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Dataset results
162 results for “Quantitative Imaging”
Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field
<p>Quantitative sodium MRI in foods: addressing sensitivity issues using single quantum Chemical Shift Imaging at high field</p>
Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans - Data
<p>Recorded raw data and result data for the paper "Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans"</p>
Quantitation of human sperm DNA accessibility and DNA nicking by NicE-view and Nick translation imaging
<ul> <li>Accessible or nicked DNA from human sperm was labelled using NicE-view, ATAC-See or nick translation.</li> <li>For nick translation and NicE-view 60 individuals were profiled, for 57 of these individuals two samples were available (1 for total sperm and 1 for sperm selected for motility via the swim-up method), while for the final 3 only total sperm was available (filename: Gill_NicE_view_CellProfiler.csv).</li> <li>For ATAC-See data from 5 individuals were generated (Gill_ATAC_See_CellProfiler.csv).</li> <li>For a further 6 individuals sperm were isolated by FACS following labelling with the DNA dye CMA3 and subjected to T4 NicE-view (Gill_NicE_view_CMA3_Cell_Profiler.csv).</li> <li>Finally a data set containing double labelling of DNA nicks and DNA accessibility was generated for 6 individuals (Gill NicE_view_Double_Labelling_CellProfiler.csv).</li> <li>For each sample 400 Z-projections were generated and quantified using CellProfiler. Data are saved in .csv files.</li> <li>A further .csv file contains metadata relating to demographic, hormonal and reproductive parameters of the study participants (Gill_NicE_view_participant.csv).</li> <li>A series of 13 R scripts (number 01-12, and a general script containing helper functions) are included to analyze the data and generate the figures found in Gill et al. (A text file containing instructions regarding directory structures is also included) as a .zip archive</li> </ul>
Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024
<p>Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024. <a href="https://doi.org/10.1111/jon.13201">https://doi.org/10.1111/jon.13201</a></p>
Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF
<p>Software tools and image files needed to reproduce the results of the image classifier evaluation in the Current Alzheimer Research manuscript of the same title.</p>
Geometric-Phase Microscopy for Quantitative Phase Imaging of Isotropic, Birefringent and SpaceVariant Polarization Samples_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-Phase Microscopy for Quantitative Phase Imaging of Isotropic, Birefringent and SpaceVariant Polarization Samples" available in open access under http://doi.org/10.1038/s41598-019-40441-9. </p>
Dataset related to article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging"
<p><em>The .zip file contains raw data related to the article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging", available from <a href="https://www.ijbs.com/v16p1363.htm">https://www.ijbs.com/v16p1363.htm</a>.</em></p> <ul> <li><em>The folder "<strong>fig 1 2 3 NIRA</strong>" contains raw data related to the experiments with niraparib presented in figures 1, 2 and 3 </em></li> <li><em>The folder "<strong>fig 1 2 3 OLA</strong>" contains raw data related to the experiments with olaparib presented in figures 1, 2 and 3 </em></li> <li><em>The folders "<strong>fig 4</strong>" and "<strong>fig 6</strong>" contain raw data related to figures 4 and 6, respectively.</em></li> </ul> <p><strong>For any additional information on how to read and reuse the dataset please contact Dr. Ubezio at paolo.ubezio@marionegri.it.</strong></p> <p> </p> <p><strong>ABSTRACT OF THE MANUSCRIPT:</strong></p> <p><strong>Rationale</strong>: Optimal intratumor distribution of an anticancer drug is fundamental to reach an active concentration in neoplastic cells, ensuring the therapeutic effect. Determination of drug concentration in tumor homogenates by LC-MS/MS gives important information about this issue but the spatial information gets lost. Targeted mass spectrometry imaging (MSI) has great potential to visualize drug distribution in the different areas of tumor sections, with good spatial resolution and superior specificity. MSI is rapidly evolving as a quantitative technique to measure the absolute drug concentration in each single pixel.</p> <p><strong>Methods</strong>: Different inorganic nanoparticles were tested as matrices to visualize the PARP inhibitors (PARPi) niraparib and olaparib. Normalization by deuterated internal standard and a custom preprocessing pipeline were applied to achieve a reliable single pixel quantification of the two drugs in human ovarian tumors from treated mice.</p> <p><strong>Results</strong>: A quantitative method to visualize niraparib and olaparib in tumor tissue of treated mice was set up and validated regarding precision, accuracy, linearity, repeatability and limit of detection. The different tumor penetration of the two drugs was visualized by MSI and confirmed by LC-MS/MS, indicating the homogeneous distribution and higher tumor exposure reached by niraparib compared to olaparib. On the other hand, niraparib distribution was heterogeneous in an ovarian tumor model overexpressing the multidrug resistance protein P-gp, a possible cause of resistance to PARPi.</p> <p><strong>Conclusions</strong>: The current work highlights for the first time quantitative distribution of PAPRi in tumor tissue. The different tumor distribution of niraparib and olaparib could have important clinical implications. These data confirm the validity of MSI for spatial quantitative measurement of drug distribution providing fundamental information for pharmacokinetic studies, drug discovery and the study of resistance mechanisms.</p>
OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images
<p>This is a dataset of OCTA images used in the development of the manuscript <em>OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images</em></p>
Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor
<p>These are data related to the manuscript titled "Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor" whose preprint can be found here: https://doi.org/10.1101/2022.05.03.490515</p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
3D-Imaging and Quantitative Subsurface Dielectric Constant Measurement Using Peak Force Kelvin Probe Force Microscopy
<p>We demonstrate a new approach to simultaneously measure the dielectric constants of buried structures and interfaces by combining peak force tapping quantitative Nano-mechanical mapping (PF QNM) and frequency-modulated Kelvin probe force microscopy (FM-KPFM). The developed method paves the way for 3D-imaging of dielectric constants of composite materials and heterostructures.</p>
Quantitative Subharmonic Breast Imaging
ClinicalTrials.gov study NCT01490892. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Source data for: Electrochemically controlled blinking of fluorophores for quantitative STORM imaging
Open the record for dataset details and reuse information.
An Omni-Mesoscope for multiscale high-throughput quantitative phase imaging of cellular dynamics and high-content molecular characterization
Open the record for dataset details and reuse information.
Data from: Quantitative single-molecule FLIM and PIE-FRET imaging of biomolecular systems
Open the record for dataset details and reuse information.
Data from: Radar wind profilers and avian migration - a qualitative and quantitative assessment verified by thermal imaging and moon watching
1. Radars of various types have been used in ornithological research for about 70 years. However, the potential of radar wind profiler (RWP) as a tool for biological purposes remains poorly understood. The aim of this study is to assess the suitability of RWP for ornithological research questions. 2. A 1290 MHz RWP at the southeastern coast of the Bay of Biscay has been known to exhibit seasonally occurring nocturnal signals attributed to migrating birds. As a first step to verify the origin of these seasonal patterns, historical radar data from 2010-2012 were analysed, and both bird patterns and temporal occurrence were identified in RWP data at different levels of the signal processing. A thermal-imaging (TI) camera in conjunction with moon watching was used as verification systems at the radar site to confirm the ornithological origin of the radar echoes. The simultaneous data on spring migration served as a basis for the identification of biological signatures (qualitative parameters) on time series level (raw data) and to derive quantitative migration parameters (flight altitude, migration traffic rates) thereof. Finally the quantitative measurements of the TI camera and the radar were compared considering meteorological conditions. 3. The approach allowed identifying reproducible criteria based on time series to calculate migration traffic rates and altitudinal flight distribution. General flight directions were only available in the final wind data. In clear weather conditions the calibration methods coincided well with the wind profiler data. 4. Findings show that wind profiler raw data offers reliable information on migration intensity, flight altitudes and flight directions in a variety of meteorological conditions. The method presented can be applied as a complement to present efforts to use weather radars for large-scale bird monitoring. Furthermore it is also interesting for the meteorological community to refine signal-processing methods.
FIGURE 8 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 8. Scatter plots showing the volume (A), length (B), width (C) and depth (D) of zooidal and avicularian chambers automatically detected and measured on the virtual dataset (N = 16) for Smittina imragueni n. sp. As in Fig. 4, the horizontal axis represents an arbitrary numbering by the software Amira. A separation between zooidal (large values on the left) and avicularian chambers (small values on the right) is clearly visible.
FIGURE 9 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 9. Scatter plots showing length (A) and width (B) of orifices and frontal pores automatically detected and measured on the virtual dataset (N = 503) for Smittina imragueni, showing a distinction between those two entities, with some datapoints plotting in between (see Discussion). As in Fig. 4, the horizontal axis represents an arbitrary numbering by the software Amira.
FIGURE 7 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 7. Virtual reconstructions of the internal structure of Smittina imragueni n. sp. (paratype SMF 40010). A, broad side of a branch. B, narrow side of a branch. C, single autozooid with suboral avicularium. D, same as C, without the suboral avicularium. E, a single suboral avicularian chamber; the two frontal protuberances are frontal pores. F, automatically detected orifices and frontal pores. Scalebars: A, B, F, 500 µm; C, D, 250 Μm; E, 50 Μm.
FIGURE 6 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 6. SEM micrographs of Smittina imragueni n. sp. A, aspect of colony fragments (right, holotype SMF 40005; left, paratype SMF 40006). B, cross-section through a branch, showing thick secondary calcification and different profiles of zooidal chambers (paratype SMF 40007). C, interior view of primary orifice and suboral avicularium (SMF 40007). D, autozooids showing a relatively early developmental stage of the peristome (SMF 40010). E, secondary orifice with an intermediately developed peristome (SMF 40006). F, immersed secondary orifice late in ontogeny; note the vertical orientation of the avicularium (SMF 40007). G, ancestrular area (paratype SMF 40008). H, transition between encrusting and erect growth (paratype SMF 40009). Scalebars: A, 2 mm; B, G, 500 µm; C, E, 100 µm; D, F, H, 200 Μm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.