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Garbage in, garbage out?
8 references·16 citing·47 second hop
Garbage in, garbage out? (2020)Garbage in, garbage out?Good enough practices in scientific computing. (2017)Good enough practices in scie…Computational Grounded Theory: A Methodological Framework (2017)Computational Grounded Theory…Jupyter Notebooks – a publishing format for reproducible computational workflows (2016)Jupyter Notebooks – a publish…Open Science: One Term, Five Schools of Thought (2013)Open Science: One Term, Five …Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups (2007)Consolidated criteria for rep…Professional Vision (1994)Professional VisionLaboratory Life: The Social Construction of Scientific Facts (1986)Laboratory Life: The Social C…The Discovery of Grounded Theory: Strategies for Qualitative Research. (1968)The Discovery of Grounded The…REFORMS: Consensus-based Recommendations for Machine-learning-based Science (2024)REFORMS: Consensus-based Reco…The Data-Production Dispositif (2022)The Data-Production DispositifPower to the People? Opportunities and Challenges for Participatory AI (2022)Power to the People? Opportun…Healthsheet: Development of a Transparency Artifact for Health Datasets (2022)Healthsheet: Development of a…An Overview of Artificial Intelligence Ethics (2022)An Overview of Artificial Int…Algorithmic fairness datasets: the story so far (2022)Algorithmic fairness datasets…Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2021)Towards Accountability for Ma…On the genealogy of machine learning datasets: A critical history of ImageNet (2021)On the genealogy of machine l…Documenting Computer Vision Datasets (2021)Documenting Computer Vision D…Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021)Do Datasets Have Politics? Di…Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application (2021)Assessing biases, relaxing mo…“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data? (2021)“Garbage in, garbage out” rev…ORES (2020)ORESLanguage (Technology) is Power: A Critical Survey of “Bias” in NLP (2020)Language (Technology) is Powe…Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020)Data and its (dis)contents: A…Between Subjectivity and Imposition (2020)Between Subjectivity and Impo…Citizen science: a study of people, expertise and sustainable development (1996)The Politics of Talk (2006)What is open peer review? 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(2005)Theory Building From Cases: Opportunities And Challenges (2007)Seeking Qualitative Rigor in Inductive Research (2012)Open science challenges, benefits and tips in early career and beyond (2019)The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update (2020)NeuroKit2: A Python toolbox for neurophysiological signal processing (2021)
this work references cited by second hop

References

  • 1968The Discovery of Grounded Theory: Strategies for Qualitative Research.46,361 cited
  • 2007Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups42,389 cited
  • 1994Professional Vision3,339 cited
  • 2016Jupyter Notebooks – a publishing format for reproducible computational workflows3,069 cited
  • 1986Laboratory Life: The Social Construction of Scientific Facts2,980 cited
  • 2017Computational Grounded Theory: A Methodological Framework702 cited
  • 2013Open Science: One Term, Five Schools of Thought678 cited
  • 2017Good enough practices in scientific computing.452 cited

Cited by

  • 2020Language (Technology) is Power: A Critical Survey of “Bias” in NLP1,044 cited
  • 2020Data and its (dis)contents: A survey of dataset development and use in machine learning research519 cited
  • 2022An Overview of Artificial Intelligence Ethics495 cited
  • 2022Power to the People? Opportunities and Challenges for Participatory AI330 cited
  • 2021Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure279 cited
  • 2021On the genealogy of machine learning datasets: A critical history of ImageNet220 cited

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2021
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
215 cited
  • 2020Between Subjectivity and Imposition150 cited
  • 2024REFORMS: Consensus-based Recommendations for Machine-learning-based Science144 cited
  • 2022The Data-Production Dispositif121 cited
  • 2022Algorithmic fairness datasets: the story so far114 cited
  • 2021“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?97 cited
  • 2020ORES82 cited
  • 2021Documenting Computer Vision Datasets82 cited
  • 2022Healthsheet: Development of a Transparency Artifact for Health Datasets78 cited
  • 2021Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application75 cited