Academic journals increasingly require data and code sharing to improve computational reproducibility, but the effectiveness of these policies remains unclear. This study evaluates the changes in reproducibility after the Open Data + FAIR policy was implemented by the International Journal of Geographic Information Science in August 2019 through a systematic audit of 351 articles published between 2020–2024, with a comparison made to 32 articles from the pre-policy period. Using a five-star reproducibility framework based on FAIR principles, we assessed data and code availability, metadata quality, and adherence to open science standards. Results show significant improvement in material availability post-policy, with most articles now including data and code compared to minimal sharing pre-policy. However, computational reproducibility likely remains limited, with most articles achieving only 1–2 star ratings due to inadequate metadata, unclear workflow documentation, and missing details about computational environments. While compliance with basic sharing requirements increased after policy introduction, it is unclear if it has facilitated the comprehensive documentation necessary for independent reproduction. These findings suggest that journal policies focused solely on availability may be insufficient for achieving computational reproducibility in GIScience research, highlighting the need for enhanced standards addressing metadata, workflow documentation, and computational environments.

Social issues, AI, and climate change are just a few of the disruptive focuses impacting science. The field of GIScience is well positioned to respond to accelerating disruptions due to the interdisciplinary nature of the field and the ability of GIScience approaches to be used in support of decision-making. This manuscript aims to start a conversation that will establish a research agenda for GIScience in an age of disruptions. We outline three guiding principles: (1) focusing on the relevance and real-world impact of research, (2) adopting systems-based thinking and contextual approaches and (3) emphasizing inclusive practices. We then outline prioritized research areas organized by what topics are important focal areas (Data and Infrastructure, Artificial Intelligence, and Causality and Generalizability), and what approaches to science we should be attentive to (Impactful Open Science, Collaborative and Convergent Science, and through Diverse Participation and Partnerships). We conclude with a call to increase impact by balancing slow science with practical and policy-oriented research. We also recognize that while broad adoption of spatial approaches is a signal of GIScience's success, we should continue to work together to advance core knowledge centered on spatial thinking and approaches.

A cornerstone of the scientific method, the ability to reproduce and replicate the results of research has gained widespread attention across the sciences in recent years. A corresponding burst of energy into how to make research more reproducible and replicable has led to numerous innovations. This article outlines some of the opportunities for geospatial researchers to contribute to and learn from the broader reproducibility literature. We review practices developed in related disciplines to improve the reproducibility and replicability of research and outline current efforts to adapt those practices to geospatial analyses. The article then highlights the open questions, opportunities, and potential new directions in geospatial research related to R&R. We stress that the path ahead will likely require a mixture of computational, geospatial, and behavioral research that collectively addresses the many sides of reproducibility and replicability issues.