Establishing Semantic Research Data Modelling in Biological Anthropology

我们建议建立生物人类学中骨学研究数据的语义模型，作为可持续研究数据管理的基础。这项技术将有助于创建全面注释的数据集，其中包含有关固有数据结构、研究设计和来源的明确声明，并对科学调查的整个过程链进行建模。其优点包括增加透明度、畅通无阻地汇集大量数据以及适合长期存储。生物人类学与其他学科相结合，通过分析人类遗骸来研究人类进化和人类与环境的相互作用。它们的微妙性质和有限的可访问性，以及广泛的研究材料、方法和上下文数据，需要良好的形式化，但灵活的数据标准化方法。尽管有各种各样的努力，构建和处理研究数据的常见做法尚未形成。以前由DFG资助的一个研究项目评估了人类遗骸收集中对研究数据管理的需求，并开发了RDFBones，这是一个创建兼容标准的正式框架，作为一种改进手段。RDFBones利用资源描述框架（RDF）和几个已建立的RDF本体来建模研究数据。核心本体保证了数据集的兼容性，而研究设计、研究方法、材料和上下文信息可以作为本体的扩展来实现。该概念受到科学界的广泛欢迎，并已被证明与其他数据处理方法很好地结合在一起。语义数据建模有可能解决现有的数据标准化问题，并实现生物人类学中全面研究数据管理的公认需求。这将需要在当前的研究中应用，在机构中建立虚拟研究环境，最后形成数据存储库。这个项目在这个方向上迈出了第一步，它开发了一个实现RDFBones的web应用程序，使研究人员能够在不具备RDF技术特定知识的情况下创建RDF数据。它将在他们的调查过程中提供支持，作为一个合作研究环境和一个展示精选数据的互联网平台。其技术目的是将常规数据条目丰富为高度注释的数据集。该软件是作为最小可行产品开发的，可以由部署应用程序的研究机构和项目定制，并通过扩展进一步配置以支持各种研究设计。第二个重点是通过与生物人类学和研究数据管理领域的专家协商，与其他数据标准化项目协调，确定建立生物人类学语义研究数据模型的可持续战略。

We propose to establish semantic modelling of osteological research data in biological anthropology as a basis for sustainable research data management. This technology will help to create comprehensively annotated datasets containing explicit statements about the inherent data structure, research design and provenance, modelling the entire process chain of scientific investigations. Advantages include increased transparency, unobstructed pooling of large bodies of data and suitability for long-term storage. Biological anthropology, interfacing with other disciplines, investigates human evolution and human-environment interactions throughout history by analysis of human remains. Their delicate nature and limited accessibility, as well as the wide range of research materials, methods and contextual data require well-formalised, yet flexible approaches to data standardisation. Despite various efforts, common practices for structuring and handling research data have not yet evolved. A previous research project funded by the DFG evaluated the demands for research data management in collections of human remains and developed RDFBones, a formalised framework for the creation of compatible standards, as a means of improvement. RDFBones draws on the Resource Description Framework (RDF) and several established RDF ontologies to model research data. A core ontology ensures compatibility of datasets, while study designs, research methods, materials and contextual information can be implemented as ontology extensions. The concept is well received by the scientific community and has proved to integrate well with other approaches to data handling. Semantic data modelling has the potential to solve existing problems with data standardisation and realise the acknowledged need for comprehensive research data management in biological anthropology. This will require application in current research, the establishment of virtual research environments at institutions and, finally, the formation of data repositories. This project takes a first step in this direction by developing a web application implementing RDFBones to enable researchers to create RDF data without specific knowledge of RDF technologies. It will support them during their investigations, serve as a collaborative research environment and an internet platform presenting selected data. Its technical purpose is the enrichment of conventional data entries into highly-annotated datasets. The software is developed as a minimum viable product that can be customised by research institutions and projects deploying the application and further configured through extensions to support various study designs. A second focus is the determination of a sustainable strategy for the establishment of semantic research data modelling in biological anthropology in coordination with other projects on data standardisation and by consultation of experts from biological anthropology and the field of research data management.