Graduate and undergraduate students involved in research projects that generate or analyze extensive datasets use several software applications for data input and processing subject to guidelines for ensuring data quality and availability. Data management guidelines are based on existing practices of the associated academic or funding institutions and may be automated to minimize human error and maintenance overhead. This paper presents a framework for automating data management processes, and it details the flow of data from generation/acquisition through processing to the output of final reports. It is designed to adapt to changing requirements and limit overhead costs. The paper also presents a representative case study applying the framework to the finite element characterization of the magnetically coupled linear variable reluctance motor. It utilizes modern widely available scripting tools particularly Windows PowerShell® to automate workflows. This task requires generating motor characteristics for several thousands of operating conditions using finite element analysis.
A key component of scientific communication is sufficient information for other researchers in the field to reproduce published findings. For computational and data-enabled research, this has often been interpreted to mean making available the raw data from which results were generated, the computer code that generated the findings, and any additional information needed such as workflows and input parameters. Many journals are revising author guidelines to include data and code availability. This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request. We assess the effectiveness of such a policy by (i) requesting data and code from authors and (ii) attempting replication of the published findings. We chose a random sample of 204 scientific papers published in the journal Science after the implementation of their policy in February 2011. We found that we were able to obtain artifacts from 44% of our sample and were able to reproduce the findings for 26%. We find this policy—author remission of data and code postpublication upon request—an improvement over no policy, but currently insufficient for reproducibility.
The association between the bacterium Fusobacterium nucleatum and human colon cancer is more complicated than it first appeared.
Here we present the Scientific Filesystem (SCIF), an organizational format that supports exposure of executables and metadata for discoverability of scientific applications. The format includes a known filesystem structure, a definition for a set of environment variables describing it, and functions for generation of the variables and interaction with the libraries, metadata, and executables located within. SCIF makes it easy to expose metadata, multiple environments, installation steps, files, and entrypoints to render scientific applications consistent, modular, and discoverable. A SCIF can be installed on a traditional host or in a container technology such as Docker or Singularity. We will start by reviewing the background and rationale for the Scientific Filesystem, followed by an overview of the specification, and the different levels of internal modules (“apps”) that the organizational format affords. Finally, we demonstrate that SCIF is useful by implementing and discussing several use cases that improve user interaction and understanding of scientific applications. SCIF is released along with a client and integration in the Singularity 2.4 software to quickly install and interact with Scientific Filesystems. When used inside of a reproducible container, a Scientific Filesystem is a recipe for reproducibility and introspection of the functions and users that it serves.
While reproducibility has been a requirement in natural sciences for centuries, computational experiments have not followed the same standard. Often, there is insufficient information to reproduce computational results described in publications, and in the recent past, this has led to many retractions. Although scientists are aware of the numerous benefits of reproducibility, the perceived amount of work to make results reproducible is a significant disincentive. Fortunately, much of the information needed to reproduce an experiment can be obtained by systematically capturing its provenance. In this paper, we give an overview of different types of provenance and how they can be used to support reproducibility. We also describe a representative set of provenance tools and approaches that make it easy to create reproducible experiments.
Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms.