An artefact repository to support distributed software engineering

The Open Source Component Artefact Repository (OSCAR) system is a component of the GENESIS platform designed to non-invasively inter-operate with work-flow management systems, development tools and existing repository systems to support a distributed software engineering team working collaboratively. Every artefact possesses a collection of associated meta-data, both standard and domain-specific presented as an XML document. Within OSCAR, artefacts are made aware of changes to related artefacts using notifications, allowing them to modify their own meta-data actively in contrast to other software repositories where users must perform all and any modifications, however trivial. This recording of events, including user interactions provides a complete picture of an artefact's life from creation to (eventual) retirement with the intention of supporting collaboration both amongst the members of the software engineering team and agents acting on their behalf

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

Optimising metadata to make high-value content more accessible to Google users

Purpose: This paper shows how information in digital collections that have been catalogued using high-quality metadata can be retrieved more easily by users of search engines such as Google. Methodology/approach: The research and proposals described arose from an investigation into the observed phenomenon that pages from the Glasgow Digital Library (gdl.cdlr.strath.ac.uk) were regularly appearing near the top of Google search results shortly after publication, without any deliberate effort to achieve this. The reasons for this phenomenon are now well understood and are described in the second part of the paper. The first part provides context with a review of the impact of Google and a summary of recent initiatives by commercial publishers to make their content more visible to search engines. Findings/practical implications: The literature research provides firm evidence of a trend amongst publishers to ensure that their online content is indexed by Google, in recognition of its popularity with Internet users. The practical research demonstrates how search engine accessibility can be compatible with use of established collection management principles and high-quality metadata. Originality/value: The concept of data shoogling is introduced, involving some simple techniques for metadata optimisation. Details of its practical application are given, to illustrate how those working in academic, cultural and public-sector organisations could make their digital collections more easily accessible via search engines, without compromising any existing standards and practices

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page

Implementing an institutional repository for digital archive communities: Experiences from National Taiwan University

This paper presents an empirical study of expanding and extending DSpace digital repository system for an academic institution. National Taiwan University created a portal web site, the Digital Archives Resource Centre (DARC), to provide the digital archives communities with preservation base for their rich research materials, and to provide the public with a helpful information retrieval service. Several modifications and extensions for the DSpace system are made in order to integrate various database resources with different formats among university departments. In this paper, we will present our empirical case in which adjustments of the DSpace system are made in areas such as data submission, metadata mapping, digital rights management, user interface and visualization. Our discussion will be focused on the useful applications in which digital archives communities benefit from the adapted DSpace system in disseminating their contents and long-term preservation within NTU campus. We will also discuss the ways in which the users benefit from the new system for searching and using digital archives

The DSpace Institutional Digital Repository System: Current Functionality

In this paper we describe DSpaceâ¢, an open source system that acts as a repository for digital research and educational material produced by an organization or institution. DSpace was developed during two years’ collaboration between the Hewlett-Packard Company and MIT Libraries. The development team worked closely with MIT Libraries staff and early adopter faculty members to produce a âbreadth-first’ system, providing all of the basic features required by a digital repository service. As well as functioning as a live service, DSpace is intended as a base for extending repository functionality, particularly to address long-term preservation concerns. We describe the functionality of the current DSpace system, and briefly describe its technical architecture. We conclude with some remarks about the future development and operation of the DSpace system

RNeXML: a package for reading and writing richly annotated phylogenetic, character, and trait data in R

NeXML is a powerful and extensible exchange standard recently proposed to better meet the expanding needs for phylogenetic data and metadata sharing. Here we present the RNeXML package, which provides users of the R programming language with easy-to-use tools for reading and writing NeXML documents, including rich metadata, in a way that interfaces seamlessly with the extensive library of phylogenetic tools already available in the R ecosystem