ReviewNanomaterials (Basel, Switzerland)2021
Synthetic Data in Quantitative Scanning Probe Microscopy.
Review in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Synthetic data-driven deep learning for label-free autonomous atomic force microscopy.Nature communications · 2026Article
- Digital Metrology for Nanoindentation: Synthetic Data Generator for Error Identification.Micromachines · 2025Article
- Synthetic data generation of health and demographic surveillance systems data: a case study in a low- and middle-income country.JAMIA open · 2025Article
- Nanometrology.Nanomaterials (Basel, Switzerland) · 2022Article
- New method for real-time visualization and quantitative characterization of early colorectal cancer in endoscopy: a pilot study.Endoscopy international open · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Synthetic data are of increasing importance in nanometrology. They can be used for development of data processing methods, analysis of uncertainties and estimation of various measurement artefacts. In this paper we review methods used for their generation and the applications of synthetic data in scanning probe microscopy, focusing on their principles, performance, and applicability. We illustrate the benefits of using synthetic data on different tasks related to development of better scanning approaches and related to estimation of reliability of data processing methods. We demonstrate how the synthetic data can be used to analyse systematic errors that are common to scanning probe microscopy methods, either related to the measurement principle or to the typical data processing paths.
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Registered trials
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