The Uneven Impact of Big Data in Science: A Literature Review and Reflective Examination of Big Data in Data-Intensive Disciplines

Data practices vary widely across scientific disciplines. While Big Data has significantly transformed research activities across various domains and has been described as a revolutionary force in scientific paradigms, its application has not been uniform across all fields. This study examines Big Data research and practices in data-intensive disciplines (DIDs), identifying its distinct features and revealing the uneven adoption and impact of Big Data across scientific domains. Our findings indicate that discussions on the epistemological concepts and definitions of Big Data in DIDs are limited, with little divergence among scholars. Machine learning emerges as a central understanding and technological focus across DIDs, closely integrated with research topics and widely driving scientific advancements. Additionally, this paper highlights the instrumental role of Big Data in scientific inquiry and underscores the disparities in its impact across different disciplines. Through this review, we aim to foster a more comprehensive understanding of Big Data’s evolving role in science, emphasizing the need for continued critical reflection as its influence continues to develop.