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The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences
14 references·0 citing·42 second hop
The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences (2026)The ODE (Overview, Data, and Execution) p…Ten simple rules for good model-sharing practices (2025)Ten simple rules for good mod…Applying the FAIR Principles to computational workflows (2025)Applying the FAIR Principles …Good modelling software practices (2024)Good modelling software pract…A survey on dataset quality in machine learning (2023)A survey on dataset quality i…How to make models more useful (2022)How to make models more usefulFair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy (2021)Fair train-test split in mach…The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism (2020)The ODD Protocol for Describi…Model Cards for Model Reporting (2019)Model Cards for Model Reporti…Machine learning on big data: Opportunities and challenges (2017)Machine learning on big data:…Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement (2015)Transparent Reporting of a mu…The journal coverage of Web of Science and Scopus: a comparative analysis (2015)The journal coverage of Web o…The ODD protocol: A review and first update (2010)The ODD protocol: A review an…Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred reporting items for…A standard protocol for describing individual-based and agent-based models (2006)A standard protocol for descr…The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies (2007)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. 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References

  • 2009Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement83,813 cited
  • 2015The journal coverage of Web of Science and Scopus: a comparative analysis5,251 cited
  • 2006A standard protocol for describing individual-based and agent-based models2,985 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2010The ODD protocol: A review and first update2,459 cited
  • 2017Machine learning on big data: Opportunities and challenges1,234 cited
  • 2020The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism899 cited
  • 2015Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement885 cited
  • 2023A survey on dataset quality in machine learning351 cited
  • 2021Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy111 cited
  • 2025Applying the FAIR Principles to computational workflows75 cited
  • 2022How to make models more useful48 cited
  • 2024Good modelling software practices10 cited
  • 2025Ten simple rules for good model-sharing practices8 cited

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