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Affiliated institutions: Arizona State University

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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.

License: CC-By Attribution 4.0 International

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Type: Research
Status: Active


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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...

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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...

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A Survey of Researcher Perceptions of Replication in Geography

A survey of researcher perceptions of replication in geography.

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Publications

Collection of pre-prints and published peer-reviewed articles

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Reproduction and Reanalysis of Malcomb et al 2014 Climate Vulnerability Model of Malawi

Holler, Udoh, Kedron & 1 more
The original study is one of the first sub-national geographic vulnerability models for a developing country, Malawi. A main contribution of the study...

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Reproduction and Reanalysis of Kang et al 2020 Spatial Accessibility of COVID-19 Health Care Resources

Holler, Burt, Udoh & 2 more
The original paper is an implementation of the enhanced two-step floating catchment area (E2FCA) method of spatial accessibility measurement in health...

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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...

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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...

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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...

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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...

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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

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Replication of Chakraborty 2021

Replication of Chakraborty 2021 analysis of unequal distribution of COVID-19 for people with disabilities

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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)

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Decentralized Infrastructure for Reproducible and Replicable Geographical Science

Conference Paper in I-GUIDE Forum 2023 - Harnessing the Geospatial Data Revolution for Sustainability Solutions

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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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