The demand for reproducible research is on the rise in disciplines concerned with data analysis and computational methods. Therefore, we reviewed current recommendations for reproducible research and translated them into criteria for assessing the reproducibility of articles in the field of geographic information science (GIScience). Using this criteria, we assessed a sample of GIScience studies from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, and we collected feedback about the assessment from the study authors. Results from the author feedback indicate that although authors support the concept of performing reproducible research, the incentives for doing this in practice are too small. Therefore, we propose concrete actions for individual researchers and the GIScience conference series to improve transparency and reproducibility. For example, to support researchers in producing reproducible work, the GIScience conference series could offer awards and paper badges, provide author guidelines for computational research, and publish articles in Open Access formats.

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Reproducibility is a cornerstone of science and thus for geographic research as well. However, studies in other disciplines such as biology have shown that published work is rarely reproducible. To assess the state of reproducibility, specifically computational reproducibility (i.e. rerunning the analysis of a paper using the original code), in geographic research, we asked geoscientists about this topic using three methods: a survey (n = 146), interviews (n = 9), and a focus group (n = 5). We asked participants about their understanding of open reproducible research (ORR), how much it is practiced, and what obstacles hinder ORR. We found that participants had different understandings of ORR and that there are several obstacles for authors and readers (e.g. effort, lack of openness). Then, in order to complement the subjective feedback from the participants, we tried to reproduce the results of papers that use spatial statistics to address problems in the geosciences. We selected 41 open access papers from Copernicus and Journal of Statistical Software and executed the R code. In doing so, we identified several technical issues and specific issues with the reproduced figures depicting the results. Based on these findings, we propose guidelines for authors to overcome the issues around reproducibility in the computational geosciences. ARTICLE HISTORY

The demand for reproducibility of research is on the rise in disciplines concerned with data analysis and computational methods. In this work existing recommendations for reproducible research are reviewed and translated into criteria for assessing reproducibility of articles in the field of geographic information science (GIScience). Using a sample of GIScience research from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, we assess the current state of reproducibility of publications in this field. Feedback on the assessment was collected by surveying the authors of the sample papers. The results show the reproducibility levels are low. Although authors support the ideals, the incentives are too small. Therefore we propose concrete actions for individual researchers and the AGILE conference series to improve transparency and reproducibility, such as imparting data and software skills, an award, paper badges, author guidelines for computational research, and Open Access publications.

Background: The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles. Methods: We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis if it (i) was actively maintained at the time the data for this paper was collected, (ii) supports the publication of executable code and data, (iii) is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers. Results: From the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents (10 applications support these formats). All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication. Conclusions: The applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process.

15The demand for reproducibility of research is on the rise in disciplines concerned with data analysis and computational methods. In this work existing recommendations for reproducible research are reviewed and translated into criteria for assessing reproducibility of articles in the field of geographic information science (GIScience). Using a sample of GIScience research from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, we assess the current state of reproducibility of publications in this field. Feedback on the assessment was collected by surveying the authors of the sample papers. The results show the reproducibility levels are low. Although authors support the ideals, the incentives are too small. Therefore we propose concrete actions for individual researchers and GIScience conference series to improve transparency and reproducibility, such as imparting data and software skills, an award, paper badges, author guidelines for computational research, and Open Access publications.

In recent years, the scientific community has called for improvements in the credibility, robustness, and reproducibility of research, characterized by higher standards of scientific evidence, increased interest in open practices, and promotion of transparency. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Currently, the impact of integrating an open and reproducible approach into the curriculum on student outcomes is not well articulated in the literature. Therefore, in this paper, we provide the first comprehensivereview of how integrating open and reproducible scholarship into teaching and learning may impact students, using a large-scale, collaborative, team-science approach. Our review highlighted how embedding open and reproducible scholarship may impact: (1) students’ scientific literacies (i.e., students’ understanding of open research, consumption of science, and the development of transferable skills); (2) student engagement (i.e., motivation and engagement with learning, collaboration, and engagement in open research), and (3) students’attitudes towards science (i.e., trust in science and confidence in research findings). Our review also identified a need for more robust and rigorous methods within evaluations of teaching practice. We discuss implications for teaching and learning scholarship in this area.

Many scientific publications report on computational results based on code and data, but even when code and data are published, the main text is usually provided in a separate, traditional format such as PDF. Since code, data, and text are not linked on a deep level, it is difficult for readers and reviewers to understand and retrace how the authors achieved a specific result that is reported in the main text, e.g. a figure, table, or number. In addition, to make use of new the opportunities afforded by data and code availability, such as re-running analyses with changed parameters, considerable effort is required. In order to overcome this issue and to enable more interactive publications that support scientists in more deeply exploring the reported results, we present the concept, implementation, and initial evaluation of bindings. A binding describes which data subsets, code lines, and parameters produce a specific result that is reported in the main text (e.g. as a figure, table, or number). Based on a prototypical implementation of these bindings, we propose a toolkit for authors to easily create interactive figures by connecting specific UI widgets (e.g. a slider) to parameters. In addition to inspecting code and data, readers can then manipulate the parameter and see how the results change. We evaluated the approach by applying it to a set of existing articles. The results provide initial evidence that the concept is feasible and applicable to many papers with moderate effort.