## 1 Introduction  
Our inability to accurately capture climatological changes of sea ice in the polar seas has created renewed interest in the dynamic interaction between sea ice and waves. This has resulted in the last few years in a number of studies that investigate the coupling between sea ice and the ocean through theoretical considerations [1; 2; 3; 4; 5; 6; 7], laboratory experiments [8; 9; 10; 11; 12], and field experiments [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Despite the advances that these studies bring, there is a growing consensus that further progress in the field can only be achieved through the collection of more observations of waves in ice. In particular, phenomena related to floe size distribution [4; 25; 26; 27], energy dissipation due to turbulence and collisions [28; 29; 30; 31], and sea ice breakup [32; 33; 34; 35] are still imperfectly modeled and understood, and advancing the state-of-the-art would require additional direct observations from the ice. Unfortunately, one faces a number of challenges in reproducing the complexity of wave-ice interaction either in experiments (due to, for example, scaling issues [9], and the diversity and realism of sea ice conditions that can be obtained in the laboratory compared with the field [12]), or in models (there, also, due to the complexity and multiscale properties of waves in ice and ice floe size distribution [36; 37]). Arguably, the current state of the art in wave-ice interaction parametrization and modeling consists of a variety of (involved and mathematically advanced) models whose formulations are relatively loosely based on some specific archetypical sea ice conditions and the associated physical mechanisms, with a number of \"tuning parameters\" that are empirically fitted to experimental or laboratory data [38; 39]. This approach is quite ad hoc and brittle, which is in stark contrast with the importance of waves in ice and their impact on both weather and climate. As a consequence, having access to large datasets of high temporal and spatial resolution direct observations of waves in ice is a key ingredient to improving both small scale wave-ice interaction parametrization, large-scale coupled wave-ice models, and our understanding of the weather and climate dynamics in the polar regions. There are a number of recent works that highlight the importance of the volume of such datasets for unveiling wave-ice interaction mechanisms. For example, [PERSON] et al. [14] showed that wave conditions that result in sea ice breakup events can be quite accurately described with a simple threshold mechanism, but in order to be able to observe such a threshold, a relatively large aggregated dataset is necessary. There are relatively few methods available for measuring waves in ice. The most common and established method considers the deployment of instrumentation in situ, providing surface truth of the sea ice motion under the influence of waves. Such instruments typically use either an Inertial Motion Unit (IMU, i.e., a combination of accelerometer, gyroscope, and magnetometer, together with some data fusion processing, e.g., [PERSON] et al. [40], [PERSON] et al. [41]), or a high-accuracy GPS [42; 43] to measure the sea ice motion and compute wave spectra. Less common techniques are using pressure sensors to measure the wave-induced pressure fluctuation to derive wave observations [23]. Remote sensing is a promising avenue to obtain observations of waves in ice across large spatial scales [44; 45] to a far larger extent compared with what can be achieved using in situ instrumentation, though the number of wave parameters that can be retrieved and the accuracy that can be achieved is still limited and a topic for open research. Moreover, remote sensing requires considerably more in situ observations for calibration and validation. As a consequence, the most-established and accurate method today is still to deploy instrumentation in situ to measure waves in ice.