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.

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