GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original models were calculated using the H2O artificial intelligence platform and output as H3 Discrete Global Grids developed by Uber (Uber Technologies Inc., 2020). The Uber grids are based on a hexagonal geometry with an average area of 5.16 km2. The Uber grids were converted to GeoTiff raster grids that approximate a 2 km by 2 km grid for this report. The full description on how the models were produced are described in Lawley and others (2021, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub repository, accessed July 1, 2021, at https://github.com/uber/h3.

GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original models were calculated using the H2O artificial intelligence platform and output as H3 Discrete Global Grids developed by Uber (Uber Technologies Inc., 2020). The Uber grids are based on a hexagonal geometry with an average area of 5.16 km2. The Uber grids were converted to GeoTiff raster grids that approximate a 2 km by 2 km grid for this report. The full description on how the models were produced are described in Lawley and others (2021, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub repository, accessed July 1, 2021, at https://github.com/uber/h3.

The dataset identifies the location and types of ANSIs that are commonly used in maps for resource management purposes. Official GEO title: ANSI *[ANSIs]: Areas of Natural and Scientific Interest *[ANSI]: Areas of Natural and Scientific Interest