22,797 research outputs found
IVOA Provenance data model: hints from the CTA Provenance prototype
We present the last developments on the IVOA Provenance data model, mainly
based on the W3C PROV concept. In the context of the Cherenkov astronomy, the
data processing stages imply both assumptions and comparison to dedicated
simulations. As a consequence, Provenance information is crucial to the end
user in order to interpret the high level data products. The Cherenkov
Telescope Array (CTA), currently in preparation, is thus a perfect test case
for the development of an IVOA standard on Provenance information. We describe
general use-cases for the computational Provenance in the CTA production
pipeline and explore the proposed W3C notations like PROV-N formats, as well as
Provenance access solutions.Comment: 4 pages, 3 figures, to appear in ADASS XXV proceedings, edited by N.
P. F. Lorente, & K. Shortridge (San Francisco: ASP), ASP Conf. Se
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
A posteriori metadata from automated provenance tracking: Integration of AiiDA and TCOD
In order to make results of computational scientific research findable,
accessible, interoperable and re-usable, it is necessary to decorate them with
standardised metadata. However, there are a number of technical and practical
challenges that make this process difficult to achieve in practice. Here the
implementation of a protocol is presented to tag crystal structures with their
computed properties, without the need of human intervention to curate the data.
This protocol leverages the capabilities of AiiDA, an open-source platform to
manage and automate scientific computational workflows, and TCOD, an
open-access database storing computed materials properties using a well-defined
and exhaustive ontology. Based on these, the complete procedure to deposit
computed data in the TCOD database is automated. All relevant metadata are
extracted from the full provenance information that AiiDA tracks and stores
automatically while managing the calculations. Such a protocol also enables
reproducibility of scientific data in the field of computational materials
science. As a proof of concept, the AiiDA-TCOD interface is used to deposit 170
theoretical structures together with their computed properties and their full
provenance graphs, consisting in over 4600 AiiDA nodes
Automatic vs Manual Provenance Abstractions: Mind the Gap
In recent years the need to simplify or to hide sensitive information in
provenance has given way to research on provenance abstraction. In the context
of scientific workflows, existing research provides techniques to semi
automatically create abstractions of a given workflow description, which is in
turn used as filters over the workflow's provenance traces. An alternative
approach that is commonly adopted by scientists is to build workflows with
abstractions embedded into the workflow's design, such as using sub-workflows.
This paper reports on the comparison of manual versus semi-automated approaches
in a context where result abstractions are used to filter report-worthy results
of computational scientific analyses. Specifically; we take a real-world
workflow containing user-created design abstractions and compare these with
abstractions created by ZOOM UserViews and Workflow Summaries systems. Our
comparison shows that semi-automatic and manual approaches largely overlap from
a process perspective, meanwhile, there is a dramatic mismatch in terms of data
artefacts retained in an abstracted account of derivation. We discuss reasons
and suggest future research directions.Comment: Preprint accepted to the 2016 workshop on the Theory and Applications
of Provenance, TAPP 201
When Provenance Aids and Complicates Reproducibility Judgments
It is well-established that the provenance of a scientific result is
important, sometimes more important than the actual result. For computational
analyses that involve visualization, this provenance information may contain
the steps involved in generating visualizations from raw data. Specifically,
data provenance tracks the lineage of data and process provenance tracks the
steps executed. In this paper, we argue that the utility of computational
provenance may not be as clear-cut as we might like. One common use case for
provenance is that the information can be used to reproduce the original
result. However, in visualization, the goal is often to communicate results to
a user or viewer, and thus the insights obtained are ultimately most important.
Viewers can miss important changes or react to unimportant ones. Here,
interaction provenance, which tracks a user's actions with a visualization, or
insight provenance, which tracks the decision-making process, can help capture
what happened but don't remove the issues. In this paper, we present scenarios
where provenance impacts reproducibility in different ways. We also explore how
provenance and visualizations can be better related
A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments
Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects
- …