These data present geologic map units for the United States (Horton and others, 2017; Wilson and others, 2015) and Australia (Raymond and others, 2012) reclassified to 31 generalized sub-type lithologic groups of igneous, metamorphic, and sedimentary rocks (Lawley and others, 2022). These generalized classifications are based on interpretation of map unit descriptions in the different map compilations. Given that map unit descriptions often contain multiple rock types, there were subjective calls necessary when assigning generalized lithologic classification. The data were developed as part of the tri-national Critical Minerals Mapping Initiative (Kelley, 2020) between the United States, Canada, and Australia, an effort to model and map prospectivity for basin-hosted Pb-Zn mineralization. A national-scale geologic map compilation for Canada is not publicly available. Therefore, Lawley and others (2021) compiled geologic source maps to produce a gridded model layer that is provided in this data release in the Child Items section “Gridded geology shapefiles for the United States, Canada, and Australia.” References Horton, J.D., San Juan, C.A., and Stoeser, D.B., 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p., https://doi.org/10.3133/ds1052. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020-3035, 2 p., https://doi.org/10.3133/fs20203035. 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. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Wilson, F.H., Hults, C.P., Mull, C.G., and Karl, S.M., comps., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, 2 sheets, scale 1:1,584,000, 196-p. pamphlet, https://doi.org/10.3133/sim3340.

These data present geologic map units for the United States (Horton and others, 2017; Wilson and others, 2015) and Australia (Raymond and others, 2012) reclassified to 31 generalized sub-type lithologic groups of igneous, metamorphic, and sedimentary rocks (Lawley and others, 2022). These generalized classifications are based on interpretation of map unit descriptions in the different map compilations. Given that map unit descriptions often contain multiple rock types, there were subjective calls necessary when assigning generalized lithologic classification. The data were developed as part of the tri-national Critical Minerals Mapping Initiative (Kelley, 2020) between the United States, Canada, and Australia, an effort to model and map prospectivity for basin-hosted Pb-Zn mineralization. A national-scale geologic map compilation for Canada is not publicly available. Therefore, Lawley and others (2021) compiled geologic source maps to produce a gridded model layer that is provided in this data release in the Child Items section “Gridded geology shapefiles for the United States, Canada, and Australia.” References Horton, J.D., San Juan, C.A., and Stoeser, D.B., 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p., https://doi.org/10.3133/ds1052. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020-3035, 2 p., https://doi.org/10.3133/fs20203035. 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. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Wilson, F.H., Hults, C.P., Mull, C.G., and Karl, S.M., comps., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, 2 sheets, scale 1:1,584,000, 196-p. pamphlet, https://doi.org/10.3133/sim3340.

These data provide geologic information, including generalized lithology, geologic age, and paleo-latitude and -longitude of geologic units, for the United States, Canada, and Australia, in an H3 Discrete Global Grid System (DGGS) hexagonal format (Uber Technologies Inc., 2020) with an average hexagon area of 5.16 square kilometers. The data are presented as the shapefile version of ASCII data developed by Lawley and others (2021) for prospectivity modeling of basin-hosted Pb-Zn mineralization in the United States, Canada, and Australia (Lawley and others, 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, https://github.com/uber/h3.

These data provide geologic information, including generalized lithology, geologic age, and paleo-latitude and -longitude of geologic units, for the United States, Canada, and Australia, in an H3 Discrete Global Grid System (DGGS) hexagonal format (Uber Technologies Inc., 2020) with an average hexagon area of 5.16 square kilometers. The data are presented as the shapefile version of ASCII data developed by Lawley and others (2021) for prospectivity modeling of basin-hosted Pb-Zn mineralization in the United States, Canada, and Australia (Lawley and others, 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, https://github.com/uber/h3.

These data provide geologic information, including generalized lithology, geologic age, and paleo-latitude and -longitude of geologic units, for the United States, Canada, and Australia, in an H3 Discrete Global Grid System (DGGS) hexagonal format (Uber Technologies Inc., 2020) with an average hexagon area of 5.16 square kilometers. The data are presented as the shapefile version of ASCII data developed by Lawley and others (2021) for prospectivity modeling of basin-hosted Pb-Zn mineralization in the United States, Canada, and Australia (Lawley and others, 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, https://github.com/uber/h3.

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. 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. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Reed, J.C., Jr., Wheeler, J.O., Tucholke, B.E., Stettner, W.R., and Soller, D.R., 2005, Decade of North American Geology Geologic Map of North America - Perspectives and explanation: Geological Society of America, v. 1, https://doi.org/10.1130/DNAG-CSMS-v1. Styron, R., and Pagani, M., 2020, The GEM global active faults database: Earthquake Spectra, v. 36, p. 160-180, https://doi.org/10.1177/8755293020944182.

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. 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. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Reed, J.C., Jr., Wheeler, J.O., Tucholke, B.E., Stettner, W.R., and Soller, D.R., 2005, Decade of North American Geology Geologic Map of North America - Perspectives and explanation: Geological Society of America, v. 1, https://doi.org/10.1130/DNAG-CSMS-v1. Styron, R., and Pagani, M., 2020, The GEM global active faults database: Earthquake Spectra, v. 36, p. 160-180, https://doi.org/10.1177/8755293020944182.

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. 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. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Reed, J.C., Jr., Wheeler, J.O., Tucholke, B.E., Stettner, W.R., and Soller, D.R., 2005, Decade of North American Geology Geologic Map of North America - Perspectives and explanation: Geological Society of America, v. 1, https://doi.org/10.1130/DNAG-CSMS-v1. Styron, R., and Pagani, M., 2020, The GEM global active faults database: Earthquake Spectra, v. 36, p. 160-180, https://doi.org/10.1177/8755293020944182.

Two shapefiles mapping the locations of ancient and modern passive margin boundaries are presented. These data are a digital recreation of the work originally published by Bradley (2008). The ancient passive margin data were used as an evidential layer to map prospectivity for sediment-hosted Pb-Zn mineral systems (Lawley and others, 2022). The ancient passive margins dataset includes additional attributes related to the boundary's orogenic setting and history, the length of the boundary, its estimated lifespan, and its modern-day country location. Although only ancient passive margin boundaries were analyzed for the United States, Canada, and Australia for this study, boundaries for the world are included in the shapefile. The modern passive margin dataset includes an identifier for the margin segment, a margin name, the associated ocean, and age ranges of basin initiation, mean age and length of the respective passive margin segment. The modern passive margin data were not used in prospectivity modeling for ancient deposits. The passive margin boundaries in both files are mapped as line segments in geographic coordinates using a WGS84 datum. References Bradley, D.C., 2008, Passive margins through earth history: Earth-Science Reviews, v. 91, no. 1-4, p. 1-26, https://doi.org/10.1016/j.earscirev.2008.08.001. 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.

Two shapefiles mapping the locations of ancient and modern passive margin boundaries are presented. These data are a digital recreation of the work originally published by Bradley (2008). The ancient passive margin data were used as an evidential layer to map prospectivity for sediment-hosted Pb-Zn mineral systems (Lawley and others, 2022). The ancient passive margins dataset includes additional attributes related to the boundary's orogenic setting and history, the length of the boundary, its estimated lifespan, and its modern-day country location. Although only ancient passive margin boundaries were analyzed for the United States, Canada, and Australia for this study, boundaries for the world are included in the shapefile. The modern passive margin dataset includes an identifier for the margin segment, a margin name, the associated ocean, and age ranges of basin initiation, mean age and length of the respective passive margin segment. The modern passive margin data were not used in prospectivity modeling for ancient deposits. The passive margin boundaries in both files are mapped as line segments in geographic coordinates using a WGS84 datum. References Bradley, D.C., 2008, Passive margins through earth history: Earth-Science Reviews, v. 91, no. 1-4, p. 1-26, https://doi.org/10.1016/j.earscirev.2008.08.001. 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.