Research Data Training – Semester 1

*UPDATE* – We have just added two new and exciting courses to our training schedule:

  • Assessing Disclosure Risk in Quantitative Data (RDS006)
  • Assessing Data Quality in Quantitative Data (RDS007)

To find out more about these courses just visit our training page.

Each semester the Research Data Support team puts together a training programme for researchers and research support staff in all schools, and at all points in their career. Our programme this year introduces a number of new courses, including one designed especially for Undergraduates planning their final year dissertation. We have also reviewed and refreshed all of our existing courses to ensure that they are not only up-to-date but also more engaging and interactive.

Full Course list:

  • Realising the Benefits of Good Research Data Management (RDS001)
  • Writing a Data Management Plan for your Research (RDS002)
  • Working with Personal and Sensitive Data (RDS003)
  • Data Cleaning with OpenRefine (RDS004)
  • Handling Data Using SPSS (RDS005)
  • Assessing Disclosure Risk in Quantitative Data (RDS006)
  • Assessing Data Quality in Quantitative Data (RDS007)
  • Data Mindfulness: Making the Most of your Dissertation (RDS009)
  • Introduction to Visualising Data in ArcGIS (RDS011)
  • Introduction to Visualising Data in QGIS (RDS012)

Full details of all these courses, with direct booking links, can be found on our training webpage https://www.ed.ac.uk/information-services/research-support/research-data-service/training

Courses can also be found and booked via the MyEd Events page.

We are always happy to deliver tailored versions of these courses suitable for a specific school, institute or discipline. Just contact us at data-support@ed.ac.uk to let us know what you need!

Kerry Miller
Research Data Support Officer
Library and University Collections

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Research Data Workshops: Electronic Notebooks Summary of Feedback

In the spring of this year (March & May) the Research Data Service ran two workshops on Electronic Notebooks (ENs) where researchers from all three colleges were invited to share their experiences of using ENs with other researchers. Presentations and demos were given on RSpace, Benchling, Jupyter Notebooks, WikiBench, and Lab Archives. Almost 70 research and support staff attended and participated in the discussions.

This post is a distillation of those discussions and we will use them to inform our plans around Electronic Notebooks over the coming year. It was obvious from the level of attendance and engagement with the discussions that there was quite a lot of enthusiasm for the idea of adopting ENs across a variety of different schools and disciplines. However, it also quickly became clear that many researchers and support staff had quite justified reservations about how effectively they could replace traditional paper notebooks. In addition to the ENs which were the subject of presentations a number of other solutions were also discussed, including; LabGuru, OneNote, SharePoint, and Wikis.

It appears that across the University there are a very wide range of platforms being used, and not all of them are intended to serve the function of an EN. This is unsurprising as different disciplines have different requirements and an EN designed for the biological sciences, such as Benchling, is unlikely to meet the needs of a researcher in veterinary medicine or humanities. There is also a huge element of personal preference involved, some researchers wish a simple system that will work straight out of the box while others want something more customisable and with greater functionality for an entire lab to use in tandem.

So, within this complex and varied landscape are there any general lessons we can learn? The answer is “Yes” because regardless of platform or discipline there are a number of common functions an EN has to serve, and a number of hurdles they will have to overcome to replace traditional paper lab books.

Firstly, let’s look at common functional requirements:

  1. Entries in ENs must be trustworthy, anyone using one has to be confident that once an entry is made it cannot be accidentally deleted or altered. All updates or changes must be clearly recorded and timestamped to provide a complete and accurate record of the research conducted and the data collected. This is fundamental to research integrity and to their acceptance by funders, or regulators as a suitable replacement for the traditional, co-signed, lab books.
  2. They must make sharing within groups and between collaborators easier – it is, in theory, far easier to share the contents of an EN with interested parties whether they are in the same lab or in another country. But in doing so they must not make the contents inappropriately available to others, security is also very important.
  3. Integration is the next requirement, any EN should be able to integrate smoothly with the other software packages that a researcher uses on a regular basis, as well as with external (or University central) storage, data repositories, and other relevant systems. If it doesn’t do this then researchers may lose the benefits of being able to record, view, and analyse all of their data in one place, and the time savings from being able to directly deposit data into a suitable repository when a project ends or a publication is coming out.
  4. Portability is also required, it must be possible for a researcher to move from one EN platform to another if, for example, they change institutions. This means they need to be able to extract all of their entries and data in a format that can be understood by another system and which will still allow analysis. Most ENs support PDF exports which are fine for some purposes, but of no use if processing or analysis is desired.
  5. Finally, all ENs need to be stable and reliable, this is a particular issue with web based ENs which require an internet connection to access and use the EN. This is also an area where the University will have to play a significant role in providing long-term and reliable support for selected ENs. They also need the same longevity as a paper notebook, the records they contain must not disappear if an individual leaves a group, or a group moves to another EN platform.

Secondly, barriers to adoption and support required:

  1. Hardware:
    1. Many research environments are not suitable for digital devices, phones / tablets are banned from some “wet” labs on health and safety grounds. If they are allowed in the lab they may not be allowed out again, so space for storage and charging will need to be found. What happens if they get contaminated?
    2. Field based research may not have reliable internet access so web based platforms wouldn’t work.
    3. There is unlikely to be space in most labs for a desktop computer(s).
    4. All of this means there will still be a need for paper based notes in labs with later transfer to the EN, which will result in duplication of effort.
  1. Cost:
    1. tablets and similar are not always an allowable research expense for a grant, so who will fund this?
    2. if the University does not have an enterprise licence for the EN a group uses they will also need to find the funds for this
    3. additional training and support my also be required
  2. Support:
    1. technical support for University adopted systems will need to be provide
    2. ISG staff will need to be clear on what is available to researchers and able to provide advice on suitable platforms for different needs
    3. clear incentives for moving to an EN need to be communicated to staff at all levels
    4. funders, publishers, and regulatory bodies will also need to be clear that ENs are acceptable for their purposes

So, what next? The Research Data Support service will now take all of this feedback and use it to inform our future Electronic Notebook strategy for the University. We will work with other areas of Information Services, the Colleges, and Schools to try to provide researchers in all disciplines with the information they need to use ENs in ways that make their research more efficient and effective. If you have any suggestions, comments, or questions about ENs please visit our ENs page (https://www.ed.ac.uk/information-services/research-support/research-data-service/during/eln). You can also contact us on data-support@ed.ac.uk.

The notes that were taken during both events can be read here Combined_discussion_notes_V1.2

Some presentations from the two workshops are available below, others will be added when they become available:

Speaker(s) Topic Link
Mary Donaldson (Service Coordinator, Research Data Management Service, University of Glasgow) Jisc Research Notebooks Study Mary_Donaldson_ELN_Jisc
Ralitsa Madsen (Postdoctoral Research Fellow, Centre for Cardiovascular Science) RSpace 2019-03-14_ELN_RSpace_RRM
Uriel Urquiza Garcia (Postdoctoral Research Associate, Institute of Molecular Plant Science) Benchling
Yixi Chen (PhD Student, Kunath Group, Institute for Stem Cell Research) Lab Archives 20190509_LabArchives_Yixi_no_videos
Andrew Millar (Chair of Systems Biology) WikiBench
Ugur Ozdemir (Lecturer – Quantitative Political Science or Quantitative IR) Jupyter Notebooks WS_Talk
James slack & Núria Ruiz (Digital Learning Applications and Media) Jupyter Notebooks for Research Jupyter_Noteable_Research_Presentation

Kerry Miller, Research Data Support Officer, Research Data Service

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New video: the benefits of RDM training

A big part of the role of the Research Data Service is to provide a mixture of online and (general/tailored) in-person training courses on Research Data Management (RDM) to all University research staff and students.

In this video, PhD student Lis talks about her experiences of accessing both our online training and attending some of our face-to-face courses. Lis emphasises how valuable both of these can be to new PhD candidates, who may well be applying RDM good practice for the first time in their career.

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It is interesting to see Lis reflect on how these training opportunities made her think about how she handles data on a daily basis, bringing a realisation that much of her data was sensitive and therefore needed to be safeguarded in an appropriate manner.

Our range of regularly scheduled face-to-face training courses are run through both Digital Skills and the Institute of Academic Development – these are open to all research staff and students. In addition, we also create and provide bespoke training courses for schools and research groups based on their specific needs. Online training is delivered via MANTRA and the Research Data Management MOOC which we developed in collaboration with the University of North Carolina.

In the video Lis also discusses her experiences using some RDS tools and services, such as DataStore for storing and backing-up her research data to prevent data loss, and contacting our team for timely support in writing a Data Management Plan for her project.

If you would like to learn more about any of the things Lis mentions in her interview you should visit the RDS website, or to discuss bespoke training for your school or research centre / group please contact us via data-support@ed.ac.uk.

Kerry Miller
Research Data Support Officer
Library and University Collections
The University of Edinburgh

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Greater Expectations? Writing and supporting Data Management Plans

“A blueprint for what you’re going to do”

This series of videos was arranged before I joined the Research Data Service team, otherwise I’d no doubt have had plenty to say myself on a range of data-related topics! But the release today of this video – “How making a Data Management Plan can help you” – provides an opportunity to offer a few thoughts and reflections on the purpose and benefits of data management planning (DMP), along with the support that we offer here at Edinburgh.

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“Win that funding”

We have started to hear anecdotal tales of projects being denied funding due – in part at least – to inadequate or inappropriate data management plans. While these stories remain relatively rare, the direction of travel is clear: we are moving towards greater expectations, more scrutiny, and ultimately into the risk of incurring sanctions for failure to manage and share data in line with funder policies and community standards: as Niamh Moore puts it, various stakeholders are paying “much more attention to data management”. From the researcher’s point of view this ‘new normal’ is a significant change, requiring a transition that we should not underestimate. The Research Data Service exists to support researchers in normalising research data management (RDM) and embedding it as a core scholarly norm and competency, developing skills and awareness and building broader comfort zones, helping them adjust to these new expectations.

“Put the time in…”

My colleague Robin Rice mentions the various types of data management planning support available to Edinburgh’s research community, citing the online self-directed MANTRA training module, our tailored version of the DCC’s DMPonline tool, and bespoke support from experienced staff. Each of these requires an investment of time. MANTRA requires the researcher to take time to work through it, and took the team a considerable amount of time to produce in order to provide the researcher with a concise and yet wide-ranging grounding in the major constituent strands of RDM.  DMPonline took hundreds and probably thousands of hours of developer time and input from a broad range of stakeholders to reach its current levels of stability and maturity and esteem. This investment has resulted in a tool that makes the process of creating a data management plan much more straightforward for researchers. PhD student Lis is quick to note the direct support that she was able to draw upon from the Research Data Service staff at the University, citing quick response times, fluent communication, and ongoing support as the plan evolves and responds to change. Each of these are examples of spending time to save time, not quite Dusty Springfield’s “taking time to make time”, but not a million miles away.

There is a cost to all of this, of course, and we should be under no illusions that we are fortunate at the University of Edinburgh to be in a position to provide and make use of this level of tailored service, and we are working towards a goal of RDM related costs being stably funded to the greatest degree possible, through a combination of project funding and sustained core budget.

“You may not have thought of everything”

Plans are not set in stone. They can, and indeed should, be kept updated in order to reflect reality, and the Horizon 2020 guidelines state that DMPs should be updated “as the implementation of the project progresses and when significant changes occur”, e.g. new data; changes in consortium policies (e.g. new innovation potential, decision to file for a patent); changes in consortium composition and external factors (such as new consortium members joining or old members leaving).

Essentially, data management planning provides a framework for thinking things through (Niamh uses the term “a series of prompts”, and Lis “a structure”. As Robin says, you won’t necessarily think of everything beforehand – a plan is a living document which will change over time – but the important things is to document and explain the decisions that are taken in order for others (and your future self is among these others!) to understand your work. A good approach that I’ve seen first-hand while reviewing DMPs for the European Commission is to leave place markers to identify deferred decisions, so that these details are not forgotten about (This is also a good reason for using a template – a empty heading means an issue that has not yet been addressed, whereas it’s deceptively easy to read free text DMPs and get the sense that everything is in good shape, only to find on more rigorous inspection that important information is missing, or that some responses are ambiguous.)

“Cutting and pasting”

It has often been said that plans are less important than the process of planning, and I’ve been historically resistant to sharing plans for “benchmarking” which is often just another word for copying. However Robin is right to point out that there are some circumstances where copying and pasting boilerplate text makes sense, for example when referring to standard processes or services, where it makes no sense – and indeed can in some cases be unnecessarily risky – to duplicate effort or reinvent the wheel. That said, I would still generally urge researchers to resist the temptation to do too much benchmarking. By all means use standards and cite norms, but also think things through for yourself (and in conjunction with your colleagues, project partners, support staff and other stakeholders etc) – and take time to communicate with your contemporaries and the future via your data management plan… or record?

“The structure and everything”

Because data management plans are increasingly seen as part of the broader scholarly record, it’s worth concluding with some thoughts on how all of this hangs together. Just as Open Science depends on a variety of Open Things, including publications, data and code, the documentation that enables us to understand it also has multiple strands. Robin talks about the relationship between data management and consent, and as a reviewer it is certainly reassuring to see sample consent agreement forms when assessing data management plans, but other plans and records are also relevant, such as Data Protection Impact Assessments, Software Management Plans and other outputs management processes and products. Ultimately the ideal (and perhaps idealistic) picture is of an interlinked, robust, holistic and transparent record documenting and evidencing all aspects of the research process, explaining rights and supporting re-use, all in the overall service of long-lasting, demonstrably rigorous, highest-quality scholarship.

Martin Donnelly
Research Data Support Manager
Library and University Collections
University of Edinburgh

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