Cars, Museums, and Data Stewardship

I was in Coventry for about a week. A mini-vacation, if I may call it that. My sister was at school during the day, which leaves me with plenty of time to wander around and play tourist. To fill some of that time, I visited the Coventry Transport Museum, and the Midland Air Museum. This post is about cars, bikes, data, and stewardship. Because apparently I can't switch off the researcher part of my brain, even on holiday. And I kept seeing beautiful connections that tagged at my data steward wannabe heart and brain strings.

I've visited a fair number of museums. There's one next to my university (Uni of Manchester) with fascinating natural history collections. Then there are museums filled with ancient artefacts, artworks, scrolls, ships, and mummies. The British Museum comes to mind. I still have complicated feelings about some of the histories behind those collections, especially when it comes to human remains. Some questions about carrying a dead body buried in respect for their life, and death, and customs across nations to put in display have never really settled with me.

But regardless of the collection, what I've always admired is the work that happens behind the scenes: the care, preservation, documentation, and sharing. Seeing that work up close in Coventry immediately made me think about data and its stewardship.

At the Coventry Transport Museum, some vehicles were donated by individuals, some by companies, and others arrived through partnerships with different organisations and regions. What struck me most were the stories behind some of the cars. A number had been maintained for years, decades, centuries, by passionate owners who invested time, money, and expertise into keeping them running and preserving their history. These vehicles did not arrive at the museum by accident. They arrived because someone cared enough to keep them alive. Once they enter the museum, responsibility shifts. The museum becomes a steward. It preserves the vehicle, documents its history, shares it with the public, and ensures that future generations can learn from it.

As I walked through the exhibits, I couldn't help thinking: isn't this exactly what we hope for in open data?

In an ideal world, researchers and organisations collect data, care for it, use it for their work and then once they're ready, they share their datasets to be used by the public. Sometimes through a platform, or through their organisation. Because they understand that this data can be useful for others to use, and trust that someone will care for their data since they've requested it to allow others to use. They contribute something valuable, and the institutions that receive that data preserve it, document it, make it discoverable, and ensure it remains useful over time. Why does openness still fail then?

1. Acknowledgement

Museums do a good job with acknowledging their contributors. Every vehicle has a story attached to it. Visitors can see where it came from, why it matters, and often who preserved it. They get a placard with that story. For example these placards.

Data should work the same way. But it rarely does. When someone contributes a dataset, that contribution should not disappear into a repository. The dataset should carry its history with it. We should know who created it, who cared for it, how it was collected, and its history. In the digital world, this recognition often takes the form of citations, metadata, data papers, governance documentation, and persistent identifiers such as DOIs. The museum placard and the dataset citation serve a surprisingly similar purpose: they connect an object to the people whose labour made it available. 

But unfortunately with data, this acknowledgement remains an issue. There are multiple instances of opting out of data sharing or participating in open data practices because of this lack of acknowledgement. Helicopter science, and failure to meet embargo terms are all examples of practices born from this lack of acknowledgement. 

2. Preservation

The preservation aspect of museum work also resonates strongly with data stewardship. In every museum I've visited, there are specialists dedicated to conservation. They restore damaged components, clean artefacts, replace deteriorated materials with appropriate replicas, document changes, and constantly monitor the condition of collections. The amount of expertise involved is remarkable. Their goal is not simply to keep an object alive. It is to keep that object understandable and usable for future audiences.

Data stewards do much the same thing. They clean datasets, improve documentation, maintain metadata, establish governance processes, manage storage, and ensure that data remains accessible and interpretable over time. Much of that work is invisible. People often celebrate the dataset itself but rarely the labour required to make it reusable.

And yet, without that labour, data becomes as inaccessible as a rusting car left in a garage. This is where the analogy becomes especially important for me. For my PhD, such data roles and their description have proven to be the difference between the Global North and South and the quality and availability of air quality data. 

3. Trust

For years we have promoted FAIR and open data principles. We have made progress, certainly, but trust remains a major challenge. In my work on air quality data in Kenya and Uganda, one issue emerged repeatedly: people and organisations had shared data in the past and felt that they had not been treated fairly. Their data was used without sufficient acknowledgment. Benefits did not flow back to them. Their contributions became invisible. The response from them was access was restricted or withdrawn.

The result is often interpreted as a lack of data in the Global South. But in many cases the problem is not the absence of data. The problem is the absence of trusted relationships and stewardship structures that make sharing worthwhile. This happens even in bigger institutions.

Imagine if the same thing happened in a museum. Someone spends years maintaining a rare vehicle and lends it to an institution, only to have their contribution erased from the story. No acknowledgment. No attribution. No recognition of the expertise and labour invested. For example the image of the Ultimate bike image below has so much history. For example it won the Millennium Product award. And you can read this on the placard because it's all written there. To exclude such important information would be unfair, and we would immediately recognise it as such. Yet similar dynamics continue to occur in data sharing environments.

This is why trust matters. There needs to be a structure that establishes trust. So that data contributors feel safe enough to share data with institutions, maintainers feel safe to request, maintain and put out data owned by others,  and more.  Therefore tools like identifiers such as DOIs become not just technical identifiers. Metadata is not just documentation. Governance records are not administrative paperwork. And data stewards or curators are not just names. Together, they are the equivalent of the museum structure of trust. They recognise contribution, establish provenance, and help build trust.

4. Knowledge sharing

Another thing that impressed me most at the museum was not the cars themselves, but the network of knowledge behind them. Many owners spent years learning how to maintain engines, source replacement parts, and adapt to changing materials and technologies. They self-learned how to so this out of passion, and more. That knowledge is valuable, and museums benefit enormously when owners share it.

Data systems are no different. People spend years collecting, cleaning, curating, and understanding datasets. They know the limitations, the quirks, the missing records, the methodological choices, and the context that makes the data meaningful. When data moves into repositories or platforms, that knowledge should move with it. 

Good stewardship is not just about preserving data. It is about preserving the knowledge surrounding data. That means creating space for communication between data owners and data stewards. Creating spaces to learn and share knowledge. By helping more people develop the skills needed to maintain high-quality datasets in the first place. Holding sessions to teach about documentation, adopting methods such as co-design and co-production where all data stakeholders work collaboratively, to develop data systems. That's exciting.

Right, of course, data is not a vintage car. It lacks the physical presence that makes museum collections so immediately compelling. But I think there is a great deal we can learn from museums. We can learn about care, preservation, attribution, trust, and respect. Most importantly, we can learn that stewardship is ultimately about relationships—between people, knowledge, and the things they choose to share with the world.

Walking through the Coventry Transport Museum reminded me that stewardship is not merely a technical task. It is a commitment to preserving value and honouring contribution.That means creating space for communication between data owners and data stewards. It means recognising expertise throughout the data lifecycle. And it means helping more people develop the skills needed to maintain high-quality datasets in the first place. Maybe that's why I want to be a data steward so bad 😁. 

All images used in this blog were taken by Pauline Karega, at the Museum of Transport in Coventry, and the Midland Air Museum.