Praetzellis, M., Buys, M., Chen, X., Chodacki, J., Davies, N., Garza, K., Nancarrow, C., Riley, B. and Robinson, E. (2023) ‘A Programmatic and Scalable Approach to making Data Management Machine-Actionable’, Data Science Journal, 22(1), p. 26. Available at: https://doi.org/10.5334/dsj-2023-026.
We are excited to announce the publication of a collaborative paper led by Maria Praetzellis. It highlights two DMP use cases for field stations, universities and research teams. We described the types of information that field station administrators wanted access to like research outputs, types of research conducted at the station and impact of the field station support. We also noted our shifting thinking for field stations on how DMPs are integrated into the workflow for research teams collecting data at field stations. All research teams need to have at least one DMP associated with the funded work, but field stations need additional details about the location-specific project. We discussed the power of shifting from DMPids at field stations to project DOIs with just a small adjustment to our metadata because both resource types utilize the DataCite Metadata Schema.
Check out the full paper here: https://doi.org/10.5334/dsj-2023-026