Article written by Dr Christiana McMahon, UCL Research Data Management Team

As we discussed in the blog post on Wednesday, there are ways to share and be transparent about your research without compromising ethical or legal requirements. Did you know that the UCL Research Data Repository (RDR) is a great way for staff and research students to archive, preserve, promote and publish these outputs created as part of the research process?

Have you ever been asked to archive other types of research outputs? UCL has RPS for articles, but did you know that you can also archive and share other outputs of research? Be it a whole dataset, or other types of material supporting your research such as photos, models, software, presentation slides, your Data Management Plan, even a poster.

By using the RDR, you can get a permanent identifier for your work in the form of a DOI and add it to your ORCID. Additionally, as you publish items in the UCL Research Data Repository, these are automatically collated together on a data repository webpage just for you.

With over 580,000 downloads globally, we have published over 950 research outputs that have been viewed in over 180 countries, so start publishing today!

• For more information contact: researchdatarepository@ucl.ac.uk.
• Access the full online catalogue: https://rdr.ucl.ac.uk/

Article written by Dr Christiana McMahon, UCL Research Data Management Team

Writing a data management plan can be difficult task to approach at the best of times. There are lots of things to consider, not just going through your project in detail, you may also need to consider external funding agency requirements, UCL’s research data expectations plus the FAIR data principles, well… advice from the Research Data Support Officers might be just what you need!

There is still plenty of time to get registered for one of the upcoming courses on how to write a data management plan! Book your place online today.

Data management plans (DMPs) describe your data management and sharing activities across the research data lifecycle and are a valuable document for you to refer to throughout your research project that can help you structure and protect your data for the long term. A fully completed DMP is usually 1-3 pages in length and can even be published as an output of your research. We recommend that they are written at the start of the research and are regularly reviewed and updated over the course of your research.

For more information on data management planning and how to get in touch with the team, visit our website.

Article written by Dr Christiana McMahon, UCL Research Data Management Team

“I want to share my data, but I can’t because…” is something we hear often.

Sometimes, it’s not possible to share evidence openly and that’s okay. Let’s take a closer look at what we can do to promote research findings and foster transparency and confidence in the research process

At UCL, staff and students are actively encouraged to share their research outputs openly with the wider academic and public communities. However, openly sharing the research evidence which underpins published findings might not always be possible as there could be ethical, legal or commercial reasons prohibiting you from doing so. Hence the phrase, “as open as possible, as closed as necessary”.

While you may not be able to share your data as an output, there are many other considerations. Can you be transparent about your processes? Can you tell others how you did the research so that they can replicate your methods? Have you considered ways to anonymise or share derived subsets of your data? What about your publications associated with your research, can they be open?

There are a huge amount of options available to you. Check out the Office for Open Science and Scholarship website for advice and support on engaging with open research principles even when the research evidence cannot be made publicly accessible.

Plus, easily access different teams across UCL helping you to engage with open research:

• Open Access,
• Research Data Management,
• UCL Press,
• Metrics and Bibliometrics,
• Transparency and Reproducibility,
• Digital Collections,
• Citizen Science,
• Co-Production and Public Engagement,
• Compliance and Assurance,
• and Copyright.

Guest post by Preeti Matharu, Jack Hindley, Victor Olago, Angharad Green (ARC Research Data Stewards), in celebration of International Love Data Week 2025

Research data is a valuable yet vulnerable asset. Research data is a valuable yet vulnerable asset. Researchers collect and analyse large amounts of personal and sensitive data ranging from health records to survey responses, and this raises an important question – whose data is it anyway?

If data involve human subjects, then participants are the original owners of their personal data. They grant permission to researchers to collect and use their data through informed consent. Therefore, responsibility for managing and protecting their data, in line with legal, regulatory, ethical requirements, and policies lie with researchers and their institution. Hence, maintaining a balance between participant rights and researcher needs.

Under the General Data Protection Regulation (GDPR) in the UK and EU, participants have the right to access, update and request deletion of their data, whilst researchers must comply with the law to ensure research integrity. However, under the Data Protection Act, research data processed in the public interest must be retained irrespective of participant rights, including the rights to erase, access and rectify. UCL must uphold this requirement while ensuring participant confidentiality is not compromised.

Information governance consists of policies, procedures and processes adopted by UCL to ensure research data is managed securely and complies with legal and operational requirements.

Support for information governance in research is now provided by Data Stewards within ARC RDM IG. That’s a long acronym, let’s break it down.

• ARC: Advanced Research Computing – UCL’s research innovative centre and provides 1. Secure digital infrastructure and 2. Teaching software.
• RDM: Research Data Management – assist researchers with data management.
• IG: Information governance – advise researchers on compliance for managing sensitive data.

Data Stewards – we support researchers with data management throughout the research study, provide guidance on data security awareness training, data security requirements for projects, and compliance with legal and regulatory standards, encompassing the Five Safes Framework principles. Additionally, we advise on sensitive data storage options, such as a Trusted Research Environment (TRE) or the Data Safe Haven (DSH).

Furthermore, we emphasise the importance of maintaining up-to-date and relevant documentation and provide guidance on FAIR (Findable, Accessible, Interoperable, Reusable) data principles.

As stated above, data can be vulnerable. UCL must implement strong security controls including encryption, access control and authentication, to protect sensitive data, such as personal health data and intellectual property. Sensitive data refers to data whose unauthorised disclosure could cause potential harm to participants or UCL.

UCL’s Information Security Management System (ISMS) is a systematic approach to managing sensitive research data to ensure confidentiality, integrity, and availability. It is a risk management process involving people, processes and IT systems. The key components include information management policy, identifying and assessing risks, implementing security controls to mitigate identified risks, training users and continuous monitoring. The ISMS is crucial in research:

1. It protects sensitive data; without stringent security measures, data is at risk of being accessed by unauthorised individuals leading to potential theft.
2. It ensures legal and regulatory compliance i.e. GDPR and UCL policies. Non-compliance results in hefty fines, legal action and reputational damage.
3. Research ethics demand participant data is handled with confidentiality. The ISMS ensures data management practices, data anonymisation, and controlled access whilst reinforcing ethical responsibility.
4. It reduces the risk of phishing attacks and ransomware.
5. It ensures data integrity and reliability – tampered or corrupted data can lead to invalid research and waste of resources.

UCL practices for Information Governance in research:

• Data security – Use encrypted storage, access controls and secure platforms like TRE or DSH.
• Ethical responsibility – Data protection office and Ethics obtain informed consent from participants and ensure they fully understand how their data will be used.
• Data Security Awareness training – Ensure researchers have completed and regularly review their training.
• Information Security Group – Conduct regular audits, identify vulnerabilities and mitigate potential risks.
• Contract Services – Review all research related contracts.

In response to the question, whose data is it anyway? Data may be generated by participants, but the overall responsibility to use, process, protect, ethically manage lies upon the researchers and UCL. Additionally, beyond compliance and good information governance, it is about ensuring research integrity and safeguarding the participants who make research possible.

In true UCL tradition, we kickstart the week with the annual Research Data Management review so take a look at our poster and see what we’ve been doing in the Library!

Guest post by Michelle Harricharan, Senior Research Data Steward, in celebration of International Love Data Week 2024.

ARC Data Stewards have completed the first phase of work on the third-party datasets project, aiming to help researchers better access and manage data provided to UCL by external organisations.

The problem:

Modern research often requires access to large volumes of data generated outside of universities. These datasets, provided to UCL by third parties, are typically generated during routine service delivery or other activities and are used in research to identify patterns and make predictions. UCL research and teaching increasingly rely on access to these datasets to achieve their objectives, ranging from NHS data to large-scale commercial datasets such as those provided by ‘X’ (formerly known as Twitter).

Currently, there is no centrally supported process for research groups seeking to access third-party datasets. Researchers sometimes use departmental procedures to acquire personal or university-wide licenses for third-party datasets. They then transfer, store, document, extract, and undertake actions to minimize information risk before using the data for various analyses. The process to obtain third-party data involves significant overhead, including contracts, compliance (IG), and finance. Delays in acquiring access to data can be a significant barrier to research. Some UCL research teams also provide additional support services such as sharing, managing access to, licensing, and redistributing specialist third-party datasets for other research teams. These teams increasingly take on governance and training responsibilities for these specialist datasets. Concurrently, the e-resources team in the library negotiates access to third-party datasets for UCL staff and students following established library procedures.

It has long been recognized that UCL’s processes for acquiring and managing third-party data are uncoordinated and inefficient, leading to inadvertent duplication, unnecessary expense, and underutilisation of datasets that could support transformative research across multiple projects or research groups. This was recognised in the “Data First, 2019 UCL Research Data Strategy”.

What we did:

Last year, the ARC Data Stewards team reached out to UCL professional services staff and researchers to understand the processes and challenges they faced regarding accessing and using third-party research datasets. We hoped that insights from these conversations could be used to develop more streamlined support and services for researchers and make it easier for them to find and use data already provided to UCL by third parties (where this is within licensing conditions).

During this phase of work, we spoke with 14 members of staff:

• 7 research teams that manage third-party datasets
• 7 members of professional services that support or may support the process, including contracts, data protection, legal, Information Services Division (databases), information security, research ethics and integrity, and the library.

What we’ve learned:

An important aspect of this work involved capturing the existing processes researchers use when accessing, managing, storing, sharing, and deleting third-party research data at UCL. This enabled us to understand the range of processes involved in handling this type of data and identify the various stakeholders involved—or who potentially need to be involved. In practice, we found that researchers follow similar processes to access and manage third-party research data, depending on the security of the dataset. However, as there is no central, agreed procedure to support the management of third-party datasets in the organization, different parts of the process may be implemented differently by different teams using the methods and resources available to them. We turned the challenges researchers identified in accessing and managing this type of data into requirements for a suite of services to support the delivery and management of third-party datasets at UCL.

Next steps:

 We have been working on addressing some of the common challenges researchers identified. Researchers noted that getting contracts agreed and signed off takes too long, so we reached out to the RIS Contract Services Team, who are actively working to build additional capacity into the service as part of a wider transformation programme.

Also, information about accessing and managing third-party datasets is fragmented, and researchers often don’t know where to go for help, particularly for governance and technical advice. To counter this, we are bringing relevant professional services together to agree on a process for supporting access to third-party datasets.

Finally, respondents noted that there is too much duplication of data. The costs for data are high, and it’s not easy to know what’s already available internally to reuse. In response, we are building a searchable catalogue of third-party datasets already licensed to UCL researchers and available for others to request access to reuse.

Our progress will be reported to the Research Data Working Group, which acts as a central point of contact and a forum for discussion on aspects of research data support at UCL. The group advocates for continual improvement of research data governance.

If you would like to know more about any of these strands of work, please do not hesitate to reach out (email: researchdata-support@ucl.ac.uk). We are keen to work with researchers and other professional services to solve these shared challenges and accelerate research and collaboration using third-party datasets.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, and join our mailing list to be part of the conversation!

Guest post by Victor Olago, Senior Research Data Steward and Shipra Suman, Research Data Steward, in celebration of International Love Data Week 2024.

Credit: Sangya Pundir, CC BY-SA 4.0 via Wikimedia Commons

The problem:

We all know sharing is caring, and so data needs to be shared to explore its full potential and usefulness. This makes it possible for researchers to answer questions that were not the primary research objective of the initial study. The shared data also allows other researchers to replicate the findings underpinning the manuscript, which is important in knowledge sharing. It also allows other researchers to integrate these datasets with other existing datasets, either already collected or which will be collected in the future.

There are several factors that can hamper research data sharing. These might include a lack of technical skill, inadequate funding, an absence of data sharing agreements, or ethical barriers. As Data Stewards we support appropriate ways of collecting, standardizing, using, sharing, and archiving research data. We are also responsible for advocating best practices and policies on data. One of such best practices and policies includes the promotion and the implementation of the FAIR data principles.

FAIR is an acronym for Findable, Accessible Interoperable and Reusable [1]. FAIR is about making data discoverable to other researchers, but it does not translate exactly to Open Data. Some data can only be shared with others once security considerations have been addressed. For researchers to use the data, a concept-note or protocol must be in place to help gatekeepers of that data understand what each data request is meant for, how the data will be processed and expected outcomes of the study or sub study. Findability and Accessibility is ensured through metadata and enforcing the use of persistent identifiers for a given dataset. Interoperability relates to applying standards and encoding such as ICD-10, ICDO-3 [2] and, lastly, Reusability means making it possible for the data to be used by other researchers.

What we are doing:

We are currently supporting a data reuse project at the Medical Research Council Clinical Trials Unit (MRC CTU). This project enables the secondary analysis of clinical trial data. We use pseudonymisation techniques and prepare metadata that goes along with each data set.

Pseudonymisation helps process personal data in such a way that the data cannot be attributed to specific data subjects without the use of additional information [3]. This reduces the risks of reidentification of personal data. When data is pseudonymized direct identifiers are dropped while potentially identifiable information is coded. Data may also be aggregated. For example, age is transformed to age groups. There are instances where data is sampled from the original distribution, allowing only sharing of the sample data. Pseudonymised data is still personal data which must be protected with GDPR regulation [4].

The metadata makes it possible for other researchers to locate and request access to reuse clinical trials data at MRC CTU. With the extensive documentation that is attached, when access is approved, reanalysis and or integration with other datasets are made possible.  Pseudonymisation and metadata preparation helps in promoting FAIR data.

We have so far prepared one data-pack for RT01 studies which is ‘A randomized controlled trial of high dose versus standard dose conformal radiotherapy for localized prostate cancer’ which is currently in review phase and almost ready to share with requestors. Over the next few years, we hope to repeat and standardise the process for past, current and future studies of Cancer, HIV, and other trials.

References:    

1. 8 Pillars of Open Science.
2. Digital N: National Clinical Coding Standards ICD-10 5th Edition (2022), 5 edn; 2022.
3. Anonymisation and Pseudonymisation.
4. Complete guide to GDPR compliance.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, and join our mailing list to be part of the conversation!

Guest post by Iona Preston, Research Data Support Officer, in celebration of International Love Data Week 2024.

Photo by Todd Quackenbush on Unsplash.

While there are a lot of general resources to support good research data management practices – for example UCL’s Research Data Management webpages – you might sometimes be looking for something a bit more specific. It’s good practice to store your data in a research data repository that is subject specific, where other people in your research discipline are most likely to search for data. However, you might not know where to begin your search. You could be looking for discipline-specific metadata standards, so your data is more easily reusable by academic colleagues in your subject area. This is where subject-specific research data management resources become valuable. Here are some resources for specific subject areas and disciplines that you might find useful: 

• The Research Data Management Toolkit for Life Sciences
  This resource guides you through the entire process of managing research data, explaining which tools to use at each stage of the research data lifecycle. It includes sections on specific life science research areas, from plant sciences to rare disease data. These sections also cover research community-specific repositories and examples of metadata standards. 
• Visual arts data skills for researchers: Toolkits
  This consists of two different tutorials covering an introduction to research data management in the visual arts and how to create an appropriate data management plan. 
• Consortium of European Social Science Data Archives
  CESSDA brings together data archives from across Europe in a searchable catalogue. Their website includes various resources for social scientists to learn more about data management and sharing, along with an extensive training section and a Data Management Expert Guide to lead you through the data management process. 
• Research Data Alliance for Disciplines (various subject areas)
  The Research Data Alliance is an international initiative to promote data sharing. They have a webpage with special interest groups in various academic research areas, including agriculture, biomedical sciences, chemistry, digital humanities, social science, and librarianship, with useful resource lists for each discipline. 
• RDA Metadata Standards Catalogue (all subject areas)
  This directory helps you find a suitable metadata scheme to describe your data, organized by subject area, featuring specific schemes across a wide range of academic disciplines. 
• Re3Data (all subject areas)
  When it comes to sharing data, we always recommend you check if there’s a subject specific repository first, as that’s the best place to share. If you don’t know where to start finding one, this is a great place to look with a convenient browse feature to explore available options within your discipline.

These are only some of the different discipline specific tools that are available. You can find more for your discipline on the Research Data Management webpages. If you need any help and advice on finding data management resources, please get in touch with the Research Data Management team on lib-researchsupport@ucl.ac.uk.  

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, and join our mailing list to be part of the conversation!

Guest post by Dr Christiana McMahon, Research Data Support Officer, in celebration of International Love Data Week 2024.

From that spark of an idea through to publishing research findings, the Research Data Management team have once again been on-hand to support staff and students.

What’s been happening?

A new version of the Research Data Repository is now available simplifying the process of archiving and preserving research outputs here at UCL for the longer-term.

In 2023 we published 200 items 151 of which were datasets.

Graph to show items published in the UCL Research Repository in 2023.

Over the past year…

• The most downloaded record was: Griffiths, David; Boehm, Jan (2019). SynthCity Dataset – Area 1. University College London. Dataset.
• The most viewed record was: Heenan, Thomas; Jnawali, Anmol; Kok, Matt; Tranter, Thomas; Tan, Chun; Dimitrijevic, Alexander; et al. (2020). Lithium-ion Battery INR18650 MJ1 Data: 400 Electrochemical Cycles (EIL-015). University College London. Dataset.
• The most cited record was: Manescu, Petru; Shaw, Mike; Elmi, Muna; Zajiczek, Lydia; Claveau, Remy; Pawar, Vijay; et al. (2020). Giemsa Stained Thick Blood Films for Clinical Microscopy Malaria Diagnosis with Deep Neural Networks Dataset. University College London. Dataset.

More information is available about the UCL Research Data Repository.  Alternatively, check our FAQs.

Data Management Plan Reviews

The RDM team can review data management plans providing researchers with feedback in-line with UCL’s expectations and funding agency requirements where these apply. In 2023, we reviewed 32 data management plans covering over 10 different funding agencies. More information is available in our website.

Mini-tutorial: Research data lifecycle

The RDM team often refer to the research data lifecycle, but what is it? Essentially, these are the different stages of the research process from planning and preparation through to archiving your research outputs, making them discoverable to the wider research community and members of the public.

The four stages:

1: Get ready – You’ve had an idea for a research study so it’s time to start making plans and getting prepared. Have you considered writing a data management plan?

• Remember, if you are in receipt of external funding, there may be data management requirements to consider.
• Feel free to reach out to Open Science and Research Support to assist you.

2: Let’s go – You are now actively researching putting all those research plans into action.

• Don’t forget to revisit your data management plan and update it to reflect your latest decision making.
• It’s also useful to consider documenting your research as you progress.

3: Ta-dah – The research is complete and it’s time to archive your research outputs to preserve them for the longer-term.

• Aim to utilise subject-specific archives and repositories where possible.
• Creating a metadata record in a public facing online catalogue with links to any related publications can be useful to building online networks of linked research outputs.
• Consider making your research outputs as openly accessible as possible remembering that controlling or restricting access is fine as long as it is justified and there is a set data access protocol in place to facilitate a data access request.
• Did you know you can archive most research outputs in the UCL Research Data Repository?

4: Wow! I think I can use this – making your research discoverable to others for potential reuse can help to maximise research opportunities

• Consider having an agreement in place between parties before sharing research outputs – issues like copyright, IP, commercialisation of (subsequent) work may be discussed here.
• For secondary researchers, time to head back to the start to begin the research process.

And so the research data lifecycle begins again!

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, and join our mailing list to be part of the conversation!

Guest post by Iona Preston, Research Data Support Officer.

Next week (February 12-16), we’re excited to be celebrating International Love Data Week. We’ll be looking at how data is shared and reused within our UCL and academic community, highlighting the support available across UCL for these initiatives. This year’s theme, “My Kind of Data,” focuses on data equity, inclusion, and disciplinary communities. We’ll be blogging and posting on X throughout the week, so please join us to learn more.

Here’s a sneak preview of what’s coming up:

• Did you know the Research Data Management team can review your data management plan and support you in publishing your data in our Research Data Repository? Find out more about our last year in review with Christiana McMahon, Research Data Support Officer.
• Have you met any members of our Data Stewards team? James Wilson, Head of Research Data Services, will be explaining how you can collaborate with them to streamline the process of managing and preserving your data, thereby supporting reproducibility and transparency in your research.
• Are you seeking tools to support best practices in data management for your specific discipline? We have some suggestions from Iona Preston, Research Data Support Officer.
• You may have heard of FAIR data – but what does that mean in practice? Join Research Data Steward Shipra Suman and Senior Research Data Steward Victor Olago as they discuss projects where they’ve supported making data FAIR.
• And, finally, to round off the week, Senior Research Data Steward Michelle Harricharan will talk about a project the Data Stewards are carrying out to better support UCL researchers in accessing and managing external datasets.

We look forward to engaging with you throughout the week and hope you enjoy learning more about research data at UCL.

And get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, and join our mailing list to be part of the conversation!