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.