# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 1 Introduction
Despite the existence of several well-established registries such as FAIRsharing [2], NAR [13], Re3 Data [14], DataCite Commons [15], Zenodo [16], Dryad, [17] FigShare [18], OpenAIRE [19], and others designed to help researchers discover scientific datasets, the process of locating and programmatically accessing relevant data resources remains remarkably difficult. The widespread adoption of the FAIR principles was intended to improve the usability of scientific data. However, in practice, adoption has been uneven and has fallen short of enabling seamless, automated access to datasets required by modern computational research tools [2]. Dynamic, interactive web interfaces continue to obscure a significant amount of structured scientific data. The term \"deep web\" or \"hidden web\" refers to these interfaces, which frequently include HTML forms, tables generated by JavaScript, cascading dropdowns, and multi-step filtering options [20]. This content is inherently resistant to indexing by traditional web crawlers and remains invisible to standard search mechanisms. Even when such resources are listed in registries like FAIRsharing, they are typically represented only by high-level documentation or homepage URLs. Users frequently need to follow a series of steps in order to access the actual data: navigate a search interface, enter parameters, decipher results from dynamically rendered tables, and manually download pertinent files. This manual, site-specific process presents a critical obstacle to intelligent systems that aim to support automated scientific discovery. Tools such as machine learning pipelines, semantic query engines, and data integration frameworks cannot easily traverse these interfaces or extract structured results without prior knowledge of how each portal operates. Currently, there is no unified, machine-readable protocol for describing how to interact with such interfaces, including how to download filtered results, submit forms, understand what inputs are needed, and parse returned tables. As a result, the vast troves of scientific data locked behind deep web forms remain largely inaccessible to automated workflows, weakening the practical realization of FAIR's Accessibility and Reusability dimensions. To bridge this gap, it is not sufficient to improve crawling technologies or rely solely on metadata registries. What is needed is a standardized, machine-executable protocol for modeling deep web interactions. Such a protocol must be domain-agnostic, interpretable by intelligent agents, and capable of abstracting complex human-facing interfaces into structured, repeatable access paths. These paths should be indexable, reusable, and composable within broader data discovery pipelines. While emerging large language models (LLMs) such as GPT-4, LLaMA, and PaLM have demonstrated promising capabilities in interpreting web content, inferring form semantics, and generating structured representations from natural language or HTML inputs [21; 22], their outputs alone lack consistency [23] without an underlying framework to structure and validate them. LLMs can recognize that a form requires a gene symbol and species identifier or predict where results will appear, but they require a consistent formalism to encode such behaviors in a way that downstream systems can reliably interpret and execute. This is where the DWCP becomes essential. DWCP offers a standardized specification to describe the semantics of deep web access: how to interact with forms, what inputs to provide, what outputs to expect, and how to extract them. LLMs serve as powerful engines to populate DWCP specifications from unstructured inputs, but DWCP itself ensures operational integrity and reusability [24]. By coupling the interpretive power of LLMs with a structured, interoperable access protocol, we move closer to a new paradigm [25] of machine-actionable scientific data discovery.