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

arXiv: https://arxiv.org/abs/1909.03486 · HTML: https://mlsanity.com/paper/1909.03486/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 30 of 30)

- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022)
- [KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes](https://mlsanity.com/paper/2506.06541/) (2025)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work](https://mlsanity.com/paper/2203.11085/) (2022)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice](https://mlsanity.com/paper/2306.06542/) (2023)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021)
- [Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making](https://mlsanity.com/paper/2302.08807/) (2023)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science](https://mlsanity.com/paper/2506.13992/) (2025)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Can Agentic AI Match the Performance of Human Data Scientists?](https://mlsanity.com/paper/2512.20959/) (2025)
- [Human-Centered AI for Data Science: A Systematic Approach](https://mlsanity.com/paper/2110.01108/) (2021)
- [Safe-DS: A Domain Specific Language to Make Data Science Safe](https://mlsanity.com/paper/2302.14548/) (2023)
- [AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science](https://mlsanity.com/paper/2603.19005/) (2026)
- [GEMS: Generative Expert Metric System through Iterative Prompt Priming](https://mlsanity.com/paper/2410.00880/) (2024)
- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [DASS Good: Explainable Data Mining of Spatial Cohort Data](https://mlsanity.com/paper/2304.04870/) (2023)
- ["We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research](https://mlsanity.com/paper/2311.18424/) (2023)
- [Collaborative Team Recognition: A Core Plus Extension Structure](https://mlsanity.com/paper/2406.06617/) (2024)
- [SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users](https://mlsanity.com/paper/2406.11232/) (2024)
- [Exploring the Innovation Opportunities for Pre-trained Models](https://mlsanity.com/paper/2505.15790/) (2025)
- [Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability](https://mlsanity.com/paper/2507.02922/) (2025)

## 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.