# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 1 Introduction  
The exponential growth of biological data over the past two decades has transformed biomedical research, enabling data-driven discovery and hypothesis generation at an unprecedented scale. However, much of these data remain locked behind dynamic, heterogeneous, and poorly integrated web interfaces, creating significant barriers to automated access and reproducible analysis. Although initiatives like the FAIR principles [1] and resources like FAIRsharing [2] aim to improve the discoverability of standards, repositories, and policies, they largely stop short of providing machine-actionable pathways to the underlying datasets. This gap impedes not only reproducibility and open science but alsothe development of intelligent systems capable of large-scale, automated data discovery and integration. Recent empirical studies underscore the magnitude of this problem. An analysis of addiction research datasets using the F-UJI tool across 5967 DOIs found that none of the six major categories (alcohol, cannabis, tobacco, cocaine, opioids, and stimulants) exceeded 30% of the overall FAIR compliance, with Findability scores around 40-59% and Reusability consistently the lowest [3]. A meta-research study of COVID-19 research data reported that while Findability reached 100% (moderate/advanced) in general-purpose repositories, Accessibility dropped to 21.5%, Interoperability to 46.7%, and Reusability to 61.3% [4]. Beyond these domain-specific studies, broader surveys highlight pervasive metadata and sharing deficiencies. [PERSON] et al. [5] reported that fewer than 7% of authors deposited data in community repositories, and only 22% used metadata standards. These findings illustrate that data remain fragmented and challenging to access, even in ostensibly open repositories. Researchers across domains encounter this problem when attempting to retrieve data spanning multiple specialized databases, each with distinct schemas and access mechanisms [6]. To illustrate this, consider a researcher interested in identifying genes and proteins linked to diseases that are annotated with pathway involvement, localization, and confidence scores similar to those from UniProtKB [7; 8; 9]. Relevant data are scattered across portals like UniProt, GeneCards, DisGeNET [10], and CTD [11], each with heterogeneous interfaces and fragmented schemas. For example, a researcher might pose a natural language query such as  
_I want to find proteins that are associated with diseases and have specific functional annotations and UniProt annotation scores._  
This query exemplifies the increasingly nuanced demands of biomedical researchers seeking integrative, context-rich protein information. Rather than issuing basic keyword-based searches, users aim to assemble datasets filtered by biological function, disease relevance, and curated confidence metrics such as UniProt annotation scores. Satisfying such queries requires the synthesis of protein-disease associations, often curated in disease-specific portals--with pathway- or domain-level annotations extracted from ontologies like Gene Ontology or Reactome [12], alongside quality indicators derived from UniProt records. However, these resources are rarely unified. Relevant data are scattered across multiple specialized repositories, each with distinct schemas, access modalities, and ranking conventions. Navigating this landscape entails not only identifying relevant repositories, but also interacting with dynamic search forms, interpreting annotation schemas, and validating score thresholds defined across heterogeneous scoring systems. The result is a workflow that is both cognitively and operationally burdensome--requiring domain expertise, familiarity with each portal's interface, and manual integration of partial results. In the absence of machine-readable access pathways, this fragmentation impedes reproducible data discovery and limits the broader utility of protein annotation in translational research contexts.