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

Proposal of a mobile learning preferences model

A model consisting of five dimensions of mobile learning preferences – location, level of distractions, time of day, level of motivation and available time – is proposed in this paper. The aim of the model is to potentially increase the learning effectiveness of individuals or groups by appropriately matching and allocating mobile learning materials/applications according to each learner’s type. Examples are given. Our current research investigations relating to this model are described

Insight Report: Contexts of use of Learning Design Support Tools

Prepared by Dr Rachel A Harris, Inspire Research Ltd with input from Seb Schmoller, Association for Learning Technology (ALT) December 2011 for The Learning Design Support Environment Project

Supporting emerging researchers in data management and curation

While scholarly publishing remains the key means for determining researchers’ impact, international funding body requirements and government recommendations relating to research data management (RDM), sharing and preservation mean that the underlying research data are becoming increasingly valuable in their own right. This is true not only for researchers in the sciences but also in the humanities and creative arts as well. The ability to exploit their own - and others’ - data is emerging as a crucial skill for researchers across all disciplines. However, despite Generation Y researchers being ‘highly competent and ubiquitous users of information technologies generally’ they appears to be a widespread lack of understanding and uncertainty about open access and self-archived resources (Jisc study, 2012). This chapter will consider the potential support that academic librarians might provide to support Generation Y researchers in this shifting research data landscape and examine the role of the library as part of institutional infrastructure. The changing landscape will impact research libraries most keenly over the next few years as they work to develop infrastructure and support systems to identify and maintain access to a diverse array of research data outputs. However, the data that are being produced through research are no different to those being produced by artists, politicians and the general public. In this respect, all libraries - whether they be academic, national, or local - will need to be gearing up to ensure they are able to accept and provide access to an ever increasing range of complex digital objects

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering