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38,240 results for “Imaging”
A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images
<p>CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Data for "Multi-band Spectropolarimetric Image Data of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Materials"
<p>This data repository contains the georeferenced spectropolarimetric data analyzed in the following article:</p> <p>Wöhler, C., Arnaut, M., Bhatt, M., 2024. Multi-band Spectropolarimetry of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Material. Astronomical Journal, accepted for publication.</p> <p>The data products are available in separate .zip archives in GEOTIFF and BSQ format. Each .zip archive contains data from eight different observations:</p> <table> <tbody> <tr> <td>Dataset</td> <td>Date</td> <td>UT time</td> <td>Phase angle</td> </tr> <tr> <td>20221114_WOP</td> <td>Nov 14th, 2022</td> <td>05:20</td> <td>64°</td> </tr> <tr> <td>20221216_WOP</td> <td>Dec 16th, 2022</td> <td>04:10</td> <td>88°</td> </tr> <tr> <td>20230225_AT</td> <td>Feb 25th, 2023</td> <td>19:20</td> <td>108°</td> </tr> <tr> <td>20230227_AT</td> <td>Feb 27th, 2023</td> <td>21:44</td> <td>85°</td> </tr> <tr> <td>20230228_AT</td> <td>Feb 28th, 2023</td> <td>19:33</td> <td>74°</td> </tr> <tr> <td>20230302_MV</td> <td>Mar 2nd, 2023</td> <td>19:03</td> <td>52°</td> </tr> <tr> <td>20230302_AT</td> <td>Mar 2nd, 2023</td> <td>19:10</td> <td>52°</td> </tr> <tr> <td>20230402_AT</td> <td>Apr 2nd, 2023</td> <td>20:03</td> <td>38°</td> </tr> </tbody> </table> <p> </p> <p>Data products of spectropolarimetric image analysis in GEOTIFF and BSQ format<br>=============================================================================</p> <p>Prefix 1: Date of data acquisition (YYYYMMDD)<br>Prefix 2: Area (WOP: Western Oceanus Procellarum; MV: Mare Vaporum; AT: Atlas)</p> <p>F : image intensity (5 bands) [DN]<br>P : degree of linear polarization (DoLP) (5 bands)<br>W : angle of linear polarization (AoLP) (5 bands) [degrees]<br>GS : relative grain size (5 bands)<br>logGS: logarithm of relative grain size (5 bands)<br>UE : across-band Umov exponent (1 band)<br>UEres: residual of across-band Umov exponent (1 band)<br>PCAP : scores on the first three principal components derived from the DoLP (3 bands) <br>PCAW : scores on the first three principal components derived from the AoLP (3 bands)<br>CIM : cluster index map (1 band)</p> <p><br>The longitude ranges of the maps are as follows:</p> <p>WOP: Longitude=[-70 -35], Latitude=[2 33]<br>MV : Longitude=[-13 12], Latitude=[-2 17]<br>AT : Longitude=[35 60], Latitude=[40 55]</p> <p>All maps are in simple cylindrical projection with a resolution of 30 pixels per degree.</p> <p>The CIM maps are provided in uint8 numerical format.<br>All other maps are provided in single-precision (32-bit) floating point numerical format.<br>The BSQ files are in binary format without header. Matlab example:<br>BSQ=multibandread('20230302_AT_P__Latitude_35_60__Latitude_35_60.bsq',[750 750 5],'single',0,'bsq','ieee-le');</p> <p> </p>
2 million histological images of breast cancer tumors with her2 labels
<p><strong>Data Description</strong><br> This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue.</p> <p>The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded.</p> <p>A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high.</p> <p>The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches.</p> <p>A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+.</p> <p>The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil</p>
Ultra-high-resolution modified RGB UAV-imaging of Alternaria solani
<p>This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation). </p> <p>This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information:</p> <ul> <li>Original patch name</li> <li>Random patch name (used during the labeling process)</li> <li>Row patch number</li> <li>Column patch number</li> <li>Block number, column block number and row block number</li> <li>Original mage name</li> <li>Coordinates of original image: latitude, longitude, altitude</li> <li>Date of flight</li> <li>Label (0: no Alternaria, 1: Alternaria)</li> </ul> <p>More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.' </p> <p> </p> <p>If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998"</p> <p> </p> <p>This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).</p> <p> </p>
Design files for a low-cost high-resolution imaging device for hyphae in soil
<p>This dataset contains the stereolithography (STL) files for the 3D-printed and cut parts of a low-cost high-resolution imaging device for hyphae in soil called <em>Hyphascope</em>. The design of <em>Hyphascope</em> was adopted from the 3D printer i3 MK3S+ by Prusa Research, with a digital microscope camera (DMC; 600× magnification) replacing the filament extruder. Repeated imaging of a soil profile with the imaging device enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>The parts were created and modified using <a href="https://www.freecad.org">FreeCAD</a> (version 0.20). STL files for the original parts are distributed under the Creative Commons Attribution 4.0 International License, STL files for the remixed parts under the GNU General Public License v2.0. For a detailed description on how to prepare and assemble the parts see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.</p> <p> </p> <div> <h2>STL files of 3D-printed parts</h2> <h3>Original parts</h3> </div> <div> <div> <ul> <li><em>dmc-attachment.stl</em></li> <li> <div><em>dmc-attachment-gear.stl</em></div> </li> <li><em>dmc-attachment-gear-wider.stl</em> (optional part)<em><br></em></li> <li><em>dmc-holder-back.stl</em></li> <li><em>dmc-holder-front.stl</em></li> <li><em>f-axis-motor-gear.stl</em></li> <li><em>f-axis-spring-end.stl</em></li> <li><em>f-axis-tighteners.stl</em></li> <li><em>frame-foot-inserts.stl</em></li> <li> <div><em>frame-foot-left.stl</em></div> </li> <li> <div><em>frame-foot-right.stl</em></div> </li> <li> <div><em>frame-hat.stl</em></div> </li> <li> <div><em>frame-hat-insert.stl</em></div> </li> </ul> </div> <h3>Remixed parts originally designed by Prusa Research</h3> <p><em>The five parts below are <strong>remi</strong></em><strong><em>xed from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts">i3 MK3S+ printable parts</a> </em></strong><em>and </em><strong><em>re-distributed under the <a href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">GNU General Public License v2.0</a></em></strong><em>.</em></p> </div> <ul> <li> <div><em>dmc-carriage-back.stl</em> (Remix of <em>x-carriage-back.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</div> </li> <li><em>dmc-carriage-front.stl </em>(Remix of <em>x-carriage.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</li> <li><em>x-end-idler-mod.stl</em> (Remix of <em>x-end-idler.stl</em>; the height was increased by 20 mm.)</li> <li><em>x-end-motor-mod.stl</em> (Remix of <em>x-end-motor.stl</em>; the height was increased by 20 mm and the counterbores of the three motor screws were moved to the opposite side.)</li> <li><em>z-axis-top-mod.stl </em>(Remix of <em>z-axis-top.stl</em>; 14.8 mm-long spacers were added.)</li> </ul> <h3>Parts designed by Prusa Research</h3> <ul> <li> <div><em>z-axis-bottom.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> <li> <div><em>z-screw-cover.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> </ul> <p> </p> <div> <h2>STL files of cut parts</h2> <h3>Original parts</h3> </div> <ul> <li><em>box-bottom.stl</em></li> <li><em>box-hook.stl</em></li> <li> <div><em>box-lid.stl</em></div> </li> <li> <div><em>box-lid-frame.stl</em></div> </li> <li><em>box-lid-valve-base.stl</em></li> <li><em>box-wall.stl</em></li> <li><em>box-wall-cables.stl</em></li> <li> <div><em>frame.stl</em></div> </li> </ul> <p> </p>
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
<p>Optical coherence tomography (OCT) is a non-invasive imaging technique that has extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology.</p> <p>The dataset consists of the following categories and images:<br>- Age-Related Macular Degeneration - 1231 images;<br>- Diabetic Macular Edema - 147 images;<br>- Epiretinal Membrane- 155 images;<br>- Normal - 332 images;<br>- Retinal Artery Occlusion - 22 images;<br>- Retinal Vein Occlusion - 101 images;<br>- Vitreomacular Interface Disease - 76 images.</p> <p>This dataset is published to provide researchers and developers with access to a large set of labeled images, which contributes to the development and improvement of algorithms for the automatic processing and analysis of OCT images for early diagnosis and monitoring of eye diseases. CSV file consists of file_name, disease, subcategory, condition, patient_id, eye, sex, year, image_width, and image_height. The dataset will be updated periodically.</p> <p> </p> <p>For more information and details about the dataset see:</p> <p>https://rdcu.be/dELrE</p> <p>https://arxiv.org/abs/2312.08255</p> <pre>@article{kulyabin2024octdl, title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods}, author={Kulyabin, Mikhail and Zhdanov, Aleksei and Nikiforova, Anastasia and Stepichev, Andrey <br> and Kuznetsova, Anna and Ronkin, Mikhail and Borisov, Vasilii and Bogachev, Alexander <br> and Korotkich, Sergey and Constable, Paul A and Maier, Andreas}, journal={Scientific Data}, volume={11}, number={1}, pages={365}, year={2024}, publisher={Nature Publishing Group UK London},<br> doi={https://doi.org/10.1038/s41597-024-03182-7} } </pre>
IODP Expedition 378 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 378 Closeup images
Close-up images taken by digital cameras as requested by the science party, typically when the section-half image is not sufficient. Close-up photographs of the areas of interest may be taken from whole-round sections, pieces, or section halves in sediments and rock.
IODP Expedition 378 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
IODP Expedition 378 Section-half images
Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.
IODP Expedition 378 Core composite images
A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.
ROCOv2: Radiology Objects in COntext Version 2, An Updated Multimodal Image Dataset
<p>Recent advances in deep learning techniques have enabled the development of systems for automatic analysis of medical images. These systems often require large amounts of training data with high quality labels, which is difficult and time consuming to generate.</p> <p>Here, we introduce Radiology Object in COntext Version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PubMed Open Access subset. Concepts for clinical modality, anatomy (X-ray), and directionality (X-ray) were manually curated and additionally evaluated by a radiologist. Unlike MIMIC-CXR, ROCOv2 includes seven different clinical modalities.</p> <p>It is an updated version of the ROCO dataset published in 2018, and includes 35,705 new images added to PubMed since 2018, as well as manually curated medical concepts for modality, body region (X-ray) and directionality (X-ray). The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical 2023. The participants had access to the training and validation sets after signing a user agreement.</p> <p>The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using the UMLS concepts provided with each image, e.g., to build systems to support structured medical reporting.</p> <p>Additional possible use cases for the ROCOv2 dataset include the pre-training of models for the medical domain, and the evaluation evaluation of deep learning models for multi-task learning.</p>
Quantification of ROIs corresponding to MQs from fluorescence in vivo imaging experiments
<p>We injected DIr labelled macrophages into mice carring immunolgical hot and cold KPC pancreatic tumors and quantified the recruitment to the tumor sites and lungs of the injected cells at different days after injection using fluorescence imaging. We hypotesized that macrophages would be recruited into tumor tissue and in prevalence into cold tumors.</p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
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>
Alpha-Galactosaminidase family GH191 protein from Environmental sample (99.2% identity to Myxococcus fulvus enzyme): X-ray diffraction images
<p><span>This submission includes a zip archive of diffraction images recorded with the Dectris EIGER X 9M detector at the DIAMOND beamline I04-1. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 9EP5. This is a case of crystal pathology – partial disorder. The model has C 2 2 21 symmetry and two molecules per asymmetric unit with occupancies 1 and 1/3. The molecule with partial occupancy overlaps with a symmetry related molecule.</span></p>
LifeWatch observatory data: phytoplankton annotated image library by FlowCam imaging for the Belgian part of the North Sea.
<p>In the framework of the Lifewatch marine observatory a number of fixed stations in the Belgian Part of the North Sea (BPNS) are sampled for phytoplankton monitoring. Samples are processed using a VS-4 FlowCAM model at 4X magnification, size range imaged is 55-300µm. The identification of the image data is done with the use of a classifier and followed by a manual validation step. These dataset comprises the full annotated image dataset which can be sampled for training of convolutional neural networks.</p>
IODP Expedition 367 Whole-round core section composite 360 degree images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
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.