The PARSEC project is designed to provide a unique opportunity for data and synthesis scientists to collaborate and exchange in real-time toward the goal of improving research outcomes, data sharing, and data reuse. It will also help pioneer new scientific and data science technologies aimed at improving both the management and conservation of global biodiversity.
We have two teams in this project with links between: a Synthesis Science team and a Data Science team. Our Synthesis Science team is employing artificial intelligence techniques to analyse satellite images and socio-economic information to better predict and mitigate the effect(s) of actions that potentially threaten the livelihoods and health of local (indigenous) communities. Like most researchers who investigate complex environmental problems, the team depends significantly on the availability of good, spatially dispersed, multidisciplinary, and time-series data.
Our Data Science team of leading environmental data management professionals, data communities (RDA, ESIP), society journals (AGU), and representatives of e-infrastructures for data attribution (e.g., DataCite and ORCID) will develop leading practices on data citation, attribution, credit, and reuse. As part of the integrated work with the synthesis-science team, the data-science team will provide a review of best practices for data management and stewardship using this effort as a case study of the wider scientific community to optimise data access and reuse. The team will also develop and implement a new tool to better track data usage and reuse for researchers.
DataCite, ORCID, Earth Science Information Partners (ESIP), the Research Data Alliance (RDA), EDI, the World Data System (WDS), Academia Sinica (Taipei) (AST), USGS John Wesley Powell Center (JWP), The Nature Conservancy (TNC).
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