DT-BASE - Training Quality Causal Model
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:
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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 |
805 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 |
Contact the Research Data Service for help interpreting this log.
| 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 |