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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 () 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.

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Abstract

This paper presents a new theory of metadata enriching and filtering. The theory emerged from a rigorous grounded theory data analysis of 57 in-depth interviews with metadata experts, library and information science researchers, librarians as well as academic library users (G. Alemu, A Theory of Digital Library Metadata: The Emergence of Enriching and Filtering, University of Portsmouth PhD thesis, Portsmouth, 2014). Partly due to the novelty of Web 2.0 approaches and mainly due to the absence of foundational theories to underpin socially constructed metadata approaches, this research adapted a social constructivist philosophical approach and a constructivist grounded theory method (K.?Charmaz, Constructing Grounded Theory: A Practical Guide through Qualitative Analysis, SAGE Publications, London, 2006). The theory espouses the importance of enriching information objects with descriptions pertaining to the about-ness of information objects. Such richness and diversity of descriptions, it is argued, could chiefly be achieved by involving users in the metadata creation process. The theory includes four overarching metadata principles - metadata enriching, linking, openness and filtering. The theory proposes a mixed metadata approach where metadata experts provide the requisite basic descriptive metadata, structure and interoperability (a priori metadata) while users continually enrich it with their own interpretations (post-hoc metadata). Enriched metadata is inter- and cross-linked (the principle of linking), made openly accessible (the principle of openness) and presented (the principle of filtering) according to user needs. It is argued that enriched, interlinked and open metadata effectively rises and scales to the challenges presented by the growing digital collections and changing user expectations. This metadata approach allows users to pro-actively engage in co-creating metadata, hence enhancing the findability, discoverability and subsequent usage of information resources. This paper concludes by indicating the current challenges and opportunities to implement the theory of metadata enriching and filtering.