NFFA-EUROPE - Hierarchical SEM Dataset

DOI

Dataset of 1,038 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories and 27 subcategories (as sub-trees) in a folder-subfolder structure. Results obtained from this dataset have been published in Modarres et al., Scientific Reports volume 7, Article number: 13282 (2017), doi:10.1038/s41598-017-13565-z The dataset is appropriate for the purposes of this study and in general for visual object recognition software research. Any scientific metadata associated to the measure is not present in the images. The dataset is therefore relevant as a whole, being the single images entirely detached from any specific information or scientific detail related to the displayed subject. This work has been done within the NFFA-EUROPE project (www.nffa.eu) and has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 654360 NFFA-Europe.

Identifier
DOI https://doi.org/10.23728/b2share.b9abc4a997f8452aa6de4f4b7335e582
Source https://b2share.eudat.eu/records/b9abc4a997f8452aa6de4f4b7335e582
Metadata Access https://b2share.eudat.eu/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.eudat.eu:b2rec/b9abc4a997f8452aa6de4f4b7335e582
Provenance
Creator Aversa, Rossella; Modarres, Mohammad Hadi; Cozzini, Stefano; Ciancio, Regina
Publisher EUDAT B2SHARE; NFFA-EUROPE Project
Contributor Chiusole, Alberto
Publication Year 2018
Rights Creative Commons Attribution (CC-BY); info:eu-repo/semantics/openAccess
OpenAccess true
Contact rossella.aversa(at)nffa.eu
Representation
Resource Type Dataset
Format tar
Size 625.3 MB; 10 files
Version 1.0
Discipline 4.1.12.1 → Computer graphics → Image processing; 4.1.16.3 → Information science → Database; 4.1.17 → Computer sciences → Artificial intelligence; 4.1.17.1.2.1 → Machine learning → Artificial neural network; 5.6.37 → Engineering → Nanomaterials; 3.4.7 → Physics → Condensed matter physics