A collaborative GIS programming course using GitHub Classroom

Anbaroğlu, Berk

doi:10.1111/tgis.12810

Cited by 9 publications

(8 citation statements)

References 102 publications

(108 reference statements)

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“…There have been many calls for researchers outside of the software development community to adopt GitHub to improve their collaborative research 3,39,53 . This call comes in light of both the continuous shift towards open-science and the increasing computational and data requirements in EEB.…”

Section: The Promise Of Github For Eeb Researchersmentioning

confidence: 99%

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Crystal‐Ornelas¹,

Edwards²,

Hébert³

et al. 2022

Preprint

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Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research, and the use of efficient methods to share, reproduce, and collaborate on code as well as any research-related documentation has become fundamental. GitHub is an online, cloud-based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols.Despite these benefits, the use of GitHub by EEB researchers is not widespread due to the lack of domain-specific information and guidelines. To help EEB researchers adopt useful features from GitHub in their own workflows, we review twelve practical ways to use the platform. We outline features ranging from low to high technical difficulty: storing code, managing projects, coding collaboratively, conducting peer review, and writing a manuscript. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research.

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Section: The Promise Of Github For Eeb Researchersmentioning

confidence: 99%

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Crystal‐Ornelas¹,

Edwards²,

Hébert³

et al. 2022

Preprint

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“…In geospatial science, scripting has become an essential skill in the field (Anbaroğlu, 2021; Brunsdon & Comber, 2021; Etherington, 2016; Palomino et al, 2017). Recent open education resources reflect this new demand for coding expertise: scripting and coding are increasingly taught in geospatial courses (Anbaroğlu, 2021; Bowlick et al, 2017; Petras et al, 2015; Reades & Rey, 2021).…”

Section: Introductionmentioning

confidence: 99%

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

Haedrich

Petras

Petrášová

et al. 2023

Transactions in GIS

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Open education materials are critical for the advancement of open science and the development of open‐source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in‐line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter, that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python‐based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi‐temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open‐source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate‐level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth.

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“…In an online collaborative geographic experiment, participants can establish a consensus on the nexus between technologies and data, as well as conduct a joint experiment at the implementation level via the web environment (Anbaroğlu, 2021;Jelokhani-Niaraki, 2021;Koski et al, 2021;MacEachren & Brewer, 2004). Notably, the collaborative geographic experiment in this article only refers to the online collaborative geographic experiment.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the common multiuser‐engaged and participatory research to solve geographic problems, such as participatory offshore wind farm planning (Mekonnen & Gorsevski, 2015), participatory mapping (Fagerholm et al, 2021), participatory urban flood assessment (Zhang et al, 2022), and participatory intercomparison of terrestrial carbon cycle models (Yue et al, 2020), collaborative geographic experiments mainly focus on the higher level on the ladder of participation (Arnstein, 1969; Balram & Dragićević, 2006b; Basco‐Carrera et al, 2017; Jankowski & Nyerges, 2001). In an online collaborative geographic experiment, participants can establish a consensus on the nexus between technologies and data, as well as conduct a joint experiment at the implementation level via the web environment (Anbaroğlu, 2021; Jelokhani‐Niaraki, 2021; Koski et al, 2021; MacEachren & Brewer, 2004). Notably, the collaborative geographic experiment in this article only refers to the online collaborative geographic experiment.…”

Section: Introductionmentioning

confidence: 99%

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

Chen

Zheng

et al. 2022

Transactions in GIS

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Geographic experiments, as a comprehensive research approach, are utilized to explore natural and social worlds. To help geographically dispersed people engage in geographic experiments and improve experimental outcomes, online collaborative geographic experiments have received increasing attention. As a new generation of geo‐analysis tools, VGEs (virtual geographic environments) can provide computer‐aided tools to serve geographic experiments. They can not only provide the collaborative GIS capabilities, but also implement geographic simulations using different cyberspace dispersed geographic models. For the complicated and interaction‐intensive online collaborative geographic experiments, VGEs also need to help experts implement geographic experiments following a well‐defined process. Nevertheless, the process is hardly considered in the existing support systems of collaborative geographic experiments. Therefore, we clarify the categories and relationships of interactions during the process of collaborative geographic experiments and design a process‐driven geo‐analysis engine. Through this engine, participants can maintain the processes of their collaborative geographic experiments, as well as implement interactions throughout the entire process. To verify the capability of the engine, it is used to support the case study on traffic noise simulation. The results suggest that the engine could assist the different geo‐analysis applications with a well‐defined process.

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A collaborative GIS programming course using GitHub Classroom

Cited by 9 publications

References 102 publications

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

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