Awesome Modeling Practices

✨Awesome modeling practices✨ curated by the OMF Early Career Scholars Working Group.

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Last updated: May 23, 2026 06:00 UTC

An opinionated list of resources to help modelers follow good modeling practices. Developed by the Early Career Scholars Working Group of the Open Modeling Foundation.

Contributions are welcomed! Please read our Contribution Guidelines before submitting your Pull Request.

Contents

Modeling Principles

  • FAIR - Findable, Accessible, Interoperable, Reusable.
  • CARE - Collective Benefit, Authority to Control, Responsibility, and Ethics. Principles for Indigenous Data Governance.
  • INSPIRE - Infrastructure for Spatial Information in Europe.
  • PAVE - Purpose, Assumptions, Validity, and Exploration. Method to communicate the suitability of models to inform a particular purpose.

Modeling Standards

  • ODD - The Overview, Design concepts, and Details protocol standardize descriptions of individual-based and agent-based models.
  • TRACE - The TRAnsparent and Comprehensive model Evaludation protocol provide standardized approach to documenting a model’s formulation, implementation, testing, and application.
  • CSDMS Standard Names - Comprehensive set of rules, patterns and standards for naming variables in models.
  • UML - The Unified Modeling Language is a standardized visual modeling language to visualize the architecture and design of a system.
  • STEP - The Standard for the Exchange of Product model data or ISO 10303 is a standard to provide interoperability between software.
  • OMF Standard - Open Modeling Foundation (in development) modeling standards.
  • ODE - The Overview, Data, and Execution protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences.

Model Libraries

  • CoMSES Model Library - Open repository containing models. Developed by CoMSES Net and based on the United States
  • OSF - Open repository containing models, research papers, data sets, software and more. Developed by the Center for Open Science and based on the United States.
  • Zenodo - Open repository containing models, research papers, data sets, software and more. Developed by CERN and based on Europe.

Societies Discussing Modeling Practices

  • OMF - The Open Modeling Foundation is an alliance of modeling organizations to develop and promote a community developed body of modeling standards and best practices.
  • CSDMS - The Community Surface Dynamics Modeling System promotes the modeling of earth surface processes by developing, supporting, and disseminating integrated software modules.
  • CoMSES Net - The Network for Computational Modeling in the Social and Ecological Sciences is an open community to improving the way we develop, share, and use computational models.

Journals Discussing Modeling Practices

Papers

  • A catalogue of Do's and Don'ts in the modeling of environmental systems

    Xifu Sun, Anthony Jakeman, Serena H. Hamilton, Volker Grimm, Randall J. Hunt, et al.

    2026Environmental Modelling & SoftwareVol. 198pp. 106893

    Offers practical "Do's and Don'ts" guide for defensible modeling in every phase.

    DOI: 10.1016/j.envsoft.2026.106893 URL

    Full record
    key
    sun_catalogue_2026
    entry type
    article
    issn
    1364-8152
    url
    https://www.sciencedirect.com/science/article/pii/S136481522600040X
    doi
    10.1016/j.envsoft.2026.106893
    abstract
    Modeling plays a vital role in understanding and managing complex environmental systems, but its credibility and quality depend heavily on a comprehensive set of defensible model activities and practices, especially when the system of interest is plagued with uncertainties and conflicting stakeholder perspectives. This paper proposes a catalogue of Do's and Don'ts to guide modelers in addressing the many pertinent considerations through the whole modeling cycle. This practical tool provides advice on approaching modeling effectively through adhering to good modeling practice. It emphasizes model choices that align with the model purpose and context, and the justification and documentation of modeling decisions and assumptions. Managing uncertainty is a core consideration. The identification, assessment and reporting of these uncertainties is important across the entire modeling process, which spans problem framing, technical design, implementation and application phases. Such good practices are critical for transparency and reliability of the modeling.
    urldate
    2026-05-06
    author
    Sun, Xifu and Jakeman, Anthony and Hamilton, Serena H. and Grimm, Volker and Hunt, Randall J. and El Sawah, Sondoss and Wang, Hsiao-Hsuan and Croke, Barry and Chen, Min
    month
    mar
    keywords
    Environmental modeling, Good modeling practice, Modeling cycle, Transparency, Uncertainty management

  • Ten simple rules for good model-sharing practices

    Ismael Kherroubi Garcia, Christopher Erdmann, Sandra Gesing, Michael Barton, Lauren Cadwallader, et al.

    2025PLOS Computational BiologyVol. 21pp. e1012702

    Presents ten practical rules to help researchers share computational models effectively and promote wider adoption of better model-sharing practices.

    DOI: 10.1371/journal.pcbi.1012702 URL

    Full record
    key
    garcia_ten_2025
    entry type
    article
    issn
    1553-7358
    url
    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012702
    doi
    10.1371/journal.pcbi.1012702
    abstract
    Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least “good enough,” and (ii) enable others to lead the change towards better model-sharing practices.
    language
    en
    number
    1
    urldate
    2026-05-06
    publisher
    Public Library of Science
    author
    Garcia, Ismael Kherroubi and Erdmann, Christopher and Gesing, Sandra and Barton, Michael and Cadwallader, Lauren and Hengeveld, Geerten and Kirkpatrick, Christine R. and Knight, Kathryn and Lemmen, Carsten and Ringuette, Rebecca and Zhan, Qing and Harrison, Melissa and Gabhann, Feilim Mac and Meyers, Natalie and Osborne, Cailean and Till, Charlotte and Brenner, Paul and Buys, Matt and Chen, Min and Lee, Allen and Papin, Jason and Rao, Yuhan
    month
    jan
    keywords
    Careers, Computer software, Metadata, Open science, Science policy, Taxonomy, User interfaces, Workshops

  • Cracking the code: Linking good modeling and coding practices for new ecological modelers

    Todd M. Swannack, Kiara C. Cushway, Carra C. Carrillo, Clementina Calvo, Kierra R. Determan, et al.

    2025Ecological ModellingVol. 499pp. 110926

    Offers guidance for new modelers building strong coding and documentation habits to support reproducible ecological models.

    DOI: 10.1016/j.ecolmodel.2024.110926 URL

    Full record
    key
    swannack_cracking_2025
    entry type
    article
    issn
    0304-3800
    shorttitle
    Cracking the code
    url
    https://www.sciencedirect.com/science/article/pii/S0304380024003144
    doi
    10.1016/j.ecolmodel.2024.110926
    abstract
    Good modeling practices are essential for producing reliable and reproducible ecological models. Inherent to good modeling practices are fundamental coding and documentation skills, which not only implement the desired modeling capabilities but also clearly outline the goals, methods, and components of a model that are necessary to reproduce the desired results. One of the largest challenges for new ecological modelers can be implementing a model into computer code. In fact, coding represents a significant barrier for entry into ecological modeling, since most ecologists have not had formal training in computer science or software development. While software packages do exist that facilitate model development, we have observed that newer modelers still struggle with developing good coding practice throughout the modeling process. During a series of agent-based modeling short-courses and full semester graduate courses, both taught in NetLogo, we identified some common challenges encountered by graduate students and environmental professionals as they learn to code an ecological model, many for the first time. We were able to categorize and provide examples of the main challenges and obstacles, which fell into three main groups that follow the steps of good modeling practice: problem scoping and conceptualization, formulation, and evaluation. We then provide guidance on how to overcome these obstacles while developing good coding and modeling practices that will result in more scientifically defensible models.
    urldate
    2026-05-06
    author
    Swannack, Todd M. and Cushway, Kiara C. and Carrillo, Carra C. and Calvo, Clementina and Determan, Kierra R. and Mierzejewski, Caroline M. and Quintana, Vanessa M. and Riggins, Christopher L. and Sams, Miranda D. and Wadsworth, Waverly E.
    month
    jan
    keywords
    Coding, Documentation, Evaluation, Good practice, Model development

  • Towards normalizing good practice across the whole modeling cycle: its instrumentation and future research topics

    Anthony J. Jakeman, Sondoss Elsawah, Hsiao-Hsuan Wang, Serena H. Hamilton, Lieke Melsen, et al.

    2024Socio-Environmental Systems ModellingVol. 6pp. 18755–18755

    Outlines research gaps and concrete actions to embed good modeling practice across the entire modeling cycle.

    DOI: 10.18174/sesmo.18755 URL

    Full record
    key
    jakeman_towards_2024
    entry type
    article
    copyright
    Copyright (c) 2024 Anthony J. Jakeman, Sondoss Elsawah, Hsiao-Hsuan Wang, Serena H. Hamilton, Lieke Melsen, Volker Grimm
    issn
    2663-3027
    shorttitle
    Towards normalizing good practice across the whole modeling cycle
    url
    https://sesmo.org/article/view/18755
    doi
    10.18174/sesmo.18755
    abstract
    Choices made in modeling matter and demand more explication since they determine how much we can trust modeling insights and predictions within their social, political and ethical contexts. Good Modeling Practice (GMP) is a key research area for strengthening and maturing the modeling field and community, through identifying, formulating and sharing knowledge about the craft of modeling. This craft represents the knowledge that modelers learn in practice about how they get things done, and how they adapt their practices to new situations. This Joint Special Issue is motivated by the importance of sharing good modeling practices from a whole modeling lifecycle viewpoint. We attempt to add conceptual clarity to this research area by defining the plethora of concepts and decision points used to characterize the choices to be made throughout the modeling process, and by synthesizing some of the existing efforts on GMP. We characterize a broad list of articles in the literature on GMP and identify a list of essential topics demanding more attention. This list is only a preliminary one as we anticipate that a more comprehensive list of knowledge gaps will be unearthed from the submissions to the Joint Special Issue collection on GMP, of which this is an introduction. We also propose a vision for GMP and suggest instrumental ways that good practice can become not just well-known but normal practice. This instrumentation focuses on journal standards, collective commitment and culture especially by research community societies, early career awards for advancing GMP, and legal requirements or accreditation. A vital instrument in all this is the design and development of a modeling curriculum that distills core requisite knowledge about modeling, as well as proven-to-work routines and practices that can be scaled up in different contexts.
    language
    en
    urldate
    2026-05-06
    author
    Jakeman, Anthony J. and Elsawah, Sondoss and Wang, Hsiao-Hsuan and Hamilton, Serena H. and Melsen, Lieke and Grimm, Volker
    month
    sep
    keywords
    fit for purpose, good modelling practice, modelling choices, modelling lifecycle

  • Good modelling software practices

    Carsten Lemmen, Philipp Sebastian Sommer

    2024Ecological ModellingVol. 498pp. 110890

    Contextualise cherry-picked and hands-on practices for supporting Good Modelling Practice, demonstrating their applications in an example.

    DOI: 10.1016/j.ecolmodel.2024.110890 URL

    Full record
    key
    lemmen_good_2024
    entry type
    article
    issn
    0304-3800
    url
    https://www.sciencedirect.com/science/article/pii/S0304380024002783
    doi
    10.1016/j.ecolmodel.2024.110890
    abstract
    Frequently in socio-environmental sciences, models are used as tools to represent, understand, project and predict the behaviour of these complex systems. Along the modelling chain, Good Modelling Practices have been evolving that ensure — amongst others — that models are transparent and their results replicable. Whenever such models are represented in software, Good Modelling meet Good Software Practices, such as a tractable development workflow, good code, collaborative development and governance, continuous integration and deployment; and they meet Good Scientific Practices, such as attribution of copyrights and acknowledgement of intellectual property, publication of a software paper and archiving. Too often in existing socio-environmental model software, these practices have been regarded as an add-on to be considered at a later stage only; modellers have shied away from publishing their model as open source out of fear that having to add good practices is too demanding. We here argue for making a habit of following a list of simple and not so simple practices early on in the implementation of the model life cycle. We contextualise cherry-picked and hands-on practices for supporting Good Modelling Practice, and we demonstrate their application in the example context of the Viable North Sea fisheries socio-ecological systems model.
    urldate
    2026-05-06
    author
    Lemmen, Carsten and Sommer, Philipp Sebastian
    month
    dec
    keywords
    Good modelling practice, Good scientific practice, Good software practice

  • Beyond guides, protocols and acronyms: Adoption of good modelling practices depends on challenging academia's status quo in ecology

    Tatiane Micheletti, Marie-Christin Wimmler, Uta Berger, Volker Grimm, Eliot J. McIntire

    2024Ecological ModellingVol. 496pp. 110829

    Identifies academic barriers to good modeling practice and proposed changes to promote broader adoption of reproducible modeling.

    DOI: 10.1016/j.ecolmodel.2024.110829 URL

    Full record
    key
    micheletti_beyond_2024
    entry type
    article
    issn
    0304-3800
    shorttitle
    Beyond guides, protocols and acronyms
    url
    https://www.sciencedirect.com/science/article/pii/S0304380024002175
    doi
    10.1016/j.ecolmodel.2024.110829
    abstract
    Implementing good modelling practices (GMP) in ecological sciences is key to improving scientific reliability. Despite the increased availability of guidelines and protocols detailing how principles such as FAIR and PERFICT can be implemented to improve good modelling practices, the sharing of code which can reproduce results and workflows remains remarkably low. In this work, we explore potential root causes of this discrepancy. We identify three key factors inherent to the current academic structure that, in our experience, might play a role in hindering a wider adoption of GMP: (1) acknowledgment of the time required to implement GMP in projects, (2) the lack of GMP and software development training among ecologists, and (3) perception of GMP as unrewarding in the short-term. We argue that there is an urgent need for systemic changes. Such changes include (1) a cultural shift to value the incorporation of GMP across projects, emphasising the need for explicit budget allocation and careful scheduling of its implementation, (2) redesigning academic curricula to explicitly include GMP and software development as fundamental disciplines in ecology, and (3) an increase in recognition of open and functional code and workflows for career advancement. We call for concerted efforts for bridging this gap, and propose a hopeful outlook emphasising the role of a new generation of scientists and tools committed to good science. Proposing concrete actions, we aim to start a discussion on challenging academia's status quo in ecology and support scientists in bringing a significant paradigm shift to ecological modelling.
    urldate
    2026-05-06
    author
    Micheletti, Tatiane and Wimmler, Marie-Christin and Berger, Uta and Grimm, Volker and McIntire, Eliot J.
    month
    oct
    keywords
    Academic reform, Ecological modelling, FAIR principles, PERFICT, Reproducibility, Scientific reliability

  • Fit-for-purpose environmental modeling: Targeting the intersection of usability, reliability and feasibility

    Serena H. Hamilton, Carmel A. Pollino, Danial S. Stratford, Baihua Fu, Anthony J. Jakeman

    2022Environmental Modelling & SoftwareVol. 148pp. 105278

    Proposes a fit-for-purpose framework to ensure models are useful, reliable, and feasible for decision support.

    DOI: 10.1016/j.envsoft.2021.105278 URL

    Full record
    key
    hamilton_fit-for-purpose_2022
    entry type
    article
    issn
    1364-8152
    shorttitle
    Fit-for-purpose environmental modeling
    url
    https://www.sciencedirect.com/science/article/pii/S1364815221003200
    doi
    10.1016/j.envsoft.2021.105278
    abstract
    Although it is widely acknowledged as a fundamental principle that models be fit-for-purpose, there remains lack of clarity on what this notion actually means and therefore how it is achieved. We contend that fitness-for-purpose must go beyond the functional use of the model to include its management, problem and project contexts. Accordingly, we propose a practical framework that considers fit-for-purpose modeling as the intersection of three requirements, in that the modeling be: useful, addressing the needs of the end user; reliable, obtaining an adequate level of certainty or trust; and feasible, within practical constraints of the project. Modeling choices, including the selection of spatial and temporal scales, system features and processes to include, and the type of model, can be better informed when the bounds of these fit-for-purpose requirements are defined. We focus on modeling in decision and management support settings, and demonstrate the framework with ecohydrological models designed for managing environmental flows. By explicitly linking its intended functional use and context to modeling choices, this framework can facilitate the design and development of environmental models that more effectively bridge science and management.
    urldate
    2026-05-06
    author
    Hamilton, Serena H. and Pollino, Carmel A. and Stratford, Danial S. and Fu, Baihua and Jakeman, Anthony J.
    month
    feb
    keywords
    Best practices, e-flows, Ecological models, Environmental water, Fit-for-purpose modeling, Model design

  • An overview of the system dynamics process for integrated modelling of socio-ecological systems: Lessons on good modelling practice from five case studies

    Sondoss Elsawah, Suzanne A. Pierce, Serena H. Hamilton, Hedwig van Delden, Dagmar Haase, et al.

    2017Environmental Modelling & SoftwareVol. 93pp. 127–145

    Shares lessons from five system-dynamics cases to guide good practice in integrated socio-ecological modeling.

    DOI: 10.1016/j.envsoft.2017.03.001 URL

    Full record
    key
    elsawah_overview_2017
    entry type
    article
    issn
    1364-8152
    shorttitle
    An overview of the system dynamics process for integrated modelling of socio-ecological systems
    url
    https://www.sciencedirect.com/science/article/pii/S136481521631091X
    doi
    10.1016/j.envsoft.2017.03.001
    abstract
    Similar to other modelling methodologies, the potential of system dynamics to contribute to system understanding and decision making depends upon the practices applied by the modeller. However lessons about many of these practices are often unreported. This paper contributes to the methodology of system dynamics modelling of socio-ecological systems by 1) examining issues modellers face during the modelling process, and 2) providing guidance on how to effectively design and implement system dynamics modelling. This is achieved through an investigation of five case studies, drawing on lessons from these experiences. This is complemented by a literature review of system dynamics applied within the context of integrated modelling and environmental DSS. The case studies cover a variety of environmental issues and system dynamics modelling methods and tools. Although we used system dynamics as the common lens from which lessons are drawn, many of these insights transcend to other integrated modelling approaches.
    urldate
    2026-05-06
    author
    Elsawah, Sondoss and Pierce, Suzanne A. and Hamilton, Serena H. and van Delden, Hedwig and Haase, Dagmar and Elmahdi, Amgad and Jakeman, Anthony J.
    month
    jul
    keywords
    Decision support, Integrated assessment, Integrated modelling, Modelling practices, Socio-ecological systems, System dynamics

Publications list generated dynamically on the OMF website.

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