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Reproducibility, Replicability, and Open Science Practices in the Geographical Sciences
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Description: Researchers in the human-environment and geographical sciences study a world of incredible complexity and enduring change. As such a persistent challenge for the field is to develop credible and reliable explanations of geographic phenomena. We examine a range of problems that pose potential challenges to the credibility of geographic research (e.g., publication bias, specification searching). Our overarching goals are to 1) describe the extent of these problems in the field, 2) explain their causes, and 3) develop interventions to mitigate their impacts on the credibility and reliability of geographic research.
Citation
Components
Template for Reproducible and Replicable Research in Human-Environment and Geographical Sciences
This template Git repository contains a folder structure, template documents, and best practice suggestions for conducting reproducible geographic res...
A Survey of Reproducibility in Geographic Research
A survey of geographic research community designed to gather information about the current understanding and use of reproducible research practices. T...
A Survey of Researcher Perceptions of Replication in Geography
A survey of researcher perceptions of replication in geography.
Publications
Collection of pre-prints and published peer-reviewed articles
Reproduction and Reanalysis of Malcomb et al 2014 Climate Vulnerability Model of Malawi
The original study is one of the first sub-national geographic vulnerability models for a developing country, Malawi. A main contribution of the study...
Reproduction and Reanalysis of Kang et al 2020 Spatial Accessibility of COVID-19 Health Care Resources
The original paper is an implementation of the enhanced two-step floating catchment area (E2FCA) method of spatial accessibility measurement in health...
Reproduction of Saffary et al 2020
The original study is one of the first spatial statistical analyses of of COVID-19 in the United States. A main contribution of the study is to provid...
Reproduction of Vijayan et al 2020
Vijayan et al. (2021) examined whether spatial patterns existed in SARS-CoV-2 age-adjusted testing rates, age-adjusted diagnosis rates, and crude posi...
Reproduction of Mollalo et al 2020
Mollalo et al. (2020) investigated county-level variations of COVID-19 incidence across the continental United States using spatial lag and spatial er...
Mainstreaming metadata into research workflows to advance reproducibility and open geographic information science
FOSS4G 2022 Conference Paper
Open Source Geographic Information Science
Advanced geography methods course at Middlebury College focused on teaching fundamental principals in geographic information science through a focus o...
A Framework for Moving Beyond Computational Reproducibility: Lessons from Three Reproductions of Geographical Analyses of COVID19
Supplementary files for manuscript https://osf.io/preprints/metaarxiv/7jqtv/ include three project components, one for each reproduction study
Replication of Chakraborty 2021
Replication of Chakraborty 2021 analysis of unequal distribution of COVID-19 for people with disabilities
Reproduction and Replication of Spielman et al.'s 2020 Evaluation of the Social Vulnerability Index
Replication study of Spielman et al's 2020 replication and reanalysis of the Social Vulnerability Index (SoVI) by Cutter, Boruff and Shirley (2003)
Decentralized Infrastructure for Reproducible and Replicable Geographical Science
Conference Paper in I-GUIDE Forum 2023 - Harnessing the Geospatial Data Revolution for Sustainability Solutions
Assessing publication bias in geographical science journals
| Registered: 2020-08-12 13:54 UTC
Publication bias refers to any situation in which some studies are more likely to be published than others based on their results. Combined with the p...
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