USGS · 70237316
No abstract available.
Explore connections, maps & timelines
Adrian Bender, Richard O. Lease, James V. Jones, Douglas C. Kreiner. 2020. Ancient rivers and critical minerals in eastern Alaska. https://doi.org/10.1029/2020eo147385
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Ecological forecasting offers critical insights for managing natural resources and safeguarding public well-being. Despite growing demand for these forecasts, progress is hindered by fragmented systems, redundant workflows, and limited interoperability. Drawing lessons from weather forecasting and recent successes like the NEON Ecological Forecasting Challenge, shared cyberinfrastructure is important for advancing ecological prediction. By adopting common standards, open-source tools, and scalable architectures, and fostering transdisciplinary collaboration, the ecological forecasting community can overcome technical and institutional barriers. Such investments could accelerate scientific understanding, improve forecast reliability, and empower decisionmakers to anticipate environmental change and respond effectively.
Coordinated work to compile existing data and apply models pairing physical understanding with machine learning could substantially improve streamflow predictions for little-known headwater basins.
The volumes and varieties of data coming from all types of scientific instrumentation around the globe and beyond are rapidly growing. To reuse and capitalize on these data effectively, scientists must be able to share and access them efficiently, which requires the data to be well managed. Many scientists intuit that research data management (RDM) done well does not mean using dusty USB drives or aging laptops for storage. Yet the path to strong data management is not always clear. How is RDM done? Who does it? For science to advance, we need to bolster cyberinfrastructure and human capacity to ensure that the data being collected are reusable by both humans and machines.