<p>Data valuation, the process of assigning monetary worth to data based on its utility, impact, and scarcity, has become increasingly critical in today’s digital landscape. As data becomes a valuable asset for organizations, governments, and individuals, effective data valuation supports informed decision-making related to the acquisition, sharing, and monetization of data. The Shapley Value, a game-theoretic concept, has emerged as a prominent approach to data valuation due to its equitable distribution of value among contributors. In this paper, we investigate the use of Shapley Value-based data valuation in Machine Learning Data Markets (MLDM). MLDM is a distributed ML platform where data is traded among agents developing individual models for a given problem. Accurate valuation is, thus, crucial for ensuring fair and efficient exchanges. We introduce the Gain Data Shapley Value (GDSV), a new data valuation method for MLDM, and conduct a comprehensive empirical study to compare its effectiveness against performance-based data valuation. Our findings demonstrate that considering the contribution of data sets to performance scores can lead to systematic improvements in learning performance in MLDM.</p>