About us
The Sequencing and Bioinformatics Metadata project is funded through the Maine-eDNA RII Track-1 EPSCoR
grant supported by the National Science Foundation award #OIA-1849227. Maine-eDNA is a Maine coast-wide
research effort to determine the efficacy of ecosystem monitoring through environmental DNA (eDNA).
The metadata project is directly related to the goals of Theme 3: Macrosystem eDNA Integration, but also
involved in Theme 1: Sustainable Fisheries and Theme 2: Harmful and Shifting Species. Our efforts
contribute to the Data Management and Data Analysis Pipeline work groups.
The Sequencing and Bioinformatics Metadata project is a collaboration between the Coordinated Operating
Research Entities (CORE) environmental DNA (eDNA) Laboratory and the Data Management Team to facilitate
the development of a web application for the management and tracking of eDNA samples.
More about Maine-eDNA
This project was supported by
The Metadata Team
Insight into environmental DNA metadata was an effort that received input from multiple collaborators across and outside of the grant. The metadata team were the effort behind compiling, modeling, and implementing Maine-eDNA Metadata.
Melissa Kimble
Data Engineer, Graduate Student, University of Maine
Interdisciplinary work on spatial data science and engineering, particularly on integration of spatiotemporal data and uncertainty characterization.
Dr. Kate Beard-Tisdale
Professor of Spatial Computing, University of Maine
Modeling, analysis and visualization of spatio-temporal phenomena.
Dr. Benjamin King
Assistant Professor of Bioinformatics, University of Maine
Utilizing genomic and computational approaches to understand the mechanisms of stress responses.
Dr. Laura Jackson
Integrative Data Scientist, University of Maine
Large-scale computation approaches to understand trait evolution in fishes with a focus on questions at the intersection of evolutionary and developmental biology.
Dr. Chaofan Chen
Assistant Professor of Computer Science, University of Maine
Techniques to enhance the interpretability and transparency of machine learning models, especially deep learning models.
Geneva York
Environmental DNA Specialist, eDNA Laboratory, University of Maine
Using environmental DNA to detect invasive species in Maine waters.
Samantha Silverbrand
Fish Biologist, U.S. Fish and Wildlife
eDNA, genetics, and marine biology.
Keijaoh Campbell
Computer Technician II, University of Maine
Applying machine learning techniques to detect and identify objects on mobile platforms.
Steven Allers
Graduate Student, University of Maine
Bioinformatics, networks, and artificial intelligence.