Data curation as anticipatory generification in data infrastructure

Data curation is crucial for data reusability. New possibilities for digital data sharing are an urgent concern for data curators, who must keep historical datasets and present data collections always ready to meet unknown future data needs. This calls for a more nuanced understanding of the temporal horizons of data curation in Information Systems research. Based on a qualitative interpretive case study of data management in an environmental monitoring infrastructure, we characterise three data curation practices to support data reuse. These practices follow three interleaving temporal perspectives: retrospective (by upgrading historical datasets), present-oriented (by monitoring ongoing data collections), and future-looking (by disseminating data). We conceptualise this work as anticipatory generification, involving continuous and temporally oriented data curation to maintain data sufficiently open-ended to anticipate future data reusability. Anticipatory generification is essential for the sustainable evolution of environmental data infrastructures. Our study contributes to the Information Systems literature by further theorising the temporal perspectives of data infrastructures and providing additional insight into how the future is anticipated in practice.

Automated summary

Data curation is crucial for data reusability, and it requires three practices: retrospective, present-oriented, and future-looking, to support data reuse. Anticipatory generification is essential for the sustainable development of environmental data infrastructures.