Research data is often treated as if it has only two moments that matter: when it is collected and when it is published. Everything in between is left to chance. Yet data that is well collected but poorly managed can become unusable within a few years: file formats fall out of support, the meaning of variables is forgotten, copies multiply and diverge, and the person who understood it moves on. Treating data as a thing to be looked after across its whole existence, rather than captured once and forgotten, is the essence of data lifecycle management. The most influential map of that lifecycle is the Digital Curation Centre’s Curation Lifecycle Model, which provides a structured way to think about the journey data takes — a journey at the heart of the research-lifecycle domain of the CASRAI Dictionary.
Why curation is continuous
The central insight of the lifecycle view is that curation is an active, continuous process, not a one-off task performed at the end. It is tempting to imagine that data can be generated freely and tidied up later. In practice, the decisions that determine whether data will survive and remain usable are made throughout: how it is structured and documented as it is created, how it is stored while in use, what is kept and what is discarded, and how it is prepared for the long term. Leaving all of this to the end means leaving it too late — documentation that was obvious at the time is forgotten, and choices that should have been deliberate are made by default. The Digital Curation Centre, a UK centre of expertise, developed its model precisely to make these activities visible and deliberate across the whole life of the data.
The shape of the model
The Curation Lifecycle Model is usually drawn as a series of concentric rings around the data at the centre. At its core sit the digital objects and databases being curated. Surrounding them are full lifecycle actions — activities that apply throughout, not at a single stage. These include description and representation information (the metadata and documentation that make data understandable), preservation planning, community watch and participation (keeping up with standards and tools), and the overarching work of curating and preserving. Around these run the sequential actions that the data passes through over time. The genius of the model is in holding both ideas at once: some curation work happens at particular moments in sequence, while other work — above all documentation and preservation planning — must be sustained continuously throughout.
The sequence of actions
The sequential part of the model traces data through its life:
- Conceptualise. Plan how data will be created and managed before any of it exists — the planning a data management plan captures, a discipline introduced at our learning hub.
- Create or receive. Generate the data, or take it in, with the metadata and documentation it needs from the outset.
- Appraise and select. Decide which data should be kept for the long term, judged against guidance and policy. Not everything need be preserved forever; deciding deliberately is itself curation.
- Ingest. Transfer the selected data into a repository or archive that will look after it.
- Preservation action. Take the steps that keep data usable over time — format migration, integrity checks and the rest.
- Store. Keep the data securely and reliably.
- Access, use and reuse. Make the data available to those entitled to it, for the purposes that justify keeping it.
- Transform. Create new data from the original, which then re-enters the lifecycle in its own right.
The model also includes occasional actions — reappraisal, migration and, where appropriate, disposal of data that should not be retained — acknowledging that curation involves honest decisions about what not to keep as well as what to preserve.
Appraisal: the decision at the centre of curation
Of all these stages, appraisal and selection deserves particular emphasis, because it is where lifecycle thinking departs most sharply from the instinct to keep everything. Storing data indefinitely is neither free nor harmless: it consumes resources, and a vast undifferentiated mass of poorly described data is hard to use. Appraisal is the disciplined judgement about what has lasting value — what should be preserved because it could be reused, verified or is too costly to reproduce — and what can responsibly be let go. Making that judgement well, against clear policy, is one of the most professional acts in data management, and the lifecycle model puts it where it belongs: a deliberate decision point, not an accident of neglect.
Preservation in service of reuse
It is worth being clear about why all this effort is undertaken. The point of preservation is not to lock data away but to keep it usable, because the ultimate purpose of curation is reuse. Data that has been appraised, documented, preserved and made accessible can be verified by others, combined with new data, and built upon in ways its creators never anticipated. This is the payoff that justifies the whole lifecycle: well-curated data is an asset that keeps giving, while neglected data is a sunk cost that decays. The model makes the connection explicit by placing reuse alongside preservation, a reminder that curation serves a purpose beyond mere safekeeping.
A consistent vocabulary across the lifecycle
For data to move smoothly through these stages — across the tools, repositories and systems involved — the information describing it must mean the same thing at every step. Metadata created at capture must be understood by the repository that ingests it; reuse depends on description that travels intact. That consistency is what the CASRAI Dictionary provides: a shared vocabulary so the information accompanying data is understood identically wherever it flows. And because curating data is genuine, recognisable contribution, the work can be described using the same framework as any other — the CRediT taxonomy, whose Data curation role names exactly this activity. The lifecycle model shows that good data does not happen by accident; sustained curation, supported by shared description, turns data collected once into data usable for years.