## 2 Materials and Methods
### Datasets  
Experiments included within this study were performed on a total of three GM datasets. These datasets originated from experimental archaeology samples in taphonomy. Each of these samples thus represent the morphological features of microscopic alterations observed on bone, using GM to quantify these morphologies for diagnostic purposes. Nevertheless, considering the objective of this study is to observe the effects of generative learning for GM data augmentation, the origin of these datasets was considered unimportant. The reason behind this lies in how, regardless of the element under study, data used for GM analysis consists of superimposed landmark coordinates (Figures 1 and 2). From these coordinates, dimensionality reduction can be used to convert each element into a single vector from which models can learn from (Figure 2). Therefore, irrespective of whether the raw landmark data was obtained from paleoanthropological specimens, little tools or carnivore feeding samples (Figure 1), all landmarks are similarly embedded into a single vector that can be used as the input to our computational models. Additional use of these three case studies was based on how each dataset was personally generated by the corresponding author, providing a means of controlling the origin of information. The 3 datasets used consists of a mixture of manually placed landmarks (Type II or III; Figure 1), as well as some computational semi-landmarks [3; 4]. The three datasets include;  
* Dataset 1 (DS1); amid tooth score dataset [16]. This dataset consists of 105 individuals from three different experimental carnivore feeding samples (labelled foxes, dogs and wolves). 3D models  
Figure 2: Workflow proposed for data augmentation tasks in geometric morphometrics. for data extraction were generated using a low-cost structured light surface scanner (David SLS-2). The topography of each 3D digital model was then used to extract 2D images where landmarks could be placed. Landmark data consist of a mixture of Type II and Type III 2D landmarks. * Dataset 2 (DS2); scratch and graze trampling dataset [15]. This dataset consists of 60 individuals from two different experimental trampling mark samples (labelled scratches and grazes). Each of the elements under study were digitized employing a 3D Digital Microscope (HIROX KH-8700), using between 100\(\times\) and 200\(\times\) magnification. Collection of landmark data was then performed following a series of measurements that established a 3D coordinate system across the model. Landmark data consist of a mixture of Type II and Type III 3D landmarks. * Dataset 3 (DS3); semi-landmark based tooth pit dataset [29]. This dataset consists of an adaptation of DS1 using 60 individuals from two carnivore feeding samples (labelled dogs and wolves). 3D models for data extraction were generated using a low-cost structured light surface scanner (David SLS-2). Landmark data consist of a mixture of 3D Type II landmarks and a mesh of semi-landmarks. These three datasets were chosen considering the dimensionality of the corresponding feature-space produced for GM analysis (\(\mathbb{R}^{14}\), \(\mathbb{R}^{39}\) and \(\mathbb{R}^{60}\) respectively). With each of these datasets presenting different dimensionalities, optimal GAN architectures could therefore be proposed so as to establish a standardized protocol, regardless of the target domain's \(\mathbb{R}^{n}\) size. These datasets were also chosen to observe the effect original sample size has on the accuracy of synthetic data. The latter was tested via minimum sample size calculations according to [PERSON]'s \(d\) (power = 0.8, d = 0.8, \(\alpha\) = 0.05, ratio = 1:1) [35]. This established a minimum sample size for two-sample statistical comparisons of 26 individuals, rounded up to 30 for simplicity. In accordance with this calculation, experiments were performed by randomly sampling 30 real individuals and comparing them with 30 synthetic individuals.