University of Edinburgh’s new Research Data Management Policy

Following a year-long consultation with research committees and other stakeholders, a new RDM Policy (www.ed.ac.uk/is/research-data-policy) has replaced the landmark 2011 policy, authored by former Digital Curation Centre Director, Chris Rusbridge, which seemed to mark a first for UK universities at the time. The original policy (doi: 10.7488/era/1524) was so novel it was labeled ‘aspirational’ by those who passed it.

"Policy"

CC-BY-SA-2.0, Sustainable Economies Law Centre, flickr

RDM has come a long way since then, as has the University Research Data Service which supports the policy and the research community. Expectation of a data management plan to accompany a research proposal has become much more ordinary, and the importance of data sharing has also become more accepted in that time, with funders’ policies becoming more harmonised (witness UKRI’s 2016 Concordat on Open Research Data).

What has changed?

Although a bit longer (the first policy was ten bullet points and could fit on a single page!), the new policy adds clarity about the University’s expectations of researchers (both staff and students), adds important concepts such as making data FAIR (explanation below) and grounding concepts in other key University commitments and policies such as research integrity, data protection, and information security (with references included at the end). Software code, so important for research reproducibility, is included explicitly.

CC BY 2.0, Big Data Prob, KamiPhuc on flickr

Definitions of research data and research data management are included, as well as specific references to some of the service components that can help – DMPOnline, DataShare, etc. A commitment to review the policy every 5 years, or sooner if needed, is stated, so another ten years doesn’t fly by unnoticed. Important policy references are provided with links. The policy has graduated from aspirational – the word “must” occurs twelve times, and “should” fifteen times. Yet academic freedom and researcher choice remains a basic principle.

Key messages

In terms of responsibilities, there are 3 named entities:

  • The Principle Investigator retains accountability, and is responsible as data owner (and data controller when personal data are collected) on behalf of the University. Responsibility may be delegated to a member of a project team.
  • Students should adhere to the policy/good practice in collecting their own data. When not working with data on behalf of a PI, individual students are the data owner and data controller of their work.
  • The University is responsible for raising awareness of good practice, provision of useful platforms, guidance, and services in support of current and future access.

Data management plans are required:

  • Researchers must create a data management plan (DMP) if any research data are to be collected or used.
  • Plans should cover data types and volume, capture, storage, integrity, confidentiality, retention and destruction, sharing and deposit.
  • Research data management plans must specify how and when research data will be made available for access and reuse.
  • Additionally, a Data Protection Impact Assessment is required whenever data pertaining to individuals is used.
  • Costs such as extra storage, long-term retention, or data management effort must be addressed in research proposals (so as to be recovered from funders where eligible).
  • A University subscription to the DMPOnline tool guides researchers in creating plans, with funder and University templates and guidance; users may request assistance in writing or reviewing a plan from the Research Data Service.

FAIR data sharing is more nuanced than ‘open data’:

  • Publicly funded research data should be made openly available as soon as possible with as few restrictions as necessary.
  • Principal Investigators and research students should consider how they can best make their data FAIR in their Data Management Plans (findable, accessible, interoperable, reusable).
  • Links to relevant publications, people, projects, and other research products such as software or source code should be provided in metadata records, with persistent identifiers when available.
  • Discoverability and access by machines is considered as important as access by humans. Standard open licences should be applied to data and code deposits.

Use data repositories to achieve FAIR data:

  • Research data must be offered for deposit and retention in a national or international data service or domain repository, or a University repository (see next bullet).
  • PIs may deposit their data for open access for all (with or without a time-limited embargo) in Edinburgh DataShare, a University data repository; or DataVault, a restricted access long-term retention solution.
  • Research students may deposit a copy of their (anonymised) data in Edinburgh DataShare while retaining ownership.
  • Researchers should add a dataset metadata record in Pure to data archived elsewhere, and link it to other research outputs.
  • Software code relevant to research findings may be deposited in code repositories such as Gitlab or Github (cloud).

Consider rights in research data:

  • Researchers should consider the rights of human subjects, as well as citizen scientists and the public to have access to their data, as well as external collaborators.
  • When open access to datasets is not legal or ethical (e.g. sensitive data), information governance and restrictions on access and use must be applied as necessary.
  • The University’s Research Office can assist with providing templates for both incoming and outgoing research data and the drafting and negotiation of data sharing agreements.
  • Exclusive rights to reuse or publish research data must not be passed to commercial publishers.

Robin Rice
Data Librarian and Head, Research Data Support
Library & University Collections

End of an era – 2017-2020 RDM Roadmap Review (part 1)

Looking back on three years that went into completing our RDM Roadmap in this period of global pandemic and working from home, feels a bit anti-climactic. Nevertheless, the previous three years have been an outstanding period of development for the University’s Research Data Service, and research culture has changed considerably toward openness, with a clearer focus on research integrity. Synergies between ourselves as service providers and researchers seeking RDM support have never been stronger, laying a foundation for potential partnerships in future.

thumbnail image of poster

FAIR Roadmap Review Poster

A complete review was written for the service steering group in October last year (available on the RDM wiki to University members). This was followed by a poster and lightning talk prepared for the FAIR Symposium in December where the aspects of the Roadmap that contributed to FAIR principles of research data (findable, accessible, interoperable, reusable) were highlighted.

The Roadmap addressed not only FAIR principles but other high level goals such as interoperability, data protection and information security (both related to GDPR), long-term digital preservation, and research integrity and responsibility. The review examined where we had achieved SMART-style objectives and where we fell short, pointing to gaps either in provision or take-up.

Highlights from the Roadmap Review

The 32 high level objectives, each of which could have more than one deliverable, were categorised into five categories. In terms of Unification of the Service there were a number of early wins, including a professionally produced short video introducing the service to new users; a well-designed brochure serving the same purpose; case study interviews with our researchers also in video format – a product of a local Innovation Grant project; and having our service components well represented in the holistic presentation of the Digital Research Services website.

Gaps include the continuing confusion about service components starting with the name ‘Data’___ [Store, Sync, Share, Vault]; the delay of an overarching service level definition covering all components; and the ten-year old Research Data Policy. (The policy is currently being refreshed for consultation – watch this space.)

A number of Data Management Planning goals were in the Roadmap, from increasing uptake, to building capacity for rapid support, to increasing the number of fully costed plans, and ensuring templates in DMPOnline were well tended. This was a mixed success category. Certainly the number of people seeking feedback on plans increased over time and we were able to satisfy all requests and update the University template in DMPOnline. The message on cost recovery in data management plans was amplified by others such as the Research Office and school-based IT support teams, however many research projects are still not passing on RDM costs to the funders as needed.

Not many schools or centres created DMP templates tailored to their own communities yet, with the Roslin Institute being an impressive exception; the large majority of schools still do not mandate a DMP with PhD research proposals, though GeoSciences and the Business School have taken this very seriously. The DMP training our team developed and gave as part of scheduled sessions (now virtually) were well taken up, more by research students than staff. We managed to get software code management into the overall message, as well as the need for data protection impact assessments (DPIAs) for research involving human subjects, though a hurdle is the perceived burden of having to conduct both a DPIA and a DMP for a single research project. A university-wide ethics working group has helped to make linkages to both through approval mechanisms, whilst streamlining approvals with a new tool.

In the category of Working with Active Data, both routine and extraordinary achievements were made, with fewer gaps on stated goals. Infrastructure refreshment has taken place on DataStore, for which cost recovery models have worked well. In some cases institutes have organised hardware purchases through the central service, providing economies of scale. DataSync (OwnCloud) was upgraded. Gitlab was introduced to eventually replace Subversion for code versioning and other aspects of code management. This fit well with Data and Software Carpentry training offered by colleagues within the University to modernise ways of doing coding and cleaning data.

A number of incremental steps toward uptake of electronic notebooks were taken, with RSpace completing its 2-year trial and enterprise subscriptions useful for research groups (not just Labs) being managed by Software Services. Another enterprise tool, protocols.io, was introduced and extended as a trial. EDINA’s Noteable service for Jupyter Notebooks is also showcased.

By far and away the most momentous achievement in this category was bringing into service the University Data Safe Haven to fulfil the innocuous sounding goal of “Provide secure setting for sensitive data and set up controls that meet ISO 27001 compliance and user needs.” An enormous effort from a very small team brought the trusted secure environment for research data to a soft launch at our annual Dealing with Data event in November 2018, with full ISO 27001 standard certification achieved by December 2019. The facility has been approved by a number of external data providers, including NHS bodies. Flexibility has been seen as a primary advantage, with individual builds for each research project, and the ability for projects to define their own ‘gatekeeping’ procedures, depending on their requirements. Achieving complete sustainability on income from research grants however has not proven possible, given the expense and levels of expertise required to run this type of facility. Whether the University is prepared to continue to invest in this facility will likely depend on other options opening up to local researchers such as the new DataLoch, which got its start from government funding in the Edinburgh and South East Scotland region ‘city deal’.

As for gaps in the Working with Data category, there were some expressions of dissatisfaction with pricing models for services offered under cost recovery although our own investigation found them to be competitively priced. We found that researchers working with external partners, especially in countries with different data protection legislation, continue to find it hard work to find easy ways to collaborate with data. Centralised support for databases was never agreed on by the colleges because some already have good local support. Encryption is something that could benefit from a University key management system but researchers are only offered advice and left to their own mechanisms not to lose the keys to their research treasures; the pilot project that colleagues ran in this area was unfortunately not taken forward.

In part 2 of this blog post we will look at the remaining Roadmap categories of Data Stewardship and Research Data Support.

Robin Rice
Data Librarian and Head of Research Data Support
Library and University Collections

Protocols.io trial… six months on!

We launched a trial of protocols.io Enterprise in December 2019, and a lot has been achieved in the first six months.

The number of registered UoE users has increased from 121 to 217 and the number of private protocols from 36 to 106 which demonstrates a significant interest in using the platform with its additional Enterprise functionality,

We have also run a number of webinars specifically for UoE staff and students which have been well attended.

While these numbers suggest interest amongst our research community in using protocols.io we have to collect better feedback before we can decide if protocols.io Enterprise is to become an ongoing service provided by the University.

That is why we are now launching this short survey about protocols.io which is open to all UoE research staff and students. The aim is to gather initial thoughts from our community and to identify people who may be prepared to contribute more in-depth feedback as the trial progresses.

The survey can be accessed at https://edinburgh.onlinesurveys.ac.uk/protocols-io-6-month-survey

To find out more about protocols.io or this trial you can read this blogpost from when the trial launched: https://libraryblogs.is.ed.ac.uk/2019/12/13/new-research-data-management-tool-on-one-year-trial-protocols-io/

Alternatively please visit our website, where you will also find links to all the protocols.io webinars we have run: https://www.ed.ac.uk/information-services/research-support/research-data-service/during/open-research-tools/protocols

Kerry Miller
Research Data Support Officer
Library & University Collections

Two new Quick Guides for good Research Data Management

The Research Data Support team have recently published two new Quick Guides, the latest in a series of short, user-friendly documents intended to help our research staff and students plan, manage and preserve their data effectively, safely, and for the long-term.

Quick Guide 5 takes the topic of “Open Research” – also known as Open Science, particularly in a European context. The drive towards research transparency and the removal of barriers to accessibility has gathered a great deal of momentum over recent years, to the extent that “Open by default” is an increasingly common approach. Open research enables scientific findings to be tested, reproduced and built upon far more quickly than traditional approaches allowed. The benefits of Open Research are being demonstrated in real time, right in front of our noses, as researchers at Edinburgh tackle various aspects of the Covid-19 pandemic. We recently tweeted about one such project which examined the effectiveness of face coverings in reducing the range travelled by breath, which of course helps transmit the virus. The data underpinning this research is freely available to everyone via Edinburgh DataShare.

The latest Quick Guide, the sixth in the series, addresses the ‘FAIR’ principles, which state that research data should – so far as possible, and appropriate – be Findable, Accessible, Interoperable and Reusable. These principles emphasise machine-actionability (i.e. the ability of automated computational systems to find, access, interoperate, and reuse data with minimal or no human intervention) as humans increasingly rely on computational means to discover and work with data as a result of the increase in volume, complexity, and creation speed of data.

These two new publications join our existing guidance on topics such as the basics of Research Data Management (RDM), RDM and data protection, and research data storage options at the University. Future topics planned include conducting research safely online, FAIR approaches to research software, and an overview of the systems and services available at Edinburgh in support of Open Research. If there is a particular topic you would find useful, please get in touch with us via data-support@ed.ac.uk or the IS Helpline.

All of our Quick Guides can be found at https://www.ed.ac.uk/information-services/research-support/research-data-service/guidance

Martin Donnelly
Research Data Support Manager
Library and University Collections