Abstract
Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for 'Plant Systems-biology Modelling' and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting till 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative public repository of secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in similar situation. We share our reflections on sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.
Highlights
Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research, as expressed for example in the UK Concordat on Open Research[1]
All the information from the PlaSMo portal are available under the PlaSMo project on the FAIRDOMHub
The migration process was smooth and we did not experience any problem with the API calls. It seems that the SEEK instance on the FAIRDOMHub production server is very robust and it handles all the requests flawlessly, unlike the test SEEK’s Docker containers we used during development
Summary
Data sharing and data re-use have become a priority for publicly- and charity-funded research, as expressed for example in the UK Concordat on Open Research[1]. Data re-use depends on reliable metadata: a detailed description of the experimental conditions, materials used, handling procedures and analysis methods. Data management goes beyond the safe storage of data, because metadata acquisition and data discovery are important aspects for effective digital preservation[2,3,4]. This creates a need for computational resources that can deliver such features
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