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Experiments for 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'

<p>This artifact accompanies the CAV 2023&nbsp;paper with the title &#39;Efficient Sensitivity Analysis for Parametric Robust Markov Chains&#39;. The artifact contains a docker file, which can be unzipped and then loaded with:</p> <pre><code>docker load -i prmc_sensitivity_cav23_docker.tar</code></pre> <p>Depending on your permissions, you may need to run this command with sudo. Please refer to the ReadMe for more information.</p> <p>The source code of the docker container is available on GitHub:&nbsp;<a href="https://github.com/LAVA-LAB/prmc-sensitivity">https://github.com/LAVA-LAB/prmc-sensitivity</a>.</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
8
Reuse readiness
8
Engagement
4

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