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Science Analytics and Synthesis (SAS) Program

Science Analytics and Synthesis (SAS) emphasizes a science data lifecycle approach to Earth systems data and information. We strive to accelerate research and decision making through data science, information delivery, advanced computing, and biodiversity analytics.

News

USGS New Supercomputer Helps Scientists Wrangle Data

USGS New Supercomputer Helps Scientists Wrangle Data

Schedule of FY23 Working Groups

Schedule of FY23 Working Groups

ScienceBase Data Release Training for USGS Authors and Data Managers

ScienceBase Data Release Training for USGS Authors and Data Managers

Publications

U.S. Geological Survey data strategy 2023–33

The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publ
Authors
Vivian B. Hutchison, Thomas E. Burley, Kyle W. Blasch, Paul E. Exter, Gregory L. Gunther, Aaron J. Shipman, Courtney M. Kelley, Cheryl A. Morris

Are researchers citing their data? A case study from the U.S. Geological Survey

Data citation promotes accessibility and discoverability of data through measures carried out by researchers, publishers, repositories, and the scientific community. This paper examines how a data citation workflow has been implemented by the U.S. Geological Survey (USGS) by evaluating publication and data linkages. Two different methods were used to identify data citations: examining publication
Authors
Grace C. Donovan, Madison Langseth

State of the data: Assessing the FAIRness of USGS data

In response to recent shifts towards open science that emphasize transparency, reproducibility, and access to research data, the US Geological Survey (USGS) conducted a study to assess the degree to which USGS data assets meet the FAIR data principles (Findable, Accessible, Interoperable, and Reusable). The USGS designed and applied a methodology for quantitative analysis of FAIR characteristics.
Authors
Vivian B. Hutchison, Tamar Norkin, Lisa Zolly, Leslie Hsu