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Data in the research lifecycle |
Lifecycle |
Data and its management change over time. Here we look at data and
research lifecycles and make connections between them.
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Ready, set, data |
Research Lifecycle Models provide a high-level overview of the stages and actions during the research lifecycle required for successful management of data. A data lifecycle shows the different phases a dataset goes through as the research project moves from "having a brilliant idea" to "making ground breaking discoveries" to "telling the world about it".
Watch the short video on the UK Data Service Data Lifecycle
Consider: What other things impact how data is managed through the lifecycle? For example, this could be funding policies which make sharing the data mandatory, or data which is part of an ongoing longitudinal study.
Data often have a longer lifespan than the research project that creates them. Follow-up projects may analyse or add to the data, and data may be reused by other researchers. Journal publishers are increasingly mandating that the data underpinning a journal article be retained and made accessible for the long term. Data needs to managed carefully to ensure long-term preservation.
- Take a look at the DCC Curation Lifecycle Model which concentrates on preservation and curation within data management.
Consider: how you might adapt this model to your local organisational context.
Data management is likely to be most effective where it is integrated into existing system-wide research and administrative processes. New software, tools and platforms are being developed particularly with this in mind.
- Read through this 2018 F1000 article Best practice data life cycle approaches for the life sciences and look carefully at Figure 2
Consider: your experience with emerging opportunities for researchers to integrate lifecycle workflows to manage their research data assets.