Confluence Retirement

Due to the feedback from stakeholders and our commitment to not adversely impact USGS science activities that Confluence supports, we are extending the migration deadline to January 2023.

In an effort to consolidate USGS hosted Wikis, myUSGS’ Confluence service is targeted for retirement. 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 Thank you for your prompt attention to this matter.

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12:30 pm  Adjourn


  1. See blog post on this meeting here.
  2. Create, update, and share your staff profile, ORCid links, or other professional page with CDI. We hope to learn about overlapping interests, geographic groupings, and more. Use this form to participate.
  3. If you maintain or use APIs, please contribute information about it so we can compile and share links back to Science Information Services (SIS). Participate here.
  4. See more about the USGS Model Catalog here.
  5. Code for coupling hydrologic models is available on GitLab for USGS employees.
  6. Explore WHISpers.


  1. Welcome and Opening Announcements
    1. Leslie Hsu
      1. Create, update, and share your staff profile, ORCid links, or other professional page with CDI. We hope to learn about overlapping interests, geographic groupings, and more. Use this form to participate.
      2. If you maintain or use APIs, please contribute information about it so we can compile and share links back to Science Information Services (SIS). Participate here.
    2. Kevin Gallagher
      1. Kevin discussed EarthMAP and CDI Funded Projects. USGS employees can find more on EarthMAP here.
        1. See slides for diagram.
        2. CDI works together to further USGS goals, especially by connecting USGS science priorities with grassroots ideas from practitioners. Community input and lessons learned that are given back to the community contribute further to finetuning projects and furthering USGS priorities. Today's project presentations exemplify the goals of the EarthMAP framework.
      2. Kevin also talked about the forthcoming USGS Model Catalog. More information here.
        1. The goals of the model catalog are to increase discoverability and use of models and link models to relevant information. A USGS Leader's Blog is forthcoming on this project.
    3. Tim Quinn
      1. The EarthMAP webinar series on integrated modeling began since the last monthly meeting. USGS employees can find more here. The next webinar will be June 23rd.
  2. Working Group Announcements
    1. See slides for full details.
    2. All group pages: Collaboration Areas
    3.  Risk
      1. June 18th: Presentations of FY19 funded projects.
      2. August 11-13: Risk annual meeting, which will be held online.
      3. [9:26 AM] Ludwig, Kristin A
        June 18, 2020 @ 1p ET - Risk Community of Practice Monthly Meeting Final project presentations from the FY19 Risk RFP awardees! June topics include risk related to landslides, coastal change, invasive species, and contaminants. Please see the link below to join on 6/18 and/or sign up for our the Risk Community of Practice to receive future announcements. Sign up here.
    4. Software Development
      1. June 25: Serverless & AWS
    5. Semantic Web
      1. June 11: Discussion of a Forbes article on the Semantic Zoo.
    6. Metadata Reviewers
      1. July 6: Continue discussion of metadata for software & code with Eric Martinez.
    7. Tech Stack
      1. June 11: ESIP infrastructure and response to support the community during the pandemic.
      2. Last meeting explored CUAHSI HydroShare.
      3. Tech Dive Webinar Wiki
    8. Data Management
      1. July 13: Collections Management at USGS with Lindsay Powers and Brian Buczowski
    9. Open Innovation
      1. June 18: Tackling the Paperwork Reduction Act with Jeff Parillo and James Sayer.
      2. Subscribe to the OI Listserv to receive OI Community meeting invites
      3. Check out the Guide to the Paperwork Reduction Act in preparation for the next Ignite OI Forum on Thursday, June 18 at 2 PM ET (past meeting videos available on Stream Channel).
      4. FedGeoDay is this Thursday and Friday online (Registration free for GOV / MIL / EDU / NGO)
      5. Check out NASA’s Summer of Citizen Science Workshops (#NASACitSci2020)
      6. Join the Federal Crowdsourcing and Citizen Science (FedCCS) Community Listserv for meetings and recordings, by sending an email to:
      7. Nicole Herman-Mercer (USGS) - Indigenous Observation Network “Data Quality from a Community-Based, Water-Quality Monitoring Project in the Yukon River Basin”
      8. Citizen Science Association (CSA) Data and Metadata Working Group - Data Quality Resource Compendium for Citizen and Community Science
    10. eDNA
      1. First meeting will be next month - a getting-to-know-you meeting. Please sign up for the email list for updates.
    11. Usability
      1. July 15: resource review
      2. Town Hall Meeting for June 17 is cancelled due to speaker conflict.
    12. Data Visualization
      1. Sophie Hou, Alicia Rhoades, Amy Puls, Ellen Bechtel, and Dionne Zoanni are re-starting the data visualization group.
      2. July 2: 1.5 hour meeting for kickoff.
      3. Sign up for the listserv here.
  3. Extending ScienceBase for Disaster Risk Reduction - Joe Bard, USGS
    1. The Kilauea volcano eruption in 2018 underlines the need for near real-time data updates.
      1. Bard helped create lava flow update maps that would inform decision-making.
      2. Previous methods for sharing latest data updates was by attaching GIS data to an email - a flawed method.
    2. When you upload GIS data to ScienceBase, web services are automatically created. 
      1. Web services are a type of software that facilitates computer to computer interaction over a network. You don't need to download data to access it; and it can be easily accessed problematically.
      2. Data updates can be automatically propagated through web services, to avoid versioning issues.
      3. Use of ScienceBase during the Kilauea volcano crisis met unforeseen issues around reliability related to hosting on the USGS server and many simultaneous connections.
    3. This project explores a cloud-based instance of Geoserver on the AWS S3 platform wherein the user can publish geospatial services to this cloud-based server. This method is more resilient to simultaneous connections and takes into account load-balancing and auto-scaling.
      1. Opens the possibility of dedicated Geoserver instances based on a team's needs
      2. On ScienceBase beta, there is a function to publish data directly to S3.
      3. The related Python tool is available on Gitlab. Makes downloading data form the internet and posting on a ScienceBase item easy.
      4. Ex: pulling in data from ASH3D and adding to an SB item.
    4. Next steps
      1. Finalize cloud hosting service deployment and configuration settings.
      2. Check load balancing and quantify performance.
      3. Explore setting up multiple Geoserver instances in the cloud.
      4. Evaluate the load balancing technologies (e.g. Cloudfront).
      5. Ensure all workflows are possible using SB Python library.
  4. Coupling Hydrologic Models with Data Services in an Interoperable Modeling Framework - Rich McDonald, USGS
    1. Why?
      1. Integrated modeling is an important component of USGS priority plans.
      2. Goal is to use an existing and mature modeling framework to test a modeling sandbox.
    2. Modeling framework
      1. Frameworks are founded on the idea of component models. Model components encapsulate a set of related functions into a usable form.
      2. Going through a BMI means that no matter what the underlying language is, the model component it can be made available as a Python component.
    3. To test the CSDMS modeling framework, the team took the PRMS modeling system and broke it down into its 4 reservoirs (surface, soil, groundwater, and streamflow) and wrapped them in a BMI. They then re-coupled them back together. Expectation is that we could couple PRMS with other models.
    4. See recording for demonstration of the tool.
      1. Note the model run-time interaction
      2. Data services example
      3. PRMS is in Fortran and we're running it in Python
      4. Code is available on Gitlab.
    5. Challenges and Takeways
      1. It takes effort to wrap a a model with BMI.
      2. New coupling opportunities possible with MODFLOW and WEBMOD.
  5. Transforming Biosurveillance by Standardizing and Serving 40 Years of Wildlife Disease Data - Neil Baertlein, USGS
    1. Over 70% of emerging infectious diseases originate in wildlife.
    2. The National Wildlife Heath Center (NWHC) has been dedicated to wildlife health since 1975.
      1. Biosurveillance the NWHC has been involved in includes: lead poisoning, West Nile Virus, Avian influenza, white-nose syndrome, and SARS-CoV2.
    3. NWHC has become a major data repository for wildlife health data.
      1. WHISPers and LIMS (laboratory information management system)
        1. WHISPers is a portal for biosurveillance data. Events are lab verified and the portal allows collaboration with various state and federal partners, and some international partners, such as Canada.
    4. Problem
      1. Need to leverage data to inform public, scientists, and decision makers
        1. Data is not FAIR (findable, accessible, interoperable, and reusable)
        2. There are nearly 200 datasets in use
        3. Data is not easy to find
        4. Data exists in various file formats
        5. Limited or no documentations
    5. 5 step process to make data FAIR
      1. Definition: creating a definition. Created a template in which we capture users responsible for data, what the file type is, where they're stored. A data dictionary was also created.
      2. Classification: provide meaning and context for data. Classifies relationships with other datasets, other databases, and identifies inconsistencies in data.
      3. Prioritization: identify high-priority datasets. High-priority datasets are ones that we need to continue to use down the road or are high-impact. Non-priority datasets can be archived.
      4. Cleansing: Next step for high-priority datasets. Includes fixing data errors and standardizing data.
      5. Migrating: map and migrate the cleansed data.
    6. How?
      1. Dedicated staff - hired 2 student service contractors
      2. Conducted interviews with lab techs, scientists, and PIs
      3. Documented datasets
      4. Organized documentation
      5. Began cleansing data
    7. Where We are
      1. 130 datasets ready for archiving and cleansing
    8. Challenges
      1. Training of staff
      2. Work is labor intensive
      3. No documentation available for some datasets
      4. Databases build with limited knowledge of database design
      5. Variation between lab and individuals
    9. Takeaways
      1. Staff have been great to work with
      2. Data collectors need to think through data collection
        1. Be intentional with data collection process - is it FAIR? Are my methods standardized? How is the data collected now and how will it be collected in the future?
      3. Documentation is important
        1. Documenting the process and management of data collection and compilation.
      4. Data migration needs dedicated resources
    10. Next Steps
      1. Finish those 200 datasets, focusing on a few migrating a few key datasets first.