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

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 -以细胞为中心的递归图像相似投影搜索-是一种计算基础设施，使高价值材料显微镜可查找，可访问，可互操作和可复制（FAIR）。CRISPS是一种多点触控、互动的研究“伙伴”，它使用人工智能来形成联想，就像人类的大脑一样，来识别图像之间的相似性。它具有永久的，不可改变的回忆，以搜索和发现基于标签和关联的科学图像集合。CRISPS将公开提供，并将在会议、用户设施和在线上进行推广，以培养一个开发人员和用户社区。公众科学素养将通过使用CRISPS探索和发现材料显微镜的互动博物馆展览来加强。该项目还为六名STEM领域代表性不足的人士提供了首次暑期研究体验。CRISPS是材料显微镜的全栈软件解决方案，无缝集成了三个新颖的软件概念。1. DataFed：一个联邦科学数据库，用于收集、整理和搜索科学数据和元数据。2. 无模式搜索：一个以单元为中心的索引和搜索元数据的工具，不需要模式。3. 递归图像相似性投影：一个交互式探索图像相似性的工具。每一项努力都是智力上的创新。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、搜索和来源图。DataFed通过可信的身份验证和使用GridFTP的安全管理文件传输，消除了协作科学的障碍。无模式搜索：一种创新的索引和ml标记化方法，使用ElasticSearch有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。递归图像相似投影：一组深度学习模型，用于进行自动对称感知显微镜特征化。当与多种学习和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。结合这些工具将促进对未发表的显微镜的创造性探索，加速发现具有新功能的新材料。CRISPS将在允许其重复使用、修改和商业化的非限制性许可下进行记录和发布。pi与学术界和工业界的利益相关者合作，在光学，电子和扫描探针显微镜的典型实验中实施CRISPS。它提供了对DataFed服务器上0.5 PB分配的公共访问，并通过CRISPS提供了索引和图像相似性搜索功能。跨学科的概念，包括科学数据管理、搜索本体和机器学习，正在被整合到材料和计算机科学的课程中；并将通过里海显微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先进网络基础设施办公室以及数学和物理科学理事会的材料研究司获得资金。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。

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.