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Dataset results
324 results for “fluorescent imaging”
Early Detection of Polyps and Colon Cancer by Fluorescence Imaging - a Dose-finding Study
ClinicalTrials.gov study NCT00285701. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of Real-time Fluorescence Imaging in Diabetic Foot Ulcers: the Impact of Colonization
ClinicalTrials.gov study NCT05873049. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ITGA6 Targeting NIR-II Fluorescence Image Guided Surgery
ClinicalTrials.gov study NCT06204835. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of ICG Fluorescence Imaging in Open Fracture and Infection Patients
ClinicalTrials.gov study NCT06793644. IPD Sharing: NO. Countries: 1. Publications: 0.
Fluorescence Image Guided Surgery Followed by Intraoperative Photodynamic Therapy for Improving Local Tumor Control in Patients With Locally Advanced or Recurrent Colorectal Cancer
ClinicalTrials.gov study NCT06307548. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Application of Three- Dimensional Visualization Combined With ICG Molecular Fluorescence Imaging in Hepatolithiasis
ClinicalTrials.gov study NCT06447181. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect and Long-Term Outcomes of Indocyanine Green Fluorescence Imaging Method Versus Modified Inflation-Deflation Method in Identification of Intersegmental Plane（IMPLANE-0529）
ClinicalTrials.gov study NCT05453721. IPD Sharing: NO. Countries: 1. Publications: 0.
Precise immuno-fluorescence canceling enables highly multiplexed imaging [scRNA-seq]
GEO Series GSE242814. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Data from: High-throughput synapse-resolving two-photon fluorescence microendoscopy for deep-brain volumetric imaging in vivo
Open the record for dataset details and reuse information.
Precise immuno-fluorescence canceling enables highly multiplexed imaging [RNA-seq]
GEO Series GSE242886. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Machine Learning-Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy - Supporting Data
<p>Supporting data for the reproduction of the results reported in Corbetta, E., Bocklitz, T., Machine learning based estimation of experimental artifacts and image quality in fluorescence microscopy (2024) [1].</p> <h2><strong>MM-IQA_Images_png and </strong><strong>MM-IQA_Images_tif</strong></h2> <p>Folder containing all the supporting datasets of the publication.</p> <ul> <li>images_manual_inspection: semisynthetic dataset used for the manual inspection of the quality metrics.</li> <li>images_lda_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.</li> <li>images_lda_prediction: datasets predicted by the LDA model. <ul> <li>images_experimental: every subfolder is a dataset composed of measurements of a different sample. Images are from publicly available datasets from [2] and [3].</li> <li>images_known_semisynthetic: knwon semisynthetic dataset used for prediction, assessment and interpretation of the trained model.</li> </ul> </li> </ul> <p>Tif files are the original data used for the study.</p> <h2><strong>MM-IQA_Source_data</strong></h2> <p>Folder containing all the supporting metadata of the publication.</p> <ul> <li>manual_inspection: quality metrics computed for the semisynthetic dataset used for the manual inspection. <ul> <li>manual_inspection_bg: indices for the selection of the background region in each sample.</li> <li>manual_inspection_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>manual_inspection_metrics: quality metrics computed for the dataset, used for the manual inspection.</li> <li>manual_inspection_samples: free parameters associated to each image of the dataset for manual inspection.</li> </ul> </li> <li>LDA_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model. <ul> <li>lda_metrics_synthetic+semisynthetic_uniform_max: quality metrics computed for the training dataset, with maximum normalization of the images. (Not used in the manuscript)</li> <li>lda_metrics_synthetic+semisynthetic_uniform_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>parameters_all_degradations: parameters used for the generation of the simulated artifacts.</li> </ul> </li> <li>LDA_prediction: datasets predicted by the LDA model. <ul> <li>experimental: quality metrics computed for measurements of different samples. Images are from publicly available datasets from [2] and [3].</li> <li>known_semisynthetic: metadata for the knwon semi-synthetic dataset used for prediction, assessment and interpretation of the trained model: <ul> <li>known_semisynthetic_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>known_semisynthetic_metrics_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>known_semisynthetic_maxnorm_lda_results: lda prediction results, when metrics are maximum normalized to the training dataset.</li> <li>known_semisynthetic_znorm_lda_results: lda prediction results, when metrics are z-score normalized to the training dataset.</li> </ul> </li> </ul> </li> </ul> <p>Source data can be used to reproduce the results of the manuscript, using the codes shared in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <h3><em>How to use the source data</em></h3> <p>The following table describes which data can be used in the scripts provided in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <table> <tbody> <tr> <td><strong>Script</strong></td> <td><strong>Data to use</strong></td> <td><strong>Details</strong></td> </tr> <tr> <td>01_quality_metrics</td> <td>Subfolders of MM-IQA_Images_png</td> <td>Include all the images to evaluate in a single subfolder in <code>/test_images</code></td> </tr> <tr> <td> </td> <td>background_idx.xlsx</td> <td>The indices for the samples to evaluate must be included in the table</td> </tr> <tr> <td> <p>01_quality_metrics_visualization</p> <p>01_quality_metrics_visualization_notebook</p> </td> <td>manual_inspection_metrics.xlsx</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td> </td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td> </td> </tr> <tr> <td> <p>02_lda_training+prediction</p> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td>As prediction dataset</td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td>As prediction dataset</td> </tr> <tr> <td> <p>02_lda_visualization</p> <p>02_lda_visualization_notebook</p> </td> <td>known_semisynthetic_maxnorm_lda_results</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_znorm_lda_results</td> <td> </td> </tr> <tr> <td>Notebook_test-iqa</td> <td>A small dataset with image data and the relative background index, if available.</td> <td>Use a limited number of images.</td> </tr> <tr> <td>Notebook_mm-iqa_workflow</td> <td>Any image dataset with the relative background indices</td> <td>For quality assessment and as prediction dataset</td> </tr> <tr> <td> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> </tbody> </table> <p> </p> <h2>MM-IQA_Scripts</h2> <ul> <li><strong>multi-marker-iqa-main</strong>: original GitLab repository for MM-IQA, version available at the date of manuscript publication.</li> <li><strong>Notebooks_peer_review</strong>: additional notebooks generated during the peer-review process with the computation of metrics for natural images and correlation measures.</li> </ul>
Figure 28 in Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging
Figure 28. Gonopod of Pseudopolydesmus caddo. Holotype (USNM, ultraviolet enhancement). A, right gonopod, ectal view. B, right gonopod, medial view.
Figure 6 in Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging
Figure 6. The characteristic prefemoral bulge in males of Pseudopolydesmus, and comparison of walking legs in Pseudopolydesmus and Polydesmus (scanning electron micrograph). A, adult male Pseudopolydesmus erasus, left leg 9, with characteristically large prefemoral bulge and thickened femur (FMNH INS3120685). B, adult female Ps. erasus, right leg 12, without prefemoral bulge (FMNH INS3120685). C, adult male Polydesmus inconstans, right leg 14, with slight prefemoral bulge and thickened femur (FMNH INS4265).
Figure 14 in Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging
Figure 14. Gonopod of Pseudopolydesmus canadensis (FMNH INS6934, scanning electron micrograph). A, left gonopod, ectal view. B, left gonopod, medial view. Both images mirrored to appear as right gonopod. Cannula removed.
Figure 1 in Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging
Figure 1. Two live examples of Pseudopolydesmus. A, Pseudopolydesmus serratus, live adult male, dorsal view (VTEC MPE01173). B, Pseudopolydesmus paludicolus, live adult female, dorsal view (VTEC MPE01167).
Figure 7 in Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging
Figure 7. Sternal tubercles in male Pseudopolydesmus canadensis, ventral view, body rings 4–8 (FMNH INS6934, ultraviolet enhancement). Visible body rings (BR4–8) and their corresponding leg pairs (LP3–11) and gonopods (GPs) are labelled. Also note the characteristic silhouette of the gonopods of Ps. canadensis, with processes e2 and e3 sharing a narrow stalk.
Intraoperative ICG Fluorescence Imaging for Peritoneal Carcinomatosis Detection
ClinicalTrials.gov study NCT04352894. IPD Sharing: NO. Countries: 0. Publications: 0.
Phase I/II Clinical Trial of NP41 for Cranial Nerve Fluorescence Imaging
ClinicalTrials.gov study NCT05043519. IPD Sharing: NO. Countries: 0. Publications: 0.
Fluorescence Imaging in Hepatobiliary Surgery
ClinicalTrials.gov study NCT03946761. IPD Sharing: NO. Countries: 0. Publications: 0.
Fluorescence Image-Guided Healing Trial
ClinicalTrials.gov study NCT04163055. IPD Sharing: NO. Countries: 0. Publications: 0.
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.