Supporting data for "Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages" by Laugesen et al. (2024)

Results data and figures for the journal paper.Dataset includes compressed Python Pickle files containing Dictionaries of NumPy arrays and metadata for each figure. This contains input and output data. Also includes image files for each figure are also included in PNG, SVG, and PDF.<br>Journal paper introduces RUVPY, a Python software library which implements the Relative Utility Value (RUV) method. This is available at https://github.com/richardlaugesen/ruvpy and can now be used by researchers and industry to quantify the value of forecast for decision making (pip install ruvpy).References<i>Laugesen, Richard and Thyer, Mark and McInerney, David and Kavetski, Dmitri, Software Library to Quantify the Value of Forecasts for Decision-Making: Case Study on Sensitivity to Damages. </i><i>http://dx.doi.org/10.2139/ssrn.5001881</i><i> (under review)</i><i>Laugesen, R., Thyer, M., McInerney, D., &amp; Kavetski, D. (2023). Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts. Hydrology and Earth System Sciences, 27(4), 873-893. </i><i>https://doi.org/10.5194/hess-27-873-2023</i>Laugesen, R., Thyer, M., McInerney, D., &amp; Kavetski, D. (2024). RUVPY software library to quantify the value of forecasts for decision-making using RUV (v0.9.0). Zenodo. https://doi.org/10.5281/zenodo.13939199