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428 results for “image map”
COCO, LVIS, Open Images V4 classes mapping
<p>This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.</p> <p>COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.</p> <p>We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using <a href="https://wordnet.princeton.edu/">wordnet</a></p> <p>This repository contains the following files:</p> <ul> <li><em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes</li> <li><em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes</li> <li><em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes</li> <li><em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy</li> <li><em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered</li> </ul> <p>This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4).</p> <p>This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:</p> <blockquote> <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> </blockquote> <p> </p> <p> </p> <p><em><strong>References:</strong></em></p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-98355-0_52">https://doi.org/10.1007/978-3-030-98355-0_52</a></p> <p>[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76.</p> <p>[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364).</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).</p> <p> </p>
Imaging spectroscopy and elemental mapping of Haughton impact melt rock: Datasets
<p>Description of archived data for manuscript Greenberger et al. (accepted, JGR Planets)<br> </p> <p>This archive contains the data underlying the results reported in the following paper:<br> Greenberger, R. N., Ehlmann, B. L., Osinski, G. R., Tornabene, L. L., & Green, R. O. Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing. Journal of Geophysical Research: Planets, accepted.<br> 1. ImageList.txt: Contains information required to connect sample names from paper with images, which often contain multiple samples.<br> 2. FieldImages.tar.gz: Imaging spectroscopy files from images of outcrops in the field<br> 3. LabImages.tar.gz: Imaging spectroscopy files from samples imaged in the laboratory<br> 4. XRF_Data.tar.gz: Elemental mapping of cut samples via mapping x-ray fluorescence</p> <p>Imaging spectroscopy files (for all below, * is image name from ImageList.txt):<br> 1. *_SWIRcalib.img files: Laboratory images of samples processed to reflectance, including a dark current subtraction, line-by-line ratio to an image of Spectralon acquired with identical lighting, and correction for the reflectance properties of Spectralon. These files are stored with BIL interleave.<br> 2. *_SWIRcalib.hdr files: Header files for (1).<br> 3. *_SWIRcalib_atmcorr.img: Images of outcrops acquired in the field processed to reflectance, including instrument level corrections (dark current subtraction and flat field correction) and atmospheric correction (dark object subtraction and correction to in-scene Spectralon calibration target).<br> 4. *_SWIRcalib_atmcorr.hdr: Header files for (3).<br> 5. mask# and mask#.hdr: Masks and associated header files where the sample or region of interest has a value of 1 and outside of the sample or region of interest has a value of 0. Imaging spectroscopy measurements of multiple samples were sometimes acquired within the same image, and each sample has its own mask. ImageList.txt shows conversions from image and mask names to sample numbers. For files with no # after mask, only one sample or region of interest is present within the image.<br> 6. *_SWIRcalibmask#_MAP, *_SWIRcalib_atmcorrmask_MAP, and corresponding .hdr files: These are image files with 12 bands, one for each lithologic classification in the paper, and associated header files. Values of 1 indicate that the lithology is present, and values of 0 indicate that it is absent. The mask files (5) were used to ignore areas outside of the sample or outcrop. The band named "Mixed 2.2 and 2.3 micron features" is Mixed Carbonate + Si-OH, and "Illite-y" is Illite-like. These files are stored with BSQ interleave.</p> <p>X-ray fluorescence (XRF) data:<br> Each folder corresponds with measurements of a single sample. These are measurements of the same surfaces of some cut samples analyzed by imaging spectroscopy. ImageList.txt gives the corresponding image cubes. All exported elements are .tsv files and are in quantized relative counts as exported by the instrument software. Data for Mg are unreliable due to its low atomic number, as is typical for XRF.</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.
<p>The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> <strong>"Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)."</strong> by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S.</p> <p>The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results.</p> <p>Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work.</p> <p>The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au)</p> <p>Thanks.</p>
Supporting Material for "Automatic Mapping of Small Lunar Impact Craters Using LROC NAC Images"
<p>The supporting material for <em>'Automatic Mapping of Small Lunar Impact Craters Using LROC NAC'.</em></p> <p>This File contains:</p> <ul> <li>Supporting Material (.pdf);</li> <li>List of True Positive detections (.csv);</li> <li>List of all ground truth and CDA detections (.csv);</li> <li>Folder (.zip) with images of the evaluation sites (.pdf); and</li> <li>Folder (.zip) with training image tiles (.png and .txt).</li> </ul> <p>Refer to Supporting Material (.pdf) for file name and header information.</p>
OMAP-23: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of vermiform appendix (FFPE) with MICS on MACSima
<p><strong>Description</strong></p> <p> OMAP-23 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human vermiform appendix sample. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). All, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region, removing Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding. The use of human IgG1 recombinant antibodies allows for the addition of uncoupled monoclonal antibodies from other species followed by a fluorescence labelled secondary reagent specific for the species of the monoclonal antibody. The multiplex system has been described already for OMAP-10 and OMAP-21. A new dye VioB515 is used for one reagents. The fluorescence is removed by cleavage. The panel contains 28 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for six anatomical structures and most cell types present in the vermiform appendix (link to ASCT+B table added). OMAP-23 follows OMAP-21, with fewer antibodies but adding antibodies for non-immune cells.</p> <p>All reagents are from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The MACSima system is continuously evolving, this is the third OMAP for the MACSima system. A representative dataset created using OMAP-23 can be found here 10.5281/zenodo.14008816 .The AVRs for the dataset can be found here (to be added).</p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
In vivo parameter maps for: Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
<p>Quantitative magnetization transfer and relaxometry maps as described in the Paper <em>Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools</em>.</p> <p>.</p>
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D).
EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping
<p>EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping </p> <p>The dataset can be unzipped and includes an image dataset and a vector dataset, which includes nine land use/land cover categories (i.e., cropland, orchard, forestland, grassland, construction land, transportation land, water body, bare land, terrace) for object-oriented remote sensing image mapping using deep learning.</p>
Weights for automatic two-dimensional bedrock fracture trace mapping from outcrop images
<p>This file contains weights for automatic two-dimensional bedrock fracture trace mapping. The weights have been trained on manually mapped traces (https://doi.org/10.5281/zenodo.7077574) that were digitized from UAV-acquired drone orthomosaics from along the shoreline of Loviisa, South-East Finland (https://doi.org/10.5281/zenodo.7077518). Code for automatic mapping and weights generation is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as Hierarchical Data Format, version 5 (HDF5).</p> <p>The work in automatic mapping was done as part of a Geological Survey of<br> Finland project, Kallioperän Rikkonaisuus, during 2021-2022.</p>
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 1. a: Map of France showing geographic position of Saint-Bauzile (source: http://d-maps.com/m/europa/france/france/ france09.gif). b: Overview of the active diatomite quarry at the Montagne d'Andance, photograph taken in 2017. c: SEM image of a frustule of pennate diatom (cf. Navicula sp.) from Saint-Bauzile. d) SEM image of frustules forming a colony of centric diatoms (cf. Diatoma sp.) from Saint-Bauzile.
Data for: Image-based evaluation of beers at an online Pint of Science festival using Projective Mapping, Check-All-That-Apply and Acceptability
<p>Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).</p>
New Ideas for Brain Modelling 4-Figure 1. Example mapping of cells presented as an image.
<p>The author has used this structure before in Greer (2016) and it is a type of entropy classifier. It attempts to reduce the error overall and is not so concerned with minimising individual associations. The paper Greer (2017) describes a classifier that is conjecturally more visual in nature than other types and it also uses a complete linking method. Instead of several levels of feature refactoring, it is a 1-level impression only. With the image classifier, each cell stores a count of every other cell it gets associated with, when averaging this can determine what cells are most similar to the pixel in question. Figure is an example of the clustering technique. If the top LHS grid is the first image to be mapped, then for cell A1, the other black cells are recorded as shown, with a count of 1. The count would then be incremented each time a cell is recorded again, for example, after the second image, cell A3 would lose a count. The idea of linking everything this way has now been used 3 times.</p>
Segmentation of Nuclei in Histopathology Images by deep regression of the distance map
<p>This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August.<br> This dataset consists of 50 annotated images, divided into 11 patients.</p> <p> </p> <p>v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren't.</p>
OMAP-21: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Palatine Tonsil with MICS on MACSima 1.5
<p>OMAP-21 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human tonsil samples. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). Most, but not all, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region. The described mutation removes the Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding of human antibodies on human tissues. Highly multiplexed imaging is achieved through cycles of immunolabeling with FITC, PE, and APC conjugated antibodies and photobleaching to eliminate fluorescence signal between imaging cycles. The panel contains 47 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for all anatomical structures and most cell types present in the human palatine tonsil. OMAP-21 follows closely OMAP-1 described for human lymph nodes (<a href="https://cdn.humanatlas.io/hra-releases/v1.4/docs/omap/omap-1-human-lymph-node-ibex.html">omap-1-human-lymph-node-ibex</a>) and OMAP-10 (<a href="https://cdn.humanatlas.io/hra-releases/v1.4/docs/omap/omap-10-palatine-tonsil-macsima.html">omap-10-palatine-tonsil-macsima</a>).</p> <p>All reagents were obtained from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The result of the analysis is included in the uploaded dataset. In brief, the software processes the raw images of the MACSima run, generates stitched images, and then allows downstream analysis including cell segmentation, cell gating, data normalization, dimension reduction plots (tSNE, UMAP), heat maps, distance analyses and cluster analyses, all of which are interactively linked together. The MACSima system is continuously evolving, this is the second OMAP for the MACSima system. A representative dataset created using OMAP-21 can be found here:<a href="https://doi.org/10.5281/zenodo.7875937"> </a><strong> 10.5281/zenodo.11281609 .</strong></p>
Red Relief Image Maps of the Ruinon Landslide, Northern Italy (2021-2022)
<p>Red Relief Image Maps of the Ruinon Landslide, Northern Italy.</p> <p>The maps are from UAV surveys carried out above the landslide on the following dates:</p> <ul> <li>06 July 2021</li> <li>29 October 2021</li> <li>02 May 2022</li> <li>05 July 2022</li> <li>05 October 2022</li> <li>29 October 2022</li> </ul> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632). They <strong>are not finely</strong> co-registered.</p> <p>The map production process details are discussed in <em>Yordanov, V.; Truong, Q.X.; Brovelli, M.A. Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques. Drones <strong>2023</strong>, 7, 85. <a href="https://doi.org/10.3390/drones7020085">https://doi.org/10.3390/drones7020085</a> </em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001 (Italian side) and partially funded by the Ministry of Science and Technology of Vietnam (MOST) (Vietnamese side) by the Bilateral Scientific Research project between Vietnam and Italy, code: NÐT/IT/21/14</p>
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.