Creating precise and data-driven battery aging models has emerged as a prominent focus within the research community [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]]. The accuracy of predictions related to a battery's State-of-Health (SoH) or Remaining Useful Life (RUL) depends on the quality of the model's training data. However, acquiring battery aging data is expensive as it requires extended equipment usage, time, and utilizing new cells until their end-of-life is reached, preventing any reuse for them [12]. The aging of a Li-ion battery is influenced by many parameters such as the temperature, the charge and discharge profiles, and the State-of-Charge (SOC) window in which the tests are performed. Consequently, open-source data can benefit the community because models can be trained, tested, and validated on more conditions. These open-source datasets even offer the potential to develop models without conducting specific aging experiments. Nevertheless, it is important to explore the dataset before using it. Given that amalgamated data from disparate sources can sometimes exhibit inconsistencies that compromise data integrity, it is essential to address this potential issue. Building upon the reviews of dos Reis et al. (2021) [13] and Hasib et al. (2021) [14], this review aims to focus on open-source datasets obtained from over 25 distinct sources, encompassing >1300 cells. The objective is to present these datasets, highlight their biases and limitations, and ultimately researchers in selecting the most suitable dataset for their specific needs.