Dust bathing events obtained from video-recordings as well as acceleration vectors recorded with accelerometer obtained in Japanese quail

Annotated base constructed to validate the methodological approach for detecting dustbathing events presented in e publication "Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis" (Fonseca et al., 2024. Ethology). For this experiment was performed where the behaviour of Japanese quail (Coturnix japonica), equipped with accelerometers, was captured through video recording within their home boxes. Experimental disign is presented in detail in Fonseca et al [1]. A total of 13 pairs of male/female quail were studied, named <b>boxes 1-13, as stated in file names</b>. The week before the experiment, a backpack was fitted onto the male, to promote habituation to it (Pellegriniet al., 2019). The backpack was 3D-printed in black plastic and had two elastic bands on their sides to be passed around the base of the animal's wings. On the morning of the experiment (between 9-11 am) the TechnoSmArt@ accelerometer-loggers Axyz was inserted into the backpack using a specially designed applicator to ensure synchronization between the acceleration time series and the two video recordings (top and side cameras). Tri-axial accelerometers were set to gather data with a sampling frequency of 25 Hz (i.e. 25 data points per second) based on previous studies that show the possibility of high-speed transitions between behaviours in this species [2]. A wire bar wall partition was inserted in the middle of the home box, dividing it into two separate, equal-sized, compartments. After the placement of accelerometers in the backpack, males were then positioned in one of these two home box compartments. Ten minutes later, the female companion bird was placed into the adjacent compartment. After a second 10-minute period, the wall partition was lifted, allowing the birds to interact. The respective video recordings began before the placement of the accelerometer and ended after the accelerometer was removed. Testing lasted 6h, thus given the 25Hz sampling frequency of the accelerometer, for each male at least 540,000 time points were obtained for the three axial compoenets (x, y and z) of the acceleration vector, corresponding to <b>dataset columns ax,ay and az</b>, respectively. For the second aim of this study, the last group (box 13) was recorded over a week-long period to demonstrate the methodology´s potential for studying dustbathing dynamics, such as daily rhythms, in long-term studies.Front and side video recordings were first scanned by an observer trained to detect dustbathing events. By strict definition, dustbathing can be characterized as a precise and orderly sequence of movements consisting of (a) pecking alternately from side to side with a closed beak; (b) scratching (one foot at a time) while sitting or squatting; (c) tossing the dust with the wings and undulating the body underneath the dust shower; and (d) occasionally rubbing head and/or body parts in the dust. Movements (b) and (c), and sometimes (a) and (d), are repeated a variable number of times. Since the initial pecking and scratching also appear in different behavioural contexts, the dust toss and body roll (undulation) were considered firm indicators of dustbathing. The time of beginning and end of each dustbathing event were determined from video recordings. Pauses longer than 5s, movement away from the site of dustbathing (usually preceded by standing and shaking), and/or the performance of another behaviour, marked the end of each dustbathing event. <b>The annotated database was constructed by assigning the correspondence between accelerometer data and video recordings, with each time point (i.e.column labeled time, expresend in seconds) from the accelerometer annotated with a label indicating whether the bird had been observed dustbathing. Labels for the dustbathing column are indicated with a 0 if the behavior is not being performed and a 1 if behavior is being performed at a given time point.</b>Maximum power of the y-axes component of the acceleration vector (ay) was estimated using the complex Morlet mother wavelet as descibed in [1]. Code provided is [3,4]. The squared absolute value of the complex wavelet coefficient is the power. When depicted in a magnitude scalogram the resulting plot is called a spectrogram (Flesia et al., 2022). The maximum value of power estimated within the range of scales 25 – 60 s is presented in the dataset as power spectrum coefficient (PSC) for the three axes, c<b>olumns PSC_x, PSC_y and PSC_z</b>.<br>本标注数据集用于验证发表于《Ethology》2024年的论文《Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis》（Fonseca等人，2024）中提出的尘浴行为检测方法。本实验以日本鹌鹑（Coturnix japonica）为研究对象，为其佩戴加速度计，并在其饲养箱内通过视频录制采集其行为数据。实验设计细节详见Fonseca等人的文献[1]。本研究共纳入13对雌雄鹌鹑，对应<b>编号1-13的饲养箱，与文件名标注一致</b>。实验前一周，为雄性鹌鹑佩戴背负装置以使其适应佩戴状态（Pellegrini等人，2019）。该背负装置采用黑色塑料3D打印而成，两侧配有两条松紧带，可环绕鹌鹑翅膀根部固定。实验当日上午（9时至11时），使用专用安装工具将TechnoSmArt@品牌的Axyz加速度记录器植入背负装置，以确保加速度时间序列与两路视频录制（顶部与侧面摄像头）实现同步。参考此前关于该物种行为间存在高速转换的研究[2]，本实验将三轴加速度计的采样频率设置为25 Hz（即每秒采集25个数据点）。在饲养箱中部安装铁丝隔栏，将箱体分为两个大小相等的独立隔间。将安装好加速度计的雄性鹌鹑放入其中一个隔间，10分钟后将雌性同伴放入相邻隔间；再经过10分钟后，移除隔栏以允许两只鹌鹑互动。视频录制始于加速度计安装前，止于加速度计移除后。本次实验持续6小时，结合加速度计25 Hz的采样频率，每只雄性鹌鹑的加速度向量三轴（x、y、z）均可获得至少540,000个时间点数据，分别对应数据集的<b>ax、ay、az三列</b>。针对本研究的第二项目标，我们对第13组（饲养箱13）进行了为期一周的录制，以验证该方法在长期研究中探究尘浴行为动态（如昼夜节律）的潜力。首先由经过尘浴行为识别训练的观察员对正面与侧面的视频录制素材进行筛查。根据严格定义，尘浴行为可被描述为一系列精准有序的动作，包括：(a) 喙部闭合，交替向两侧啄取；(b) 蹲坐时单爪轮流抓挠；(c) 用翅膀扬起尘土，并在扬起的尘幕下起伏身体；(d) 偶尔在尘土中摩擦头部或身体其他部位。动作(b)与(c)，有时连同(a)与(d)，会重复可变次数。由于初始啄取与抓挠动作也会出现在其他行为场景中，因此将尘土扬起与身体起伏（摆动）视为尘浴行为的可靠判定指标。通过视频录制确定每一次尘浴事件的起始与结束时间。单次尘浴事件的结束标志包括：停顿时长超过5秒、离开尘浴地点（通常伴随站立与抖毛动作），或进行其他行为。<b>本标注数据库通过建立加速度计数据与视频录制的对应关系构建完成：为加速度计的每个时间点（即标注为time的列，单位为秒）标注对应标签，以表明该时刻鹌鹑是否正在进行尘浴行为。尘浴行为列的标签规则为：若当前时刻未发生尘浴行为则标记为0，若正在发生则标记为1。</b>加速度向量y轴分量（ay）的最大功率通过复Morlet母小波进行估算，具体方法详见文献[1]。相关代码已公开[3,4]。复小波系数的平方绝对值即为功率值。当以幅值尺度图形式展示时，所得图像称为频谱图（Flesia等人，2022）。在25–60秒的尺度范围内估算得到的最大功率值，将作为三轴的功率谱系数（Power Spectrum Coefficient, PSC）存入数据集，对应列分别为<b>PSC_x、PSC_y和PSC_z</b>。