# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
In future versions of FAIRFind, we plan to support an exportable format where users can retrieve ready-to-integrate code segments corresponding to DWCP entries. These code modules will encapsulate the form submission logic, result parsing routines, and access metadata necessary to port specific data views into downstream applications. This functionality will further promote reuse, interoperability, and transparency, while extending FAIRFind's role from a discovery interface to a programmable access layer. ### Maintenance and Sustainable Development

Making sure FAIRFind is developed and maintained sustainably over time is one of the main priorities. As web standards, data formats, and user needs change, the system is

Figure 6: FAIRFind interface responding to a population-scale genomic data query. The system parses a complex natural language question concerning diabetics and retrieves a list of relevant datasets, as indexed in the target data portal. The table shown is extracted from a dynamic web page and includes downloadable GWAS datasets and summaries. built with a modular architecture to support updates and newer technological integrations. We aim to keep FAIRFind as an open-source project so that there can be shared-community-driven collaborative work to improve the functionality and respond to new issues like managing dynamic web interfaces and changing API standards. Moreover, we plan to add extensive documentation alongside automated testing pipelines, which will regulate both code quality and the sustainment of the project over time. With these approaches, we try to guarantee that FAIRFind continues to be a powerful and flexible tool designed to support the implementation of the principles of FAIR and open science in the long run. ### Security and Privacy Considerations

Privacy and security are crucial factors to take into account when using deep web resources. This ethical and responsible operation of the FAIRFind system ensures the integrity of accessed resources and the protection of any sensitive information discovered during data discovery and processing. FAIRFind adheres to publicly stated terms of service and respects all robots.txt directives to avoid disrupting target servers or violating resource owners' policies [48]. Rate limits and random delays between requests are added by the crawler and form submission modules to lessen the chance of overloading servers or activating anti-bot defenses. FAIRFind does not currently try to access private or session-specific data; instead, it only interacts with publicly accessible, unauthenticated content. This design decision removes the possibility of unintentionally gathering or disclosing private or sensitive data. To protect data in transit, all communication with target websites uses HTTPS when it is available, and all gathered data and intermediate artifacts are safely stored on access-controlled servers. ### Potential and Use Cases

Applications of the FAIRFind system in a variety of real-world research settings have shown how useful it is for automating access to complex, diverse scientific datasets. It has efficiently retrieved structured data from specialized portals in domains such as protein dynamics and molecular simulations, taxonomic classification of microbial and eukaryotic species, spatial transcriptomics, microbiome and metagenomic studies, genetic association studies, neuroimaging, and brain atlas resources. The system's ability to analyze a wide range of data from different repositories and domains, such as imaging, proteomics, phenotypic, genomic, and environmental data, is demonstrated by these use cases. By automating the navigation of dynamic interfaces and extracting structured, reproducible workflows, FAIRFind helps modern, data-intensive science by relieving researchers of the time-consuming task of manually integrating data.