Enabling Collaborative Numerical Modeling in Earth Sciences using Knowledge Infrastructure

Bandaragoda, C.; Castronova, Anthony M.; Istanbulluoglu, Erkan; Strauch, Ronda; Nudurupati, Sai Siddhartha; Phuong, Jimmy; Adams, J. M.; Gasparini, N. M.; Barnhart, Katherine R.; Hutton, Eric; Hobley, Daniel E. J.; Lyons, Nathan J.; Tucker, G. E.; Tarboton, David G.; Idaszak, Ray; Wang, Shaowen

doi:10.1016/j.envsoft.2019.03.020

Cited by 22 publications

(8 citation statements)

References 61 publications

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“…These enabling infrastructures move researchers away from the drudgery of data management and wrangling and back to intuitive and productive workflows, thus accelerating scientific progress. While there have been numerous efforts to build cyberinfrastructures (Droegemeier et al., 2005; Ludäscher et al., 2006; Nemani, 2012), renewed efforts are needed to couple infrastructures with research software and practices to enable more efficient Earth system science research (Bandaragoda et al., 2019).…”

Section: Open Science Focus Areasmentioning

confidence: 99%

From Open Data to Open Science

Ramachandran

Bugbee

Murphy³

2021

Earth and Space Science

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Open science, as both a concept and a term, is increasing in popularity and usage. However, definitions, interpretations, and perceptions as to what the term "open science" means varies. Some definitions are fairly narrow and only focus on providing more open access to science as a body of knowledge. These narrow definitions place an emphasis on openly sharing scientific knowledge as early as possible in the research process (University of Cambridge, 2020). On the other hand, broader definitions of open science acknowledge that science is both a body of knowledge and a systematic method for thinking. Broad definitions place an emphasis on encouraging a culture of openness (Bartling & Friesike, 2014) that includes the entire process of conducting science (National Academies of Sciences, Engineering, & Medicine, 2018a, 2018b) and encourages open collaboration and access to knowledge (Vicente-Saez & Martinez-Fuentes, 2018). In its broadest definition, the term "open science" refers to a paradigm shift in how the methods of science are conducted. This expansive vision of open science acknowledges that rapid technology changes, primarily driven by the Internet, may enable a second scientific revolution that fundamentally changes research methods and standards across science. To complicate matters, the term "open science" is sometimes used interchangeably to represent various principles that support the broader idea of open science itself. These principles include ideas such as open data, open source software, open journal access, and reproducibility. For example, reproducibility, or the ability to verify another scientist's results, is enabled by the principles of open data, open code, and transparent methodologies, yet reproducibility itself is not equivalent to open science.While open science definitions are variable and ambiguous, the value of open science as both a concept and a paradigm change is accepted by the majority of the scientific community. Open science not only benefits the scientific endeavor itself but has also been shown to benefit individual researchers through increased citations and media attention, a larger collaborative network, and exposure to new career and funding opportunities (

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Section: Open Science Focus Areasmentioning

confidence: 99%

From Open Data to Open Science

Ramachandran

Bugbee

Murphy³

2021

Earth and Space Science

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“…With the development of Web services technology and SOA (Service Oriented Architecture), simulation resources are increasingly deployed and published as Web services in geographic modeling and simulation systems to support sharing and reuse (Wen et al, 2017;Chen et al, 2020;Niknejad et al, 2020), such as eHabitat, a web processing service for ecosystem simulation (Dubois et al, 2013), and AWARE, a water resource monitoring and prediction framework (Granell et al, 2010;Gan et al, 2020). In addition, based on the SOA, for the development of open science (Woelfle et al, 2011;Nosek et al, 2015), different geographers and experts have conducted preliminary explorations on open geographic modeling and simulation research modes, such as SWATShare (Rajib et al, 2016), HydroShare (Bandaragoda et al, 2019;Gan et al, 2020), and OpenGMS (Open Geographic Modeling and Simulation Systems) (Wen et al, 2013;Yue et al, 2015Yue et al, , 2018Yue S S et al, 2016;Wang et al, 2018Wang et al, , 2020Zhang et al, 2019Zhang et al, , 2020Chen et al, 2020).…”

Section: Operational Architecture Analysis Of Geographic Modeling and Simulation Systemsmentioning

confidence: 99%

“…To serve research on complex geographic systems, geographic modeling and simulation systems need to support collaborative work with different types of role engagement. In collaborative geographic modeling and simulation, participants with different roles can conduct different forms of geographic collaboration, including resource-based collaboration, knowledge-based collaboration, and interactive collaboration (Voinov et al, 2018;Bandaragoda et al, 2019;Elsawah et al, 2020). (1) In resource-based collaboration, different participants engage in geographic modeling and simulation by contributing their resources.…”

Section: Collaborative Geographic Modeling and Simulationmentioning

confidence: 99%

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Chen

Zhou

et al. 2021

Sci. China Earth Sci.

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Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

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“…These include a model domain representation (the model grid), physical process components, and utilities that support use and extension of the package. Landlab's modular design lowers the barriers of entry to computational research, reduces researcher time, and supports publication of reproducible scientific research products (e.g., Bandaragoda et al, 2019). Development and maintenance of Landlab follows modern software development standards such as version control, integrated testing and documentation, continuous integration, and multiplatform binary distribution (e.g., Adorf et al, 2019;Hwang et al, 2017;Mandli et al, 2016;Poisot, 2015;Taschuk and Wilson, 2017;Wilson et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Short communication: Landlab v2.0: a software package for Earth surface dynamics

et al. 2020

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Abstract. Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.

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Enabling Collaborative Numerical Modeling in Earth Sciences using Knowledge Infrastructure

Cited by 22 publications

References 61 publications

From Open Data to Open Science

From Open Data to Open Science

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Short communication: Landlab v2.0: a software package for Earth surface dynamics

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