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Inland Seas Education Association (ISEA), located in Suttons Bay, Michigan, has served as a unique Great Lakes education organization since 1989. Utilizing a 77’ schooner, thousands of children and adults have sailed Suttons Bay (Lake Michigan) to learn about plankton, fish, benthic organisms, and water quality in the Great Lakes; while also learning how to be sailors and stewards. With each sail, ISEA collects scientific field data on the bay--an impressive collection which now spans over 30 years. However, to this point, data have been housed in a mix of platforms, including Microsoft Access and Excel. These data are not easily accessible to anyone outside of ISEA’s internal staff (e.g., for use in the classroom, research, or to inform policy). With different formats, these data are also difficult to analyze as one cohesive set. Additionally, data are not currently collected with a formal monitoring plan in place, making it difficult to adapt the monitoring program into the future. Finally, with ISEA’s primary goal being education, the organization is questioning the value of continuing to collect and manage all of these data for years to come. ISEA recruited our student team to address these issues. Over the course of 16 months, we addressed ISEA’s science monitoring and data management challenges through literature comparisons, interviews, on-site observations, and discussions with potential end-users or similar organizations. In exploring solutions for ISEA, our objectives were to: 1. Explore elements of a science strategy to guide the shipboard monitoring program; 2. Consider alternatives for data storage, management, sharing, and use; and 3. Provide recommendations to ISEA for strengthening these two aspects of their program. We concluded that ISEA’s data has tremendous value for education, both for the general public and school-age children. It also has potential value for other audiences (i.e., researchers and policymakers) if some quality assurance and quality control measures are enacted. Thus, we created a set of recommendations to help ISEA strengthen their science monitoring program and make their data accessible to these external end users. Recommendations Use Data for Education ⚓ Prioritize uses of data for educational purposes (rather than for scientific purposes) when making decisions related to data collection and recording, data management and sharing, and what QA/QC and documentation practices to adopt. ⚓ Make raw data and data summaries accessible to teachers and students who have attended an ISEA program; as well as to other interested community members, researchers, and organizations. 2 Choosing a Database Management System (DBMS) ⚓ When deciding what DBMS to use, consider factors of accessibility, flexibility, and capabilities for data analysis and sharing. ⚓ Create a new project in the online citizen science platform FieldScope, to serve as the primary DBMS. Considerations for Future Science Monitoring Strategy ⚓ When deciding which parameters to record, consider ease of recording onboard as well as end-user needs and interests. [These must be balanced because sometimes data recording may conflict with the priority education program] ⚓ Create a formal process for cataloging newly discovered or uncommon organisms. Quality Assurance/Quality Control (QA/QC) ⚓ Simplify the approaches to recording on datasheets, including but not limited to reading and writing requirements. ⚓ Standardize data recording formats to avoid inconsistencies between volunteer instructors. ⚓ Add a data confidence checkbox to demonstrate trust in data quality, to establish the context for “data of known quality”, and to provide context for Secchi depths. ⚓ Digitize everything on the datasheets to reduce the need for the data entry inputter to make decisions out of context. ⚓ Create an education-grade QAPP to document data quality for end users. Parameter-Specific QA/QC Improvements ⚓ Record Secchi depth measurements in half-meter increments to align with scientific protocols. ⚓ Record only presence-absence data for plankton and benthos to reduce volunteer work. [Volunteer attention needs to be maximized for student learning, so recording needs to be simple enough not to interfere with education tasks] ⚓ Record only the first values at the water quality station to optimize data quality. [Since this station uses water that has sat onboard for a while, only the first data points are an accurate reflection of the water, given the methods ISEA uses.] ⚓ Continue current practices for recording fish and temperature data, as these are easy and accurately performed by volunteers. ⚓ Stop recording weather observations on datasheets but incorporate them into the data confidence checkbox to provide context for quality of other data. Stop recording microplastics data onboard. If a research partner is interested in analyzing the samples, providing technical support, and providing the data back to ISEA, then data can be stored in ISEA’s database. [Since identification onboard is challenging, ISEA should store research-grade data from the external researcher in ISEA’s microplastics database instead of the onboard data. This will be more accurate and valuable to share.]