Dashboard Prototype Workspace

This repository contains a large majority of our team's draft work and early approaches to developing a dashboard.

You'll notice there is a collection of folders found under the Scripts folder, these detail early iterations of our workflow, as well as, ideas for future scaffolding. To see the final version of the dashboard contents workflow, please visit our official repository: https://github.com/GaiaFuture/CLM5_PPE_Emulator

File Structure and Contents

Prototype
│   README.md
│   gitignore
│   Data_Generation_Data_Repo.ipynb (notebook with the script to generate archived data)     
│
└───Results 
    │   contains files associated with the data our team generated. This includes
    |   preprocessed data, the predictions generated by the emulator,
    |   and our data visualizations
│
└───Environment_Details 
    │   contains files with the necessary components to create the same virtual
    |   environment we used to test our dashboard
│
└───Script 
    │   contains files used by the students to generate the functionalities that
    |   will appear in the final product. This is broken up into notebooks outlining
    |   the interactivity and design of the dashboard in the Dashboard folder, and
    |   adding the final capabilities to the emulator in the Emulator folder. 

Data Access

Archived Data can be accessed here: https://datadryad.org/stash/dataset/doi:10.5061/dryad.vq83bk422

References

1.7. Gaussian Processes. (n.d.). Scikit-Learn. Retrieved March 15, 2024, from https://scikit-learn/stable/modules/gaussian_process.html

A Visual Exploration of Gaussian Processes, Görtler, et al., Distill, 2019. https://distill.pub/2019/visual-exploration-gaussian-processes/

Good enough practices to manage your project data—Managing your data. (n.d.). Retrieved March 15, 2024, from https://ucsb-library-research-data-services.github.io/project-data-management/manage.html

Gaussian Processes for Machine Learning. Rasmussen, C. E., & Williams, C. K. I. (2006). MIT Press. Accessed 3 March 2024.

Gaussian Processes in Machine Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds) Advanced Lectures on Machine Learning. Rasmussen, C.E. (2004). ML 2003. Lecture Notes in Computer Science(), vol 3176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28650-9_4

Improved generalized Fourier amplitude sensitivity test (FAST) for model assessment. Fang, S., Gertner, G.Z., Shinkareva, S. et al. | Statistics and Computing 13, 221–226 (2003). https://doi.org/10.1023/A:1024266632666

Pickle - Python Object Serialization. Python Documentation, docs.python.org/3/library/pickle.html. Accessed 13 May 2024.

Sklearn.Gaussian_process.Kernels.DotProduct. Scikit, scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html. Accessed 13 May 2024.

Sklearn.Metrics.Pairwise.Polynomial_kernel. Scikit, scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html. Accessed 13 May 2024.

Authors and Contributors

Heather Childers (Project Manager)
email: [email protected]
Sofia Ingersoll (Communications Manger)
email: [email protected]