references.bib

@software{allaire,
  title = {Quarto},
  author = {Allaire, J. J. and Teague, Charles and Xie, Yihui and Dervieux, Christophe},
  doi = {10.5281/zenodo.5960048},
  abstract = {Quarto is an open-source scientific and technical publishing system built on Pandoc.},
  organization = {Zenodo},
  keywords = {computer science,dynamic documents,exact sciences,pandoc,r (programming language)}
}

@article{eyring2016,
  title = {Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization},
  author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
  date = {2016-05-26},
  journaltitle = {Geoscientific Model Development},
  volume = {9},
  number = {5},
  pages = {1937--1958},
  publisher = {Copernicus GmbH},
  issn = {1991-959X},
  doi = {10.5194/gmd-9-1937-2016},
  abstract = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions:  - How does the Earth system respond to forcing? - What are the origins and consequences of systematic model biases?  - How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios?  This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
  langid = {english},
  keywords = {climate change,computer simulations,data interpolation,data science,databases,environmental sciences,exact sciences,interdisciplinary fields,meteorology,modeling,open data,open science,worldclim}
}

@article{fick2017,
  title = {WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas},
  shorttitle = {WorldClim 2},
  author = {Fick, Stephen E. and Hijmans, Robert J.},
  date = {2017},
  journaltitle = {International Journal of Climatology},
  volume = {37},
  number = {12},
  pages = {4302--4315},
  issn = {1097-0088},
  doi = {10.1002/joc.5086},
  abstract = {We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15\% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.},
  langid = {english},
  keywords = {climate,climate modeling,data interpolation,databases,environmental sciences,exact sciences,geography,interdisciplinary fields,meteorology,modeling,open data,open science,probability and statistics,worldclim}
}

@book{grolemund2014,
  title = {Hands-on programming with R: Write your own functions and simulations},
  shorttitle = {Hands-on programming with R},
  author = {Grolemund, Garrett},
  date = {2014},
  publisher = {O'Reilly Media},
  location = {Sebastopol, CA},
  url = {https://rstudio-education.github.io/hopr},
  abstract = {Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.},
  isbn = {978-1-4493-5901-0},
  langid = {american},
  pagetotal = {250},
  keywords = {data engineering,data science,exact sciences,probability and statistics,programming,r (programming language)}
}

@article{harris2020,
  title = {Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset},
  author = {Harris, Ian and Osborn, Timothy J. and Jones, Phil and Lister, David},
  date = {2020-04-03},
  journaltitle = {Scientific Data},
  shortjournal = {Sci Data},
  volume = {7},
  number = {1},
  pages = {109},
  publisher = {Nature Publishing Group},
  issn = {2052-4463},
  doi = {10.1038/s41597-020-0453-3},
  abstract = {CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.},
  langid = {english},
  keywords = {atmospheric dynamics,climate,climate modeling,computer simulations,data interpolation,databases,environmental sciences,exact sciences,geography,interdisciplinary fields,meteorology,modeling,open data,open science,probability and statistics,worldclim}
}

@software{hijmans2024,
  title = {{{geodata}}: Download geographic data},
  shorttitle = {{{geodata}}},
  author = {Hijmans, Robert J. and Barbosa, Márcia and Ghosh, Aniruddha and Mandel, Alex},
  date = {2024},
  doi = {10.32614/CRAN.package.geodata},
  abstract = {Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.},
  organization = {CRAN},
  keywords = {apis,computer science,exact sciences,nosource,probability and statistics,r (programming language),r packages,software engineering,spatial data,spatial data analysis}
}

@software{hijmans2026,
  title = {{{terra}}: Spatial sata analysis},
  shorttitle = {{{terra}}},
  author = {Hijmans, Robert J.},
  date = {2026},
  doi = {10.32614/CRAN.package.terra},
  abstract = {Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on {$<$}https://rspatial.org/{$>$} to get started.},
  organization = {CRAN},
  keywords = {computer science,exact sciences,nosource,probability and statistics,r (programming language),r packages,spatial data,spatial data analysis}
}

@online{hijmansa,
  title = {GADM: Database of global administrative areas},
  shorttitle = {WorldClim},
  author = {Hijmans, Robert J.},
  url = {https://gadm.org},
  abstract = {GADM wants to map the administrative areas of all countries, at all levels of sub-division. We provide data at high spatial resolutions that includes an extensive set of attributes. This is a never ending project, but we are happy to share what we have.},
  langid = {english},
  keywords = {datasets,exact sciences,open data,open science,probability and statistics,spatial data,spatial data analysis,worldclim}
}

@article{hjorth2020,
  title = {LevelSpace: A NetLogo extension for multi-level agent-based modeling},
  shorttitle = {LevelSpace},
  author = {Hjorth, Arthur and Head, Bryan and Brady, Corey and Wilensky, Uri},
  date = {2020},
  journaltitle = {Journal of Artificial Societies and Social Simulation},
  shortjournal = {JASSS},
  volume = {23},
  number = {1},
  pages = {4},
  issn = {1460-7425},
  doi = {10.18564/jasss.4130},
  abstract = {Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM – coupling of heterogenous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.},
  langid = {english},
  keywords = {agent-based modeling,complexity science,computer science,exact sciences,interdisciplinary fields,netlogo,netlogo extensions}
}

@software{hornik2025,
  title = {ISOcodes: Selected ISO codes},
  shorttitle = {ISOcodes},
  author = {Hornik, Kurt and Buchta, Christian},
  date = {2025},
  doi = {10.32614/CRAN.package.ISOcodes},
  abstract = {ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes.},
  organization = {CRAN},
  keywords = {computer science,exact sciences,iso,programming,r (programming language),r packages,standards}
}

@article{lang2017,
  title = {{{checkmate}}: Fast argument checks for defensive R programming},
  shorttitle = {terra},
  author = {Lang, Michel},
  date = {2017},
  journaltitle = {The R Journal},
  volume = {9},
  number = {1},
  doi = {10.32614/RJ-2017-028},
  abstract = {Dynamically typed programming languages like R allow programmers to write generic, flexible and concise code and to interact with the language using an interactive Read-eval-print-loop (REPL). However, this flexibility has its price: As the R interpreter has no information about the expected variable type, many base functions automatically convert the input instead of raising an exception. Unfortunately, this frequently leads to runtime errors deeper down the call stack which obfuscates the original problem and renders debugging challenging. Even worse, unwanted conversions can remain undetected and skew or invalidate the results of a statistical analysis. As a resort, assertions can be employed to detect unexpected input during runtime and to signal understandable and traceable errors. The package checkmate provides a plethora of functions to check the type and related properties of the most frequently used R objects and variable types. The package is mostly written in C to avoid any unnecessary performance overhead. Thus, the programmer can conveniently write concise, well-tested assertions which outperforms custom R code for many applications. Furthermore, checkmate simplifies writing unit tests using the framework testthat (Wickham 2011) by extending it with plenty of additional expectation functions, and registered C routines are available for package developers to perform assertions on arbitrary SEXPs (internal data structure for R objects implemented as struct in C) in compiled code.},
  langid = {american},
  keywords = {computer science,defensive programming,exact sciences,nosource,r (programming language),r packages}
}

@software{muller2025,
  title = {{{here}}: A simpler way to find your files},
  shorttitle = {{{here}}},
  author = {Müller, Kirill},
  date = {2025},
  doi = {10.32614/CRAN.package.here},
  abstract = {Constructs paths to your project's files. Declare the relative path of a file within your project with 'i\_am()'. Use the 'here()' function as a drop-in replacement for 'file.path()', it will always locate the files relative to your project root.},
  organization = {CRAN},
  keywords = {computer science,exact sciences,programming,r (programming language),r packages}
}

@article{oneill2017,
  title = {The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century},
  shorttitle = {The roads ahead},
  author = {O’Neill, Brian C. and Kriegler, Elmar and Ebi, Kristie L. and Kemp-Benedict, Eric and Riahi, Keywan and Rothman, Dale S. and family=Ruijven, given=Bas J., prefix=van, useprefix=true and family=Vuuren, given=Detlef P., prefix=van, useprefix=true and Birkmann, Joern and Kok, Kasper and Levy, Marc and Solecki, William},
  date = {2017},
  journaltitle = {Global Environmental Change},
  shortjournal = {Global Environmental Change},
  volume = {42},
  pages = {169--180},
  issn = {0959-3780},
  doi = {10.1016/j.gloenvcha.2015.01.004},
  abstract = {Long-term scenarios play an important role in research on global environmental change. The climate change research community is developing new scenarios integrating future changes in climate and society to investigate climate impacts as well as options for mitigation and adaptation. One component of these new scenarios is a set of alternative futures of societal development known as the shared socioeconomic pathways (SSPs). The conceptual framework for the design and use of the SSPs calls for the development of global pathways describing the future evolution of key aspects of society that would together imply a range of challenges for mitigating and adapting to climate change. Here we present one component of these pathways: the SSP narratives, a set of five qualitative descriptions of future changes in demographics, human development, economy and lifestyle, policies and institutions, technology, and environment and natural resources. We describe the methods used to develop the narratives as well as how these pathways are hypothesized to produce particular combinations of challenges to mitigation and adaptation. Development of the narratives drew on expert opinion to (1) identify key determinants of these challenges that were essential to incorporate in the narratives and (2) combine these elements in the narratives in a manner consistent with scholarship on their inter-relationships. The narratives are intended as a description of plausible future conditions at the level of large world regions that can serve as a basis for integrated scenarios of emissions and land use, as well as climate impact, adaptation and vulnerability analyses.},
  keywords = {adaptation,climate change,environmental sciences,interdisciplinary fields,mitigation,narratives,scenarios,shared socioeconomic pathways}
}

@software{rcoreteam,
  title = {R: A language and environment for statistical computing},
  author = {{R Core Team}},
  location = {Vienna, Austria},
  url = {https://www.r-project.org},
  organization = {R Foundation for Statistical Computing},
  keywords = {computer science,exact sciences,nosource,probability and statistics,programming languages,r (programming language)}
}

@software{ushey2025,
  title = {{{renv}}: Project environments},
  shorttitle = {{{renv}}},
  author = {Ushey, Kevin and Wickham, Hadley},
  date = {2025},
  doi = {10.32614/CRAN.package.renv},
  abstract = {A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.},
  organization = {CRAN},
  keywords = {computer science,exact sciences,programming,r (programming language),r packages}
}

@software{vartanian2026,
  title = {{{logolink}}: An interface for running NetLogo simulations from R},
  shorttitle = {{{logolink}}},
  author = {Vartanian, Daniel},
  date = {2026},
  location = {CRAN},
  doi = {10.32614/CRAN.package.logolink},
  abstract = {logolink is an R package that simplifies setting up and running NetLogo simulations from R. It provides a modern, intuitive interface that follows tidyverse principles and integrates seamlessly with the tidyverse ecosystem. The package is designed to work with NetLogo 7.0.1 and above. Earlier versions are not supported. See NetLogo’s Transition Guide to upgrade your models if needed.},
  keywords = {computer science,exact sciences,logolink (r package),netlogo,nosource,probability and statistics,r (programming language),r packages,software engineering}
}

@software{vartanian2026a,
  title = {{{orbis}}: Spatial data analysis tools},
  shorttitle = {{{orbis}}},
  author = {Vartanian, Daniel},
  date = {2026},
  url = {https://danielvartan.github.io/orbis},
  abstract = {orbis provides tools for spatial data analysis in R. It follows tidyverse principles and is designed to work with the r-spatial collection of packages.},
  organization = {GitHub},
  keywords = {computer science,data science,exact sciences,geospatial data science,nosource,probability and statistics,r (programming language),r packages,spatial data analysis}
}

@software{vartanian2026b,
  title = {LogoActions: GitHub Actions for the NetLogo community},
  shorttitle = {LogoActions},
  author = {Vartanian, Daniel},
  date = {2026},
  doi = {10.5281/zenodo.18102378},
  abstract = {LogoActions is a collection of GitHub Actions designed to facilitate the setup and execution of NetLogo models within GitHub workflows. These actions enable researchers and developers to automate the installation of NetLogo, run and test simulations, and integrate NetLogo with other tools and platforms, such as Quarto, logolink and pyNetLogo.},
  organization = {GitHub},
  keywords = {agent-based models,automation,computer science,continuous integration,exact sciences,interdisciplinary fields,netlogo,netlogo models,nosource,probability and statistics,simulations,unit tests}
}

@software{vartanian2026g,
  title = {Logônia: Plant growth response model in NetLogo},
  shorttitle = {Logônia},
  author = {Vartanian, Daniel and Garcia, Leandro and Carvalho, Aline Martins},
  date = {2026},
  doi = {10.5281/zenodo.21332539},
  abstract = {Logônia is a NetLogo model that simulates the growth response of a fictional plant, Logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.},
  organization = {Zenodo},
  keywords = {climate change,computer science,environmental sciences,exact sciences,interdisciplinary fields,logoclim,netlogo,nosource,probability and statistics,worldclim}
}

@article{wickham2011,
  title = {{{testthat}}: Get started with testing},
  shorttitle = {{{testthat}}},
  author = {Wickham, Hadley},
  date = {2011},
  journaltitle = {The R Journal},
  shortjournal = {The R Journal},
  volume = {3},
  number = {1},
  pages = {5},
  issn = {2073-4859},
  doi = {10.32614/RJ-2011-002},
  abstract = {Software testing is important, but many of us don’t do it because it is frustrating and boring. testthat is a new testing framework for R that is easy learn and use, and integrates with your existing workflow. This paper shows how, with illustrations from existing packages.},
  langid = {english},
  keywords = {computer science,libraries,r (programming language),r packages,test units}
}

@online{wickham2023c,
  title = {The tidy tools manifesto},
  author = {Wickham, Hadley},
  date = {2023},
  url = {https://tidyverse.tidyverse.org/articles/manifesto.html},
  urldate = {2023-07-18},
  abstract = {tidyverse},
  langid = {english},
  organization = {Tidyverse},
  keywords = {data engineering,data science,engineering,guia de estilo,nosource,programming,r (programming language),software engineering}
}

@book{wickham2023e,
  title = {R for data science: Import, tidy, transform, visualize, and model data},
  shorttitle = {R for data science},
  author = {Wickham, Hadley and Çetinkaya-Rundel, Mine and Grolemund, Garrett},
  date = {2023},
  edition = {2},
  publisher = {O'Reilly Media},
  location = {Sebastopol, CA},
  url = {https://r4ds.hadley.nz},
  abstract = {Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.},
  isbn = {978-1-4920-9740-2},
  langid = {american},
  pagetotal = {576},
  keywords = {data engineering,data science,exact sciences,probability and statistics,programming,r (programming language)}
}

@book{wickhama,
  title = {The tidyverse style guide},
  author = {Wickham, Hadley},
  url = {https://style.tidyverse.org},
  langid = {english},
  keywords = {computer science,exact sciences,guia de estilo,nosource,r (programming language),standards}
}

@book{wickhamc,
  title = {Tidy design principles},
  author = {Wickham, Hadley},
  url = {https://design.tidyverse.org},
  langid = {english},
  keywords = {computer science,exact sciences,guia de estilo,nosource,r (programming language),standards}
}

@software{wilensky1999a,
  title = {NetLogo},
  author = {Wilensky, Uri},
  date = {1999},
  location = {Evanston, IL},
  url = {https://www.netlogo.org},
  organization = {Center for Connected Learning and Computer-Based Modeling, Northwestern University},
  keywords = {agent-based modeling,complexity science,computer science,exact sciences,interdisciplinary fields,netlogo,programming}
}