It is commonly agreed that in-silico scientific experiments should be executable and repeatable processes. Most of the current approaches for computational experiment preservation and reproducibility have focused so far on two of the main components of the experiment, namely data and method.
In this paper we propose a new approach that addresses the third cornerstone of experimental reproducibility: the equipment. This work focuses on the set of software and hardware components that are involved in the execution of a scientific workflow. In order to demonstrate the feasibility of our proposal, we describe a use case scenario on the Text Analytics domain and the application of our approach to it. From the original workflow we document its execution environment by means of a set of semantic models and a catalogue of resources and we generate an equivalent infrastructure for re-executing it.
In this section we include the files containing the datasets of our experiment and a set of queries for retrieving information from them.
|Idafen Santana-Perez||(firstname.lastname@example.org) Ontology Engineering Group, UPM|
|María Pérez-Hernández||(email@example.com) Ontology Engineering Group, UPM|