Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Towards a generic research data management infrastructure

Until recent years, a focused and centralized strategy for the annotation, storage and curation of research data is something that has not been widely considered within academic communities. The majority of research data sits, fragmented, on a variety of disk structures (Desktops, network & external hard drives) and is usually managed locally, with little interest paid to policies governing how it is backed up, disseminated and organized for short or long term reuse. Recognition of how current practices and infrastructure present a barrier to research, has resulted in several recent academic programmes which have focused on developing comprehensive frameworks for the management and curation of research data1-3. Many of these frameworks (such as the Archer suite of e- Research tools1), however, are large and complex, and have an overreliance on new and novel technologies making them unwieldy and difficult to support. The paper discusses the development of a simpler framework for the management of research data through its full lifecycle, allowing users to annotate and structure their research in a secure and backed up environment. The infrastructure is being developed as a pilot system and is expected to work with data from approximately a dozen researchers and manage several Terabytes of data. The technical work is a strand of the MaDAM (Manchester Data Management) project at The University of Manchester which is funded by the JISC Managing Research Data Programme.

A visual exploration workflow as enabler for the exploitation of Linked Open Data

Abstract. Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers to understand the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and ex-ploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the work-flow’s potential impact on data consumers which bridges the offer: published Linked Open Data; and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70 % of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to un-derstand the potential of Linked Open Data.