Calling data scientists, developers, designers and storytellers for a six week-long, social-good hackathon supporting Chesapeake Bay conservation.

In celebration of the 50-year anniversary of Earth Day, Booz Allen Hamilton, Inc. (Booz Allen) has partnered with the Chesapeake Monitoring Cooperative (CMC) to host a social good hackathon to explore data monitoring the health of the watershed. From August 3rd to September 14th, we invite data scientists, developers, designers, problem solvers, and storytellers to explore CMC’s data and create solutions to address some of their core challenges. By exploring the Bay’s chemical and wildlife observations and their intersection with other geospatial, temporal, environmental and demographic data, we hope to further empower decision-making and inspire action for watershed restoration.

Why This Issue? Why Now?

The CMC is leading the first federally-supported citizen science water quality data initiative and supported by four partner organizations, the Alliance for the Chesapeake Bay (Alliance), the Izaak Walton League of America (IWLA), Dickinson College's Alliance for Aquatic Resource Monitoring (ALLARM), and the University of Maryland Center for Environmental Science (UMCES).  The CMC is at its next big steppingstone where it seeks assistance in demonstrating and leveraging the value of the data they have collected (made available in the Chesapeake Data Explorer). To support these goals, we are asking hackathon participants to develop their own open source solutions in one of the four hackathon tracks:

Challenge 1: Develop a Restoration Case Study (Time Series / Visualization Challenge)

Using data from CMC, the Chesapeake Bay Program, and supplementary sources, tell a story about how water quality has changed over time in the Chesapeake Bay watershed.

Challenge 2: Identify Data Gaps (GIS / Mapping Challenge)

With one or more visualizations, demonstrate how and where CMC’s data fills the gaps in the Chesapeake Bay Program’s database, and where data gaps in the watershed still exist. Provide an analysis that recommends locations and parameters that CMC should prioritize for new data collection, and why.

Challenge 3: Model Water Pollution (Machine Learning / Modeling Challenge)

CMC’s water quality indicators can be linked to types of pollution in the tributaries of the Chesapeake Bay. Analyze potential causes and/or build a predictive model for pollution in a section of the Bay using CMC, CBP, and supplementary geospatial datasets.

Challenge 4: Design a Water Quality Report Card (Design / Web Dev Challenge)

Design a local or regional version of the Chesapeake Bay report card that ties water quality to the values of communities living in the watershed.  

Timeline:

Submission Period: August 3, 2020 (12:00pm ET) - September 14, 2020 (11:45pm ET)

Judging Period: September 15, 2020 (9:00am ET) - September 25, 2020 (5:00pm ET)

Winners Announced: Winners will ne notified by September 25, and winners given the opportunity to present their solutions on Tuesday, September 29. (Details on winner presentations coming.)

In addition, Hack the Bay has teamed up with additional social-good focused speakers that will be sharing their knowledge of water and environmental justice. Keep an eye out for those announcements as the Hackathon begins! 

View full rules

Prizes

Present to Industry Leaders (4)

While there will be winners announced per each track, we encourage you to share resources, information and ideas freely with others to support each other and expand your understanding of the Bay. The most compelling entries in each track will be invited to virtually present their findings to a distinguished panel from the Chesapeake Monitoring Cooperative, Booz Allen Hamilton, and other experts in the field. Winners will also be recognized in post-event press and publications about the event.

Devpost Achievements

Submitting to this hackathon could earn you:

Eligibility

The Hackathon is open only to individuals who are 18 years of age or older as of August 3rd, 2020. If you wish to register as a team, we ask that you work with a maximum of 6 eligible individuals per team. You are not eligible to participate or receive any Prize in the Hackathon if you are a resident of a country designated by the United States Treasury’s Office of Foreign Assets Control (see http://www.treasury.gov/resource-center/sanctions/SDN-List/Pages/default.aspx for additional information). By engaging in this Hackathon and accepting any prize awarded to you, you are affirmatively representing to Booz Allen Hamilton that you are not prohibited from accepting the prize offered to you by any laws, regulations, policies or other rules applicable to you (“Applicable Rules”), including Applicable Rules imposed upon you by any employer or by any government entity with which you or a family member are affiliated. Booz Allen reserves the right to require you to demonstrate, to Booz Allen’s satisfaction, that no Applicable Rules prohibit you from accepting any prize and to withhold any prize offered under this competition if you cannot reasonably demonstrate your eligibility in a timely fashion. 

Requirements

Build an open-sourced solution in at least one of the four hackathon tracks: 

  • Developing a Restoration Case Study
  • Identifying Data Gaps
  • Modeling Water Pollution
  • Designing a Water Quality Report Card

 

 

After you and your team have explored the challenge, enter the following to be competitive for the finalist selection:

• 3-6 slides outlining the solution and key findings

• Link to a public GitHub repository or shared folder with your hackathon solution. This should include code, visualizations, maps, or any design files/templates developed for your project.

• Short 5-minute video explaining your results and process (hosted on YouTube or Vimeo). 

Entries should cite data sources, borrowed code, models, or references to papers or other research.

Judges

Liz Chudoba
Alliance for the Bay

Emily Bialowas

Emily Bialowas
Izaak Walton League

Peter Tango

Peter Tango
Chesapeake Bay Program Office

Caroline Donovan

Caroline Donovan
University of Maryland Center for Environmental Science

Judging Criteria

  • Robustness
    How well did the team understand and address the challenge (in the context of CMC’s goals and Chesapeake Bay issues?) How thoroughly did the team explain their methods (citing references and leveraging best practices in analysis/design?)
  • Scalability
    Can the work be reproduced (did the entry include relevant code/templates for developing the solution?) Can the solution be scaled over the Chesapeake Bay watershed?
  • Creativity
    How well did the solution address the challenge in a unique and compelling way?

themes

  • Machine Learning/ AI
  • Communication
  • Social Good