IIT Dataverse (Istituto Italiano di Tecnologia)
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Colourimetric and Spectral Data Bowls Pompeii
No full textThe database contains colourimetric and spectral data from bowls of pigments found in Casa dei Pittori al Lavoro. The data were acquired using Konica-Minolta Spectrophotometer CM-700d/600d. Each folder is related to a single bowl, and it is named after the inventory number of the bowl. Each folder contains: - one or multiple images of the analysed bowl (.jpg). Note that some bowls do not have pictures available. - raw data (excel file) with colourimetric coordinates and spectral values. - one or more images of the plotted spectrum (.tiff). Please be aware that due to the shape of the bowls and the configuration of the instrument, some measurement are not reliable
Replication Data for: One-Step Enhancer: Deblurring and Denoising of OCT Images
No full textOCT datasets of pork larynx and rabbit eyes, supporting the findings of Li, S.; Azam, M.A.; Gunalan, A.; Mattos, L.S. One-Step Enhancer: Deblurring and Denoising of OCT Images. Appl. Sci. 2022, 12, 10092. https://doi.org/10.3390/app121910092
Replication data for: ROFT: Real-time Optical Flow-aided 6D Object Pose and Velocity Tracking
No full textThis dataset contains the numerical data reporting the results of the algorithms:
- ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
- PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
- se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
on the Fast-YCB dataset as reported in the publication ROFT: Real-time Optical Flow-aided 6D Object Pose and Velocity Tracking.</i
IIT Dataverse Short Deposit Checklist
No full textPDF version of "IIT Dataverse Deposit Checklist", a short guideline to dataset creation in IIT Dataverse
Replication Data for "A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging"
No full textThis repository contains all the data required to replicate results shown in "A deep learning approach for online cell identification and trace extraction in functional two-photon calcium imaging"
Abstract
In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments
Code for: A self-adapting approach for the detection of bursts and network bursts in neuronal cultures
No full textThis dataset contains MATLAB (Mathworks Inc.) scripts for performing burst and network burst detection on neuronal cultures' electrophysiological recordings obtained by planar multi-site Micro-Electrode Arrays. The algorithms for burst and network burst detection have been described in the journal article: Pasquale, V., Martinoia, S. & Chiappalone, M. A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. J Comput Neurosci 29, 213–229 (2010). https://doi.org/10.1007/s10827-009-0175-1