Dataset from 2025 Leonardo Grando Ph.D. Thesis. This dataset is used in three Thesis related publications also. The thesis abstract is: Unmanned aerial vehicles (UAVs), also called drones, can be used in critical applications, such as disaster recovery support or precision agriculture. However, their autonomy is limited due to their small size and capacity. New forms of energy can increase the autonomy of drones and improve the coordination of their processes, both work and decision-making for recharging their energy sources. This work aims to develop coordination proposals for the recharging process of these devices, aiming to increase their autonomy. In this research, drones are considered IoT devices, operating together, in the form of a swarm. Drones need to decide whether or not to recharge their batteries at a recharging station. This work presents two main results: first, the identification of three gaps in the literature from a systematic review on the process of coordinating drone recharging in the context of applications in agriculture and disasters. The second result was the development and documentation of an agent-based simulation (ABM) model in NetLogo software, in which 12,000 simulation runs were performed. In this model, a swarm of drones is considered acting autonomously and without communication with each other when deciding whether to recharge. For this model, two decision-making policies were proposed, called Baseline (BL) and ChargerThreshold (CT), which were developed and tested, in addition to three indicators to evaluate robustness and efficiency in different situations. The literature review showed that there are few practical studies in the context of recharging drone swarms. The development of a simulation base, such as this work, as well as the creation of indicators to evaluate simulation scenarios, are important contributions to this research area, as they serve as a basis for future developments. The results highlighted three types of gaps found in the literature and showed that both policies work well in situations with lower energy demand. However, in scenarios with higher battery consumption, the CT policy proved to be more efficient, allowing the set of drones to perform the expected work autonomously. The simulation results highlight the potential of the proposed decision-making strategies to optimize drone coordination in real-world scenarios, as well as showing the potential for using this approach in other applications, such as the electric vehicle recharging process.