Data management

About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.

Time and space efficient algorithms for two-party authenticated data structures

Abstract: Authentication is increasingly relevant to data management Data is being outsourced to untrusted servers and clients want to securely update and query their data For example, in database outsourcing, a client's database is stored and maintained by an untrusted server Also, in simple storage systems, clients can store very large amounts of data but at the same time, they want to assure their integrity when they retrieve them In this paper, we present a model and protocol for two-party authentication of data structures Namely, a client outsources its data structure and verifies that the answers to the queries have not been tampered with We provide efficient algorithms to securely outsource a skip list with logarithmic time overhead at the server and client and logarithmic communication cost, thus providing an efficient authentication primitive for outsourced data, both structured (eg, relational databases) and semi-structured (eg, XML documents) In our technique, the client stores only a constant amount of space, which is optimal Our two-party authentication framework can be deployed on top of existing storage applications, thus providing an efficient authentication service Finally, we present experimental results that demonstrate the practical efficiency and scalability of our scheme

Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions

Abstract: As long as empirical research has existed, researchers have been doing “data management” in one form or another. However, funding agency mandates for doing formal data management are relatively recent, and academic libraries’ involvement has been concentrated mainly in the last few years. The National Science Foundation implemented a new mandate in January 2011, requiring researchers to include a data management plan with their proposals for funding. This has prompted many academic libraries to work more actively than before in data management, and science librarians in particular are uniquely poised to step into new roles to meet researchers’ data management needs. This study, a survey of science librarians at institutions affiliated with the Association of Research Libraries, investigates science librarians’ awareness of and involvement in institutional repositories, data repositories, and data management support services at their institutions. The study also explores the roles and responsibilities, both new and traditional, that science librarians have assumed related to data management, and the skills that science librarians believe are necessary to meet the demands of data management work. The results reveal themes of both uncertainty and optimism—uncertainty about the roles of librarians, libraries, and other campus entities; uncertainty about the skills that will be required; but also optimism about applying “traditional” librarian skills to this emerging field of academic librarianship.

SEEK: a systems biology data and model management platform

Abstract: Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

Total Quality Management: An Introductory Text

Abstract: Quality Management - The Basics. The Basics of Management. Quality Management Concepts. Total Quality Management. Quality Management Writers. The Three Views of Quality. The Five Functions of Total Quality Management. Quality Planning. Quality of Design. Organisational Structure and Design. Leadership. Group Dynamics. Human Resource Management. Cultural and Change and Management. Control. Statistical Process Control. Quality Economics. Quality Standards. Integrated Total Quality Management - The Future.