AwesomeGeodataTable - Towards a community-maintained searchable table for data sets easily usable as predictors for spatial machine learning

In the field of spatial machine learning, access to high-quality data sets is a crucial factor in the success of any analysis or modeling project, especially in subsurface hydrology. However, finding and utilizing such data sets can be a challenging and time-consuming process. This is where AwesomeGeodataTable comes in. AwesomeGeodataTable aims to establish a community-maintained searchable table of data sets that are easily usable as predictors for spatial machine learning starting with the focus on subsurface hydrology. With its user-friendly interface and currently small but growing number of data sets, AwesomeGeodataTable will make it easier for researchers and practitioners to find and use the data they need for their work. It brings the usability of existing data set collections to a next level through adding features for filtering and searching meta information on data sets. This talk will introduce attendees to the AwesomeGeodataTable project, its goals and features, and how they can get involved in maintaining and extending its database and expanding its features and user experience. Overall, AwesomeGeodataTable is a valuable resource for anyone working in the field of spatial machine learning, and we hope to see it become a widely used and respected resource in the community.