42,028 research outputs found

    Moving data into and out of an institutional repository: Off the map and into the territory

    Get PDF
    Given the recent proliferation of institutional repositories, a key strategic question is how multiple institutions - repositories, archives, universities and others—can best work together to manage and preserve research data. In 2007, Green and Gutmann proposed how partnerships among social science researchers, institutional repositories and domain repositories should best work. This paper uses the Timescapes Archive—a new collection of qualitative longitudinal data— to examine the challenges of working across institutions in order to move data into and out of institutional repositories. The Timescapes Archive both tests and extends their framework by focusing on the specific case of qualitative longitudinal research and by highlighting researchers' roles across all phases of data preservation and sharing. Topics of metadata, ethical data sharing, and preservation are discussed in detail. What emerged from the work to date is the extremely complex nature of the coordination required among the agents; getting the timing right is both critical and difficult. Coordination among three agents is likely to be challenging under any circumstances and becomes more so when the trajectories of different life cycles, for research projects and for data sharing, are considered. Timescapes exposed some structural tensions that, although they can not be removed or eliminated, can be effectively managed

    Learning objects and learning designs: an integrated system for reusable, adaptive and shareable learning content

    Get PDF
    This paper proposes a system, the Smart Learning Design Framework, designed to support the development of pedagogically sound learning material within an integrated, platform-independent data structure. The system supports sharing, reuse and adaptation of learning material via a metadata-driven philosophy that enables the technicalities of the system to be imperceptible to the author and consumer. The system proposes the use of pedagogically focused metadata to support and guide the author and to adapt and deliver the content to the targeted consumer. A prototype of the proposed system, which provides proof of concept for the novel processes involved, has been developed. The paper describes the Smart Learning Design Framework and places it within the context of alternative learning object models and frameworks to highlight similarities, differences and advantages of the proposed system

    Pattern Reification as the Basis for Description-Driven Systems

    Full text link
    One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

    Lessons learned: structuring knowledge codification and abstraction to provide meaningful information for learning

    Get PDF
    Purpose – To increase the spread and reuse of lessons learned (LLs), the purpose of this paper is to develop a standardised information structure to facilitate concise capture of the critical elements needed to engage secondary learners and help them apply lessons to their contexts. Design/methodology/approach – Three workshops with industry practitioners, an analysis of over 60 actual lessons from private and public sector organisations and seven practitioner interviews provided evidence of actual practice. Design science was used to develop a repeatable/consistent information model of LL content/structure. Workshop analysis and theory provided the coding template. Situation theory and normative analysis were used to define the knowledge and rule logic to standardise fields. Findings – Comparing evidence from practice against theoretical prescriptions in the literature highlighted important enhancements to the standard LL model. These were a consistent/concise rule and context structure, appropriate emotional language, reuse and control criteria to ensure lessons were transferrable and reusable in new situations. Research limitations/implications – Findings are based on a limited sample. Long-term benefits of standardisation and use need further research. A larger sample/longitudinal usage study is planned. Practical implications – The implementation of the LL structure was well-received in one government user site and other industry user sites are pending. Practitioners validated the design logic for improving capture and reuse of lessons to render themeasily translatable to a new learner’s context. Originality/value – The new LL structure is uniquely grounded in user needs, developed from existing best practice and is an original application of normative and situation theory to provide consistent rule logic for context/content structure

    Designing Traceability into Big Data Systems

    Full text link
    Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems need to be designed from the outset to support usage of such Items across the spectrum of business use rather than from any specific application view. The design philosophy advocated in this paper is to drive the design process using a so-called description-driven approach which enriches models with meta-data and description and focuses the design process on Item re-use, thereby promoting traceability. Details are given of the description-driven design of big data systems at CERN, in health informatics and in business process management. Evidence is presented that the approach leads to design simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore July 2015. arXiv admin note: text overlap with arXiv:1402.5764, arXiv:1402.575
    • …
    corecore