Automated Author ProfileOiticica, Pedro Ramon
Oiticica, Pedro Ramon
Current S-Index
3.2
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
1.6
Average Dataset Index per dataset
Total Datasets
2
Total datasets for this author
Average FAIR Score
65.4%
Average FAIR Score per dataset
Total Citations
0
Total citations to the author's datasets
Total Mentions
0
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
- Overview and Data SourceThis dataset contains optical microscopy images of plasmonic biosensors based on gold nanoislands (AuNI) on glass substrates, used for detecting SARS-CoV-2 virus particles. It also includes extracted image features for machine learning-based detection. The dataset was generated for the study by Oiticica P. R. A. et al. (2025) (https://doi.org/10.1021/acssensors.4c03451). The Images were acquired using a Zeiss Axio Lab.A1 microscope with a 40x objective (400X magnification). Various handcrafted and deep-learning-based Convolutional Neural Networks (CNN) were used for feature extraction. Machine learning models, including LDA, KNN, SVM, and RF, were trained to classify images based on SARS-CoV-2 virus concentration. The highest classification accuracy (91.6%) was achieved using the MobileNetV3_small feature extractor combined with an SVM classifier, to detect the SARS-CoV-2 virus with concentrations as low as 1 PFU/mL. This approach has potential applications for detecting other viruses and analytes.2. Data Structure and DescriptionThis dataset contains 858 optical microscopy images in .TIF format (RGB, 1920×2560 resolution), captured under standardized conditions. The test categories are:Positive tests: SARS-CoV-2 virus at dilutions from 1×10⁻⁴ to 1×10⁵ PFU/mL (10 classes).Negative tests: RSV virus (1×10³ to 1×10⁵ PFU/mL), blank tests with PBS/MgCl₂, and probe images (immunosensors before testing).File Naming Format: {sensor_number}_{label_name}.tifExample: s012_cov_03.tif refers to an image of sensor s012 after testing with SARS-CoV-2 at dilution 03. The correspondence between dilution numbers and PFU/mL is in imageinfo.csv table.The Features folder contains tables (.h5 format) with extracted image features for all images, with a table for each computer vision method.Feature Table Columns:filename: Image filename.label_name: Test label combining analyte and dilution code.concentration: PFU/mL concentration.analyte: Analyte type ('CoV inat', 'RSV inat', 'PBSMgCl2', and 'No' for immunosensor before tests.)vision_type: Feature extraction method (Handcrafted or CNN).feature_vector_rgb: Feature vector extracted from RGB images.feature_vector_gray: Feature vector extracted from grayscale images.The table imageinfo.csv contains the first 4 columns of the feature tables.3. CitationIf you use this dataset, please cite our paper:Oiticica, Pedro R. A. and Angelim, Monara K. S. C. and Soares, Juliana C. and Soares, Andrey C. and Proença-Módena, José L. and Bruno, Odemir M. and Oliveira Jr, Osvaldo N. (2025). "Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV‑2 Virus". ACS Sensors. DOI: 10.1021/acssensors.4c03451.5. External SourcesGitHub: https://github.com/praoiticica/COVID-plasmonic-sensor-ML.
Authors
- Oiticica, Pedro Ramon ;
- Angelim, Monara ;
- Soares, Juliana ;
- Soares, Andrey ;
- Proença-Módena, josé ;
- Bruno, Odemir ;
- Oliveira, Osvaldo
0 Citations0 Mentions65% FAIR1.6 Dataset Index
10.17632/z4js67w5vcFebruary 2025
- Overview and Data SourceThis dataset contains optical microscopy images of plasmonic biosensors based on gold nanoislands (AuNI) on glass substrates, used for detecting SARS-CoV-2 virus particles. It also includes extracted image features for machine learning-based detection. The dataset was generated for the study by Oiticica P. R. A. et al. (2025) (https://doi.org/10.1021/acssensors.4c03451). The Images were acquired using a Zeiss Axio Lab.A1 microscope with a 40x objective (400X magnification). Various handcrafted and deep-learning-based Convolutional Neural Networks (CNN) were used for feature extraction. Machine learning models, including LDA, KNN, SVM, and RF, were trained to classify images based on SARS-CoV-2 virus concentration. The highest classification accuracy (91.6%) was achieved using the MobileNetV3_small feature extractor combined with an SVM classifier, to detect the SARS-CoV-2 virus with concentrations as low as 1 PFU/mL. This approach has potential applications for detecting other viruses and analytes.2. Data Structure and DescriptionThis dataset contains 858 optical microscopy images in .TIF format (RGB, 1920×2560 resolution), captured under standardized conditions. The test categories are:Positive tests: SARS-CoV-2 virus at dilutions from 1×10⁻⁴ to 1×10⁵ PFU/mL (10 classes).Negative tests: RSV virus (1×10³ to 1×10⁵ PFU/mL), blank tests with PBS/MgCl₂, and probe images (immunosensors before testing).File Naming Format: {sensor_number}_{label_name}.tifExample: s012_cov_03.tif refers to an image of sensor s012 after testing with SARS-CoV-2 at dilution 03. The correspondence between dilution numbers and PFU/mL is in imageinfo.csv table.The Features folder contains tables (.h5 format) with extracted image features for all images, with a table for each computer vision method.Feature Table Columns:filename: Image filename.label_name: Test label combining analyte and dilution code.concentration: PFU/mL concentration.analyte: Analyte type ('CoV inat', 'RSV inat', 'PBSMgCl2', and 'No' for immunosensor before tests.)vision_type: Feature extraction method (Handcrafted or CNN).feature_vector_rgb: Feature vector extracted from RGB images.feature_vector_gray: Feature vector extracted from grayscale images.The table imageinfo.csv contains the first 4 columns of the feature tables.3. CitationIf you use this dataset, please cite our paper:Oiticica, Pedro R. A. and Angelim, Monara K. S. C. and Soares, Juliana C. and Soares, Andrey C. and Proença-Módena, José L. and Bruno, Odemir M. and Oliveira Jr, Osvaldo N. (2025). "Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV‑2 Virus". ACS Sensors. DOI: 10.1021/acssensors.4c03451.5. External SourcesGitHub: https://github.com/praoiticica/COVID-plasmonic-sensor-ML.
Authors
- Oiticica, Pedro Ramon ;
- Angelim, Monara ;
- Soares, Juliana ;
- Soares, Andrey ;
- Proença-Módena, josé ;
- Bruno, Odemir ;
- Oliveira, Osvaldo
0 Citations0 Mentions65% FAIR1.6 Dataset Index
10.17632/z4js67w5vc.1February 2025