Confluence Retirement

In an effort to consolidate USGS hosted Wikis, myUSGS’ Confluence service is scheduled for retirement on January 27th, 2023. The official USGS Wiki and collaboration space is now SharePoint. Please migrate existing spaces and content to the SharePoint platform and remove it from Confluence at your earliest convenience. If you need any additional information or have any concerns about this change, please contact myusgs@usgs.gov. Thank you for your prompt attention to this matter.

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Aparna Bamzai-Dodson, USGS, presented on the Climate Scenarios Toolbox (now renamed to the Climate Futures Toolbox!), an open-source tool that helps users formulate future climate scenarios for adaption planning. Scenario planning is a way to consider the range of possible outcomes by using projections based on climate data to develop usually 3-5 plausible divergent future scenarios (ex: hot and dry; moderately hot with no precipitation change; and warm and wet). Resource managers and scientists can use these scenarios to help predict the effects of climate change and attempt to select appropriate adaptation strategies. However, climate projection data can be difficult to work with in areas of discovery, access, and usage, involving multiple global climate model repositories, downscaling techniques, and file formats. The Climate Futures Toolbox aims to take the pain out of working with climate data. 

Collection of photos of people collaborating around climate scenarios and adaptation planning graphs.Image Added

The creators of the Toolbox wanted a way to make working with climate data easier by lowering the barrier to entry, automating common tasks, and reducing the potential for errors. The Climate Futures Toolbox uses a seamless R code workflow to ingest historic and projected climate data and generate summary statistics and customizable graphics. Users are able to contribute open code to the Toolbox as well, building on its existing capabilities and empowering a larger user community. The Climate Futures Toolbox was created in collaboration with University of Colorado-Boulder's Earth Lab, the U.S. Fish and Wildlife Service, and the National Park Service. 

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The first step required building a cloud infrastructure, with the help of Cloud Hosting Solutions (CHS). This involves connecting the edge computing (camera and raspberry PI footage of a stream) to an Amazon Web Services (AWS) IoT system and depositing camera footage and derivative products into a S3 bucket. The code for this portion of the product is in a preliminary GitLab repository that is projected to be published as a part of the long-term project. The team is also still working toward building the infrastructure through to data serving and dissemination. 

Workflow for getting streamflow data into a cloud computing system.Image Added

Other successes accomplished with this project so far include auto-provisioning (transmitting location and metadata) of edge computing systems to the cloud; establishing global actions (data is transmitted to the cloud framework and can roll into automated processing, like extracting video into frames); and building automated time-lapse computation. 

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In order to successfully create a centralized AIS viewer, community standards need to be established so that data can be checked for quality and validity, especially within the FAIR data framework (Findable, Accessible, Interoperable, and Reusable). To establish community standards and successfully integrate eDNA into NAS, the project team accomplished several objectives: 

List of steps taken in integrating eDNA data into the Nonindigenous Aquatic Species DatabaseImage Added

1) Experimental Standards 

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