# How Data Scientists Review the Scholarly Literature

arXiv: https://arxiv.org/abs/2301.03774 · HTML: https://mlsanity.com/paper/2301.03774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 151 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Explaining Relationships Between Scientific Documents](https://mlsanity.com/paper/2002.00317/) (2020)
- [Augmenting Scientific Creativity with an Analogical Search Engine](https://mlsanity.com/paper/2205.15476/) (2022)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery](https://mlsanity.com/paper/2108.05669/) (2021)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [How Work From Home Affects Collaboration: A Large-Scale Study of Information Workers in a Natural Experiment During COVID-19](https://mlsanity.com/paper/2007.15584/) (2020)
- [From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks](https://mlsanity.com/paper/2204.10254/) (2022)
- [Augmenting Scientific Creativity with Retrieval across Knowledge Domains](https://mlsanity.com/paper/2206.01328/) (2022)
- [ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology](https://mlsanity.com/paper/2003.05108/) (2020)
- [POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation](https://mlsanity.com/paper/2105.09204/) (2021)
- [ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives](https://mlsanity.com/paper/2204.04694/) (2022)
- [Searching to Learn with Instructional Scaffolding](https://mlsanity.com/paper/2111.14584/) (2021)
- [Argo Scholar: Interactive Visual Exploration of Literature in Browsers](https://mlsanity.com/paper/2110.14060/) (2021)
- [Understanding the "Pathway" Towards a Searcher's Learning Objective](https://mlsanity.com/paper/2208.07275/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond Text: Characterizing Domain Expert Needs in Document Research](https://mlsanity.com/paper/2504.12495/) (2025)

## Abstract (arXiv)

Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a deluge of research and coordinated efforts to develop AI tools intended to uncover the research frontier. Despite these trends indicative of the information overload faced by data scientists, no prior work has examined the specific practices and challenges faced by these scientists in an interdisciplinary field with evolving scholarly norms. In this paper, we close this gap through a set of semi-structured interviews and think-aloud protocols of industry and academic data scientists (N = 20). Our results while corroborating other knowledge workers' practices uncover several novel findings: individuals (1) are challenged in seeking and sensemaking of papers beyond their disciplinary bubbles, (2) struggle to understand papers in the face of missing details and mathematical content, (3) grapple with the deluge by leveraging the knowledge context in code, blogs, and talks, and (4) lean on their peers online and in-person. Furthermore, we outline future directions likely to help data scientists cope with the burgeoning research literature.