Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Towards Ontology-Based Modeling of Technical Documentation and Operation Data of the Engineering Asset

Management of engineering assets within an organization is a crucial interdisciplinary approach that aims to optimize their performance and guarantee their overall effectiveness through efficient decision making. This task is always largely supported by official technical documentation created by the asset manufacturer which describes in detail the asset's functionality, architecture as well all necessary information such as testing, operation and maintenance specifications. This valuable information has to be accessible and comprehensive since it essentially dictates the target asset configuration, operation and maintenance modes and strategies in order to guarantee the asset's performance and availability. However, current technical documentations mainly consist of textual and graphical documents that often are poorly written and constructed, misleading, unavailable, outdated and are read by users with as little effort as possible. This results in a poor connection of the operating asset with its original documentation that prevents the asset from reaching its full potential. In this work, we will propose the new concept of using ontologies as a form of documentation that accompanies the official technical documentation and is created by the manufacturer and provided to the customer. We will also propose the use of a generic asset management ontology model that asset users can be based on to create their own domain asset ontology. Finally, we will demonstrate with examples how the use of the ontology and its reasoning mechanism is ideal to identify potential problems in the operation, configuration and maintenance of the asset, as well as potentially discover areas for improvement. We expect that eventually this concept will gather all the knowledge necessary to assist in the decision making process in order to improve the asset's availability, longevity and quality of operations