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Authors: Sarah Higginson (EDRC), Catherine Jones (UKERC Energy Data Centre) and James Curwen (UKERC)

In November 2024, staff from 20 UK energy research centres gathered for the annual Cross-consortium Engagement Meeting (CCEM). The CCEM enables UK’s energy research consortia and funding councils to strengthen connections, collaborate on specific projects and discuss topics of shared interest in cross-cutting areas of programme management.

This meeting focussed on managing research data: why it matters, the challenges involved, and what is being done across the UK research environment to make data more accessible, usable and available for the long-term. This demonstrates the commitment to share and improve practice in this area.

In this blog, we explore the outcomes and how centres like the Energy Demand Research Centre (EDRC) and the UK Energy Research Centre (UKERC), who co-lead the CCEM, can improve their data management for their own researchers and beyond.

Setting the Scene

Paul Richards from UKRI’s Open Research team explained the policy environment for the sharing of publicly funded research data, including the useful guiding principle that data should be “As open as possible, as closed as necessary”. Open data can increase impact by giving research greater global reach. It also ensures that publicly funded energy research benefits society by providing economic and environmental opportunities.

Following the policy framework, Catherine Jones (UKERC Energy Data Centre), Sarah Higginson (EDRC) and Cristina Magder (UK Data Service) discussed practical solutions/approaches to complying with UKRI policy. This included the importance of researchers making conscious data management decisions at the start of their work and using data management plans as a tool to support this process.

Finally, participants were split into groups to discuss the experiences and challenges in their consortia. Energy researchers, and their consortia, are multi-disciplinary and bring different domain expectations, terminology and standards, enabling us to draw out interesting insights.

Insights from the workshop

The barriers to data sharing

Barriers fell into three main categories:

1. Legal/ privacy concerns: Commercially sensitive data is often difficult to access and usually not possible to share. Researchers need to be aware who owns the data they use and make sure they are able to use and share it. Personal data needs to be anonymised or pseudonymised before it can be shared, and this can be a lot of work, particularly for qualitative researchers. Sharing data internationally can be complicated for various reasons due to the different data laws in other countries.
2. Cultural differences: Different disciplines have different domain approaches and expectations to data management and sharing, one of the things that complicates data management and sharing in multi/ inter-disciplinary consortia. When working with commercial partners, sensitivity to competition can be an issue, sometimes similarly for academics not wanting to advantage colleagues in similar fields by sharing their data, especially where a lot of time and effort have gone into the collection and/ processing of that data. Lack of awareness of the importance of, or necessity for, data management and a lack of training are important barriers, that need to be addressed by doctoral training centres and consortia alike. Researchers at all career stages need to become more aware of funder expectations and where to find relevant guidance. The UK Data Service (UKDS) and UKERC Energy Data Centre (EDC)are both excellent sources of support and information.
3. Technical challenges: There are technical barriers to good data sharing, such as being able to find data when it might be shared in a variety of different repositories. There is also a lack of data standardisation, in structure and format, for example, or even in knowing what should be shared – raw data /processed data/ final analysis or a combination, particularly when it comes to models – and little knowledge about what metadata to provide. There is also an opportunity to set domain expectations around these issues. The quality of data can also be an issue. Publicly available data is not always current and so does not provide the most accurate or recent information.

Helpful approaches to good data management

One of the advantages of talking to other consortia is that good practice is shared. The FAIR principles (that data should be Findable, Accessible, Interoperable and Reusable) were widely but not universally known. Clearly, awareness of them is the first step and, again, UKDS and EDC are helpful resources in this respect.

Operationalising the FAIR principles is more challenging because the interdisciplinary nature of energy research leads to a plethora of data repositories, different types and standards of data and so on. However, having discussions within consortia, and across them where possible about how to approach these challenges, and experimenting with solutions, is helpful.

For example, one consortium had taken the decision to prioritise data management by making it a work package, which obviously means it would get attention and allow plenty of space for this sort of reflection. Another shared the advantages of having clear licenses for data, so that permissions are thought about early on in the project. The ethics process is also key in this respect and should help researchers at the project level think about data management (and sharing, which is not always thought about), though we have observed some contradictions within and across institutions in ethics procedures.

Examples of good data sharing were shared too, including the Northern Power Grid Open data portal, the Place Based Carbon Calculator (one of the projects in EDRC) and the CREST Stochastic Demand Model. Funders and regulators in some sectors are increasingly encouraging open data practices.

Data sharing could be improved by continuing the standardisation and simplification of the data management plans and embedding the expectation to share data within them. Some consortia make it an explicit condition of payment that researchers within the institutions involved in the consortium share their data, raising awareness within institutions, and so helping to shape and improve practices there.

Meanwhile, in other areas of the research landscape, some funders will not fund researchers unless they have shared data in previous projects, and many publishers now insist on data being shared before they agree to publish. However, there need to be carrots as well as sticks. Researchers should be better recognised and rewarded for work in this area, which is starting to happen by means of some of these mechanisms.

Continuing to experiment, reflect and share what works in these areas through, for example, the creation of specific domain sub-groups/discussion sessions, can only be helpful. Two authors of this blog have also suggested the creation of a set of data management principles in their paper Data Synergy in Times of Crisis. These are:

• Assume that data will be shared. Using the “as open as possible, as closed as necessary” approach sets the expectation that data will be shared unless there is an (articulated) research reason not to do so.
• Put the data where people will look for it. Critical mass is important and having data located next to similar data will aid discovery.
• Encourage conscious data management decisions. A process that facilitates and records explicit decisions about how the data will be managed and shared, is better than making implicit decisions which may impact on the ability to share data in the future.
• Ensure the ethics process is supportive of data sharing. Consider how the ethics process can support future data sharing and what adjustments can be made to enable this at the start of the process.
• Consider reproducibility and transparency of the research process from the start. Gathering the provenance of the data process cannot be done effectively in retrospect.

Consortia have the opportunity to value, and publish, data as an outcome of research. As an energy research community, we can highlight good practice, as we have started to do here. However, without repositories and appropriate funding, there is a risk that project outputs, such as data or grey literature might disappear. UKDS and EDC are good places to deposit energy related data for future use and preservation, and EDC is also widening their collection remit as part of UKERC 2024-2029 to include grey literature, so mitigating this risk. As previously mentioned, relevant support and guidance is available from UKDS and the EDC, and the EDC has updated their brief guides series.

While supporting energy researchers to share data will always be a federated activity due to the multidisciplinary nature of energy research, relevant specialised data repositories, such as the UKDS and the EDC, form a vital part of the UKRI’s Digital Research Infrastructure.

What will we do next?

It can be difficult to move from theory into practice without reinventing processes where there are opportunities for knowledge sharing and common practices. However, one result of this CCEM will be the establishment of a cross-consortium ‘data manager group’ to provide mutual advice, support and provide some standardisation in data management practices across the community – again, coordinated by the authors. If you would like to join, please get in touch with S.L.Higginson@bham.ac.uk or catherine.jones@stfc.ac.uk