Metadata Quality for Digital Libraries

The quality of metadata in a digital library is an important factor in ensuring access for end-users. Several studies have tried to define quality frameworks and assess metadata but there is little user feedback about these in the literature. As collections grow in size maintaining quality through manual methods becomes increasingly difficult for repository managers. This research presents the design and implementation of a web-based metadata analysis tool for digital repositories. The tool is built as an extension to the Greenstone3 digital library software. We present examples of the tool in use on real-world data and provide feedback from repository managers. The evidence from our studies shows that automated quality analysis tools are useful and valued service for digital libraries

Archival Quality and Long-term Preservation: A Research Framework for Validating the Usefulness of Digital Surrogates

Digital archives accept and preserve digital content for long-term use. Increasingly, stakeholders are creating large-scale digital repositories to ingest surrogates of archival resources or digitized books whose intellectual value as surrogates may exceed that of the original sources themselves. Although digital repository developers have expended significant effort to establish the trustworthiness of repository procedures and infrastructures, relatively little attention has been paid to the quality and usefulness of the preserved content itself. In situations where digital content has been created by third party firms, content quality (or its absence in the form of unacceptable error) may directly influence repository trustworthiness. This article establishes a conceptual foundation for the association of archival quality and information quality research. It outlines a research project that is designed to develop and test measures of quality for digital content preserved in HathiTrust, a large scale preservation repository. The research establishes methods of measuring error in digitized books at the data, page, and volume level and applies the measures to statistically valid samples of digitized books, adjusting for inter-coder inconsistencies and the effects of sampling strategies. The research findings are then validated with users who conform to one of four use-case scenarios: reading online, printing on demand, data mining, and print collection management. The paper concludes with comments on the implications of assessing archival quality within a digital preservation context.Andrew W. Mellon FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86643/1/J20 Conway Archival Quality 2011.pd

Overview of bladder heating technology: matching capabilities with clinical requirements.

Moderate temperature hyperthermia (40-45°C for 1 h) is emerging as an effective treatment to enhance best available chemotherapy strategies for bladder cancer. A rapidly increasing number of clinical trials have investigated the feasibility and efficacy of treating bladder cancer with combined intravesical chemotherapy and moderate temperature hyperthermia. To date, most studies have concerned treatment of non-muscle-invasive bladder cancer (NMIBC) limited to the interior wall of the bladder. Following the promising results of initial clinical trials, investigators are now considering protocols for treatment of muscle-invasive bladder cancer (MIBC). This paper provides a brief overview of the devices and techniques used for heating bladder cancer. Systems are described for thermal conduction heating of the bladder wall via circulation of hot fluid, intravesical microwave antenna heating, capacitively coupled radio-frequency current heating, and radiofrequency phased array deep regional heating of the pelvis. Relative heating characteristics of the available technologies are compared based on published feasibility studies, and the systems correlated with clinical requirements for effective treatment of MIBC and NMIBC

Web Archive Services Framework for Tighter Integration Between the Past and Present Web

Web archives have contained the cultural history of the web for many years, but they still have a limited capability for access. Most of the web archiving research has focused on crawling and preservation activities, with little focus on the delivery methods. The current access methods are tightly coupled with web archive infrastructure, hard to replicate or integrate with other web archives, and do not cover all the users\u27 needs. In this dissertation, we focus on the access methods for archived web data to enable users, third-party developers, researchers, and others to gain knowledge from the web archives. We build ArcSys, a new service framework that extracts, preserves, and exposes APIs for the web archive corpus. The dissertation introduces a novel categorization technique to divide the archived corpus into four levels. For each level, we will propose suitable services and APIs that enable both users and third-party developers to build new interfaces. The first level is the content level that extracts the content from the archived web data. We develop ArcContent to expose the web archive content processed through various filters. The second level is the metadata level; we extract the metadata from the archived web data and make it available to users. We implement two services, ArcLink for temporal web graph and ArcThumb for optimizing the thumbnail creation in the web archives. The third level is the URI level that focuses on using the URI HTTP redirection status to enhance the user query. Finally, the highest level in the web archiving service framework pyramid is the archive level. In this level, we define the web archive by the characteristics of its corpus and building Web Archive Profiles. The profiles are used by the Memento Aggregator for query optimization

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Digital Preservation Services : State of the Art Analysis

Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe

Building information modeling – A game changer for interoperability and a chance for digital preservation of architectural data?

Digital data associated with the architectural design-andconstruction process is an essential resource alongside -and even past- the lifecycle of the construction object it describes. Despite this, digital architectural data remains to be largely neglected in digital preservation research – and vice versa, digital preservation is so far neglected in the design-and-construction process. In the last 5 years, Building Information Modeling (BIM) has seen a growing adoption in the architecture and construction domains, marking a large step towards much needed interoperability. The open standard IFC (Industry Foundation Classes) is one way in which data is exchanged in BIM processes. This paper presents a first digital preservation based look at BIM processes, highlighting the history and adoption of the methods as well as the open file format standard IFC (Industry Foundation Classes) as one way to store and preserve BIM data