Workshop

Co-producing adaptable applications and trainings using USGS data to enhance data literacy in K-12 education.

Expanding documentation, accessibility, and flexibility of a powerful AI tool for wildlife aerial photo-survey processing

Teach USGS personnel Git within code.usgs.gov to develop, track, share, and publish their code.

This project will share techniques developed in two AI/ML competitions run in Fall 2022, Automated Map Georeferencing, and Automated Map Feature Extraction with USGS stakeholders. We will develop a strategy to operationalize successful approaches, benefiting any activity that uses legacy map data.

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.

We propose a summit for USGS and Tribal nation partners to share critical knowledge of past and future flooding. The summit will build on USGS research investigating changing flood conditions, improve access to flood information, and aid in building climate resilient communities.

FAIR is an international set of principles for improving the findability, accessibility, interoperability, and reusability of research data and other digital products. The PIs for this CDI project planned and hosted a workshop of USGS data stakeholders, data professionals, and managers of USGS data systems from across the Bureau’s Mission Areas. Workshop participants shared case studies that foste

Deep learning is a computer analysis technique inspired by the human brain’s ability to learn. It involves several layers of artificial neural networks to learn and subsequently recognize patterns in data, forming the basis of many state-of-the-art applications from self-driving cars to drug discovery and cancer detection. Deep neural networks are capable of learning many levels of abstraction, an

Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. To address this need, the USA National Phenology Network (USA-NPN) created a suite of “Pheno Forecast” ma

USGS will soon transition to the international metadata standards known collectively as ISO 19115. The open-ended nature of ISO benefits with much greater flexibility and vocabulary to describe research products. However, that flexibility means few constraints that can guide authors and ensure standardized, robust documentation across the bureau. This project proposed that the USGS data community

An additional product not originally on the funded activities list was organized for the newly-formed Science Data Coordinator Network. The Network invited 2 representatives from each of the USGS geographic areas, and several mission areas, to participate in a Data Management Workshop. The Workshop was organized by Viv Hutchison and taught by Tom Chatfield of the Bureau of Land Management. The cou