mlsanityData to 7 Oct 2026

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

arXiv 1909.03486 ↗First posted 8 Sep 2019cs.CY cs.AI cs.HCcited by 30

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Abstract (arXiv)

In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2019.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 7; 2024: 4; 2025: 8; 2026: 2.

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  1. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  2. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  3. 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
  4. 2025KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakescited by 30
  5. 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
  6. 2021How Much Automation Does a Data Scientist Want?cited by 20
  7. 2022Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Workcited by 15
  8. 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Designcited by 15
  9. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  10. 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Modelscited by 12
  11. 2021Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoMLcited by 12
  12. 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Makingcited by 11
18 more
  1. 2024Steering AI-Driven Personalization of Scientific Text for General Audiencescited by 7
  2. 2025AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Sciencecited by 7
  3. 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Educationcited by 5
  4. 2025Can Agentic AI Match the Performance of Human Data Scientists?cited by 3
  5. 2021Human-Centered AI for Data Science: A Systematic Approachcited by 2
  6. 2023Safe-DS: A Domain Specific Language to Make Data Science Safecited by 2
  7. 2026AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Sciencecited by 2
  8. 2024GEMS: Generative Expert Metric System through Iterative Prompt Primingcited by 1
  9. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  10. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  11. 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writingcited by 1
  12. 2023DASS Good: Explainable Data Mining of Spatial Cohort Datacited by 0
  13. 2023"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare researchcited by 0
  14. 2024Collaborative Team Recognition: A Core Plus Extension Structurecited by 0
  15. 2024SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Userscited by 0
  16. 2025Exploring the Innovation Opportunities for Pre-trained Modelscited by 0
  17. 2025Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainabilitycited by 0
  18. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 0