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The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) Allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that operate on such a provenance model. (3) Define provenance in a(More)
This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed(More)
Scientific discourse occurs both in the academic literature and, increasingly, on the Web. What is discussed in the literature influences what is discussed on the web, and the reverse. However, the study of this discourse has largely been isolated based on medium either using bibliometrics for academic literature or webometrics for Web-based communication.(More)
NOMADS is a mobile agent system that supports strong mobility (i.e., the ability to capture and transfer the full execution state of mobile agents) and safe Java agent execution (i.e., the ability to control resources consumed by agents, facilitating guarantees of quality of service while protecting against denial of service attacks). The NOMADS environment(More)
SUMMARY The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to(More)
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that(More)
Very large scale computations are now becoming routinely used as a methodology to undertake scientific research. In this context, 'provenance systems' are regarded as the equivalent of the scientist's logbook for in silico experimentation: provenance captures the documentation of the process that led to some result. Using a protein compressibility analysis(More)
E-science experiments typically involve many distributed services maintained by different organisations. After an experiment has been executed, it is useful for a scientist to verify that the execution was performed correctly or is compatible with some existing experimental criteria or standards, not necessarily anticipated prior to execution. Scientists(More)
As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises:(More)