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98 results for “image generation”
Expert and AI-generated annotations of the tissue types for the RMS-Mutation-Prediction microscopy images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div> <p>This dataset contains 2 components:</p> <ol> <li>Annotations of multiple regions of interest performed by an expert pathologist with eight years of experience for a subset of hematoxylin and eosin (H&E) stained images from the RMS-Mutation-Prediction image collection [1,2]. Annotations were generated manually, using the Aperio ImageScope tool, to delineate regions of alveolar rhabdomyosarcoma (ARMS), embryonal rhabdomyosarcoma (ERMS), stroma, and necrosis [3]. The resulting planar contour annotations were originally stored in ImageScope-specific XML format, and subsequently converted into Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) representation using the open source conversion tool [4].</li> <li>AI-generated annotations stored as probabilistic segmentations.</li> </ol> <p><strong>WARNING</strong>: After the release of IDC v20 (v2 of this data record), it was discovered that a mistake had been made during data conversion that affected the newly-released segmentations accompanying the "RMS-Mutation-Prediction" collection. Segmentations released in v20 for this collection have the segment labels for alveolar rhabdomyosarcoma (ARMS) and embryonal rhabdomyosarcoma (ERMS) switched in the metadata relative to the correct labels. Thus segment 3 in the released files is labelled in the metadata (the SegmentSequence) as ARMS but should correctly be interpreted as ERMS, and conversely segment 4 in the released files is labelled as ERMS but should be correctly interpreted as ARMS. This mistake was fixed in the version v3 of this record (IDC data release v21).</p> <p>Many pixels from the whole slide images annotated by this dataset are not contained inside any annotation contours and are considered to belong to the background class. Other pixels are contained inside only one annotation contour and are assigned to a single class. However, cases also exist in this dataset where annotation contours overlap. In these cases, the pixels contained in multiple contours could be assigned membership in multiple classes. One example is a necrotic tissue contour overlapping an internal subregion of an area designated by a larger ARMS or ERMS annotation. The ordering of annotations in this DICOM dataset preserves the order in the original XML generated using ImageScope. These annotations were converted, in sequence, into segmentation masks and used in the training of several machine learning models. Details on the training methods and model results are presented in [1]. In the case of overlapping contours, the order in which annotations are processed may affect the generated segmentation mask if prior contours are overwritten by later contours in the sequence. It is up to the application consuming this data to decide how to interpret tissues regions annotated with multiple classes. The annotations included in this dataset are available for visualization and exploration from the National Cancer Institute Imaging Data Commons (IDC) [5] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov</a>) as of data release v18. Direct link to open the collection in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.</p> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset, as well as the publication describing the original dataset <a href="https://paperpile.com/c/NHiBXI/njdR">[2]</a> and publication acknowledging IDC <a href="https://paperpile.com/c/NHiBXI/uJJZ">[5]</a>.</p> <h3>References</h3> </div> </div> <div> <p>[1] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: 10.1158/1078-0432.CCR-22-1663.</p> <p>[2] Clunie, D., Khan, J., Milewski, D., Jung, H., Bowen, J., Lisle, C., Brown, T., Liu, Y., Collins, J., Linardic, C. M., Hawkins, D. S., Venkatramani, R., Clifford, W., Pot, D., Wagner, U., Farahani, K., Kim, E., & Fedorov, A. (2023). DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8225132" rel="noopener">https://doi.org/10.5281/zenodo.8225132</a></p> <p>[3] Agaram NP. Evolving classification of rhabdomyosarcoma. Histopathology. 2022 Jan;80(1):98-108. doi: 10.1111/his.14449. PMID: 34958505; PMCID: PMC9425116,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425116/</p> <p>[4] Chris Bridge. (2024). ImagingDataCommons/idc-sm-annotations-conversion: v1.0.0 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10632182" rel="noopener">https://doi.org/10.5281/zenodo.10632182</a></p> <p>[5] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</p> </div>
Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.
<p><strong>Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.</strong></p> <p><strong>Introduction</strong>: For AI-based classification tasks in computed tomography, a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, this should be drawn from clinical routine data, using State-of-the-art scanners, evaluated in a blinded manner, and verified with a reference test.</p> <p><strong>Methods: </strong>2659 consecutive CTs performed between 01/2016 and 01/2021 with reported pleural effusion were retrospectively included. Pathology reports from thoracocentesis or biopsy within 7 days of CT were used as reference standard (n = 335). Two radiologists (4 and 10 PGY) blindly assessed chest CTs (n=335, 81 empyemas) for pleural CT features and ICC was determined. In addition, both pleural CT features and radiological diagnosis were extracted from written radiological reports. If needed, consensus was achieved using an experienced radiologist's opinion (29 PGY). We assessed the correlation of these findings with the following patient outcomes: mortality and median hospital stay.</p> <p><strong>Results: </strong>Specificity and sensitivity for clinical detection of empyema (N=81) were 90.94 (95%-CI 86.55-94.05) and 72.84 (95%-CI: 61.63-81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen's kappa = 0.41-0.82). Features describing pleural enhancement or thickening achieved the highest accuracy with 87.02% and 81.49%, respectively. Empyema was associated with a longer hospital stay (median= 20 versus 14 days), and findings consistent with pleural carcinosis impacted mortality. </p>
Aminoacyl-tRNA synthetase gene alignments from multiple Sileneae species generated from full-length transcripts using Iso-Seq and raw microscopy image files
<p>Trimmed and untrimmed alignments for the final aminoacyl-tRNA synthetases in <em>Sileneae </em>species and <em>Arabidopsis thaliana. W</em>e investigated the evolution of subcellular localization of aaRS enzymes in five different species from the plant lineage <em>Sileneae</em> that has experienced extensive and rapid mitochondrial tRNA loss. By analyzing full-length mRNA transcripts with single-molecule sequencing technology (PacBio Iso-Seq) and searching genome sequences, we found instances of predicted retargeting of an ancestrally cytosolic aaRS to the mitochondrion as well as scenarios where enzyme localization does not appear to change despite functional tRNA replacement.</p> <p>Nikon .nd2 raw microscopy files for the transient expression and imaging of predicted transit peptides and colocalization assays in <em>N. benthamiana</em> epithelial cells. The amino acid sequence plus 10 upstream amino acids of the protein body were fused to GFP and co-transfected with an eqFP611-tagged transit peptide from a known mitochondrially localized protein (isovaleryl-CoA dehydrogenase).</p>
(SEN12MS) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>SEN12MS </strong>NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(capsicum) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>capsicum</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
Cross-correlation coefficient maps generated in the paper "Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths" by Narita et al. published in Journal of Geophysical Research - Planets
<p>This data archive contains the cross-correlation coefficient maps. Unzipping the compressed file, the following directories corresponding to different wavelength pairs appear. </p> <p> IR1_IR2/ : 0.9 micron & 2.02 micron<br> UVI283_UVI365/ : 283 nm & 365 nm<br> IR2_UVI365/ : 2.02 micron &. 365 nm<br> IR2_UVI283/ : 2.02 micron & 283 nm<br> IR2_LIR/ : 2.02 micron & 10 micron</p> <p>If usual unzip tools do not work, the use of 7zip is recommended:<br> https://www.7-zip.org/download.html</p> <p>Each directory contains CSV files for the longitude-latitude distribution of the correlation coefficient. The 2880 longitude grids cover the longitude range of 0 - 360 degrees, and the 1440 latitude grids cover the latitude range of -90 - +90 degrees, with a pixel resolution of 0.125 degree/pixel. Invalid regions are filled with the value of 1.1.</p> <p>Each filename is composed of the date, the instrument (wavelength), and the time. For example, for the file "20160720_ir2_150821_hp6_IR1_150209_hp6_sb24.csv":</p> <p> 20160720 : July 20, 2016<br> ir2 : 2.02 micron filter of IR2 camera<br> 150821 : IR2 exposure at 15:08:21<br> hp6: High-pass filtering size is 6 deg x 6 deg<br> IR1 : 0.9 micron filter of IR1 camera<br> 150209 : IR1 exposure at 15:02:09<br> sb24 : Sliding box size is 24 deg x 24 deg</p>
Planar Graph Classifier - Generated Graph Images
<p>These images were generated with networkx layout algorithms and matplotlib graph visualization from the graph input datasets of the Planar Graph Classifier. Some randomization in size and color was used for a slight augmentation effect.</p>
(nirscene) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>nirscene</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p> <p> </p>
The impact from the contrast of the generated images
<p>The contrast of the generated images was dull for the S2IGAN code, as given. By enhancing brightness and contrast, this problem could be solved. However, we found this is only needed for human evaluation: On 20 selected images, the IS score for S2IGAN was the same for the raw (greyish) output and the enhanced output samples. </p>
PhysicsGen - Can Generative Models Learn from Images to Predict Complex Physical Relations?
<p>This dataset comprises 300,000 pairs of images designed for the advancement of generative model applications in physical simulations. Each pair consists of an input image and its corresponding output image that represents a physical simulation. The dataset aims to facilitate research into whether generative models can effectively learn and reproduce complex physical dynamics from visual data, potentially replacing traditional differential equation-based methods with significant computational speedups.</p> <p>Data, baseline models and evaluation code: <a href="https://www.physics-gen.org">https://www.physics-gen.org</a></p>
Image Datasets for "Virtual tissue microstructure reconstruction across species using generative deep learning"
<p>Training and velautaion image datasets used in the manuscript "Virtual tissue microstructure reconstruction across species using generative deep learning"</p>
Accelerating Whole-Sample Polarization-Resolved Second Harmonic Generation imaging in Mammary Gland Tissue via Generative Adversarial Networks
<p>Authors:</p> <p>Arash Aghigh, Jysiane Cardot, Melika Saadat Mohammadi, Gaëtan Jargot, Heide Ibrahim, Isabelle Plante, François Légaré</p> <p>Affiliations:</p> <p> 1. Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada.<br> 2. Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Québec, Canada.</p> <p>Corresponding Author:</p> <p>Arash Aghigh, arash.aghigh@inrs.ca</p> <p>Description:</p> <p>This dataset accompanies the research on improving whole-sample Polarization-Resolved Second Harmonic Generation (P-SHG) imaging in mammary gland tissue using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The novel approach significantly reduces imaging time while maintaining high image quality and analytical accuracy, demonstrating a reduction in imaging time by more than 95%. This method also minimizes laser-induced photodamage, lowers costs of optical components, and increases the accessibility and applicability of P-SHG imaging in various fields.</p> <p>Keywords:</p> <p>Polarization-Resolved Second Harmonic Generation, P-SHG, Generative Adversarial Networks, GAN, ESRGAN, Mammary Gland Imaging, Super-Resolution, Image Upscaling, Deep Learning, Biomedical Imaging</p> <p>Funding Information:</p> <p> • Canada Foundation for Innovation<br> • Fonds de recherche du Québec–Nature et technologies<br> • Natural Sciences and Engineering Research Council of Canada<br> • New Frontiers Research Fund<br> • NSERC CREATE program (scholarship for Arash Aghigh)</p> <p>Related Identifiers:</p> <p> • GitHub repository for ChaiNNer program: https://github.com/chaiNNer-org/chaiNNer<br> • Download links for models used: https://openmodeldb.info</p> <p>Additional Information:</p> <p>Animal studies were conducted according to the procedures provided by the Canadian Council on Animal Care. The protocol (2005-02) was reviewed and approved by the Institutional Committee for Animal Protection of the Laboratoire National de Biologie Expérimentale (LNBE), the animal facilities based at the Institut National de Recherche Scientifique (INRS).</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Generation of human induced pluripotent stem cells-derived cortical neurons for high throughput imaging of neurite morphology and neuron maturation
<p>Figure 5: Whole cell patch clamping showed the differentiated neurons are functional.</p>
Generation of human induced pluripotent stem cells-derived cortical neurons for high throughput imaging of neurite morphology and neuron maturation
<p>Figure 3 - Neurite outgrowth dataset. Comparing neurite outgrowth at Day1 and 15 post-seeding cortical neural progenitors. </p>
Maestro Platform-Generated Dataset of Classified Bird Images
<p>The bird dataset, mentioned in the publication titled "Evaluation of Maestro, an extensible general-purpose data gathering and data classification platform" comprises two files: a zip file containing the classified files and a JSON file that includes the corresponding classification results.</p>
Maestro Platform-Generated Dataset of Classified Bird Images
<p>The bird dataset, mentioned in the publication titled "Maestro: An Extensible General-Purpose Data Gathering and Data Classification Platform," comprises two files: a zip file containing the classified files and a JSON file that includes the corresponding classification results.</p>
Resolution Enhancement and Deblurring of Porous Media μ-CT Images based on Super Resolution Generative Adversarial Network
<p>The above is the Data2 of Super Resolution Generative Adversarial Network based on High-Resolution Representation Learning.</p>
Dataset for automated image-based generation of finite element models for masonry buildings
<p>This repository contains the dataset used for computing finite element models for masonry buildings via image-based approach. The method that uses this data set was presented in the paper "Automated image-based generation of finite element models for masonry buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1007/s10518-023-01726-7</p>
Image datasets used in the paper "Revealing invisible cell phenotypes with conditional generative modeling"
<p>- BBBC021_selection_128 is a selection of the BBBC021 image dataset from the Broad Bioimage Benchmarck Collection from the Broad Institute</p> <p>- golgi_256_subset is a subset (one plate) of the Golgi Dataset we used (which is about 3 times larger). It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- translocation_256 is the translocation Dataset we used. It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- LRKK2_256 is the Parkinson LRKK2 mutation dataset we used. It was generated by Ksilink, Strasbourg, France</p> <p>- smala_256 is the Malaria dataset we used. It was generated by IRD, Paris, France and acquired by the Histopathology Platform at Institut Pasteur in Paris, France. </p>
ScienceDex guides
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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.