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Dataset results
478 results for “artifact”
Assessment of the Artifical Intelligence Assisted Registration Versus Conventional Point Based Registration on Cone Beam-computed Tomography (CBCT) With Heavy Metal Artifacts
ClinicalTrials.gov study NCT06273332. IPD Sharing: NO. Countries: 1. Publications: 0.
Pilot Study to Compare ISOVUE®-250 and VISIPAQUE™ 270 for Motion Artifact and Pain in Peripheral DSA
ClinicalTrials.gov study NCT00740207. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Metal Artifact Reduction Sequence MRI for Surgical Decision-Making in Infected Unicompartmental Knee Arthroplasty
ClinicalTrials.gov study NCT07208968. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Virtual Single-energy Imaging and De-metallic Artifact Technology in Reducing Spinal Metallic Artifacts
ClinicalTrials.gov study NCT04955483. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of FOV and mA on Metal Artifacts on CBCT
ClinicalTrials.gov study NCT04935944. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of a New MRI Technique to Reduce Breathing-Related Artifacts in Brain Imaging
ClinicalTrials.gov study NCT07305948. IPD Sharing: NO. Countries: 1. Publications: 0.
Recognition of cellular RNAs by the S9.6 antibody creates pervasive artifacts when imaging RNA:DNA hybrids
GEO Series GSE141833. Homo sapiens. 18 samples. Type: Other.
[E-MTAB-587] PCR_artifacts
GEO Series GSE30616. Arabidopsis thaliana. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Traceability Solutions for Supporting Intermingled-Bilingual Artifacts
<p>Bilingual software engineering dataset for issues and commits</p>
Artifact_image
<p>An example image.</p>
SPLReePlan Evaluation Artifacts
<p>Data set containing the artifacts of the SPLReePlan Evaluation</p>
The COAD-Artifact Dataset
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Limestone votive artifact, Escoural, Portugal
Votive limestone artefact of circular shape. In the center it has a circular perforation, on both sides, around the hole, a protruding edge is visible. It was discovered in the neolithic necropolis of the Escoural Cave. It belongs to the collection of the Museu Nacional de Arqueologia, but is preserved today in the collection of Museum of São Domingos, in Montemor-o-Novo. Source: Objaverse 1.0 / Sketchfab
GhOST Artifact Evaluation
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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>
Archaeology artifact 2
An archaeological statue from an unknown place in Asia. Size about 1m. Made with Reality Capture (11 pictures). Source: Objaverse 1.0 / Sketchfab
77-47-Q470 Pumice Artifact
Pumice Artifact, Hot Springs Village, Port Moller, Alaska CAT# 77-47-Q470 Okada excavations HHQ, Level 5. Hot Springs 1A. 2000-1600 BCE The Hot Springs site is a massive village on the shore of Port Moller, on the Alaska Peninsula side of the southern Bering Sea. It was excavated by several different teams over the last 100 years. The main occupations are from 2000 BCE-1000 BCE, and from 100 CE to 800 CE. The Hot Springs artifacts are presented as a result of the research conducted under grants NSF 0137756, NSF 1204020, NSF 1139266, and NSF 1321411. H. Maschner, Principal Investigator. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing completed at Global Digital Heritage. Source: Objaverse 1.0 / Sketchfab
Bird Bone Artifact, XFP-119, Sanak Island, AK
Carved and polished Bird Bone Artifact, XFP-119, Sanak Island, Alaska. XFP-119-78. Likely 400-100 BCE. XFP-119 is a group of house and other depressions along the beach within the area of the Historic town of Sanak. There are at least three components dating approximately 400 BCE, 100BCE, and 1250-1410 CE. These artifacts were scanned with either a Faro Edge Arm or a Minolta Vivid 9i. Processed in Geomagic or Polyworks. 4-8 photos were used for texture in Geomagic Wrap. The Sanak Island artifacts are presented as a result of the research conducted under grants NSF 0326584, NSF 0508101, NSF 1139266, NSF 1321411. H. Maschner, Principal Investigator. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing completed at Global Digital Heritage. Fieldwork and analysis done with the permission and collaboration of the Pauloff Harbor Tribe and the Sanak Corporation. Source: Objaverse 1.0 / Sketchfab
Supplementary Material for Mining Cost Awareness in the Infrastructure as Code Artifacts of Cloud-based Applications
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Correct the beegfs VM in ATC'24 Artifact Evaluation of Monarch
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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.