2021-10-01, 14:30–15:15, Malena Libman
Many innovative analysis approaches presented in scientific publications are hard or impossible to reproduce. This slows down the uptake of new ideas and makes it harder for others to improve on these ideas. Just as open science in general needs open source software, it is clear that open and reproducible spatial data science needs open source GIS. In this talk, I will share my vision for the future of spatial data science in academia and industry, related challenges and potential beyond the core community of geospatial experts.
Many innovative analysis approaches presented in scientific publications are hard or impossible to reproduce. This slows down the uptake of new ideas and makes it harder for others to improve on these ideas. Just as open science in general needs open source software, it is clear that open and reproducible spatial data science needs open source GIS. In this talk, I will share my vision for the future of spatial data science in academia and industry, related challenges and potential beyond the core community of geospatial experts.
Graser, Anita (1)
(1) Austrian Institute of Technology
Track –Panel
Topic –Community & participatory FOSS4G
Level –1 - Principiants. No required specific knowledge is needed.
Language of the Presentation –English
Spatial data scientist | QGIS & MovingPandas @ MobilityDB | More details: anitagraser.com
- Twitter: @underdarkGIS
- Linkedin: Anita Graser