Data Envelopment Analysis (Dea) approach In efficiency transport manufacturing industry in Malaysia

The objective of this study was to measure of technical efficiency, transport manufacturing industry in Malaysia score using the data envelopment analysis (DEA) from 2005 to 2010. The efficiency score analysis used only two inputs, i.e., capital and labor and one output i.e., total of sales. The results shown that the average efficiency score of the Banker, Charnes, Cooper - Variable Returns to Scale (BCC-VRS) model is higher than the Charnes, Cooper, Rhodes - Constant Return to Scale (CCR-CRS) model. Based on the BCC-VRS model, the average efficiency score was at a moderate level and only four sub-industry that recorded an average efficiency score more than 0.50 percent during the period study. The implication of this result suggests that the transport manufacturing industry needs to increase investment, especially in human capital such as employee training, increase communication expenses such as ICT and carry out joint ventures as well as research and development activities to enhance industry efficiency

Stewardship of the evolving scholarly record: from the invisible hand to conscious coordination

The scholarly record is increasingly digital and networked, while at the same time expanding in both the volume and diversity of the material it contains. The long-term future of the scholarly record cannot be effectively secured with traditional stewardship models developed for print materials. This report describes the key features of future stewardship models adapted to the characteristics of a digital, networked scholarly record, and discusses some practical implications of implementing these models. Key highlights include: As the scholarly record continues to evolve, conscious coordination will become an important organizing principle for stewardship models. Past stewardship models were built on an "invisible hand" approach that relied on the uncoordinated, institution-scale efforts of individual academic libraries acting autonomously to maintain local collections. Future stewardship of the evolving scholarly record requires conscious coordination of context, commitments, specialization, and reciprocity. With conscious coordination, local stewardship efforts leverage scale by collecting more of less. Keys to conscious coordination include right-scaling consolidation, cooperation, and community mix. Reducing transaction costs and building trust facilitate conscious coordination. Incentives to participate in cooperative stewardship activities should be linked to broader institutional priorities. The long-term future of the scholarly record in its fullest expression cannot be effectively secured with stewardship strategies designed for print materials. The features of the evolving scholarly record suggest that traditional stewardship strategies, built on an “invisible hand” approach that relies on the uncoordinated, institution-scale efforts of individual academic libraries acting autonomously to maintain local collections, is no longer suitable for collecting, organizing, making available, and preserving the outputs of scholarly inquiry. As the scholarly record continues to evolve, conscious coordination will become an important organizing principle for stewardship models. Conscious coordination calls for stewardship strategies that incorporate a broader awareness of the system-wide stewardship context; declarations of explicit commitments around portions of the local collection; formal divisions of labor within cooperative arrangements; and robust networks for reciprocal access. Stewardship strategies based on conscious coordination involve an acceleration of an already perceptible transition away from relatively autonomous local collections to ones built on networks of cooperation across many organizations, within and outside the traditional cultural heritage community

Metadata quality : implications for library and information science professionals

Purpose - In contrast with recent studies noting the necessity of library and information science (LIS) skills in digital library and repository projects, this study aims to examine the impact of metadata quality requirements on how LIS professionals apply their skills outside a library setting. Design/methodology/approach - The paper reviews the concept of metadata quality and examines the implications of this for LIS professionals by reviewing the differences between the context of the library community and other relevant communities of practice. Findings - The paper argues that, although much needed, LIS skills require contextualisation before application outside library settings. Research limitations/implications - Many of the new opportunities for and settings of LIS skills are immature - consequently this analysis may date as the context of these settings mature. Current trends, however, suggest that it will not. Practical implications - Training in LIS skills should take account of how they might apply differently outside libraries. Librarians co-operating with colleagues outside the library should appreciate the potential metadata 'compromises' they might have to make and why they are necessary. Originality/value - The paper provides food for thought on the increasing number of LIS professionals working outside library settings

Extracting, Transforming and Archiving Scientific Data

It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 201

Enforcing public data archiving policies in academic publishing: A study of ecology journals

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.Comment: 35 pages, 1 figure, 1 tabl

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

A Dual Model of Open Source License Growth

Every open source project needs to decide on an open source license. This decision is of high economic relevance: Just which license is the best one to help the project grow and attract a community? The most common question is: Should the project choose a restrictive (reciprocal) license or a more permissive one? As an important step towards answering this question, this paper analyses actual license choice and correlated project growth from ten years of open source projects. It provides closed analytical models and finds that around 2001 a reversal in license choice occurred from restrictive towards permissive licenses.Comment: 14 pages, 6 figure