APICURON: a database for credit & Recognition of biocuration activities

The consolidation of massive data sources over the past decade has revolutionized scientific inquiry, placing data at the center of modern research paradigms[17]. This reliance has led to a paradigm where all decision-making is data-driven. This shift is equally transformative in scientific research, particularly in biology, where recent, significant breakthroughs have been directly attributable to data-driven approaches leveraging Artificial Intelligence[16]. In this context, the demand for Findable, Accessible, Interoperable, and Reusable (FAIR) and high-quality data is paramount for sustainable, modern research. This necessity highlights the critical role of Biocuration: the essential process of manually transforming unstructured biological information into a structured, computable, and FAIR format. However, accurately quantifying, tracking, and formally attributing the intense intellectual effort of biocurators remains a significant challenge. The work described in this PhD Thesis addresses this gap through the development and implementation of APICURON, a web-based service designed to provide formalized Credit & Recognition for Biocuration efforts. The system aggregates curation events from disparate resources, utilizing a standardized schema to capture curator activity. With a granular efficient computation workflow that allows a continuous timely reaction to user activity. Additionally, to demonstrate APICURON’s efficacy and interoperability, this work also encompassed the development and major update of DisProt, the database of Intrinsically Disordered Proteins. DisProt served as the primary pilot implementation for APICURON.By operating simultaneously as the infrastructure developer and the database maintainer, this work validated APICURON’s ability to capture granular curation activity and display attribution metrics within a live, production-level resource. Crucially, the system’s maturity was accelerated by a group of early adopters, specifically through integration with major EMBL-EBI resources. While technically successful, this expansion revealed significant privacy-related challenges regarding General Data Protection Regulation (GDPR) compliance. The regulatory requirement to exclude data lacking explicit user consent introduced workflow complexities and underscored the need for a standardized privacy mechanism. In response, this thesis proposes the APICURON Consent Management Platform: an open solution specifically designed to manage consent in Data Sharing scenarios, a critical use case often overlooked by commercial offerings, thereby ensuring lawful data processing and enabling seamless future integrations. Building on this successful implementation, along with its challenges, the scope of the thesis extends to address a broader challenge: the lack of recognition for diverse research outputs beyond traditional publications. Modern researchers frequently engage in software development, dataset creation, and educational training, activities often invisible in scholarly publications and standard indicators. Leveraging its flexible architecture, APICURON was adapted to track these varied workflows. Ultimately, this thesis establishes a comprehensive ecosystem for scientific attribution beyond traditional publications that bridges technical implementation with regulatory compliance. By making hidden labor visible and overcoming privacy barriers, this work lays the foundation for a more equitable and sustainable reward system suited to the era of data-driven research.