The Atlas of Data Science Research | Zendy

Sergio Picascia | Zendy; Stefano Montanelli | Zendy; Silvia Salini | Zendy; Stefano Verzillo | Zendy

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The Atlas of Data Science Research

Author(s) -

Sergio Picascia,

Stefano Montanelli,

Silvia Salini,

Stefano Verzillo

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3618442

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The origin and evolution of Data Science (DS) have been a subject of ongoing debate, with perspectives varying across disciplines. Understanding the development of this field requires a data-driven approach that systematically analyzes the scientific literature and provides a practical method for its exploration. In this paper, we present the “Atlas of Data Science Research” (DS-Atlas), an interactive visualization tool designed to study the landscape of the DS field. The DS-Atlas is built on a dataset of approximately 1.3 million scientific publications from the Elsevier Scopus database, leveraging Natural Language Processing, Large Language Models, and dimensionality reduction techniques to generate a semantic representation of the DS research. The DS-Atlas provides interactive operations to explore the dataset by allowing users to focus on specific areas, filter by keywords and/or time periods, and uncover thematic connections and research trends. Examples of concrete tasks that can be addressed by DS-Atlas are discussed to show how the proposed solution can support scholars in the data-driven analysis of the data science literature. As a further DS-Atlas contribution, the paper illustrates an analysis of the Data Science discipline in terms of geographical distribution of influential authors, institutions, and journal in the field. The DS-Atlas is publicly available online for exploration and testing.

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