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Lukens, Sarah committed Apr 26, 2024
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- 📘 [ISO 15926-4:2019](https://www.iso.org/standard/73830.html) - Reference data for recording information about process plants.
- 📘 [ISO 14224:2016](https://www.iso.org/obp/ui/#iso:std:iso:14224:en) - Bases for the collection of reliability and maintenance (RM) data for equipment in oil and gas industry.

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#### Ontologies
- 📃 [ROMAIN](https://content.iospress.com/articles/applied-ontology/ao190208) - Maintenance management ontology.
- 📃 [An ontology for maintenance activities and its application to data quality.](https://content.iospress.com/articles/semantic-web/sw233299) - Paper describing an ontology for maintenance activities, which is extensible across industries.
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## TLP Research

### Human Centric TLP Research
#### Human Centric TLP Research

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## Human Centric TLP Research
- 📃 [Avoiding Past Mistakes in Unethical Human Subjects Research: Moving From Artificial Intelligence Principles to Practice](https://ieeexplore.ieee.org/abstract/document/10417823) - a brief history of the events that prompted today's ethical codes to protect human rights in research, key ethical principles from the [Belmont Report](https://web.archive.org/web/20240402052729/https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html), and how these ethical principles apply to AI research.
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- 📃 [Human Centric Technology Insertion](https://www.nist.gov/publications/where-do-we-start-guidance-technology-implementation-maintenance-management-0) - Provides a comprehensive look at technology insertion in the maintenance management workflow using well established error mitigation frameworks.
- 📃 [MWO Categorization Errors](https://www.nist.gov/publications/categorization-errors-data-entry-maintenance-work-orders) - Analyzes human error in recording maintenance work order data into CMM systems.

### TLP Representations & Embeddings
#### TLP Representations & Embeddings
- 📃 [Condition Monitoring Annotations with BERT and Technical Language Substitution](https://www.papers.phmsociety.org/index.php/phme/article/view/3356) - Substituting out-of-vocabulary technical words with natural language terms can improve language model performance on other language domains for pre-trained BERT.


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