APICURON：面向分布式研究数据生态系统的信用归因响应式基础设施
APICURON: a reactive infrastructure for credit attribution across distributed research data ecosystems
AI总结：
研究人员推出更新后的 APICURON 平台，这一信用归因基础设施可实时捕获生物数据整理事件，关联 ORCID 形成可验证工作单元，已集成至生物知识库，还支持非传统研究产物的贡献认可。
AI中文摘要：
数据驱动的生物学依赖于专业生物 curator（数据整理人员）生成的结构化知识，但这项工作在传统学术评估中大多未得到认可。为弥合这一差距，我们推出了更新后的 APICURON 平台，这是一个信用归因基础设施，可正式认可这些科学贡献。该系统不依赖延迟的批量报告，而是在整理事件发生时对其进行捕获，并将其转换为可验证的工作单元。这种设计允许独立资源定义和更新自身的认可模型，同时保留每项贡献的历史记录。对于研究人员而言，APICURON 会突出显示其近期活动与终身成就，并通过 ORCID 将已验证的活动与持久的学术档案关联起来。APICURON 已成功集成到多个生物知识库和数据资源中，展示了其在多样化工作流程中的应用。除生物数据资源外，它还支持对非传统研究 artefacts（产物）的认可，包括培训材料和研究软件，且不对贡献施加僵化的定义。
英文摘要：
Data-driven biology relies on structured knowledge generated by expert biocurators, yet this work remains largely unrecognized in traditional academic assessments. To bridge this gap, we present the updated APICURON platform, a credit-attribution infrastructure that formally acknowledges these scientific contributions. Rather than relying on delayed batch reporting, the system captures curation events as they happen and transforms them into verifiable units of work. This design allows independent resources to define and update their own recognition models while preserving the historical record of each contribution. For researchers, APICURON highlights recent activity alongside lifetime achievements and connects verified activities to persistent academic profiles via ORCID. APICURON has been successfully integrated across biological knowledgebases and data resources, demonstrating its application to diverse workflows. Extending beyond biodata resources, it also supports recognition of non-traditional research artefacts, including training materials and research software, without imposing a rigid definition of contribution.
