the pharma industry.

The Southampton University Chemistry groups’ work with electronic lab notebooks (ELN) has taught them a thing or two about what people actually record and do with paper and digital notebooks. The greater sense of ownership researchers have of paper notebooks affects their willingness to make the switch. ELN take-up is difficult where there are specific research values around data ownership, and they are investigating ways to encourage submission of ELNs.

Decision making

Humanities scholars tend to deal with the subjectivity of research decision-making differently to scientists. Iris spoke about artists and scholars concern to understand motivations and influences. She pointed out that history and archaeology are concerned with similar problems as scientific reproducibility – doing forensic studies of ‘how people got there’. Generalising across the humanities, the view tends to be much more that provenance is debatable, while for scientists it’s a record of the path of their research that does not need or deserve debate.

These differences can be productive though; some present commented on the value of capturing motivations in science.  There is also value in drawing out the role of decisions that can’t be planned for e.g. apparently arbitrary decisions about what is looked at, or selected for analysis. Conventional recording processes rarely allow for this in the sciences, and appraisal processes emphasise deliberate and rational choice.

Having light shed on arbitrary decision-making from models of the creative process may help to incorporate into the scientific record metadata on how ideas are made, and how spontaneity is dealt with. As Simon Coles pointed out research depends on the ability to deviate from a plan on the fly. And as Neil Jefferies remarked, collaborations often depend on people knowing that they share a similar feeling about the problem at hand that comes from their aggregated history. Exposing those aspects that influence how decision are made could help with reproducibility, and are challenging to record.

Workflow and lifecycle

If you have tried to apply research data lifecycle models in practice and thought ‘ok that’s fine for the fly-by overview, but life’s not like that’, you will probably appreciate the problem CREAM is trying to tackle.  One of the marked similarities the project found was between the procedural blending model and models of the research lifecycle more common in the sciences.

There are well-known barriers to the practicality of documenting more fine-grained and realistic metadata; the prime one being justifying the expense of doing it. But there are nuances to the cost-benefit trade-offs. Obviously automating the metadata gathering helps, but only of the metadata is more meaningful and useful than that which is handcrafted but based on fallible memory and hindsight-based rationalisations about what happened.  This is where the CREAM collaborators believe workflow models that allow for provenance metadata to be applied prospectively may help.  So far, they said they had been pleasantly surprised at how much the fluidity of the artistic view could be useful to shed light on scientific process and, from that artistic perspective the potential of scientific workflow techniques for recording process.

Dissemination

Ownership and attribution issues were the key ones highlighted at this point in the discussion. Copyright and plagiarism concerns drive a reluctance to record research processes.  For some present that pointed to the need to enable a hierarchy of access to data. Workflows for research data sharing must allow for much of the data to be kept to known collaborators for much of the time. The RDM community’s general invocation to be as open as possible, as quickly as feasible, can drown out that message.

Fiona Murphy, who coupled her humanities background with her experience in scientific publishing, highlighted an important question – how important is it who actually makes the observations that create data? From a reproducibility perspective these observations should, in principle at least, be independent of the observer making them, but how is that actually viewed in practice?  Some of the scientists present were happy to acknowledge that some people are better than others at making observations. Had there been more humanists or sociologists of science in the room this might have sparked further debate about epistemology, or about how researchers’ biographies and social networks actually affect what research gets done.

Other participants reiterated the earlier point that science reporting also tends to play down the creative elements of the research process. And from her arts background Iris Garrelf mentioned that the convention of working within a genre, and following its rules of provenance (among other things), has similarities with the call for reproducibility in science.

Conclusions

Two main points wrapped up this session; firstly that scientific and humanistic datasets can be used by researchers on the other side of the divide for purposes neither imagined, so it makes sense to have common data management frameworks. The other was to encourage researchers, and others involved in the RDM field, to go beyond a mandate-fulfilling view of reproducibility. Records of process aren’t just useful for re-treading your own path, they can be a resource for doing things outside your own field.

From my own point of view I liked this workshop a lot, and was pleased to see there’s a similar one planned for IDCC [8]. Many of the themes will be familiar to provenance researchers and also touch on sociology of science. I was also reminded of Arthur Koestler’s ‘bisociation’ theory of creative thinking [9]. I used that in my very first published journal article [1989, lost to digital rot, but papyrus still available!] so it had plenty of personal resonances.

CREAM are pursuing a novel approach, and more recent parallels struck me. On the sociological side of things some work by the Information Systems group at the LSE on ‘Collective agility, paradox and organizational improvisation’ based on a study of particle physics research processes in the GridPP collaboration [10].  A little more current than that, Also the Research Data Alliance has several groups addressing the ‘planning and agility’ theme. These are the interest group on Active Data Management Plans [11], plus another on ‘De-constructing the Data Lifecycle- Agile Curation’ [12].

CREAM is part of a flurry of tech development aiming for better record-making tools in research. The hope is they’ll offer metadata that’s actually useful for research before it’s done, as well as more accurate about how it’s done, all with less effort and higher usability than the traditional lab record or artists notepad. The results remain to be seen.

Photo credit: ‘just spilled milk‘ by Post Memes CC-BY-2.0

[1] More on the Jisc Research Data Spring projects is available at: https://www.jisc.ac.uk/rd/projects/research-data-spring

[2] For example: Cerys Willoughby, Colin Bird, Jeremy Frey (2015) User-Defined Metadata: Using Cues and Changing Perspectives, International Journal of Digital Curation 2015, 10(1), pp. 18-47 doi:10.2218/ijdc.v10i1.343

[3] More on Iris Garrelf’s work including the journal ‘Reflections on Process in Sound’ available at: http://irisgarrelfs.com/

[4] Nine by Nine, available at: http://www.ninebynine.org/

[5] See Wikipedia entry ‘The Two Cultures’ available at: https://en.wikipedia.org/wiki/The_Two_Cultures

[6] Iris Garrelfs (2015) ‘From inputs to outputs: an investigation of process in sound art practice’ University of London. Available at: http://irisgarrelfs.com/thesis

[7] W3C (2013) PROV-Overview: An Overview of the PROV Family of Documents. Available at: http://www.w3.org/TR/prov-overview/

[8] Metadata in Action workshop, IDCC16, Amsterdam, 25 Feb 2016 http://www.dcc.ac.uk/events/idcc16/workshops#Workshop%2010

[9] Arthur Koestler (1964) The Act of Creation, London: Penguin Books

[10] Zheng, Yingqin and Venters, Will and Cornford, Tony (2011) ‘Collective agility, paradox and organizational improvisation: the development of a particle physics grid’ Information Systems Journal. DOI: 10.1111/j.13652575.2010.00360.x Pre-print available at: http://eprints.lse.ac.uk/30029/1/Collective_agility_%28LSERO%29.pdf

[11] Research Data Alliance, ‘Active Data Management Plans’ IG, see: https://rd-alliance.org/groups/active-data-management-plans.html

[12] Research Data Aliiance, ‘Deconstructing the Data Life Cycle- Agile Curation’ Birds of a Feather Group, see: https://rd-alliance.org/groups/deconstructing-data-life-cycle-agile-curation.html

 

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What staff resource is allocated to RDM Services, and is it enough?

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This post is about the first part of a three-part briefing on the responses to our survey carried out in May 2015. The briefing is available on our survey page. A basic report giving analysis of these responses was sent to the survey participants at th…

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Variety in RDM requirements, a curse or the spice of life?

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  The RDMF14 meeting in York 9-10th November had ‘research data (and) systems’ as it’s main focus. Those brackets signified the expectation that integration would be a common talking point, and it did turn out to be an omni-present theme. There were three breakout sessions, with ‘integration’ the headline topic for one, ‘shared services’ another, and ‘requirements’ a third. This is a summary of that breakout session on requirements, and I’ve aimed below to distil the conversation around six issues:

Is ‘cathedral thinking’ hampering RDM?

In his welcoming comments DCC colleague Martin Donnelly drew attention to the RDMF meeting a year ago, where we attempted to list ‘systems’ or software platforms relevant to RDM. We came up with around 80, even excluding discipline-specific tools. Alongside that as a backdrop for the breakouts we had Arkivum CTO Mathew Addis panel presentation. This referenced software engineer Eric Raymond’s famous essay ‘The Cathedral and the Bazaar’ [1], which critiqued the then prevalent software development model; top-down, expert-driven creation of a monolithic software suite (the Cathedral).

The more open approach governed by co-development maxims like ‘given enough eyeballs all bugs are shallow’, and ‘release early, release often’ tends to result in a more diverse choice of solutions (the Bazaar). Mathew cited the excellent ‘101 Innovations in Scholarly Communication’ digest of tools to ease the research workflow [2], claiming that ‘researchers don’t use cathedrals they use bazaars’.

The point was to warn against ‘cathedral thinking’ in RDM. There was perhaps a suggestion that institutions tend towards that, in piecing together one-size-fits-all systems driven from the top-down by funder policy. There was also I think a definite hint that the task of integrating the many available solutions is presently under-resourced, and should not fall on the shoulders of any single supplier.

The requirements breakout that followed was facilitated by Robert Darby and Jane Williams, and took up this cue. Discussion began around the issues of ‘whose requirements?’, the challenge of prioritising them, and dealing with generic vs disciplinary tensions. Most of the conversation was on recurring ‘itches’ that the (UK) RDM community does yet have particularly settled responses to, including the core metadata requirements, ensuring that compliance needs are fulfilled, and providing secure storage for collaboration, and then getting data producers to help move the contents to an appropriately structured location inside or outside the institution.  Towards the end the discussion turned to requirements gathering approaches.

What about the pareto principle?

The difficulty in prioritising requirements that come from engagement with researchers kicked off the ‘whose requirements’ discussion. Caroline Hargreaves experience of finding that U.of Manchester research groups don’t view the research cycle in a sufficiently similar way seemed to resonate. Business analysis strategies of providing 80% of the solution by addressing the vital 20% of the problem (the ‘Pareto principle’) are very challenging to apply in this context without making some fairly hard-nosed choices. Caroline suggested several pragmatic strategies: –

  • Prioritise the needs of the biggest projects, partly because their PIs already have to deal with data sharing and standardisation issues to deliver their project, and getting PI buy-in is one of the big RDM service challenges.
  • Concentrate on shared approaches that might open doors to grant income sources that are new to particular faculty or school groups, such as cyber-security and (for health-related research) the Information Governance Toolkit.

Considering the prospect of dealing with the myriad forms of ‘smaller science’ data types and needs in a more ad-hoc way, Martin Wolf questioned whether this is really a problem, asking ‘”is thinking of RDM as ‘one thing’ holding us back?”. One answer was that the composition of ‘the RDM system’ is strongly influenced by where the RDM service sits, affecting the ‘flavour’ of what the system does. A quick show of hands gave a roughly 4:2:1 split between Library, Research & Enterprise, and IT Service.

Rather than dwell on whether any of the relevant stakeholders would be more susceptible to ‘cathedral thinking’, Torsten Reimer asserted the more pressing issue is coordination between them. That ought to happen as a result of senior management saying ‘this is a university-wide thing’. So while at Imperial the coordination post happens to be in the research office, the point of that was to get to a joined-up project plan, and from there to joined-up practice, or as close to that as one can.

Is disciplinary metadata a red herring?

The tension between generic and domain-specific needs is probably most acute when determining what the ‘core’ metadata fields are. As an aside, this is something DCC has articulated a view on. Although we have not been as vocal about it as we might, it is set out in Alex Ball’s recommendations for the Research Data Discovery Service pilot [3].

The EPrints Recollect schema offers a broader menu of choices, and Datacite and OpenAire schemas are a narrower starting point. But the fact this is a still evolving topic speaks volumes about the messiness of the problem. RDM services can be promoted as all encompassing systems, and for the metadata discussion it was a moot point whether that signifies ‘cathedral’ thinking or holistic thinking; the key point is that everyone needs discipline-agnostic metadata. Getting there is the issue.

One participant remarked that academic champions in their institution’s steering group had pointed at failed efforts to establish common schema in their own domains, but they expected that everything should gravitate towards some ‘basic’ fields. This led to the ‘red herring’ suggestion, countered by the point that some disciplines do have agreed schemas and they are very important to them, so the question remains ‘what do you take as a general approach to the specific’. Five tips from the discussion were:

  1. Focus on the ‘must do’ things; everyone needs to know what data is there, where it is, and to make it discoverable. It is more important to try to establish exchange and sharing as a way of working across the institution than worry too much about disciplinary details.
  2. Tell researchers you can and will take their domain specific metadata if they have it. Doing something is better than nothing, even if is just adding it to the deposited collection as another file.
  3. Telling researchers what disciplinary standard to adopt is unlikely to work, especially where a norm is not established. Better to drop hints than to push too hard.
  4. It is good practice to check that the description does actually identify what data is being deposited, even if it is presented as a readme file rather than structured metadata.
  5. Make a start and get some data out – if researchers see their colleagues doing it and getting a benefit from it they will take it up.

What compliance reporting metrics are needed?

Robert Darby’s question about how the group were meeting needs to monitor institutional compliance requirements – ‘it’s easy, right?’ brought some hollow laughs. If it’s sensible to target development at requirements that represent the biggest ‘itches’ this is one that might be described as sore or ticklish, depending on your disposition.

Torsten Reimer had remarked in his keynote that any institution slavishly trying to comply with EPSRC’s expectations to the letter is missing the point that institutions and the broader RDM community should be ahead of the funder’s, rather than simply responding to them. Tosten has recently written about the difficulties of OA reporting [4]. Nevertheless he was the first to admit that, however similar RDM and Open Access are in other respects, OA is technically easier to implement and the workflows and systems, including compliance reporting, are relatively more advanced.

There was a shared view that RDM compliance is not so clear-cut as OA. Whatever drives the institution’s RDM policy, the broad scope of ‘the requirements’ to implement them makes them difficult to squeeze into meaningful metrics. The discussion focused on Data Management Planning and Data access statements. It seemed sensible to focus the requirements lens where support is newer, as there are fewer entrenched institutional practices to get in the way of developing standard metrics.

The discussion started at the publication end of the research cycle, and the requirement (from RCUK) to include data access statements in publications, to marry these up with supporting data. The need for automation here was probably the most sorely scratched itch of the whole discussion, with some participants having to spend considerable amounts of time manually checking each publication for a data access statement. There was frustration in the knowledge that the information is already out there, but not flowing between the various systems within institutions, and between them and the funders and publishers. Identifiers were one way forward, provided there is support from agencies like Crossref and ORCID to link them up.

RDM services, as someone remarked, need to know about publications before they are published, and much of the information held about them ‘out there’ is already ‘out here’ in the heads of PIs. So the group’s gaze turned back to DMPs. Data Management Plans join up institutional stakeholders, and there is the potential for each of these to use the DMP as a gateway to resources and source of planning information; Jane Williams gave the example of storage at Robert Gordon University.

Nobody disputed the importance of DMPs, and some present mentioned using DMPonline to support them. However straightforward it may be to report on the numbers of DMPs submitted, there was some scepticism that it makes sense to rely on them as a source of information on later-stage RDM activity. One institutional survey had found 60% of researchers saying they had never written a DMP, and most of those who had written one never looked at it subsequently. So the idea of the DMP as a working document may have appeal for some of us, but it is some way from realisation.

How to share securely at a reasonable cost?

The requirements and cost issues around ‘file and sync’ Dropbox-style applications were a key issue for some of those in the service development phase. The use cases here ranged from a space for the PhD student to share their work with their supervisor, to large research projects needing a space to share securely with external collaborators. Several of the available third-party cloud storage solutions were discussed, mostly from the compliance and cost perspectives.

In this case the compliance issues were around Data Protection and cybersecurity, with non-EU hosting seen as problematic for human-subjects data. There was interest in the potential of CASB (Cloud Access Security Broker) software to meet institutional requirements for encryption . One participant mentioned an options paper that may be shareable (if and when it is, we’ll update this post to link to it).

Funding body compliance was recognised as having a bearing on the cost model, given that post-project storage costs cannot be charged to grants unless met up-front. In theory that should not be an issue for ‘active’ storage, but in reality the lack of awareness among researchers of institutional solutions for both active and longer- term storage meant that many were paying unnecessarily. We talked about cloud providers business models, typically a per-head charge in the case of file share and sync providers. Powerfolder was one of the offerings mentioned here as worth considering for a low per-head charge and European hosting.

The key point that Kevin Ashley made here was to take a long-term view. Whatever supplier is offering the best deal currently is unlikely to be the same 3 years hence, and it is crucial to avoid being locked in to a supplier, whether by technology, or terms and conditions that penalise migration. A useful starting point here is Jisc’s framework agreements.

How can we better identify the right ‘itches to scratch’?

The point was made that it is better to base solution choices on good understanding of the requirements than by what suppliers are offering. This led naturally to the final themes of the discussion; how those requirements (or ‘itches to scratch’) are being identified.

Most of those present it seemed had not employed ‘user experience’ or UX approaches such as use case scenarios and personas in developing their RDM systems. Some mentioned surveying researchers, and others using interviews and meetings to try to bring researchers in through the project process, providing opportunities to comment on specs and sign-off of the project results. Generally it seemed that services had inferred what tools or support activity would be needed from high-level policy compliance requirements, and borrowed from the approaches of more mature RDM services. Leeds University’s functional requirements for a data repository were cited as an example.

Another point was that RDM service roles are often sandwiched together from various people’s other jobs, and they do not necessarily have the collective skills or resourcing to carry out extensive co-design of their RDM solution. Researchers, it was remarked, tend to be too specific in their needs for those to quickly be translated into a generic ‘solution’. Coupled with that, the opportunity to design based on user experience may also be limited by researcher scepticism that their data can have any reuse value without attention to their specific requirements.

The discussion ended with three points that seemed pretty useful to me:

  1. Being open about what we do is the best way to deal with the issue that most people don’t have the time to go out and speak to everyone.
  2. There’s a need for useful information on tools and services, including providers of development support with open source tools
  3. Instead of engaging with users based on their organisational groupings it may be better to group them by the tools they use – perhaps echoing the ‘more user groups’ plea made by John Beaman earlier in the day.

DCC can support each of these to some extent. We have a How-to guide on discovering requirement [5]. We also plan to share examples of requirements approaches, and update our Tools and Services catalogue [6]. No doubt we should also consider support for user groups in our planning.

Getting back to the headline question, to finish with some personal conclusions, whether ‘too much variety’ in RDM solution design is spice or chaos depends on your perspective. Most of us in the requirements breakout were involved in developing solutions, or in brokering knowledge of problems and solutions, rather than researchers or other users.

Asking whether we do so from a ‘cathedral’ or ‘bazaar’ development perspective seemed to me a useful framework to start the discussion. The ‘bazaar’ approach was proposed in the context of open source software design, where a variety of modifications may evolve from a minimal prototype to a preferred solution. But only if the conditions are right, and the relative size of the development and user communities is recognised to be one of the most important of those conditions. The RDM development community within institutions is a small hard-pressed one, scrabbling around for crumbs to try to serve a very large user community with ridiculously limited resources. Perhaps that discourages co-design. It would be deeply unfortunate if it led to institutions buying into ‘glass cathedrals’ that seem to ‘do transparency’ in a clear way, but won’t allow the modification thing.

[1] Wikipedia (n.d.) The Cathedral and the Bazaar, available at: https://en.wikipedia.org/wiki/The_Cathedral_and_the_Bazaar

[2] Kramer, B., and; Bosman, J. (2015): 101 Innovations in Scholarly Communication – the Changing Research Workflow. figshare. http://dx.doi.org/10.6084/m9.figshare.1286826

[3] Ball, A. (2014) UK Research Data Registry Mapping Schemes, available at: http://www.dcc.ac.uk/projects/research-data-registry-pilot

[4] Reimer, T. (2015, Oct.30) ‘Why OA reporting is difficult’, Blog post at: https://wwwf.imperial.ac.uk/blog/openaccess/2015/10/30/imperial-college-…

[5] Whyte, A and Allard, S. (Eds) 2014. ‘How to Discover Research Data Management Service Requirements’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides

[6] DCC Tools and Services Catalogue. Available at: http://www.dcc.ac.uk/resources/external/tools-services

 

Photo credit. Romana Klee on flickr CC-BY-SA 2.0

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Guidance publications – Are we getting it right?

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At the end of September we ran a ‘quick poll’ asking for feedback on the current and proposed guidance publications that DCC produces with Jisc support. This aimed to consult the UK institutional RDM community on topics and priorities, and complemented broader conversations that DCC colleagues Magdalena Getter and Diana Sisu had over the summer to better understand how our output is perceived.

For the poll we targeted subscribers of the Jiscmail research-dataman list and asked a few questions about the topical focus of the guidance, and which formats are seen as most useful.  By next week we’ll have agreed how to respond to the results in a way that ties in with Jisc’s Research at Risk programme. As soon as we can after that, we’ll post a publications schedule to the list and on these pages.

The poll brought responses from 30 people, and almost all gave some feedback on our current output. They picked 12 of our recent publications, and rated them using these three criteria:

  • Do they provide informative content?
  • Is that content clearly presented?
  • Does the publication help you address your organisation’s data management challenges?

We used a five-point scale from ‘strongly agree’ to ‘strongly disagree’, and scored each publication from 2 to -2 according to how many responses there were on each point on that scale.

The table below shows the results. In some cases individual titles have been rated by only one or two people. While it would be wrong to compare the titles on that basis on such small figures, it’s one way we’ll do so from now on. And I’m happy to say that 11 of the 12 publications passed the ‘agree’ test on all three criteria (one case study wasn’t thought helpful enough).  Averaging the scores gave a 70% average score overall, with 80% for ‘informative’, and 73% for ‘clearly presented’.  

For us the bottom line is, ‘does the guidance help people address their organisation’s data management challenges?’ The average score on this was 55% so there is definite room for improvement, and I hope we can make our forthcoming output even more practical. Underlining that need for practicality, the format that people see the greatest need for can be summed up in two words; “examples, please!”

Respondents also commented on how we might have done better on the current publications. We can incorporate some of these ideas in future updates of the titles (named in brackets), and through our new output, e.g:

  • “Estimating storage requirements remains to be a key issue.  More suggestions on this and how others have estimated need are required!” (How to Discover Requirements)
  •  “It’s very useful. Include ‘Consent’ in section 3. Update ‘About this work’ and combine with ‘About DCC’. Supply electronically with space for local University contact details.” (DCC Checklist for a Data Management Plan)
  • “Maybe some practical examples; this area is becoming more nuanced by discipline and so more subject or discipline specific guides/case studies and examples would be useful” (Five steps to decide what data to keep)
  • “Split into two publications – one on creating citations for existing datasets, one for creating the citation for your own dataset, [and] including data access statement” (How to cite datasets and link to publications)
  •  “More details about particular techniques and links to more examples and tools that we could use” (How to Develop RDM Services)
  • “More focus on clarifying some of the issues raised in the recent RCUK concordat. We need more precise requirements on issues like preservation and curation” (Funder policy summary)

Stepping back from individual titles, we also want to improve on how we gather this feedback. We’ll be consulting more widely and deeply in future, using these or similar metrics, and badgering people for comments both online and wherever we set out our publications stall.

 

 Guidance publication titles and rating scores

(strongly agree=2; agree=1, not sure=0, disagree=-1, strongly disagree=-2)

Number of responses

(n)

Informative

(a)

Clearly presented

(b)

Helps meet challenges

(c)

Score average

(a+b+c)/n
How to License Research Data 5 9 6 6 4.2
How to Discover Requirements for RDM Services 4 6 5 3 3.5
How to Appraise & Select Research Data for Curation 3 5 5 3 4.3
DCC Checklist for a Data Management Plan (fold out) 2 2 3 2 3.5
Five steps to decide what data to keep 2 3 2 4 4.5
Funder policy summary 2 3 3 3 4.5
How to Track the Impact of Research Data with Metrics 2 4 4 2 5.0
Five Things You Need to Know About RDM and the Law 1 2 2 1 5.0
How to Cite Datasets and Link to Publications 1 1 1 1 3.0
How to Develop a Data Management and Sharing Plan 1 2 1 1 4.0
How to Develop RDM Services 2 3 3 2 4.0
RDM strategy: moving from plans to action 1 1 2 -1 2.0
“All”/ “various” 2 4 4 4 6.0
  28 45 41 31 4.2

 

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Jisc research data spring takes its next leap forward

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The fate of 15 projects funded through the first round of Jisc’s Research Data Spring programme was sealed over two days last week (July 13 and 14) at Imperial College, London. It was a great opportunity to sample what has already been done, and I was really impressed by how much these projects have accomplished in a mere 3 months. Below I’ve summarised some key points I took from the presentations, including links so you can judge for yourself.

This was workshop 2 in the Research Data Spring process, and as Kevin Ashley outlined in a previous blog, the projects that successfully bid for three months of initial development funding were making the case for up to £40k of additional investment. I’ve no connection to the judging, and assume some projects below won’t make it further. But those successful this time around will go through the same hoops again, most likely in December.  Third-round winners get another £60K to build what are judged at that point to be the services most likely to succeed.

As in the first ‘sandpit’ workshop, project teams made four-minute pitches to a panel of judges on day two, who then grilled them for another four minutes. There was also the opportunity on the first day to share slightly longer overviews of project achievements, and their approaches to collaboration, and to catch up during the scheduled breaks. I picked out four themes based on those framing the original project ideas, omitting ‘shared services’ given that’s a common aspiration. So the themes I’m using are:

  1. Improving the researchers experience
  2. Deposit and sharing tools
  3. Seamless integration
  4. Analytics for research data activity

To finish I enter into the judgemental swing of things by picking some co-design favourites.

1. Improving the researchers experience

AMASED is one of two RDS projects with eyes on the enormous reuse potential of research data that cannot be publicly shared for privacy reasons. It exploits DataSHIELD, a toolkit originating in an EU-funded project. Basically the idea is to ‘take the analysis to the data’ without the user actually seeing the raw data at any point.  DataSHIELD is enabling bioscience researchers to analyse patient-identifying data across studies. Conventionally they would do these meta-analyses by pooling datasets from studies, e.g. those that share similar variables, to enable more powerful analysis of larger or richer samples.

The DataSHIELD software has great potential to facilitate data reuse, and save money in this context. That’s because data pooling conventionally requires some level of disclosure, which has stringent ethical constraints that also limit the opportunities for analysis.  Working around those ought to allow more people to do more analyses, and AMASED is testing how that can extend to other domains and applications.

So far the project has been attempting to flesh out two further use cases, and seems to have made some progress on one. That is to enable readers to analyse confidential data referenced in data articles from publisher F1000 Research’s data journal. The other test use case is to allow analysis of a digitised books dataset held by the British Library. I missed their presentation of work done so far, but I guess an important challenge will be assessing how far automated data cleaning can compensate for researchers’ inability to ‘eyeball’ the data.

Jisc has funded many projects digitising cultural heritage collections over the years, but the potential to analyse these using high performance computing (HPC) has been largely untapped. HPC has not typically meshed with the kinds of questions arts and humanities want to ask, or that new computation techniques can answer. The Enabling complex analysis of Large Scale Digital Collections project has made some impressive inroads here, working with digital humanities researchers to ingest a range of data types and sources, and to understand and facilitate the kinds of advanced query they want to perform.

The aim is to make humanities researchers into independent users of HPC facilities, by offering the service and ‘recipe knowledge’ to cater for the 5 or 6 searches that 90% of them want to do. The project team’s hackathon with researchers at UCL to use the university’s HPC facilities has already produced some intriguing analyses on the correlation of epidemiological data on disease outbreaks in the UK with mentions of these in historic texts. The project looks full of potential for HPC facilities to win points for impact, as much as for arts and humanities researchers to take advantage of real computational power. So if you’re up for that, bluclobber is the word!

The Sound Matters project grabbed my attention for the way it has developed its framework for archiving and reusing sound recordings. It’s aim is to facilitate “interrogation and relational playback of sound in its own terms”, as described on the project blog. The focus is on humanities researchers who use fieldwork to gather sounds and speech.

The project started with a straightforward and elegant model for understanding these researchers’ interactions with archived recordings. What really impressed me was the effort that has gone into working with this interdisciplinary community to take the framework further. This has ranged from interviews and a co-design event to an online community event, which was publicised through social media and used ‘virtual board’ Mural.ly to capture comments on the framework. This then fed into a co-design event.  There’s a lovely video here https://vimeo.com/133219234 with a foretaste of prototypes to come if, as I hope, they get further support. Maybe there’s something about the medium conveying the message in a more interesting way, or maybe it’s the embedding of a remix and reuse culture in this discipline. Whatever the key ingredients, this project seems to me to effortlessly make the case for putting RDM support in the hands of researchers who really understand what it takes to make their work reusable. 

Clipper addresses a gap in the tools available for working with digital audio and video clips. The emphasis is on ‘non-destructive editing’ of a/v materials. That means making it easy to work collaboratively with clips, annotate, organise and share them, while leaving the a/v data itself untouched. So far the project has been using a proof-of-concept demo to test demand, and get feedback, and has generated a fair amount of interest particularly in the cultural heritage sector.

I gather this tool shares some characteristics with tools used for marking up and analysing a/v data in linguistics and qualitative social research (CAQDAS tools). These cater for discipline- or methodology-specific analysis, but tend to limit the ability to share and reuse the results independently of the tool. Clipper is taking a relatively simple and generic approach to annotation. Potentially it seems a great solution for researchers who use digital a/v from archived collections. It steps aside from archiving the a/v data itself, or providing access to it, so researchers who create a/v research data may need alternative tools. They will also still need to find a repository suitable for hosting video collections. But for making clips and annotation shareable, Clipper may have hit on a niche. It would be great if the tool could go a step further and support citation, perhaps going some way towards meeting the recommendations for citing dynamic data coming out of the Research Data Alliance’s Working Group on Data Citation.

2. Deposit and sharing tools

The idea of ‘sheer curation’ is to capture metadata on the fly from research activity, rather than trying to trawl it up afterwards from reluctant researchers. If an appropriate way to describe the challenge is ‘inventing an elixir of youth for the research data archive’ you might be sceptical that anyone could claim to have done so in the space of 3 months. But of course the two RDS project teams working towards that end – Artivity and CREAM – have been doing so for a lot longer than that. The projects are taking contrasting approaches; if I understand correctly Artivity is starting from the particular and working up to the general, while CREAM takes a broader ‘from first principles’ approach.

Artivity is about capturing contextual metadata from digital artists as they use the tools on their desktop. It builds on much previous work on frameworks to make the semantic desktop idea concrete, and implements one such framework for two graphic illustration tools, recording the user’s actions. The aim is to appeal to artists who want to self-archive a provenance record of their work, and to art and design historians who want to curate the techniques of digital artists.

The impressive demo showed metadata being captured automatically, with some nice splicing and dicing of stats about user activity. I hope the project can address the questions around take-up, which seem quite sizeable ones. On the ‘desktop’ side, can the project deal with the fact that artists and designers mostly use Adobe tools on Mac OSX, not open source Linux tools?  On the semantics side, is the ‘context’ that’s being recorded consistently meaningful to art and design historians as a record of ‘technique’?

CREAM has some things in common with Artivity (as well as Thanasis Velios who is on both teams). This project also explores the use of metadata to record research as it happens, on ‘live objects in real time’, rather than assigning it to an archived object, and it also conceives a general framework for managing and understanding that metadata. That framework is being shaped using Nine By Nine’s Annalist,  a prototype out-of-box tool for semi-structured data collection and organisation, that ‘supports evolution, collaboration and reuse’ of linked open data. The project partners are using this with their lab notebooks to work up four case studies in different disciplinary contexts; chemistry at Southampton and STFC, arts at University of the Arts London, and earth systems in Edinburgh.

These are mostly ‘big science’ contexts, where the processing of research data is strongly determined by the planning of its collection, ‘known unknowns’ about the data, and ‘known knowns’ about the equipment generating it. The project partners are also big names in the development of linked data tools and standards, the other element of the project’s framework. Automated validation and quality checking are the practical benefits they believe to be in reach. The project also seems to be filling a niche in provenance standards, essentially by demonstrating how it may be planned in advance (with no Tardis or other time machine involved).

DataVault could just as easily fit under other headings, as by making it easier to package data for archiving it is aiming to improve researchers’ experience, and doing that will involve ‘seamless integration’. Data Vault takes a remorselessly pragmatic line. RDM has been focused on making more data more openly accessible, and on principle quite rightly. But institutions and many researchers face the reality that much of the data they need to archive cannot be shared publically, at least until their share-ability has been determined.

DataVault has set its sights on a basic level of archiving for the broadest range of data. The concept is basically to take research data that is no longer being actively used, describe and package it, and move it to cheaper storage such as Amazon Glacier, local tape backup infrastructure, or Arkivum.  This involves two main functions, the first being the interface to handle the storage and retrieval of data packages from an underlying storage management layer, and the other a policy engine to manage the packaging and description, and apply information security, retention and any other controls.

The emphasis in phase one of the project has been to build a working prototype, using bagit to package files with datacite metadata and some basic project information. I can’t find the prototype online but you can follow the project progress here. In phase 2 the plan is to offer more storage options (some initial discussion with Arkivum was mentioned), tackle authentication and authorisation, demonstrate CRIS integration, and build the capability to handle retention and review policies.  The policy engine sounds a fairly major piece of work, and probably critical to making the concept work. I suspect the non-technical issues around governing the data access terms and conditions will be the biggest challenge there.

Giving Researchers Credit

In a year of data policy mandates that are (probably wrongly) seen as big sticks to prod researchers with, this project promises carrots for all. The idea targets institutional repositories, and leverages publisher efforts to promote data papers. On the face of it it’s a simple concept – provide a one-click ‘submit a data paper’ button. This would (it is hoped) incentivise deposit by making it easier to transfer all relevant details from an IR to a relevant publisher platform for publication as a data paper. There, the data, metadata and methods can be worked up as needed, then peer reviewed, and indexed to increase visibility. If this takes off, everyone gets better metadata. IR’s win by engaging more effectively with depositors, publishers win by getting more submissions, and the depositor wins by getting the effort to create and describe their data recognised by the community as a formal research output.

In its first three months the project surveyed publishers and repositories to gauge their interest, and reportedly found it in 16 publishers and 34 repositories. In practice, repository and publisher workflows can be tricky to integrate. The project is drawing on the Research Data Alliance’s working group on Data Publishing Workflows. This has recently been investigating the rich variety of repository deposit workflows (interest declaration – I’ve had minor involvement in that work). Instead of each publisher-repository pair attempting to negotiate their own solution, the project intends to develop a helper application, probably deploying the SWORD protocol. The end result should offer a ‘straw man’ reference implementation, hopefully beginning in the next phase by linking up Oxford University’s Bodleian Library with publisher F1000 Research.

Software reuse, repurpose and reproducibility

Despite wide awareness of their importance to research reproducibility, approaches to software citation and preservation are less mature than those for research data. This project (RRR) aims to help close that gap, initially by drafting guidance on the use of DataCite DOIs for software citation, and recommending changes to DataCite metadata to support software.  These recommendations were presented at a recent DataCite meeting and are being considered further by that organisation’s metadata working group, as well as Force 11’s software citation group, which is impressively impactful for three month’s work. 

The possibly more intriguing element of RRR is the software it intends to deliver and has started developing. This is intended to apply the principle that software citation entails keeping software in a runnable state, making good on the slogan “discoverability is runnability”. The project involves recomputation.org, and I gather follows its approach of offering tools to make available virtual machines in which experiments have been validated to run. The premise is (I believe) that preserving research software in a virtual machine makes the reproducibility more straightforward to address. If, as planned, the tools gain the acceptance of the project’s heavyweight partners we can expect to hear a lot more about the approach. I would hope that also addresses licensing issues for software held in the virtualised machine.

Unlocking thesis data

There seems an undeniable case for institutional repositories to make the data underpinning PhD theses persistently identifiable and available online. For one thing, the EPSRC requires it for studentships they fund. So this project might have been forgiven for pressing ahead and developing some form of plug-in without taking the time to get extensive input from stakeholders and users. However they have done exactly that. Although some of the dialogue they kicked off took place in workshops at IDCC and elsewhere, before their successful RDS bid, the team has gone on to survey the sector, and follow that up with mini case studies. Their report identifies the steps needed to apply PIDs to theses and have them harvested by EThOS, the British Library’s national thesis publication service.

There are of course many more differences between theses and journal articles than their length, as the project’s work shows. There are more complex relationships to data and ‘supplementary materials’, different cultures and workflows around examination and deposit, and consequently different sets of user and depositor relationships to deal with. The project found no institution minting DOIs for theses. It also found that so far EThOS has not successfully harvested a single ORCID identifier from institutional systems. Their report considers various reasons for that, and how support could be improved in EThOS and institutional repositories, building on that available in EPrints and through CRIS systems such as PURE.

3. Seamless integration

Streamlining deposit

Most researchers’ experience with data sharing is through uploading the supplementary material for a journal article, to its publisher’s platform. For the increasing number of publishers wanting to recommend a data repository, rather than deal with supp mat themselves, the issue is how get the necessary handshaking set up so it’s straightforward for depositors. The Open Journal System (OJS) is one of the more widely used platforms with 25k installations worldwide, a potentially large market for a data repository integration solution. The Streamlining Deposit project is pitching at this with what seems a tempting offer; plug-ins for Figshare initially, and for Dryad, other repositories, and the Jisc Publications Router if they get further funding. This should save depositors’ time, and enable more journals to make their supplementary materials citable using the repository-supplied DOIs.

It was impressive to see that authors and editors have been interviewed to establish their priorities, and that a working plug-in has already been developed. One of the proposed next steps is the Dryad data repository, which is well known for its model of linking data to articles. This approaches this problem from the other direction, as it were, but the project should be pushing at an open door (no pun intended), building on the work already done to integrate the Journal of Open Health Data with this repository.

Filling the digital preservation gap

This project fills an elephantine gap in the RDM room; how institutions can effectively support preservation beyond the ‘bit level’ guarantee of file integrity, to deal with issues around format obsolescence (for example). Funders tell institutions ‘go preserve your data’, leaving it to them to work out what level of preservation will be needed to keep data accessible. The EPSRC of course also points people to DCC guidance, which includes many pointers to good practice. Perhaps wrongly, we have not been particularly forceful in identifying what specific preservation steps institution should aspire to offer, knowing that even giving researchers a common steer on where to put data is a challenge.  The slow development of services in this area I think reflects the complexity of the gap between what is desirable and feasible, rather than it being one of those issues nobody is prepared to talk about.

At any rate, this project is taking a pragmatic step beyond talking about it. Leading open source tool Archivematica offers comprehensive support for preserving digital content. The project is investigating how to use it effectively for research data, particularly where data repositories or catalogues are already being established. The main issues here are around the range of formats to be dealt with, and the workflows to deal with them. I won’t say any more other than to highly recommend the project report, and wonder whether collaboration with DataVault wouldn’t make sense.

Integrated RDM for Small and Specialist Institutions

The largest RDS project also has the longest name, which in full is; “A consortial approach to building an integrated RDM system – small and specialist”. Part of this consortium is CREST (Consortium for Research Excellence, Support and Training) a sub-group of the GuildFE body, which represents the “small and specialist” institutions of the project title. The 22 institutions in CREST are a test bed for shared services, whose potential is being explored through three case studies, one focusing on ‘improving’ EPrints for Art and Design institutions, another on ‘streamlining’ PURE, and a third examining workflows for RDM.

The workflows study struck me as a very useful piece of work. The report, published by Arkivum, provides examples of different institutions’ approaches to RDM service delivery. Each identifies key points and outlines a scenario and high-level workflow.  I’d love to see DCC contributing more of these examples. I also look forward to understanding what the first two case studies accomplish, and will be very interested to learn further details of the level of integration envisaged in CREST.

4. Analytics for research data activity

 

DMAOnline

The DMA in the title is ‘data management admin’ and the premise of this project is quite straightforward; RDM managers will sleep better at night if they have real-time data to hand on key indicators of compliance.  What these key indicators are is identified in the five use cases addressed in Lancaster University’s prototype. Several deal with monitoring data management plans, others storage utilisation, and others compliance with RCUK requirements on data access statements.

The live demo showed stats drawn from real data in Lancaster’s systems, an achievement that has had to overcome the organisational barriers of working with different stakeholders groups as well as system barriers.  Additional funding will bring  further integration with DMPonline using the forthcoming API. The team also hopes to exploit synergy with the Filling the Digital Preservation Gap project, and include stats on preserved datasets in the dashboard.

The next phase would also bring further user studies, and it will be very interesting to see how the institutions waiting to test the dashboard find it adaptable to their needs. In principle the idea sounds a winner, and I hope the effort to contextualise the dashboard and its contents proves to be doable as well as sociologically fascinating!

Extending the Organisational Profile Document

The Organisational Profile Document or OPD cut its teeth in the equipment.data project and is the brainchild of the Southampton team behind data.ac.uk and, fresh of the wiki chopping board, research.data.ac.uk. Essentially it’s a template for research organisations to publish as linked open data some basic organisational characteristics. These were originally intended to ease the flow of information needed to locate expensive EPSRC-funded equipment, by making it more machine readable.

The ‘extension’ proposed in this project (by DCC’s own Joy Davidson) is is to include details of RDM services in the profile, those details spanning a range of ‘hard and soft’ infrastructure not unlike the service model put forward in our How to Develop RDM Services guide. By pointing to some online evidence that these services exist, the idea is to make the OPD a vehicle for finding them more easily.

So the rationale is to make RDM Services more discoverable by building a machine-readable collection of RDM Service profiles. That’s something I’d love to see DCC developing even if the proposal doesn’t make it through the next phase. We have a growing list of institutions that have self-penned portraits of their services in our ‘where are they now’ series of blog posts. So we know that institutions are looking to set out their stall, and want to make the results go further. So fingers crossed for that one.   

And the winner is?

Similarities between the judging process in RDS workshops and BBC TV’s ‘Dragon’s Den’ panel of investors are difficult to avoid and probably not entirely coincidental.  The process certainly forces projects to present their work in the best possible light, and in less time than most elevator rides on the London Underground.  I’m personally not convinced that three months into a project is the best time to do that. Anyway, I’m not intending to pass judgement on the RDS process. The criteria the judges themselves used are available here and I’m sure they have more information to base their decision on than the pitches.  Jisc’s criteria could be summarised as; 

  • Is the work done in scope for RDS?
  • Are the use cases clear?
  • Is there evidence of community engagement?
  • Is there a realistic sustainability plan?
  • Can the team deliver?
  • Are existing outputs clear, and are planned outputs feasible?

The judges and Jisc will have made their decisions on that by the time this article is online, so any favoritism on my part won’t affect the outcome (I should be so lucky). Personally I was interested in how much genuine co-design has been carried out, and would be going forward. To what extent are the teams using any process to “empower, encourage, and guide users to develop solutions for themselves” (1). The overviews on day one gave a little more indication of that than the pitches. So I didn’t find it particularly easy to tell, but if I was dishing out prizes (and excluding projects with any DCC involvement) four projects stood out as having engaged most with their prospective users and stakeholders;

  • Sound Matters
  • Enabling Complex Analysis
  • Unlocking Thesis Data
  • Integrated RDM for Small and Specialist Institutions

Some of the other projects were funded to get on and develop software, and may well have deeper engagement with users in phase 2, so no criticism is intended. I’ll be surprised if many of the projects go away empty handed, and don’t envy those deciding who should be in that category.

(1) ‘Co-design’ Wikipedia entry. Retrieved from https://en.wikipedia.org/wiki/Co-design

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Capturing the state of play on UK institutions’ research data management support

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Driven by the need to better manage their digital assets, research institutions are building links between research information systems and other platforms. There’s a large appetite for linking research data catalogues, publications and data repositories. Doing so can help institutions to support research, promote its visibility and track impact.

The range of platforms and capabilities is constantly evolving though, and as we found in for example in last November’s Research Data Management Forum (RDMF12), it can be difficult for institutions to navigate the options, feel confident they are making the right choices, and identify where shared solutions may be more effective.  

Jisc and DCC are committed to offering more assistance to institutions on RDM support. So we are surveying the sector to allow us to make better information available on what institutions are doing, and to inform what further steps we can take to help.

First up is Jisc’s survey on Services and Systems for Research Data.  This survey is aiming to help scope Jisc’s response to a need that we have heard increasingly frequently; for shared storage and repository services.  As John Kaye’s blog post sets out in more detail, the questions are grouped around six categories of platform:

  • Active research data storage and archival and preservation storage for research data
  • Archival and preservation solutions
  • Current research information systems (CRIS)
  • Institutional repositories
  • Research data catalogue or registries
  • Research software

The responses will identify what is already in place, what plans institutions have, and what issues need attention.

Just when Jisc’s survey closes on May 15th, DCC will be launching our second annual Institutional RDM survey. Where Jisc’s survey focuses on platforms the DCC questions take a broader service development view.  The questions closely follow last year’s to aid comparison, with a few improvements to reflect last year’s responses (which are available here). Our main goals for this survey are: –   

  • Understand how institutions are supporting research data management (RDM) through policy and organisational change
  • Identify how far RDM policies have been ratified, and how far policy implementation has progressed towards service delivery
  • Help prioritise Jisc and DCC provision of support in this area

The survey is also timed to coincide with the EPSRC’s first step to assess institutions’ compliance with its policy expectations; a questionnaire survey of Pro Vice Chancellors for Research in those institutions EPSRC funds. Sarah Jones’ blog post recently outlined the guidance currently on offer from Jisc and ourselves on this.

So are all these surveys really needed? We believe so, but appreciate the concern about overload this flurry of RDM-related questionnaires may raise. The justification we believe is this:

  • Jisc’s survey and ours have complementary but different sets of questions, and different people may have the knowledge to answer them
  • The EPSRC survey asks institutions to report to them on meeting the funding body’s expectations. While the DCC survey also asks about progress, it is aimed at managers responsible for RDM services. It is geared to finding out their expectations of support, and is an opportunity to share views confidentially. It is also going to a wider set if institutions- all those participating in the REF2014 research assessment.

We believe the sector really benefits from having an up to date picture of it’s own activity. Like last year, DCC will be providing our respondents with early access to the results.
 

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Sharing RDM Services – Where to Now?

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The RDMF12 meeting on 18/19 November ran under the banner “Linking Data and Repositories (and other systems)” so it was a natural topic for a breakout group to discuss opportunities for sharing these across institutions. We came up with a l…

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Final Results from the DCC RDM 2014 Survey

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Several months ago we carried out our 2014 survey of senior managers in UK Higher Education Institutions who are involved in decision-making about their institution’s Research Data Management services.  The survey gave us a snapshot of progr…

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From RDM strategy to action -a glass half full!

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Recently DCC carried out an online survey of senior managers involved in decision-making on Research Data management in UK Higher Education Institutions. If you took part in it you may have already seen some of the results. As a small payback for the t…

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