Towards the FAIRification of Scanning Tunneling Microscopy Images

被引：0

作者：

Tommaso Rodani ^{[1
]}

Elda Osmenaj ^{[2
]}

Alberto Cazzaniga ^{[1
]}

Mirco Panighel ^{[2
]}

Cristina Africh ^{[2
]}

Stefano Cozzini ^{[1
]}

机构：

[1] AREA Science Park

[2] CNR-IOM, Consiglio Nazionale delle Ricerche–Istituto Officina dei

来源：

Data Intelligence | 2023年 / 5卷 / 01期

关键词：

D O I：

暂无

中图分类号：

TN16 [电子光学仪器]; TP391.41 [];

学科分类号：

0803 ; 080401 ; 080203 ;

摘要：

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.

引用

页码：27 / 42

页数：16

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