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Improving reproducibility of data science pipelines through transparent provenance capture
16 references·4 citing·37 second hop
Improving reproducibility of data science pipelines through transparent provenance capture (2020)Improving reproducibility of data science…Reproducibility vs. Replicability: A Brief History of a Confused Terminology (2018)Reproducibility vs. Replicabi…Artificial intelligence faces reproducibility crisis (2018)Artificial intelligence faces…Automatically Tracking Metadata and Provenance of Machine Learning Experiments (2017)Automatically Tracking Metada…A survey on provenance: What for? What form? What from? (2017)A survey on provenance: What …M odel DB (2016)M odel DBGoods (2016)GoodsTitian: Data Provenance Support in Spark. (2015)Titian: Data Provenance Suppo…Titian (2015)TitiannoWorkflow: Capturing and Analyzing Provenance of Scripts (2015)noWorkflow: Capturing and Ana…SPADE: Support for Provenance Auditing in Distributed Environments (2012)SPADE: Support for Provenance…RAMP (2011)RAMPPutting lipstick on pig (2011)Putting lipstick on pigLayering in provenance systems (2009)Layering in provenance systemsChoosing a Data Model and Query Language for Provenance (2008)Choosing a Data Model and Que…VisTrails (2006)VisTrailsTransparent result caching (1997)Transparent result cachingAn LLM-guided platform for multi-granular collection and management of data provenance (2025)An LLM-guided platform for mu…Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance (2024)Supporting Better Insights of…Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers (2023)Data pipeline quality: Influe…A Model-Driven Approach for Systematic Reproducibility and Replicability of Data Science Projects (2022)A Model-Driven Approach for S…Chimera: a virtual data system for representing, querying, and automating data derivation (2002)The Open Provenance Model core specification (v1.1) (2010)A systematic review of provenance systems (2018)Decision Provenance: Harnessing Data Flow for Accountable Systems (2018)A Survey on Collecting, Managing, and Analyzing Provenance from Scripts (2019)Provenance Collection Support in the Kepler Scientific Workflow System (2006)Taverna: a tool for building and running workflows of services (2006)Provenance and scientific workflows (2008)Big data provenance: Challenges, state of the art and opportunities (2015)Scientific workflow management and the Kepler system (2005)The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud (2013)Probabilistic demand forecasting at scale (2017)Automating large-scale data quality verification (2018)Artificial Intelligence Governance For Businesses (2022)DataHub: Collaborative Data Science & Dataset Version Management at Scale (2014)Principles of dataset versioning (2015)The data civilizer system (2017)Data Integration and Machine Learning (2018)Data market platforms (2020)Provenance for Computational Tasks: A Survey (2008)YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts (2015)ReproZip (2016)Sustainable computational science: the ReScience initiative (2017)Beyond Accuracy: What Data Quality Means to Data Consumers (1996)Taverna: a tool for the composition and enactment of bioinformatics workflows (2004)Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010)The fallacy of inscrutability (2018)FAIR Computational Workflows (2019)This Thing Called Fairness (2019)Reproducible Research in Computational Science (2011)Journal of Computational Neuroscience (2013)What does research reproducibility mean? (2016)Open science, reproducibility, and transparency in ecology (2018)Reproducibility in machine learning for health research: Still a ways to go (2021)Insights From “The Machine Stops ” to Better Understand Rational Assumptions in Algorithmic Decision Making and Its Implications for Organizations (2019)Artificial intelligence and the conduct of literature reviews (2021)The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021)
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References

  • 2018Artificial intelligence faces reproducibility crisis567 cited
  • 2006VisTrails480 cited
  • 2018Reproducibility vs. Replicability: A Brief History of a Confused Terminology426 cited
  • 2017A survey on provenance: What for? What form? What from?298 cited
  • 2012SPADE: Support for Provenance Auditing in Distributed Environments252 cited
  • 2016Goods171 cited
  • 2016M odel DB157 cited
  • 2009Layering in provenance systems146 cited
  • 2015noWorkflow: Capturing and Analyzing Provenance of Scripts129 cited
  • 2011Putting lipstick on pig117 cited
  • 2015Titian101 cited
  • 2015Titian: Data Provenance Support in Spark.85 cited
  • 2017Automatically Tracking Metadata and Provenance of Machine Learning Experiments78 cited
  • 2011RAMP70 cited
  • 2008Choosing a Data Model and Query Language for Provenance65 cited
  • 1997Transparent result caching65 cited

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  • 2023Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers

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50 cited
  • 2024Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance15 cited
  • 2025An LLM-guided platform for multi-granular collection and management of data provenance8 cited
  • 2022A Model-Driven Approach for Systematic Reproducibility and Replicability of Data Science Projects8 cited