Abstract

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project.

Highlights

  • Replicability and reproducibility are essential aspects of the scientific method that add consistency to scientific contributions in any discipline

  • The SIOSE-INNOVA project is organised into two complementary parts: (1) a technical innovation part that consists in verifying which open source technologies provide the best solutions to exploit certain facets of the SIOSE database, and (2) an applied part that involves the implementation of these new technologies in real case studies

  • We explain two different cases where using Docker containers made a difference, and we propose the adoption of Unified Modeling Language (UML) diagrams for documenting and visually explaining how Docker helped in making some computational experiences more reproducible

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Summary

Introduction

Replicability and reproducibility are essential aspects of the scientific method that add consistency to scientific contributions in any discipline. It is considered that something cannot be understood as being reproducible without it being open [18]

The SIOSE-INNOVA project
Objectives
Open Reproducible Research in the Geospatial Domain
Collaborative Development and Code Sharing
Geospatial data availability
Linked solutions in the geospatial context
A Brief Look at the Adoption of Containerisation in GISc Projects
Enabling Research Reproducibility in the SIOSE-INNOVA Project
Benchmarking SQL and NoSQL Database Models
An ETL Process Using Containerisation
Findings
Concluding Remarks
Full Text
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