How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation

By Michael J. Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Liao, Casey Dugan, Thomas Erickson. International Conference on Human Factors in Computing Systems, 2019

311 citations recorded by the source corpus.

With the rise of big data, there has been an increasing need for practitioners in this space and an increasing opportunity for researchers to understand their workflows and design new tools to improve it. Data science is often described as data-driven, comprising unambiguous data and proceeding through regularized steps of analysis. However, this view focuses more on abstract processes, pipelines, and workflows, and less on how data science workers engage with the data. In this paper, we build on the work of other CSCW and HCI researchers in describing the ways that scientists, scholars, engineers, and others work with their data, through analyses of interviews with 21 data science professionals. We set five approaches to data along a dimension of interventions: Data as given; as captured; as curated; as designed; and as created. Data science workers develop an intuitive sense of their data and processes, and actively shape their data. We propose new ways to apply these interventions analytically, to make sense of the complex activities around data practices.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Safety Research

View Paper: https://www.semanticscholar.org/paper/398b154013db9d8025bf60f910bc156dedd9b40e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Michael J. Muller
2. Amy X. Zhang
3. Alexander G. Gray
4. Horst Samulowitz
5. Parikshit Ram
6. Dakuo Wang

In Information Systems and Management

1. Michael J. Muller
2. Amy X. Zhang
3. Lora Aroyo
4. Justin D. Weisz
5. Dakuo Wang
6. Paul M. Leonardi

In Safety Research

1. Werner Geyer
2. Michael J. Muller
3. Amy X. Zhang
4. Justin D. Weisz
5. Nithya Sambasivan
6. Dakuo Wang

Citations in the corpus

• Between Subjectivity and Imposition (2020, background): “ed promise of mechanical neutrality” [41]. However, these systems require, in many cases, the intervention of human discretion in their deployment [2, 64], and even more frequently, in their creation [18, 22, 35, 36, 39, 48, 51, 61, 66, 67]. Moreover, critical research has argued that data-driven sy”
• Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies (2021, unknown): “theory [91, 103] and affinity diagramming [14, 64, 148, 149].”
• “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “ship have long examined the practices of collaboration, problem formulation, and sensemaking, by humans behind the datasets, including data collectors and scientists, [69, 86, 127], and are designing computational artefacts for dataset development [53].”
• How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, methodology): “ed code pipelines, and more consequential decisions and products. Meanwhile, research around data science topics has also increased rapidly within the HCI and CSCW community in the past several years [34, 50, 51, 54, 67, 81, 92, 93, 95]. From existing literature, we have learned that the data scienc”
• Human-AI Collaboration in Data Science (2019, background): “on citizen data scientists to help analyze non-proprietary data in events such as hackathons [27] or online challenges [32]. These models, while often successful, are not without their own challenges [48]. In addition to operational innovations in conducting data science, researchers and practitione”