# A Knowledge Graph Framework for Dementia Research Data
## 3 Materials and Methods
### Data Transformation Design Patterns  
One of the aims of the KG is to integrate raw research data to enable explicit connections with knowledge concepts. We propose a set of design patterns to support the data instantiation of patient/subject study visits, phenotype observations arising from these visits, measurements/analyses derived from samples collected from different specimens, and neuropsychological test results. In all these patterns, OBI is the central ontology employed to enable the relating of clinical and research concepts with specific entities of the biomedical domain. Figures 1-3 illustrate the main patterns through simplified concept map figures, depicting the main ontology classes and properties involved, identified with a pseudo-CURIE of the format PREFIX, namely, \"class label\", where prefix is the OBO ontology prefix. The first pattern models the relations between study protocol/visit encounters, the agents involved, and the resulting outputs. The pattern mainly utilizes concepts defined in the Neurodegenerative Disease Data Ontology (NDDO) [91] (integrated in NIO) and the Ontology for General Medical Science (OGMS). The pattern supports a proper logical definition of longitudinal protocols, common in dementia research studies. Figure 1: Concept map of the visits (light blue) and clinical (orange) design patterns, depicting the main ontology classes employed to model data entities. Clinical history phenotypes are characterized through observations at a study visit or from existing records. The framework leverages a pattern that relates visits with specific clinical administration, the finding, and the observed phenotype, usually a phenotype or disease concept from MONDO or HP. Relevant metadata can also be linked to the OGMS clinical entities, such as dates, agents involved, and locations. This pattern is shared across medical history, physical examination, and diagnosis processes. Figure 1 illustrates both the visits and clinical patterns. A critical component of research data encompasses various assay measurements and proteomic datasets. We employ OBI's assay design patterns [92] to capture the multiple aspects involved in this process. These patterns enable the comprehensive integration of data pertaining to the assay, the specimen, and the molecule or material under examination, such as a protein or leukocyte count. Several relevant ontologies, including GO, PR, and Cell Ontology (CL), supply the necessary terminologies. We leverage entities from UBERON to denote the anatomical origin of the sample. This pattern facilitates the preservation of crucial metadata about processes, encompassing information about the type of assay, the specimen or sample employed, experimental conditions such as freeze-thaw cycles, and the date and time of collection. Such metadata is of considerable value for resource  
Figure 3: Exemplification of the neuropsychological test design pattern, through a CERAD recall test. Figure 2: Concept map of the experimental measurements design pattern. management and can significantly aid research analyses. For instance, the type of tube in which a sample was collected could influence assay results and should be accounted for in linear models. Overall, it provides a more comprehensive context of the conditions under which experiments are conducted, enhancing the reproducibility and reliability of experimental outcomes. Analyses derived from neuroimaging techniques, including segmentation measurements from tools such as Freesurfer [93] and Automatic Sub Hippocampal Segmentations (ASHS) [94], along with white matter evaluations from Diffusion Tensor Imaging (DTI) [95] and peak width of skeletonized mean diffusivity (PSMD) [96], play an indispensable role in dementia research.