10.13012/B2IDB-3357538_V2
Data-Theoretic: DT-BASE - Training Quality Causal Model
Pence, Justin
Justin
Pence
0000-0002-9065-2349
University of Illinois
Mohaghegh, Zahra
Zahra
Mohaghegh
University of Illinois
Pence, Justin
Justin
Pence
0000-0002-9065-2349
U.S. National Science Foundation (NSF)
https://doi.org/10.13039/100000001
1535167
University of Illinois at Urbana-Champaign
2017
Dataset
Data-Theoretic
Training
Organization
Probabilistic Risk Assessment
Training Quality
Causal Model
DT-BASE
Bayesian Belief Network
Bayesian Network
Theory-Building
2017-12-15
2
Creative Commons Attribution 4.0 International (CC BY 4.0)
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
10.13012/B2IDB-3357538_V1
10.13012/B2IDB-3357538_V3