Smart Lake Erie Citizen Science Summit
Dates: March 14-15, 2022
Steering Committee: Co-leads: Chris Winslow (Ohio State University) & Max Herzog (Cleveland Water Alliance); Committee: Andrea Paine (Huron River Watershed Council), Casey Godwin (UM CIGLR), Laura Johnson (Heidelberg University), & Steve Ruberg (NOAA-GLERL)
CIGLR Research Theme: Advanced Warning Systems
Description: The Smart Lake Erie Citizen Science Summit, and the collaborative planning and design efforts leading up to it, will provide a framework for legitimizing water quality data collected by citizen scientists across the Lake Erie region. This framework will not only enable more consistent and reliable baseline monitoring across Lake Erie and its watersheds, it will open up a valuable regional data source capable of baselining advanced observing systems, feeding modelling tools and serving as the basis for new data products that support sustainable water resource decision-making. CWA is looking to CIGLR to cover the expense of hosting the broad coalition of participants and provide the logistical support needed to organize and execute the proposed summit. The summit is designed to be held in-person at CIGLR-affiliated institutions in Ann Arbor, Michigan to facilitate organic collaboration and partnership building. The summit is intended to take place in November 2021 to enable adequate planning and coordination around the core framing of the standards as well as to allow for the resolution of the COVID-19 pandemic. If in-person meeting restrictions are still in place by the time of the summit, CWA is prepared to build on experience executing virtual events throughout 2020 and 2021 to pivot the summit to a digital platform. Otherwise, this summit will not significantly differ in format or cost from those previously funded through the SWG program.
Products: The primary product of the Smart Lake Erie Citizen Science Summit is a collaboratively developed set of standards for the collection, management, and use of Lake Erie citizen science data. These Smart Lake Standards will integrate existing frameworks and new innovations from Federal, Academic, and Private Sector research leaders, Citizen Science Groups and the State Environmental Agencies of Michigan, Ohio, and New York to ensure alignment across the Lake Erie water data ecosystem. The Standards will be framed as a set of operating procedures and technical benchmarks intended for use by community groups, government entities, and research institutions. Ultimately, the Standards are intended to enable current and future cross-sector partnerships to leverage credible citizen science in addressing our most pressing Lake Erie basin research and management needs. While the process will center the seven communities currently participating in the Initiative, the Standards will serve as a general framework that can be adopted groups across Lake Erie and the Great Lakes.
- This product will serve as the basis for a multi-year process of adoption and implementation by the stakeholders participating in the Summit. This coalition intends to leverage the Smart Lake Standards to integrate, harmonize and accelerate the collection and use of citizen science data across Lake Erie and its watersheds. The Standards will also be shared more broadly as a white paper which communicates the frameworks needed to enable usable and accessible citizen science data. A draft set of priorities for working groups, which correspond to potential white paper sections, can be found below. These foci may change as planning and program work develop leading up to the Summit.
- Target Properties and Contaminants: Establish criteria for selecting target properties and contaminants for consideration, such as regional and/or community-specific priorities, profile relevance and availability of traditional benchmark(s) against which findings can be compared.
- Database Needs & Structures: Identify likely needs and uses for aggregated data, such as: trends over time, geographically specific analysis, analysis by watershed profile or community organization, and raw data vs. derived data.
- Per-Field Season Protocols: Establish the baseline number of samples, schedule, determinants of sampling priorities, and reporting formats for a given field season to ensure sampling is statistically significant, relevant and comparable across participating communities.
- Per Sample Baseline Data: Establish ideal baseline logistical and water-property information that should be recorded when each sample is collected to maximize the utility of data collected.
- Lab Equipment and Methodology: Define baseline lab equipment and methodologies to be used across the network of communities for water-sample analysis (data credibility and inter-group comparison) and to enable benchmarking of results for validation of new technologies.
- Data Variables & Data Records: Establish definitions for individual data variables and data units to ensure data continuity between records across the network. Establish tagging, order and/or location of each variable to be sampled to ensure consistent aggregation into database fields.
- Data Aggregation & Access: Establish platform(s) and/or methods for aggregating data collected across communities and configure data access most to help optimize the research support available (as well as to develop the potential market for consumers of the network’s data).