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Data for "Serpentinite-rich Gouge in a Creeping Segment of the Bartlett Springs Fault, Northern California: Comparison with SAFOD and Implications for Seismic Hazard" Data for "Serpentinite-rich Gouge in a Creeping Segment of the Bartlett Springs Fault, Northern California: Comparison with SAFOD and Implications for Seismic Hazard"
This report presents the mineral chemistry dataset that was used in a published study of serpentinite-rich gouge from an actively creeping trace of the Bartlett Springs Fault in northern California. The fault gouge consists of porphyroclasts of antigorite serpentinite, talc, chlorite, and tremolite-actinolite in a sheared matrix of the same materials. The compositions of spinels in the
Spatial layers generated by the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) applied in Southern Wyoming Spatial layers generated by the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) applied in Southern Wyoming
All data layers included in this data release were created using the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) tool, which relies on spatial inputs on species distributions and likelihood of restoration success to select parcels for sagebrush restoration. The PReSET is a workflow that relies on the prioritizr package in program R to identify parcels for effective and
Changes in Organic Carbon Source and Storage with Sea Level Rise-Induced Transgression in a Chesapeake Bay Marsh Changes in Organic Carbon Source and Storage with Sea Level Rise-Induced Transgression in a Chesapeake Bay Marsh
This data set includes a variety of bulk organic carbon, lignin-phenol biomarker, and sedimentation rate data from a set of sites in the Blackwater Marsh in Chesapeake Bay. At each site, a short core was taken, and all data is organized according to depth below the marsh surface. Data includes: Bulk %OC, bulk %N, atomic carbon:nitrogen ratios, stable carbon isotopes (d13C) Lignin-phenol...
Waveform Data and Metadata used to National Earthquake Information Center Deep-Learning Models Waveform Data and Metadata used to National Earthquake Information Center Deep-Learning Models
These data were used to train the Machine Learning models supporting the USGS software release "NEIC Machine Learning Applications Software" (https://doi.org/10.5066/P9ICQPUR), and its companion publication in Seismological Research Letters "Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center" (https://doi.org/XXXXX)...
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa
This geodatabase reflects the U.S. Geological Survey's (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa...
Alpine Vegetation Trends in Glacier National Park, Montana 2019 Alpine Vegetation Trends in Glacier National Park, Montana 2019
This dataset is focused on alpine plant species presence/absence, species turnover, and trends in species abundance on four mountain summits in Glacier National Park, Montana, USA. This dataset consists of data from 2019, the fourth survey of the project which is on a five year survey schedule. Two summit sites were established in 2003 on Dancing Lady and Bison Mountain, east of the...