The European digital information landscape: how can LIBER contribute?

This paper looks at a snapshot of the current state of digitisation in the information landscape. It then looks at what LIBER can contribute to that landscape through portal development, funding, identifying and documenting best practice, lobbying at a European level, and managing the transition from paper to digital delivery, including the issue of digital preservation. The paper ends by trying to identify how the user will use the digitised resources which are increasingly being made available by libraries

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Preliminary results in tag disambiguation using DBpedia

The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area

Incremental scoping study and implementation plan

This report is one of the first deliverables from the Incremental project, which seeks to investigate and improve the research data management infrastructure at the universities of Glasgow and Cambridge and to learn lessons and develop resources of value to other institutions. Coming at the end of the project’s scoping study, this report identifies the key themes and issues that emerged and proposes a set of activities to address those needs. As its name suggests, Incremental deliberately adopts a stepped, pragmatic approach to supporting research data management. It recognises that solutions will vary across different departmental and institutional contexts; and that top-down, policy-driven or centralised solutions are unlikely to prove as effective as practical support delivered in a clear and timely manner where the benefits can be clearly understood and will justify any effort or resources required. The findings of the scoping study have confirmed the value of this approach and the main recommendations of this report are concerned with the development and delivery of suitable resources. Although some differences were observed between disciplines, these seemed to be as much a feature of different organisational cultures as the nature of the research being undertaken. Our study found that there were many common issues across the groups and that the responses to these issues need not be highly technical or expensive to implement. What is required is that these resources employ jargon-free language and use examples of relevance to researchers and that they can be accessed easily at the point of need. There are resources already available (institutionally and externally) that can address researchers’ data management needs but these are not being fully exploited. So in many cases Incremental will be enabling efficient and contextualised access, or tailoring resources to specific environments, rather than developing resources from scratch. While Incremental will concentrate on developing, repurposing and leveraging practical resources to support researchers in their management of data, it recognises that this will be best achieved within a supportive institutional context (both in terms of policy and provision). The need for institutional support is especially evident when long-term preservation and data sharing are considered – these activities are clearly more effective and sustainable if addressed at more aggregated levels (e.g. repositories) rather than left to individual researchers or groups. So in addition to its work in developing resources, the Incremental project will seek to inform the development of a more comprehensive data management infrastructure at each institution. In Cambridge, this will be connected with the library’s CUPID project (Cambridge University Preservation Development) and at Glasgow in conjunction with the Digital Preservation Advisory Board

Building a Texas Water Data Hub as a model for National Water Data Infrastructure

Findable, Accessible, Interoperable, Reusable (FAIR) water data is a buzz word in the industry for good reason (Making Public Data FAIR, 2018). Without these objectives, poor water data across the United States will continue to cripple the ability of decision makers to manage and develop sustainable practices (Building Data Infrastructure, 2022). In an effort to implement these standards, this research was designed to first understand the past and current water data infrastructure throughout Texas and the United States and then create a findable, accessable, interoperable, and reusable (FAIR) water data hub (Making Public Data FAIR, 2018). An important part of this effort was to include stakeholders and decision makers from the water data industry. This research provides an overview of initial data collection and follows with detailed updates to water categorization and standards, stakeholder engagement and best practices, the creation of the Texas Water Data Hub and finally, recommendations to expand this state effort to a national level. The discussion speaks to the complexity of organizing water data due to the overlapping needs of such a project. The conclusion points out the additional challenges to scaling up these procedures to a national level. All of these efforts are part of building FAIR water data and is essential in our increasing need and care of water

requirements and use cases

In this report, we introduce our initial vision of the Corporate Semantic Web as the next step in the broad field of Semantic Web research. We identify requirements of the corporate environment and gaps between current approaches to tackle problems facing ontology engineering, semantic collaboration, and semantic search. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. We propose an initial layout for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

The use of tagging to support the authoring of personalisable learning content

This research project is interested in the area of personalised and adaptable learning and in particular within an e-learning context. Brusilovsky (1996) and Santally (2005) stress the importance of adaptive systems within e-learning. Karagiannikis and Sampson et al. (2004) argue that personalised learning systems can be defined by their capability to adapt automatically to the changing attitudes of the “learning experience” which can, in turn, be defined by the individual learner characteristics, for example the type of learning material. The project evolved to cover areas including personalised learning, e-learning environments, authoring tools, tagging, learning objects, learning theories and learning styles. The main focus at the start of the project was to provide a personalised and adaptable learning environment for students based on their learning style. During the research, this led to a specific interest about how an academic can create, tag and author learning objects to provide the capability of personalised adaptable e-learning for a learner. Research undertaken was designed to gain an understanding of personalised and adaptive learning techniques, e-learning tools and learning styles. Important findings of this research showed that e-learning platforms do not offer much in the way of a personalised learning experience for a learner. Additionally, the research showed that general adaptive systems and adaptive systems incorporating learning styles are not commonly used or available due to issues with flexibility, reuse and integration. The concept of tagging was investigated during the research and it was found that tagging is underused within e-learning, although the research shows that it could be a good ‘fit’ within e-learning. This therefore led to the decision to create a general purpose discriminatory tagging methodology to allow authors to tag learning objects for personalisation and reuse. The main focus for the evaluation of this tagging methodology was the authoring side of the tagging. It was found that other research projects have evaluated the personalisation of learning content based on a learner’s learning style (see Graf and Kinshuk (2007)). It was therefore felt that there was a sufficient body of existing evidence in this area whereas there was limited research available on the authoring side. The evaluation of the discriminatory tagging methodology demonstrated that the methodology could allow for any discrimination between learners to be used. The example demonstrated within this thesis includes discriminating according to a learner’s learning style and accessibility type. This type of platform independent flexible discriminatory methodology does not exist within current e-learning platforms or other e-learning systems. Therefore, the main contribution of this thesis is therefore a platform independent general-purpose discriminatory tagging methodology

Bibliographic Control in the Digital Ecosystem

With the contributions of international experts, the book aims to explore the new boundaries of universal bibliographic control. Bibliographic control is radically changing because the bibliographic universe is radically changing: resources, agents, technologies, standards and practices. Among the main topics addressed: library cooperation networks; legal deposit; national bibliographies; new tools and standards (IFLA LRM, RDA, BIBFRAME); authority control and new alliances (Wikidata, Wikibase, Identifiers); new ways of indexing resources (artificial intelligence); institutional repositories; new book supply chain; “discoverability” in the IIIF digital ecosystem; role of thesauri and ontologies in the digital ecosystem; bibliographic control and search engines