PReServ: Provenance Recording for Services

The importance of understanding the process by which a result was generated in an experiment is fundamental to science. Without such information, other scientists cannot replicate, validate, or duplicate an experiment. We define provenance as the process that led to a result. With large scale in-silico experiments, it becomes increasingly difficult for scientists to record process documentation that can be used to retrieve the provenance of a result. Provenance Recording for Services (PReServ) is a software package that allows developers to integrate process documentation recording into their applications. PReServ has been used by several applications and its performance has been benchmarked

Architecture for Provenance Systems

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 in this document may be applied to different technologies

An Architecture for Provenance Systems

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 in this document may be applied to different technologies

Provenance-based validation of E-science experiments

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. Scientists may also want to review and verify experiments performed by their colleagues. There are no existing frameworks for validating such experiments in today's e-Science systems. Users therefore have to rely on error checking performed by the services, or adopt other ad hoc methods. This paper introduces a platform-independent framework for validating workflow executions. The validation relies on reasoning over the documented provenance of experiment results and semantic descriptions of services advertised in a registry. This validation process ensures experiments are performed correctly, and thus results generated are meaningful. The framework is tested in a bioinformatics application that performs protein compressibility analysis

{\em Ab initio} Quantum Monte Carlo simulation of the warm dense electron gas in the thermodynamic limit

We perform \emph{ab initio} quantum Monte Carlo (QMC) simulations of the warm dense uniform electron gas in the thermodynamic limit. By combining QMC data with linear response theory we are able to remove finite-size errors from the potential energy over the entire warm dense regime, overcoming the deficiencies of the existing finite-size corrections by Brown \emph{et al.}~[PRL \textbf{110}, 146405 (2013)]. Extensive new QMC results for up to $N=1000$ electrons enable us to compute the potential energy $V$ and the exchange-correlation free energy $F_{xc}$ of the macroscopic electron gas with an unprecedented accuracy of $|\Delta V|/|V|, |\Delta F_{xc}|/|F|_{xc} \sim 10^{-3}$. A comparison of our new data to the recent parametrization of $F_{xc}$ by Karasiev {\em et al.} [PRL {\bf 112}, 076403 (2014)] reveals significant deviations to the latter