A Provenance-Based Infrastructure to Support the Life Cycle of Executable Papers

AbstractAs publishers establish a greater online presence as well as infrastructure to support the distribution of more varied information, the idea of an executable paper that enables greater interaction has developed. An executable paper provides more information for computational experiments and results than the text, tables, and figures of standard papers. Executable papers can bundle computational content that allow readers and reviewers to interact, validate, and explore experiments. By including such content, authors facilitate future discoveries by lowering the barrier to reproducing and extending results. We present an infrastructure for creating, disseminating, and maintaining executable papers. Our approach is rooted in provenance, the documentation of exactly how data, experiments, and results were generated. We seek to improve the experience for everyone involved in the life cycle of an executable paper. The automated capture of provenance information allows authors to easily integrate and update results into papers as they write, and also helps reviewers better evaluate approaches by enabling them to explore experimental results by varying parameters or data. With a provenance-based system, readers are able to examine exactly how a result was developed to better understand and extend published findings

DEEP: a provenance-aware executable document system

The concept of executable documents is attracting growing interest from both academics and publishers since it is a promising technology for the dissemination of scientific results. Provenance is a kind of metadata that provides a rich description of the derivation history of data products starting from their original sources. It has been used in many different e-Science domains and has shown great potential in enabling reproducibility of scientific results. However, while both executable documents and provenance are aimed at enhancing the dissemination of scientific results, little has been done to explore the integration of both techniques. In this paper, we introduce the design and development of DEEP, an executable document environment that generates scientific results dynamically and interactively, and also records the provenance for these results in the document. In this system, provenance is exposed to users via an interface that provides them with an alternative way of navigating the executable document. In addition, we make use of the provenance to offer a document rollback facility to users and help to manage the system's dynamic resources

Perspectives on automated composition of workflows in the life sciences [version 1; peer review: 2 approved]

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.Stian Soiland-Reyes was supported by BioExcel-2 Centre of Excellence, funded by European Commission Horizon 2020 programme under European Commission contract H2020-INFRAEDI-02-2018 823830. Carole Goble was supported by EOSC-Life, funded by European Commission Horizon 2020 programme under grant agreement H2020-INFRAEOSC-2018-2 824087. We gratefully acknowledge the financial support from the Lorentz Center, ELIXIR, and the Leiden University Medical Center (LUMC) that made the workshop possible. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer Reviewed"Article signat per 33 autors/es: Anna-Lena Lamprecht , Magnus Palmblad, Jon Ison, Veit Schwämmle , Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin , Paul Groth , Hans Ienasescu, Pratik Jagtap, Matúš Kalaš , Vedran Kasalica, Alireza Khanteymoori , Tobias Kuhn12, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert9, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft "Postprint (published version

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure