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Data-Theoretic: DT-BASE - Training Quality Causal Model
Citation:
Pence, Justin; Mohaghegh, Zahra (2017): Data-Theoretic: DT-BASE - Training Quality Causal Model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3357538_V2
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Dataset Description

Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level.

(M1, M2, and M3) Weight metadata are based on probability language from: Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report. The URL to the report: https://www.ipcc.ch/ipccreports/tar/vol4/english/index.htm. The language can be found firstly in “Summary for Policymakers” section, in PDF format.

Weight Metadata:
LowerBound_Probability, UpperBound_Probability, Qualitative Language
0.99, 1, Virtually Certain
0.9, 0.99, Very Likely
0.66, 0.9, Likely
0.33, 0.66, Medium Likelihood
0.1, 0.33, Unlikely
0.01, 0.1, Very Unlikely
0, 0.01, Extremely Unlikely
Keywords Data-Theoretic; Training; Organization; Probabilistic Risk Assessment; Training Quality; Causal Model; DT-BASE; Bayesian Belief Network; Bayesian Network; Theory-Building
License CC BY
Funder U.S. National Science Foundation (NSF) - Grant: 1535167
Corresponding Creator Justin Pence
Downloaded 89 times
Version DOI Comment Publication Date
3 10.13012/B2IDB-3357538_V3 Updated the model 2018-01-11
2 10.13012/B2IDB-3357538_V2 Corrected a typo in .csv file 2017-12-15
1 10.13012/B2IDB-3357538_V1 2017-12-13
57.8 KB File

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