Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

Railing for safety: Job demands, job control, and safety citzenship role definition.

This study investigated job demands and job control as predictors of safety citizenship role definition, that is, employees ’ role orientation toward improving workplace safety. Data from a survey of 334 trackside workers were framed in the context of R. A. Karasek’s (1979) job demands–control model. High job demands were negatively related to safety citizenship role definition, whereas high job control was positively related to this construct. Safety citizenship role definition of employees with high job control was buffered from the influence of high job demands, unlike that of employees with low job control, for whom high job demands were related to lower levels of the construct. Employees facing both high job demands and low job control were less likely than other employees to view improving safety as part of their role orientation

Smart Certification Of Mixed Criticality Systems

Abstract. High integrity applications, such as those performing safety or security critical functions, are usually built to conform to standards such RTCA DO-178B [1] or UK Def Stan 00-55 [2]. Typically such standards define ascending levels of criticality each of which requires a different and increasingly onerous level of verification. It is very common to find that real systems contain code of multiple criticality levels. For example, a critical control system may generate a non-critical usage log. Unless segregation can be demonstrated to a very high degree of confidence, there is usually no alternative to verifying all the software components to the standard required by the most critical element, leading to an increase in overall cost. This paper describes the novel use of static analysis to provide a robust segregation of differing criticality levels, thus allowing appropriate verification techniques to be applied at the subprogram level. We call this fine-grained matching of verification level to subprogram criticality smart certification.

Consistent and Coherent Treatment of Uncertainties and Dependencies in Fatigue Crack Growth Calculations Using Multi-Level Bayesian Models

These files have been uploaded as a reference for the following submission to the Elsevier Journal of Reliability Engineering and System Safety: 'Consistent and Coherent Treatment of Uncertainties and Dependencies in Fatigue Crack Growth Calculations Using Multi-Level Bayesian Models' by D.Di Francesco, M.H.Faber, M.Chryssanthopoulos and U.Bharadwaj

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes