How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
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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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- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing16 Nov 2025
- 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks3 Jul 2025
- 2025Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability25 Jun 2025
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- 2024SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users17 Jun 2024
- 2024Collaborative Team Recognition: A Core Plus Extension Structure7 Jun 2024
- 2023"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research30 Nov 2023
- 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education9 Nov 2023
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice10 Jun 2023
- 2023DASS Good: Explainable Data Mining of Spatial Cohort Data10 Apr 2023
- 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design6 Mar 2023
- 2023Safe-DS: A Domain Specific Language to Make Data Science Safe28 Feb 2023
- 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making17 Feb 2023
- 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning7 Dec 2022
- 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm6 Jul 2022
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata6 Jun 2022
- 2022Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work21 Mar 2022
- 2021Human-Centered AI for Data Science: A Systematic Approach3 Oct 2021
- 2021Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML9 Apr 2021
- 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models29 Jan 2021
- 2021AutoDS: Towards Human-Centered Automation of Data Science13 Jan 2021
- 2021How Much Automation Does a Data Scientist Want?7 Jan 2021
- 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
- 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
- 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
- 2025KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakescited by 30
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
- 2021How Much Automation Does a Data Scientist Want?cited by 20
- 2022Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Workcited by 15
- 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Designcited by 15
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
- 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Modelscited by 12
- 2021Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoMLcited by 12
- 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Makingcited by 11
12 most cited of 29 All 29 (JSON)
- 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems30 Apr 2026
- 2026AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science19 Mar 2026
- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing16 Nov 2025
- 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks3 Jul 2025
- 2025Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability25 Jun 2025
- 2025KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes6 Jun 2025
- 2025AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science25 May 2025
- 2025Exploring the Innovation Opportunities for Pre-trained Models21 May 2025
- 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages15 May 2025
- 2024Steering AI-Driven Personalization of Scientific Text for General Audiences15 Nov 2024
- 2024GEMS: Generative Expert Metric System through Iterative Prompt Priming1 Oct 2024
- 2024SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users17 Jun 2024
12 newest of 29 All 29 (JSON)