Code & Data
Supporting Open and Reproducible Science
The MaRRS lab has a strong committment to open and reproducible science. As such, our academic publications are often accompanied by open access data and the computer code needed to replicate the published work, and to help apply it to other problems. The MaRRS lab also leads efforts to produce advanced but accessible tools for a variety of remote sensing applications (e.g. OysterNet, see https://github.com/patrickcgray/oyster_net and MorphoMetriX, see https://joss.theoj.org/papers/10.21105/joss.01825). This also includes the development of browser-based systems that are designed to provide wide access of tools to researchers and educators (e.g. The WhaleScale, see https://whalescale-one.vercel.app/). Finally, our commitment to diversity and inclusivity also focuses on capacity building as part of our field campaigns where possible. For example, we have worked with First Nations in Canada and the Aleut community in Alaska to build local capacity for applying UAS in natural resource management. See below for links to data and code repositories generated by MaRRS lab members.
On Github
Check out contributions of MaRRS lab members on Github
https://doi.org/10.7924/r4cv4jq6jData and scripts from: A semi-automated method for estimating Adelie penguin colony abundance from a fusion of multispectral and thermal imagery collected with Unoccupied Aircraft Systems
Citation:
Bird, C. N., Duprey, A. H., Dale, J., & Johnston, D. W. (2020). Data and scripts from: A semi-automated method for estimating Adelie penguin colony abundance from a fusion of multispectral and thermal imagery collected with Unoccupied Aircraft Systems. Duke Research Data Repository. https://doi.org/10.7924/r4cv4jq6j
https://doi.org/10.7924/r4cv4gx0hData from: Deep learning for coastal resource conservation: automating detection of shellfish reefs
Data Citation:
Data from: Ridge, J.T., Gray, P.C., Windle, A.E. and Johnston, D.W. (2020), Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sens Ecol Conserv. doi:10.1002/rse2.134. Duke Data Repository. https://doi.org/10.7924/r4cv4gx0h
https://doi.org/10.7924/r4w956k1qData from: Modeling salt marsh vegetation height using Unoccupied Aircraft Systems and Structure from Motion.
Data Citation:
DiGiacomo, A. E., Bird, C. N., Pan, V. G., Dobroski, K., Atkins-Davis, C., Johnston, D. W., Ridge, J. T. (2020). Data from: Modeling salt marsh vegetation height using Unoccupied Aircraft Systems and Structure from Motion. Duke Research Data Repository. https://doi.org/10.7924/r4w956k1q
https://doi.org/10.7924/r4wd3x28bData and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS
Citation:
Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A. S., Johnston, D. W. (2020). Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Duke Research Data Repository. https://doi.org/10.7924/r4wd3x28b
https://doi.org/10.7924/r4dn45v9gData from: A deep learning approach for large-scale detection and enumeration of Black-browed Albatrosses and Southern Rockhopper Penguins in drone imagery
Data Citation:
Hayes, M. C., Gray, P. C., Harris, G., Sedgwick, W. C., Crawford, V. D., Chazal, N., Crofts, S., & Johnston, D. W. (2020). Data from: A deep learning approach for large-scale detection and enumeration of Black-browed Albatrosses and Southern Rockhopper Penguins in drone imagery. Duke Research Data Repository. https://doi.org/10.7924/r4dn45v9g
https://doi.org/10.5061/dryad.7482v2nData from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
Data Citation:
Gray, Patrick C., K. C. Bierlich, S. A. Mantell, A. S. Friedlaender, J. A. Goldbogen and
D. W. Johnston. 2019. Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry, Dryad, Dataset, https://doi.org/10.5061/dryad.7482v2n
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