Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft

Some challenges facing scientific software developers: The case of molecular biology

It is apparent that the challenges facing scientific software developers are quite different from those facing their commercial counterparts. Among these differences are the challenges posed by the complex and uncertain nature of the science. There is also the fact that many scientists have experience of developing their own software, albeit in a very restricted setting, leading them to have unrealistic expectations about software development in a different setting. In this paper, we explore the challenges facing scientific software developers focusing especially on molecular biology. We claim that the nature and practice of molecular biology is quite different from that of the physical sciences and pose different problems to software developers. We do not claim that this paper is the last word on the topic but hope that it serves as the inspiration for further debate

Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic

Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic (March 25 - 27, 2018 -- The University of New Hampshire) paired two of NSF\u27s 10 Big Ideas: Navigating the New Arctic and Growing Convergence Research at NSF. During this event, participants assessed economic, environmental, and social impacts of Arctic change on New England and established convergence research initiatives to prepare for, adapt to, and respond to these effects. Shipping routes through an ice-free Northwest Passage in combination with modifications to ocean circulation and regional climate patterns linked to Arctic ice melt will affect trade, fisheries, tourism, coastal ecology, air and water quality, animal migration, and demographics not only in the Arctic but also in lower latitude coastal regions such as New England. With profound changes on the horizon, this is a critical opportunity for New England to prepare for uncertain yet inevitable economic and environmental impacts of Arctic change

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment