## 1 Introduction  
In modern wind turbines, a plethora of operating data are acquired with high temporal frequency [1, 2] by a vast number of sensors [3, 4]. However, usually only a selection of these sensor data is stored. Furthermore, the data are typically aggregated as 10-min average values, sometimes accompanied by the standard deviations or the maxima and minima measured in these intervals. This temporal aggregation of a signal, also referred to as downsampling, saves a lot of space upon storage and reduces the bandwidth needed when transferring the data, both connected to cost savings. Unfortunately, much of the information on short timescales that might be valuable to better model and track the behavior and condition of wind turbines is inevitably lost in this process [4, 5, 6]. Aggregating data induces an information loss regardless of the source of data, even though its impact depends on the downsampling rate with respect to the behavior and the resolution of the raw signal. Albeit, the consequences arising from this signal conversion depend strongly on the further use of the data. Understanding these consequences, by knowing the properties of a signal after its transition to lower resolutions, will therefore help to optimize both data storage and costs while at the same time providing the best possible signal quality for analytic investigations. In this study we investigate operating data of wind turbines recorded by the supervisory control and data acquisition system (SCADA). SCADA is a control system which among other functionalities allows for monitoring of wind turbines and receives input from a net of sensors that measure various operating variables such as wind speed, active power, temperatures, pressures, speeds, and environmental conditions through time. As we only investigate operating data, we will refer to \"SCADA operating data\" as simply \"SCADA data\" throughout the paper. Please note that although we developed the methods and algorithms to quantify information loss for wind turbine data, they are not necessarily restricted to our use case. While the considerations regarding specific consequences are limited to the wind energy field only, the approach can also be extended to other fields of application. In many technological sectors data are considered a key asset to foster growth and innovation--wind energy is no exception. In the need of clean energy, wind power prospers globally: During the year 2020, 111 GW of new capacity were installed worldwide [7]. In Europe alone, wind power capacity amounted to 220 GW by the end of 2020. It is desired to grow between 80 and 105 GW over 2021-2025 out of which 29 GW are planned to be installed offshore [8]. Therefore, using data for better and more efficient operational strategies will be pivotal to further reduce the costs and to sustain the competitiveness of wind production compared to conventional energy sources. On the one hand, a profound data basis can support methods for accurate power output predictions. An overview about possible wind energy forecasts was given by [PERSON] and [PERSON] [9]. On the other hand, monitoring the health status of turbines supports the improvement of reliability by understanding and anticipating failures. The current state of using SCADA data for condition monitoring was summarized by [PERSON] and [PERSON] [4]. Each prevented turbine fault will avert a subsequent standstill and an involved loss of revenue. In the end this also reduces the costs for operation and maintenance (O&M) that currently account for up to 30% of an onshore wind turbine's levelized cost of electricity (LCOE) and up to 25% of the much higher LCOE of an offshore turbine [10]. These costs are thus still a major burden for the wind energy industry. Research can help to minimize such costs by approaches of early fault detection or health monitoring methods. One possible solution addresses mechanical components by monitoring, e.g., the drivetrain or transmission elements through rich high-frequency data.