Mid-scale RI-1 (M1:IP): EduceLab: Infrastructure for Next-Generation Heritage Science

该项目将为遗产科学（HS）创建一个以数据为中心的仪器平台EduceLab。HS将科学测量和调查的工具、技术和严谨性应用于不同的遗产背景，如文物和策展藏品、生物遗骸和人类影响的环境。拟议的中等规模仪器生态系统是围绕关键科学能力设计的，研究界认为这些能力对于解决HS环境中具有挑战性的变异性至关重要和基础。生态系统包括材料表征；高级多模态成像（断层扫描、摄影、摄影测量）；用于捕获、构建和处理大规模数据集的网络基础设施和方法；以及用于现场数据采集和初始评估的移动和灵活部署的仪器。EduceLab被视为一个整体生态系统，能够解决HS中以前无法解决的研究问题，它将根据与不同HS社区和选区相匹配的使用模式，作为操作集群：用于精确测量和分析（BENCH）的固定、受控环境中的实验室设备；可以在现场部署的移动设备，用于无法移动的收藏品和景观（移动）；以及用于设想、构建和测试定制仪器配置的多变环境，以解决不受实验室分析和现场能力影响的环境（FLEX）。这些集群将通过网络基础设施（CYBER）相互连接，从数据采集到结构化分析，在整个基础设施中提供高效的数据流，支持数据科学和人工智能新技术的应用。EduceLab将作为一个新的中等规模的研究基础设施——一个可变的科学透镜——用于对引人注目的传统科学研究问题进行严格的、数据驱动的探索，产生新的发现以及基础科学进步。总部设在肯塔基大学威廉·s·韦伯人类学博物馆内，EduceLab将在不同的国家和国际利益相关者和选民群体之间建立凝聚力：学员/学生；HS和数据采集活动重叠领域的研究人员；从事积极文物收藏和相关研究工作的机构的从业人员；社区成员对正在进行的保护、保存、研究、传播和教育活动进行投资。EduceLab的组织将允许严格的科学探索，面对自然和文化遗产背景带来的独特挑战。EduceLab生态系统将满足这些问题对测量、计算和仪器解决方案的敏捷性和创新的持续需求。这些进步将反过来不断促进HS所涉及的一系列科学学科的知识、增长和创新，包括数据科学、计算机科学、计算机视觉、成像科学、系统工程、化学和材料工程、人类学和网络基础设施。拟议的仪器与美国国家科学基金会的“大创意”计划非常一致，包括：不断增长的融合研究（EduceLab和HS本身就是一项融合活动，融合了许多科学学科，并依靠强有力的合作取得成功）；利用数据革命（EduceLab的活动将是数据驱动和数据密集型的，依靠新兴的计算工具在化学、物理和计算机科学等学科中发挥作用，以捕捉物体及其环境的本质）；和NSF INCLUDES （EduceLab生态系统以一种文化知情的方式将STEM与传统领域结合起来，从战略上提高代表性不足群体对STEM领域的兴趣和参与，并提高社会公众对科学技术的素养和参与度）。该项目由中等规模研究基础设施-轨道1计划、信息和智能系统、刺激竞争研究的既定计划（EPSCoR）和先进网络基础设施办公室共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will create EduceLab, a data-centric instrument platform for Heritage Science (HS). HS applies the tools, techniques, and rigor of scientific measurement and inquiry to diverse heritage contexts, such as artifacts and curated collections, biological remains, and human-impacted environments. The proposed mid-scale instrument ecosystem is designed around key scientific capabilities that the research community has embraced as crucial and fundamental to addressing the challenging variability of HS contexts. The ecosystem includes materials characterization; advanced multimodal imaging (tomography, photography, photogrammetry); cyberinfrastructure and methodologies for capturing, structuring, and processing large-scale data sets; and mobile and flexibly-deployed instrumentation for in-situ data acquisition and initial evaluations. Viewed as a holistic ecosystem uniquely capable of addressing previously insoluble research questions in HS, EduceLab will be commissioned as operational clusters based on usage patterns that match the diverse HS communities and constituencies: laboratory equipment in a fixed, controlled environment for precise measurement and analysis (BENCH); mobile equipment that can be deployed on-site for collections and landscapes that cannot travel (MOBILE); and a protean environment for envisioning, building, and testing custom instrument configurations that address contexts impervious to laboratory analysis and on-site capabilities (FLEX). The clusters will be interconnected via cyberinfrastructure (CYBER) that powers efficient data flow through the entire infrastructure, from data acquisition to structured analysis, supporting the application of new techniques in data science and artificial intelligence. EduceLab will function as a novel Mid-scale Research Infrastructure – a variable scientific lens – for the rigorous, data-driven exploration of compelling heritage science research questions, engendering new discoveries as well as fundamental scientific advancement. Headquartered within the University of Kentucky’s William S. Webb Museum for Anthropology, EduceLab will build cohesion across a diverse group of national and international stakeholders and constituents: trainees/students; researchers in HS and in areas where data acquisition activities overlap; practitioners at institutions with active heritage collections and connected research efforts; and community members with investments in the ongoing conservation, preservation, study, dissemination, and educational activities.EduceLab in its organization will allow rigorous scientific exploration in the face of the unique challenges posed by natural and cultural heritage contexts. The EduceLab ecosystem will answer ongoing demands for agility and innovation in measurement, computational, and instrumentation solutions to these problems. Such advances will in turn continuously promote knowledge, growth, and innovation across a range of scientific disciplines involved in HS, including data science, computer science, computer vision, imaging science, systems engineering, chemical and materials engineering, anthropology, and cyberinfrastructure. The proposed instrumentation strongly aligns with the National Science Foundation’s “Big Ideas” initiatives, including: Growing Convergence Research (EduceLab and HS is a convergence activity in itself, blending a number of scientific disciplines and relying on robust collaborations for success); Harnessing the Data Revolution (EduceLab activities will be data-driven and data-intensive, relying on emerging computational tools at play across disciplines like chemistry, physics, and computer science to capture the essence of objects and their environments); and NSF INCLUDES (the EduceLab ecosystem combines STEM with heritage fields in a culturally informed way to strategically grow the interest and participation of underrepresented groups in STEM fields and increase societal public literacy and engagement with science and technology.).This project is jointly funded by Mid-Scale Research Infrastructure-Track 1 program, Information and Intelligent Systems, the Established Program to Stimulate Competitive Research (EPSCoR), and the Office of Advanced Cyberinfrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.