What Should Be Saved? The Impact of Austerity on Data Rescue

At a time where research institutions globally are being faced with diminishing budgets, methods for prioritising data for preservation are essential. This talk will detail our application of the Hoffman et al. data rescue framework (DRF) in a recent project to prioritise datasets for rescue, plan workload, anticipate potential obstacles, and approximate resources required. I will detail our novel points-based adaptation of the DRF which facilitated the decision-making process of which dataset to save, accounting for our limited budget. I will also describe how we used this adaptation to quantitatively compare the dataset before and after rescue, taking the FAIR principles into account. The methodology I will describe is likely applicable to countless similar datasets currently held in inaccessible locations and gives a step-by-step structured process for data curation professionals to follow from prioritising data through to publication. It could greatly improve efficiency and prioritisation of data rescues if adopted by other institutions, particularly those affected by scarcity of budget and resource.