扫描隧道显微镜图像的精细化研究

IF 1.3 3区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Data Intelligence Pub Date : 2022-08-10 DOI:10.1162/dint_a_00164

Tommaso Rodani, E. Osmenaj, A. Cazzaniga, M. Panighel, C. Africh, S. Cozzini

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引用次数: 1

摘要

摘要在本文中，我们描述了为使符合FAIR的扫描隧道显微镜（STM）图像成为科学档案而开发的数据管理实践和服务。作为第一步，我们提取了数据集每个图像的仪器元数据，以创建一个结构化数据库。然后，我们通过利用人工注释、机器学习技术和仪器元数据过滤的管道，用有关表面结构和组成的信息丰富了这些元数据。为了直观地探索图像和元数据，并提高数据集的可访问性和可用性，我们开发了“STM浏览器”，作为一种集成在的里雅斯特高级数据服务（TriDAS）网站中的web服务。在这些数据服务和工具之上，我们提出了W3C PROV标准的实现来描述STM图像的来源元数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Towards the FAIRification of Scanning Tunneling Microscopy Images

ABSTRACT In this paper, we describe the data management practices and services developed for making FAIR compliant a scientific archive of Scanning Tunneling Microscopy (STM) images. As a first step, we extracted the instrument metadata of each image of the dataset to create a structured database. We then enriched these metadata with information on the structure and composition of the surface by means of a pipeline that leverages human annotation, machine learning techniques, and instrument metadata filtering. To visually explore both images and metadata, as well as to improve the accessibility and usability of the dataset, we developed “STM explorer” as a web service integrated within the Trieste Advanced Data services (TriDAS) website. On top of these data services and tools, we propose an implementation of the W3C PROV standard to describe provenance metadata of STM images.

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来源期刊

Data Intelligence COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

6.50

自引率

15.40%

发文量

审稿时长

8 weeks