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 these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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A cross‐disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

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 application, we derive a set of generic use cases for a provenance system. In order to support these, we address the following fundamental questions: what is provenance? how to record it? what is the performance impact for grid execution? what is the performance of reasoning? In doing so, we define a technologyindependent notion of provenance that captures interactions between components, internal component information and grouping of interactions, so as to allow us to analyse and reason about the execution of scientific processes. In order to support persistent provenance in heterogeneous applications, we introduce a separate provenance store, in which provenance documentation can be stored, archived and queried independently of the technology used to run the application. Through a series of practical tests, we evaluate the performance impact of such a provenance system. In summary, we demonstrate that provenance recording overhead of our prototype system remains under 10% of execution time, and we show that the recorded information successfully supports our use cases in a performant manner.

Abstract. Knowing the provenance of a data item helps in ascertaining its trustworthiness. Various approaches have been proposed to track or infer data provenance. However, these approaches either treat an executing program as a black-box, limiting the fidelity of the captured provenance, or require developers to modify the program to make it provenance-aware. In this paper, we introduce DataTracker, a new approach to capturing data provenance based on taint tracking, a technique widely used in the security and reverse engineering fields. Our system is able to identify data provenance relations through dynamic instrumentation of unmodified binaries, without requiring access to, or knowledge of, their source code. Hence, we can track provenance for a variety of well-known applications. Because DataTracker looks inside the executing program, it captures high-fidelity and accurate data provenance.

In this paper we propose a new provenance model which is tailored to a class of workflow-based applications. We motivate the approach with use cases from the astronomy community. We generalize the class of applications the approach is relevant to and propose a pipeline-centric provenance model. Finally, we evaluate the benefits in terms of storage needed by the approach when applied to an astronomy application. General TermsDocumentation, Performance

Abstract. The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, ecommerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustnessmeasure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.

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