###### Abstract  
The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality datasets. This work uses existing terminologies, definitions, and core features of STPs to conduct a multidimensional data analysis that explores and evaluates the 21 core features which describe the key internal factors of an STP. The core features are gathered from a reliable and updatable dataset of Spanish STPs. The methodological framework can be replicated for other STP contexts and is based on descriptive techniques and machine-learning tools. The results of the study provide an overview of the general situation of STPs in Spain, validate the existence and characteristics of three types of STPs, and identify the typical features of STPs. Moreover, the prototype STP can be used as a benchmark so that other STPs can identify the features that need to be improved. Finally, this work makes it possible to carry out classifications of STPs, in addition to prediction and decision making for innovation ecosystems. science and technology parks; multidimensional data analysis; core features; dataset; classification; decision making; machine learning +
Footnote †: journal: Applied sciences  
Moreover, the growing availability of data and the advent of artificial intelligence (AI) have introduced new opportunities along with complexities when it comes to assessing and managing STPs. The application of AI tools for data collection, cleaning, and analysis is becoming increasingly vital for understanding the multifaceted nature of these innovation ecosystems. This evolving landscape calls for a systematic approach to redefine and categorise STPs, establish standardised metrics, and harness the power of AI for comprehensive evaluation. The specific problems detected in the field of STPs are the lack of: (i) critical standards in the definition, classification, and valuation of STPs; (ii) standardised and manageable sources of information in the field of STPs; and (iii) specific technologies for analysing STP ecosystems and enabling informed decision making. Problems (i) and (ii) have been recently addressed in a previous work by the authors [2]. Following on from this line of research, the present study addresses problem (iii) with the development of technologies oriented towards an in-depth analysis and informed decision making for STPs. This problem has not been explored in the literature to date. Recently, [PERSON] et al. [2] have developed different resources that are of crucial interest for this study, which are summarised next: the definition of three types of STPs; the core features that define the key internal factors that affect STPs; and a methodology to build standardised datasets of STPs applied to the use case of Spanish STPs. On the one hand, short definitions of the three main STPs models were discussed and provided (Table 1). These definitions were considered for the expert labelling for the case study of Spanish STPs, also described by [PERSON] et al. [2]. As explained, slight differences can be noted between the characteristics of an STP, as well as regarding their performance and impact. [PERSON] et al. [2] also analyses the internal and external key factors affecting STPs identified in the literature. Internal factors can be malleable and external factors are totally outside the \"control\" of the STP decision-making management process. Hence, this work, in line with the dataset presented in [2], focuses on internal factors alone. Nevertheless, the methodology presented will allow the incorporation of as many factors (endogenous and exogenous) as required. Table 2 presents each internal key factor together with their corresponding descriptive core feature(s) that were considered in the study.