Towards the FAIRification of Scanning Tunneling Microscopy Images

Rodani, T.; Osmenaj, E.; Cazzaniga, A.; PANIGHEL, Mirco; Africh, C.; Cozzini, S.

doi:10.1162/dint_a_00164

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Towards the FAIRification of Scanning Tunneling Microscopy Images

Rodani, T.; Osmenaj, E.; Cazzaniga, A. et al.

2023 • In Data Intelligence, 5 (1), p. 27 - 42

Peer reviewed Dataset

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https://hdl.handle.net/10993/61754

DOI
10.1162/dint_a_00164

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Keywords :

scanning tunneling microscopy; FAIR principles; open science; machine learning; provenance; data management; metadata

Abstract :

[en] 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.

Disciplines :

Physics

Author, co-author :

Rodani, T.

Osmenaj, E.

Cazzaniga, A.

PANIGHEL, Mirco ; CNR - Istituto Officina dei Materiali (IOM), Trieste, Laboratorio TASC, Strada Statale 14, km 163.5, 34149 Basovizza, Italy

Africh, C.

Cozzini, S.

External co-authors :

yes

Language :

English

Title :

Towards the FAIRification of Scanning Tunneling Microscopy Images

Publication date :

2023

Journal title :

Data Intelligence

Volume :

Issue :

Pages :

27 - 42

Peer reviewed :

Peer reviewed

Focus Area :

Physics and Materials Science

Additional URL :

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150034147&doi=10.1162%2fdint_a_00164&partnerID=40&md5=d5506bc5422dabb548ccb3b0ef3322ea

European Projects :

H2020 - 101007417 - NEP - Nanoscience Foundries and Fine Analysis - Europe|PILOT

Funders :

Union Européenne

Funding text :

This work has been supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 857650, EOSC-Pillar project and European Union's Horizon 2020 research and innovation programme under grant agreement No. 101007417 within the framework of the NFFA-Europe Pilot Joint Activities.

Data Set :

https://doi.org/10.5281/zenodo.5799773

Available on ORBilu :

since 01 August 2024

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Bibliography

Wilkinson, M., et al.: Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 1–9 (2016)
GO FAIR: FAIRification Process. Available at: https://www.go-fair.org/fair-principles/fairification-process/
Jacobsen, A., et al.: A generic workflow for the data FAIRification process. Data Intelligence 2(1–2), 56–65 (2020)
Sinaci, A.A., et al.: From raw data to FAIR data: the FAIRification workflow for health research. Methods of Information in Medicine 59. S01, e21–e32 (2020).
Scienta Omicron: VT SPM Lab. Available at: https://scientaomicron.com/en/products-solutions/SPM/VT-SPM-Lab
TriDAS: Trieste Advanced Data Services. Available at: https://tridas.nffa.eu/
Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling provenance metadata. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 773–776 (2013)
Tommaso, R., et al.: Dataset of Scanning Tunneling Microscopy (STM) images of graphene on nickel. Version 1.0. Zenodo (2021)
Tommaso, R.: t0m-R/STM images 0.1.0. Version 0.1.0. (2020)
Zou, Z.Y., et al.: Strain release at the graphene-Ni(100) interface investigated by in-situ and operando scanning tunnelling microscopy. Carbon 172, 296–301 (2021)
Sala, A., Zou, Z., Carnevali, V., et al.: Quantum Confinement in Aligned Zigzag “Pseudo-Ribbons” Embedded in Graphene on Ni(100). Advanced Functional Materials p. 2105844 (2021)
Zou, Z., et al.: Honeycomb on Square Lattices: Geometric Studies and Strain Analysis of Moir’e Structures at a Symmetry-Mismatched Interface. The Journal of Physical Chemistry C 124(46), 25308–25315 (2020)
Zou, Z., et al.: Operando atomic-scale study of graphene CVD growth at steps of polycrystalline nickel. Carbon 161, 528–534 (May 2020)
Zou, Z., et al.: Graphene on nickel (100) micrograins: Modulating the interface interaction by extended moir’e superstructures. Carbon 130, 441–447 (2018)
Carnevali, V., et al.: Doping of epitaxial graphene by direct incorporation of nickel adatoms. Nanoscale 11(21), 10358–10364 (2019)
Patera, L.L., et al.: Real-time imaging of adatom-promoted graphene growth on nickel. Science 359(6381), 1243–1246 (2018)
Africh, C., et al.: Switchable graphene-substrate coupling through formation/dissolution of an intercalated Ni-carbide layer. Scientific Reports 6(1), 19734 (2016)
Patera, L.L., et al.: Temperature-Driven Changes of the Graphene Edge Structure on Ni(111): Substrate vs Hydrogen Passivation. Nano Letters 15(1), 56–62 (2014)
Bianchini, F., et al.: Atomic Scale Identification of Coexisting Graphene Structures on Ni(111). The Journal of Physical Chemistry Letters 5(3), 467–473 (2014)
Patera, L.L., et al.: In Situ Observations of the Atomistic Mechanisms of Ni Catalyzed Low Temperature Graphene Growth. ACS Nano 7(9), 7901–7912 (2013)
Puppo, S.D., et al.: Tuning graphene doping by carbon monoxide intercalation at the Ni(111) interface. Carbon 176, 253–261 (2021)
Fiori, S., et al.: Inside out growth method for high-quality nitrogendoped graphene. Carbon 171, 704–710 (2021)
Perilli, D., et al.: Mechanism of CO Intercalation through the Graphene/Ni(111) Interface and Effect of Doping. The Journal of Physical Chemistry Letters 11(20), 8887–8892 (2020)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35(8), 1798–1828 (2013)
Le Cun, Y., et al.: Handwritten digit recognition: applications of neural network chips and automatic learning. IEEE Communications Magazine 27(11), 41–46 (1989)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. Available at: http://www. deeplearningbook.org
Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Huh, M., Agrawal, P., Efros, A.: What makes ImageNet good for transfer learning? NeurIPS LSCVS 2016 Workshop (2016)
Modarres, M., et al.: Neural Network for Nanoscience Scanning Electron Microscope Image Recognition. Scientific Reports 7 (2017)
Aversa, R., et al.: Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification. Data Intelligence 2(4), 513–528 (2020)
Cazzaniga, A.: Representation Learning and Hierarchical Clustering for microscopy images. MHPC Thesis, Scuola Internazionale Superiore di Studi Avanzati (2020). Available at: http://hdl.handle.net/20.500.11767/119181
Deng, J., et al.: Imagenet: A large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp. 248–255 (2009)
He, K., et al.: Deep Residual Learning for Image Recognition. (2016). https://doi.org/10.1109/CVPR.2016.90
Rodani, T.: Machine Learning techniques and visualization tools for STM images at CNR-IOM labs (2020). Available at: https://doi.org/10.5281/zenodo.5801169
Bokeh Development Team: Bokeh: Python library for interactive visualization (2020). Available at: https://bokeh.org/
Panighel, M.: rescipy-project/spym: v0.7.0. Version v0.7.0. (2021). https://doi.org/10.5281/zenodo.5792910
Grinberg, M.: Flask web development: developing web applications with python. O’Reilly Media, Inc., (2018)
Gil, Y., et al.: Examining the Challenges of Scientific Workflows. Computer 40(12), 24–32 (2007)
Belhajjame, K., et al.: Prov-dm: The prov data model. W3C Recommendation 14, 15–16, (2013)
PROV-Overview: An Overview of the PROV Family of Documents. Project Report (2013). Available at: https://eprints.soton.ac.uk/356854/
NFFA: NFFA Glossary. Available at: https://www.nffa.eu/apply/data-policy/glossary/
MDMC: Integrated Model and Data Driven Materials Characterization. Available at: https://jl-mdmc-helmholtz.de
Lebo, T., et al.: PROV-O: The PROV Ontology. English. W3C Recommendation. United States: World Wide Web Consortium, (2013)
Trung Dong, H.: Prov Python (2014). Available at: https://github.com/trungdong/prov
Devaraju, A., Huber, R.: F-UJI – An Automated FAIR Data Assessment Tool. Version v1.0.0, (2020). https://doi.org/10.5281/zenodo.4063720
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Tech. Rep., Technical Report CMU-CALD-02–107, Carnegie Mellon University (2002)
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT press (2012)
Spinuso, A., Atkinson, M., Magnoni, F.: Active provenance for Data-Intensive workflows: engaging users and developers. 2019 15th International Conference on eScience (eScience), IEEE, pp. 560–569 (2019)
Panighel, M. rescipy-project/nxstm: v0.3.0. Version v0.3.0. (2021), https://doi.org/10.5281/zenodo.5792931
Něcas, D., Klapetek, P.: Gwyddion: an open-source software for SPM data analysis. Open Physics 10(1), 181–188 (2012)