Representing Dataset Quality Metadata using Multi-Dimensional Views

Data quality is commonly defined as fitness for use. The problem of identifying quality of data is faced by many data consumers. Data publishers often do not have the means to identify quality problems in their data. To make the task for both stakeholders easier, we have developed the Dataset Quality Ontology (daQ). daQ is a core vocabulary for representing the results of quality benchmarking of a linked dataset. It represents quality metadata as multi-dimensional and statistical observations using the Data Cube vocabulary. Quality metadata are organised as a self-contained graph, which can, e.g., be embedded into linked open datasets. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as analysing data versions, browsing datasets by quality, and link identification. We finally discuss how data cube visualisation tools enable data publishers and consumers to analyse better the quality of their data.Comment: Preprint of a paper submitted to the forthcoming SEMANTiCS 2014, 4-5 September 2014, Leipzig, German

How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics

A large number of services for research data management strive to adhere to the FAIR guiding principles for scientific data management and stewardship. To evaluate these services and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing metric frameworks. The retrieval of spatially and temporally annotated images can exemplify such a use case. The prototypical implementation indicates that currently no research data repository achieves the full score. Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the images. These and other insights can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data.Comment: This is a preprint for a paper accepted for the 2018 IEEE conferenc

Reports Of Conferences, Institutes, And Seminars

This quarter\u27s column offers coverage of multiple sessions from the 2016 Electronic Resources & Libraries (ER&L) Conference, held April 3–6, 2016, in Austin, Texas. Topics in serials acquisitions dominate the column, including reports on altmetrics, cost per use, demand-driven acquisitions, and scholarly communications and the use of subscriptions agents; ERMS, access, and knowledgebases are also featured

EJT editorial standard for the semantic enhancement of specimen data in taxonomy literature

This paper describes a set of guidelines for the citation of zoological and botanical specimens in the European Journal of Taxonomy. The guidelines stipulate controlled vocabularies and precise formats for presenting the specimens examined within a taxonomic publication, which allow for the rich data associated with the primary research material to be harvested, distributed and interlinked online via international biodiversity data aggregators. Herein we explain how the EJT editorial standard was defined and how this initiative fits into the journal's project to semantically enhance its publications using the Plazi TaxPub DTD extension. By establishing a standardised format for the citation of taxonomic specimens, the journal intends to widen the distribution of and improve accessibility to the data it publishes. Authors who conform to these guidelines will benefit from higher visibility and new ways of visualising their work. In a wider context, we hope that other taxonomy journals will adopt this approach to their publications, adapting their working methods to enable domain-specific text mining to take place. If specimen data can be efficiently cited, harvested and linked to wider resources, we propose that there is also the potential to develop alternative metrics for assessing impact and productivity within the natural science

The challenge of the e-Agora metrics: the social construction of meaningful measurements

'How are we progressing towards achieving sustainable development in the EU's desired knowledge society?' Current lists of indicators, indices and assessment tools, which have been developed for measuring and displaying performance at different spatial levels, show that progress has been made. However, there are still a very large number of indicators, perhaps the majority, most specifically those which relate to social and political issues, that are difficult to capture. Issues such as intergenerational equity, aesthetics and governance come into this category. 'How is it possible to measure these and capture their full meaning and represent this back meaningfully to disparate groups of stakeholders in a society?' This paper will discuss these issues, highlighting the need for new methods and an alternative view of how to go about the capture and representation of the types of data with which we need to wor