Elements: CRISPS: Cell-Centric Recursive Image Similarity Projection Searching

元素：CRISPS：以细胞为中心的递归图像相似性投影搜索

• 批准号: 2246463
• 负责人: Joshua Agar
• 金额: $ 59.98万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246463&HistoricalAwards=false
• 关键词: Elements; CRISPS; Cell; Centric; Recursive

Materials scientists use microscopy to determine the structure, order, and periodicity that affect properties. The core challenge is that only a fraction of microscopy data is published. Thus, most of the information from these costly experiments is lost to an abyss. CRISPS - Cell-Centric Recursive Image Similarity Projection Searching – is a computing infrastructure to make high-value materials microscopy Findable, Accessible, Interoperable, and Reproducible (FAIR). CRISPS is a multitouch, interactive research "buddy" that uses artificial intelligence to form associations like the human mind to identify similarities between images. It has permanent, immutable recollection to search and discover collections of scientific images based on labels and associations. CRISPS will be made openly available and will be promoted at conferences, in user facilities, and online to foster a community of developers and users. Public scientific literacy will be enforced through interactive museum exhibits that use CRISPS to explore and discover materials microscopy. The program also supports a first summer research experience for six under-represented persons in STEM.CRISPS is a full-stack software solution for materials microscopy that seamlessly integrates three novel software concepts. 1. DataFed: a federated scientific database for collecting, collating, and searching scientific data and metadata. 2. Schema-Free Search: a tool for cell-centric indexing and searching metadata without schemas. 3. Recursive Image Similarity Projections: a tool to interactively explore image similarity. Each of these efforts is intellectually innovative. DataFed provides an automated, secure, scalable generalized scientific data repository that supports metadata schemas, searches, and provenance graphs. DataFed removes barriers to collaborative science through trusted authentication and secure managed file transfers using GridFTP. Schema-Free Search: an innovative index and ML-tokenization methodology to search unstructured metadata efficiently using ElasticSearch. Scientists will discover schemas and ontologies through an interactive graphical user interface (GUI). Recursive Image Similarity Projections: a collection of deep learning models to conduct automatic symmetry-aware microscopy featurization. When coupled with manifold learning and a GUI, this software tool will enable filtering, similarity exploration, and rapid labeling of materials microscopy. Combining these tools will facilitate creative inquiry into unpublished microscopy, accelerating the discovery of new materials with novel functionalities. CRISPS will be documented and released under a non-restrictive license which allows its reuse, modification, and commercialization. The PIs work with stakeholders in academia and industry to implement CRISPS for typical experiments in optical, electron, and scanning probe microscopies. It provides public access to a 0.5 PB allocation on a DataFed server with indexing and image similarity searching functionality through CRISPS. Interdisciplinary concepts, including scientific data management, search ontologies, and machine learning, are being integrated into courses in materials and computer science; and will be broadly shared through the Lehigh Microscopy School and conference tutorials.This proposal receives funds through the Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate and the Division of Materials Research in the Mathematical and Physical Sciences Directorate.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.

材料科学家使用显微镜来确定影响性能的结构，顺序和周期性。核心挑战是只有一小部分显微镜数据被公布。因此，从这些昂贵的实验中获得的大部分信息都消失在深渊中。CRISPS（Cell-Centric Recursive Image Similarity Projection Searching）是一种计算基础设施，可使高价值材料显微镜变得可发现、可解释、可互操作和可再现（FAIR）。CRISPS是一个多点触摸、交互式的研究“伙伴”，它使用人工智能来形成像人类思维一样的关联，以识别图像之间的相似性。它具有永久的，不可改变的回忆，以搜索和发现科学图像的基础上的标签和协会的集合。CRISPS将公开提供，并将在会议、用户设施和在线上推广，以培养开发人员和用户社区。公众的科学素养将通过互动博物馆展览来加强，这些展览使用CRISPS来探索和发现材料显微镜。该计划还支持六个代表性不足的人在STEM的第一个夏季研究经验。CRISPS是材料显微镜的全栈软件解决方案，无缝集成了三个新的软件概念。1. DataFed：用于收集、整理和搜索科学数据和元数据的联合科学数据库。2. Schema-Free Search：一个以单元为中心的索引和搜索元数据的工具，不使用模式。3. Recursive Image Similarity Projections：一个交互式探索图像相似性的工具。这些努力都是智力创新。DataFed提供了一个自动化、安全、可扩展的通用科学数据存储库，支持元数据模式、搜索和出处图。DataFed通过使用GridFTP的可信身份验证和安全托管文件传输消除了协作科学的障碍。Schema-Free Search：一种创新的索引和ML标记化方法，使用ElasticSearch高效地搜索非结构化元数据。科学家将通过交互式图形用户界面（GUI）发现模式和本体。Recursive Image Similarity Projections：一组深度学习模型，用于进行自动的图像感知显微镜特征化。当与流形学习和GUI相结合时，该软件工具将实现过滤，相似性探索和材料显微镜的快速标记。结合这些工具将有助于对未发表的显微镜进行创造性的探索，加速发现具有新功能的新材料。CRISPS将在非限制性许可证下记录和发布，该许可证允许其重复使用，修改和商业化。PI与学术界和工业界的利益相关者合作，为光学，电子和扫描探针显微镜的典型实验实施CRISPS。它通过CRISPS提供对DataFed服务器上0.5 PB分配的公共访问，并具有索引和图像相似性搜索功能。跨学科概念，包括科学数据管理、搜索本体和机器学习，正在被纳入材料和计算机科学课程;并将通过利哈伊显微镜学校和会议教程广泛分享。该提案通过计算机和信息科学与工程理事会的高级网络基础设施办公室以及数学和信息科学部的材料研究部门获得资金。该奖项反映了NSF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。