Development of a pilot data management infrastructure for biomedical researchers at University of Manchester – approach, findings, challenges and outlook of the MaDAM Project

Management and curation of digital data has been becoming ever more important in a higher education and research environment characterised by large and complex data, demand for more interdisciplinary and collaborative work, extended funder requirements and use of e-infrastructures to facilitate new research methods and paradigms. This paper presents the approach, technical infrastructure, findings, challenges and outlook (including future development within the successor project, MiSS) of the ‘MaDAM: Pilot data management infrastructure for biomedical researchers at University of Manchester’ project funded under the infrastructure strand of the JISC Managing Research Data (JISCMRD) programme. MaDAM developed a pilot research data management solution at the University of Manchester based on biomedical researchers’ requirements, which includes technical and governance components with the flexibility to meet future needs across multiple research groups and disciplines

An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Data Management Practices at UVM: Findings from the Quantitative Phase

This article reports on the second quantitative phase of an exploratory sequential mixed methods research design focused on researcher data management practices and related institutional support and services. The study aims to understand data management activities and challenges of faculty at the University of Vermont (UVM), a higher research activity Research University, in order to develop appropriate research data services (RDS). Data was collected via a survey, built on themes from the initial qualitative data analysis from the first phase of this study. The survey was distributed to a nonrandom census sample of full-time UVM faculty and researchers (P=1,190); from this population, a total of 319 participants completed the survey for a 26.8% response rate. The survey collected information on five dimensions of data management: data management activities; data management plans; data management challenges; data management support; and attitudes and behaviors towards data management planning. Frequencies, cross tabulations, and chi-square tests of independence were calculated using demographic variables including gender, rank, college, and discipline. Results from the analysis provide a snapshot of research data management activities at UVM, including types of data collected, use of metadata, short- and long-term storage of data, and data sharing practices. The survey identified key challenges to data management, including data description (metadata) and sharing data with others; this latter challenge is particular impacted by confidentiality issues and lack of time, personnel, and infrastructure to make data available. Faculty also provided insight to RDS that they think UVM should support, as well as RDS they were personally interested in. Data from this study will be integrated with data from the first qualitative phase of the research project and analyzed for meta-inferences to help determine future research data services at UVM

Requirements analysis in the implementation of integrated PLM, ERP and CAD systems

Product Lifecycle Management (PLM) system implementation is a major investment when the technology is used in manufacturing companies. This paper provides an analysis of the requirements for the integration of PLM systems with Enterprise Resource Planning (ERP) systems incorporating the design aspects of Computer Aided Design and Manufacturing (CAD/CAM) within the product development process. PLM implementation deals with various existing product data and information generated over years both from CAD and ERP systems. Data integration is very challenging and has important impact on future decisions while creating new processes. The information management plays very important role not only in PLM implementation but also in the way this will be used in future production. Therefore it is very important to analyse how product information is transferred to PLM system. It also need to be investigated that what, when and how the data will flow from and to PLM systems

Participatory sensing as an enabler for self-organisation in future cellular networks

In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells

Animal health and welfare in organic livestock production in Europe– current state and future challenges

Existing data on animal health and welfare in organic livestock production systems in the European Community countries are reviewed in the light of the demands and challenges of the recently implemented EU regulation on organic livestock production. The main conclusions and recommendations of a three-year networking project on organic livestock production are summarised and the future challenges to organic livestock production in terms of welfare and health management are discussed. The authors conclude that, whilst the available data are limited and the implementation of the EC regulation is relatively recent, there is little evidence to suggest that organic livestock management causes major threats to animal health and welfare in comparison with conventional systems. There are, however, some well-identified areas, like parasite control and balanced ration formulation, where efforts are needed to find solutions that meet with organic standard requirements and guarantee high levels of health and welfare. It is suggested that, whilst organic standards offer an implicit framework for animal health and welfare management, there is a need to solve apparent conflicts between the organic farming objectives in regard to environment, public health, farmer income and animal health and welfare. The key challenges for the future of organic livestock production in Europe are related to the feasibility of implementing improved husbandry inputs and the development of evidence-based decision support systems for health and feeding management. (HOVI, M., A. SUNDRUM and S. M. THAMSBORG (2003): Animal health and welfare in organic livestock production in Europe – current state and future challenges. Livestock production science 80, 41-53.

Data Management: The Data Life Cycle

The scientific site of Kiel provides support for projects with data management requirements due to project size or interdisciplinarity. This infrastructure is the Kiel Data Management Infrastructure (KDMI) and was initially created by SFB574, SFB754, Excellence Cluster ‘The Future Ocean‘ and the GEOMAR | Helmholtz Centre for Ocean Research Kiel. To achieve public data availability from publicly funded projects by the end of the funding period it is necessary to initiate the data acquisition during the data creation process. Accordingly the KDMI uses a three level approach to achieve this goal in SOPRAN III. Data management is al- ready involvedin the planning of expeditions or experiments. The resulting schedule for data files can be used by the project coordinationto increase the efficeny of data sharing within SOPRAN III. The scientists provide files with basic metainformation, which are available within the virtual research environment as soon as possible to all project members. Final data will be transferred to PANGAEA for long term availability when the data are analysed and interpreted in a scientific publication or by the end of SOPRAN III. The Kiel Data Management Team offers a portal for all GEOMAR and University Kiel marine projects. This portal will be used in SOPRAN III in combination with PANGAEA to fulfill the project’s data management requirements and to enhance the data sharing within SOPRAN III by a file sharing environment for preliminary data not yet suitable for PANGAEA

Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

The future of big data in facilities management : opportunities and challenges

Purpose: This paper explores the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the AEC industry with an emphasis on Facilities Management (FM). Design/methodology/approach: Following a comprehensive literature review, the Big Data concept was investigated through two scoping workshops with industry experts and academics. Findings: The value in data analytics and Big Data is perceived by the industry; yet the industry needs guidance and leadership. Also, the industry recognises the imbalance between data capturing and data analytics. Large IT vendors’ developing AEC industry focused analytics solutions and better interoperability among different vendors are needed. The general concerns for Big Data analytics mostly apply to the AEC industry as well. Additionally however, the industry suffers from a structural fragmentation for data integration with many small-sized companies operating in its supply chains. This paper also identifies a number of drivers, challenges and way-forwards that calls for future actions for Big Data in FM in the AEC industry. Originality/value: The nature of data in the business world has dramatically changed over the past 20 years. This phenomenon is often broadly dubbed as “Big Data” with its distinctive characteristics, opportunities and challenges. Some industries have already started to effectively exploit “Big Data” in their business operations. However, despite many perceived benefits, the AEC industry has been slow in discussing and adopting the Big Data concept. Empirical research efforts investigating Big Data for the AEC industry are also scarce. This paper aims at outlining the benefits, challenges and future directions (what to do) for Big Data in the AEC industry with a FM focus