Reproducible Research Publication Workflow:A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output

In this paper we present the Reproducible Research Publication Workflow(RRPW)as an example of how generic canonical workflows can be applied to a specific context.The RRPW includes essential steps between submission and final publication of the manuscript and the research artefacts(i.e.,data,code,etc.)that underlie the scholarly claims in the manuscript.A key aspect of the RRPW is the inclusion of artefact review and metadata creation as part of the publication workflow.The paper discusses a formalized technical structure around a set of canonical steps which helps codify and standardize the process for researchers,curators,and publishers.The proposed application of canonical workflows can help achieve the goals of improved transparency and reproducibility,increase FAIR compliance of all research artefacts at all steps,and facilitate better exchange of annotated and machine-readable metadata.

Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.

Reasons,challenges,and some tools for doing reproducible transportation research

This paper introduces reproducible research(RR),and explains its importance,benefits,and challenges.Some important tools for conducting RR in Transportation Research are also introduced.Moreover,the source code for generating this paper has been designed in a way so that it can be used as a template for researchers to write their future journal papers as dynamic and reproducible documents.

General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

General noise cost functions have been recently proposed for support vector regression(SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind,three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm(NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall–Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.