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Provenance is a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing. Provenance can be used to trace the source of ingredients in food industry, record the intermediate and final results of scientific workflows, and follow the origin of online posts and news. Data provenance becomes a significant metadata in validating the origin of information and asserting its quality. It has been adopted in many significant domains for different purposes, for example, validating experimental results in scientific workflow systems, improving health services in healthcare, trustworthiness of data from sensor networks, and managing access control systems. In particular, the provenance of information is crucial in deciding whether information is to be trusted. In order to establish trust and confirm the quality and originality using provenance information, it is important to share provenance among collaborators in scientific workow systems or publicly over open environments such as the Web. PROV is a recent W3C specification for sharing provenance over the Web. However, sharing provenance may expose confidential information such as the medical history of a patient, the identity of an agent, and the bank account details of an individual. It is therefore crucial for the sensitive and confidential information of provenance data to be obscured to enable trustworthiness prior to sharing provenance in open environments such as the Web. This research work describes PROV-GTS, a provenance graph transformation system, whose principled definition is based on PROV properties, and which seeks to preserve graph integrity by avoiding false independencies and false dependencies while obscuring restricted provenance information. The system is formally established as a template based framework and formalised using category theory concepts, such as functors, diagrams,and natural transformation. PROV-GTS is shown to preserve graph connectivity, to be terminating and to be confluent with deterministic and consistent rule applications. The performance evaluation based on real-world provenance graphs demonstrates a high connectivity preservation with minimum graph reduction.

Scientists within the materials engineering community produce a wide variety of data, ranging from large 3D volume densitometry files (voxel) generated by microfocus computer tomography (μCT) to simple text files containing results from tensile tests. Increasingly they need to share this data as part of international collaborations. The design of a suitable database schema and the architecture of a flexible system that can cope with the varying information is a continuing problem in the management of heterogeneous data. We discuss the issues with managing such varying data, and present a model flexible enough to meet users’ diverse requirements. Metadata is held using a database and its design allows users to control their own data structures. Data is held in a file store which, in combination with the metadata, gives huge flexibility and means the model is limited only by the file system. Using examples from materials engineering and medicine we illustrate how the model can be applied. We will also discuss how this data model can be used to support an institutional document repository, showing how data can be published in a remote data repository at the same time as a publication is deposited in a document repository. Finally, we present educational material used to introduce the concepts of research data management. Educating students about the challenges and opportunities of data management is a key part of the solution and helps the researchers of the future to start to think about the relevant issues early on in their careers. We have compiled a set of case studies to show the similarities and differences in data between disciplines, and produced documentation for students containing the case studies and an introduction to the data lifecycle and other data management practices. Managing in-use data and metadata is just as important to users as published data. Appropriate education of users and a data staging repository with a flexible and extensible data model supports this without precluding the ability to publish the data at a later date.

We aim to develop a formal framework to reason about provenance in distributed systems. We take as our starting point an extension of the asynchronous pi-calculus where processes are explicitly assigned principal identities. We enrich this basic setting with provenance annotated data, dynamic provenance tracking and dynamically checked trust policies. We give several examples to illustrate the use of the calculus in modelling systems where principals base their trust in the quality of data on the provenance information associated with it. We consider the role of provenance in the calculus by relating the provenance tracking semantics to a plain one in which no provenance tracking or checking takes place. We further substantiate this by studying bisimulation-based behavioural equivalences for the plain and annotated versions of the calculus and contrasting the discriminating power of the equivalences obtained in each case. We also give a more denotational take on the semantics of the provenance calculus and look at notions of well-formedness and soundness for the provenance tracking semantics. We consider two different extensions of the basic calculus. The first aims to alleviate the cost of run time provenance tracking and checking by defining a static type system which guarantees that in well-typed systems principals always receive data with provenance that matches their requirements. The second extension looks at the ramifications of provenance tracking on privacy and security policies and consists of extending the calculus with a notion we call filters. This gives principals the ability to assign different views of the provenance of a given value to different principals, thus allowing for the selective disclosure of provenance information. We study behavioural equivalences for this extension of the calculus, paying particular attention to the set of principals composing the observer and its role in discriminating between systems.

Data can be stored in the form of amorphous and crystalline marks within a chalcogenide thin film. Commonly Ge. Therefore Ga:La:S:Cu shows potential as a future electrical phase change data storage material.

The ability to store large volumes of data is increasing faster than processing power. Some existing data management methods often result in data loss, inaccessibility or repetition of simulations. We propose a framework which promotes collaboration and simplifies data management. In particular we have demonstrated the proposed framework in the scenario of handling large scale data generated from biomolecular simulations in a multiinstitutional global collaboration. The framework has extended the ability of the Python problem solving environment to manage data files and metadata associated with simulations. We provide a transparent and seamless environment for user submitted code to analyse and post-process data stored in the framework. Based on this scenario we have further enhanced and extended the framework to deal with the more generic case of enabling any existing data file to be post processed from any .NET enabled programming language.

A hypothesis of this dissertation is that utilising metadata linked into the ontological structure for the tasks of supporting information retrieval, offers an advantage over the traditional full text searching techniques. The "à la" (Associative Linking of Attributes) concept presented in this thesis demonstrates how a system for managing document collections in an organisational setting, could be enhanced to support the information delivery process. It can be considered as a pre-Semantic Web application, a recommender facility that provides assistance to locating items by utilising hypertextually linked metadata. The contribution of this thesis lies in three areas: •    Novel approach to metadata linking: using ontological zzstructures and ZigZag linking; •    Novel approach to using the metadata network for document retrieval: "Query by association"; •    Evaluation that compares the usage of the " à la" prototype system with the traditional approach. The "a la" prototype evaluation study compares free text querying using a full text search approach with the "a la" method for finding relevant documents. Precision (fraction of relevant search results) and serendipity (fraction of novelty or positively surprising items in a search result) were the chosen metrics. The study findings indicate that " à la" performs better in carrying out general-concept queries that fall within a project's knowledge domain. The evaluation concludes that " à la" can successfully enrich a typical document management system in order to improve the user's searching experience, by bringing not only the expected relevant search results, but also the items which are serendipitous. Moreover, browsing metadata connected in a Zigzag fashion can better aid understanding of the existing domain. This can in turn benefit knowledge engineers in their first step towards building a more formal organisational ontology. It is concluded that the " à la" method has the potential to be applicable towards establishing principles of the Semantic Web within a corporate environment.