DT-BASE - Training Quality Causal Model
Cite this dataset:
Pence, Justin; Mohaghegh, Zahra (2018): DT-BASE - Training Quality Causal Model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3357538_V3
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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 language can be found in the “Summary for Policymakers” section, in the 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
Subject Physical Sciences
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 602 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
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RelatedMaterial update: {"uri"=>[nil, "10.1016/j.ress.2018.12.020"], "uri_type"=>[nil, "DOI"], "datacite_list"=>[nil, "IsSupplementTo"], "note"=>[nil, ""], "feature"=>[nil, false]} 2024-02-01T18:56:22Z
RelatedMaterial update: {"citation"=>["", "Pence, J. (Creator), Mohaghegh, Z. (Creator) (Dec 15 2017). Data-Theoretic: DT-BASE - Training Quality Causal Model. University of Illinois Urbana-Champaign. 10.13012/B2IDB-3357538_V2"], "note"=>[nil, ""], "feature"=>[nil, false]} 2024-02-01T18:56:22Z
RelatedMaterial create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1016/j.ress.2018.12.020", "uri"=>nil, "uri_type"=>nil, "citation"=>"Pence, J., Sakurahara, T., Zhu, X., Mohaghegh, Z., Ertem, M., Ostroff, C., & Kee, E. (2019). Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis. Reliability Engineering & System Safety, 185, 240-260.", "dataset_id"=>408, "selected_type"=>"Article", "datacite_list"=>nil} 2019-06-20T15:27:32Z
Creator update: {"identifier"=>["", "0000-0003-1062-3853"]} 2019-06-20T15:27:32Z
Dataset update: {"subject"=>[nil, "Physical Sciences"]} 2018-02-26T15:43:41Z