ScienceBase
Use ScienceBase and the ScienceBase Data Release process to publicly release your USGS data
Use ScienceBase and the ScienceBase Data Release process to publicly release your USGS data
Data Management Website
Visit the Data Management Website to learn more about best practices and policy requirements
Visit the Data Management Website to learn more about best practices and policy requirements
Community for Data Integration
Join the Community for Data Integration to connect with your community on data topics
Join the Community for Data Integration to connect with your community on data topics
Science Data Management
The Science Analytics and Synthesis (SAS) Science Data Management Branch (SDM) provides Bureau-wide leadership to optimize and share USGS science data management practices and workflows. SDM designs, supports, and builds community around enterprise tools and services that align with the USGS Science Data Lifecycle to ensure scientific data are fully described, preserved, and publicly accessible.
Discover SDM Tools
Sources/Usage: Public Domain. View Media Details
- Use ScienceBase and the ScienceBase Data Release process to publicly release your USGS data
- Visit the Data Management Website to learn more about best practices and policy requirements.
- Discover data through the Science Data Catalog.
- Create metadata with the Metadata Wizard.
- Browse USGS and external models in the USGS Model Catalog.
- Learn more about our other tools.
Engage with the SDM Team
Sources/Usage: Public Domain. View Media Details
- Join the Community for Data Integration to connect with your community on data topics.
- Consult with SDM to plan large data releases, work with USGS enterprise systems, develop complex workflows, and more.
- Collaborate with us on Bureau data integration strategies and information services.
News
April ScienceBase Data Release Training for USGS Authors and Data Managers
April ScienceBase Data Release Training for USGS Authors and Data Managers
January/February ScienceBase Data Release Training for USGS Authors and Data Managers
January/February ScienceBase Data Release Training for USGS Authors and Data Managers
October ScienceBase Data Release Training for USGS Authors and Data Managers
October ScienceBase Data Release Training for USGS Authors and Data Managers
Publications
Community for data integration 2019 project report
The U.S. Geological Survey Community for Data Integration annually supports small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 14 projects supported in fiscal year 2019 and outlines their goals, activities, and accomplishments. Proposals in 2019 were encouraged to addre
By
Ecosystems Mission Area, Water Resources Mission Area, Science Synthesis, Analysis and Research Program, Science Analytics and Synthesis (SAS) Program, Volcano Hazards Program, Community for Data Integration (CDI), Geology, Geophysics, and Geochemistry Science Center, Geosciences and Environmental Change Science Center, National Wildlife Health Center, Oklahoma-Texas Water Science Center, Southwest Biological Science Center, Volcano Science Center, Western Geographic Science Center, Wetland and Aquatic Research Center , Woods Hole Coastal and Marine Science Center, Science Data Management
Community for data integration 2020 annual report
The Community for Data Integration is a community of practice whose purpose is to advance the data integration capabilities of the U.S. Geological Survey. In fiscal year 2020, the Community for Data Integration held 11 monthly forums, facilitated 13 collaboration areas, and supported 13 projects. The activities supported the broad U.S. Geological Survey priority of producing building blocks for do
Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data
In 2016, an interdisciplinary, international group of 53 scientists introduced a framework named “the FAIR Principles” for addressing 21st century scientific data challenges. The FAIR Principles are increasingly used as a guide for producing digital scientific products that are findable, accessible, interoperable, and reusable (FAIR), especially to enable use of such products in automated systems.