Analysis and synthesis of metadata goals for scientific data

Abstract

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 (>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.

Data and metadata research in the social sciences and humanities: An approach from data repositories in these disciplines

“…We agree with the argument given by Willis, Greenberg and White (2012) that creators of metadata schemes are more likely to change and adapt or enhance an existing scheme than to create a new one. Once the DC has been installed, it is easier to adapt than it is to adopt a new schema.…”

Research data and metadata curation as institutional issues

“…Data curation infrastructures, for example, developed in the context of cross-disciplinary organizations should either be discipline agnostic, or highly customizable (Mayernik, Choudhury et al, 2012;Witt, 2012). Third, studies of data and metadata practices and standards suggest that many relevant issues are independent of discipline (Mayernik, 2011;Willis, Greenberg, & White, 2012), and that intradisciplinary data practices can be as variable as interdisciplinary practices, as illustrated in studies of astronomy (Norris et al, 2006;Wynholds, Fearon, Borgman, & Traweek, 2011), ecology , and the quantitative social sciences (Pienta, Alter, & Lyle, 2010).…”

An Information Theory Calculator for Understanding Information and Library Science Applications

“…These labels assist in the retrieval of desired information, with library material often being accessed through searches of subject headings in an online public access catalog. Databases may be searched for metadata or attribute labels, and the metadata may provide further information about various aspects of the data in the database, such as its origin [6].…”