This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1080/24694452.2020.1806028. This is version 1 of this Preprint.
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Abstract
Reproducible research is often perceived as a technological challenge, but it is rooted in the challenge to improve scholarly communication in an age of digitisation. When computers become involved and researchers want to allow other scientists to inspect, understand, evaluate, and build upon their work, they need to create a research compendium that includes the code, data, computing environment, and script-based workflows used. Here, we present the state of the art for approaches to reach this degree of computational reproducibility, addressing literate programming and containerisation, while paying attention to working with geospatial data (digital maps, GIS). We argue that all researchers working with computers should understand these technologies to control their computing environment, and we present the benefits of reproducible workflows in practice. Example research compendia illustrate the presented concepts and are the basis for challenges specific to geography and geosciences. Based on existing surveys and best practices from different scientific domains, we conclude that researchers today can overcome many barriers and achieve a very high degree of reproducibility. If the geography and geosciences and communities adopt reproducibility and the underlying technologies in practice and in policies, they can transform the way researchers conduct and communicate their work towards increased transparency, understandability, openness, trust, productivity, and innovation.
DOI
https://doi.org/10.31223/X5XH0Z
Subjects
Computer Sciences, Earth Sciences, Environmental Sciences, Higher Education
Keywords
reproducible research, reproducibility, open science, computational reproducibility, Scholarly communication
Dates
Published: 2022-03-28 09:52
Last Updated: 2022-03-28 16:52
License
CC BY Attribution 4.0 International
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Data Availability (Reason not available):
No data was collected as part of this work.
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