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328 results for “segmentation dataset”
Shadow Detection/Texture Segmentation Computer Vision Dataset
<p>A simple computer vision dataset for shadow detection and texture analysis, specifically created to help test shadow detection algorithms (and texture segmentation algorithms) for mobile robots - that is, shadow detection with an <em>active (moving) camera</em>.</p> <p>The dataset is focused around texture analysis, so each image sequence contains shadows moving in front of a number of various textured surfaces. The dataset contains four main subfolders: "active", "artificial", "kondo", and "static". The "static" folder contains ground-truthed image sequences of textured surfaces with shadows moving over them, and the "active" folder contains ground-truthed image sequences of a camera travelling over textured surfaces. The "artificial" folder contains a computer-generated 3D scene with computer-generated ground truth, but note that texture is absent from all images within. Finally, the "kondo" folder contains a series of extremely challenging images captured from a webcam mounted to a Kondo bipedal robot. This final dataset is challenging because it contains a high level of noise, flicker and interference from electrical lighting, and the poor lighting conditions make for complex shadows with large penumbrae.</p>
An Annotated and Segmented Acoustic Dataset of 7 Picidae Species
<p>The proposed dataset contains 1669 labeled audio files from the following Picidae species doing 3 types of birdsongs: call, drumming and song. Audio data are organized in thirteen folders: twelve containing the labeled audios of each birdsong type from every Picidae specie listed in what follows and one folder containing the background sound samples. </p> <p>1- DendrocoposLeucotos - call.</p> <p>2- DendrocoposLeucotos - drumming.</p> <p>3- DendrocoposMajor - call.</p> <p>4- DendrocoposMajor - drumming.</p> <p>5- DendrocoposMedius - call.</p> <p>6- DendrocoposMedius - song.</p> <p>7- DendrocoposMinor - call.</p> <p>8- DendrocoposMinor - drumming.</p> <p>9- DryocopusMartius - call.</p> <p>10- DryocopusMartius - drumming.</p> <p>11- JynxTorquilla - song.</p> <p>12- PicusViridis - song.</p> <p>13- Silence (or background noise).</p>
A Dataset for Sanskrit Word Segmentation
<p>The work was accepted in Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, colocated with ACL 2017</p> <p>The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments. </p>
Ureteroscopy Lumen Segmentation Dataset
<p>This is a dataset for lumen segmentation. The dataset is composed of 1,754 endoscopic images from 23 patients undergoing Ureteroscopy. The dataset is composed of a total 2,187 endoscopic images with its respective masks highlighting the lumen in .png format. The dataset is divided by folders in train/val/test, train and val folders are divided in two sub-folders (image / label) For each image there is a corresponding mask with the same name. The training and validation data is the one described in <strong>[1]</strong>. The test folder is composed of 3 patient cases, test_01, test_02 and test_03. For more details please check the referred publication associated with this dataset. </p><p> </p><p>J. F. Lazo et al., "A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 9203-9210, doi: 10.1109/ICPR48806.2021.9411924.</p>
Tunnel Excavation Face Segmentation Dataset
<p>the dataset contain some tunnel excavation face images with segmentations</p>
SPACEtomo training dataset for Yeast lamella map segmentation using nnU-Netv2
<p><strong>Training data used to train a nnU-Net 2D segmentation model for TEM montages of cryoFIB-milled lamellae from Yeast.</strong></p> <p>Medium magnification map pieces (2.283 nm/pixel) from starved <em>S. cerevisiae</em> were manually segmented into 17 categories (see publication) and used to train a 2D nnU-Net. </p> <p>This training dataset contains 50 images and segmentations of starved Yeast cells (kindly provided by Anna Bieber and Cristina Capitanio) and 10 additional images of Baker's Yeast under exponential growth conditions.</p>
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p> <table> <tbody> <tr> <td>Dataset </td> <td>Task</td> <td>Model Details on Zenodo</td> </tr> <tr> <td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td> <td>Segment Liver from CT scans</td> <td>https://zenodo.org/record/8274976</td> </tr> <tr> <td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td> <td>Segment Kidney, Cyst, and Tumors from CT Scans</td> <td>https://zenodo.org/records/8277846</td> </tr> <tr> <td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td> <td>Segment Liver from the MR scans</td> <td>https://zenodo.org/record/8290124</td> </tr> <tr> <td>Data from m <a href="../record/6624726">pi-cai</a></td> <td>Segment Prostate region from MR scans</td> <td>https://zenodo.org/record/8290093</td> </tr> </tbody> </table>
Neural Image Segmentation for Redacted Text Detection Dataset
<p>This dataset was created for the "<span>Redacted Text Detection Using Neural Image </span><span>Segmentation Methods</span>" project, and contains roughly 1000 pages with manually annotated redactions in Dutch documents released under the WOO, with the trained model files and the model outputs also included in the dataset. More details on the usage of the dataset and models can be found on Github: https://github.com/RubenvanHeusden/NeuralRedactedTextDetection/</p>
Lysosome training dataset for live cell anatomy segmentation
Open the record for dataset details and reuse information.
ER training dataset for live cell anatomy segmentation
Open the record for dataset details and reuse information.
Pan-Cancer-Nuclei-Seg-DICOM: DICOM converted Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology 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=Pan-Cancer-Nuclei-Seg-DICOM" target="_blank" rel="noopener">Pan-Cancer-Nuclei-Seg-DICOM</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>This collection contains automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types earlier published in [2] (<a href="https://doi.org/10.7937/TCIA.2019.4A4DKP9U">https://doi.org/10.7937/TCIA.2019.4A4DKP9U</a>) stored in DICOM Bulk Annotation and DICOM Segmentation formats.</div> <div> </div> <div>DICOM Bulk Annotation nuclei annotations are stored as closed polygons along with the area of each nuclei. DICOM Segmentation version contains binary segmentations obtained by rasterizing the polygon contours. </div> <div> </div> <div>The annotations correspond to digital pathology images from the TCGA-BLCA,TCGA-BRCA,TCGA-CESC,TCGA-COAD,TCGA-GBM,TCGA-LUAD,TCGA-LUSC,TCGA-PAAD,TCGA-PRAD,TCGA-READ,TCGA-SKCM,TCGA-STAD,TCGA-UCEC,TCGA-UVM collections available in NCI Imaging Data Commons.</div> <div> </div> <div>To learn how these files are organized and how to access the content programmatically, see this documentation page: <a href="https://highdicom.readthedocs.io/en/latest/ann.html">https://highdicom.readthedocs.io/en/latest/ann.html</a>.</div> <div> </div> <div>Conversion of the nuclei segmentations from the original format into DICOM ANN and SEG representations was done using the code available in <a href="https://doi.org/10.5281/zenodo.13871765">10.5281/zenodo.10632181</a>.</div> <div> </div> <div>Annotations corresponding to this container ID in the source failed to convert due to the pixel matrix being too large to store: <code>TCGA-OL-A66K-01Z-00-DX1</code></div> <div> </div> <div>The following container IDs from the source annotations have failed due to inability to find the annotated images using the container IDs:</div> <div> <pre><code>TCGA-CU-A3QU-01Z-00-DX1 TCGA-A2-A0D1-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-BH-A0B2-01Z-00-DX1 TCGA-E2-A15E-01Z-00-DX1 TCGA-E2-A1IP-01Z-00-DX1 TCGA-F4-6857-01Z-00-DX1 TCGA-12-0773-01Z-00-DX4 TCGA-35-3621-01Z-00-DX1 TCGA-49-4486-01Z-00-DX1 TCGA-33-4587-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX2 TCGA-D9-A4Z6-01Z-00-DX1 TCGA-EE-A17Y-01Z-00-DX1 TCGA-EE-A29R-01Z-00-DX1 TCGA-EE-A2A0-01Z-00-DX1 TCGA-EE-A2MS-01Z-00-DX1 TCGA-ER-A199-01Z-00-DX1 TCGA-ER-A1A1-01Z-00-DX1 TCGA-ER-A2NC-01Z-00-DX1 TCGA-FS-A1Z7-06Z-00-DX10 TCGA-FS-A1Z7-06Z-00-DX11 TCGA-FS-A1Z7-06Z-00-DX12 TCGA-FS-A1Z7-06Z-00-DX13 TCGA-FS-A1ZN-01Z-00-DX10 TCGA-FS-A1ZN-01Z-00-DX11 TCGA-FS-A1ZW-06Z-00-DX10 TCGA-FS-A1ZW-06Z-00-DX11 TCGA-GN-A261-01Z-00-DX1 TCGA-GN-A266-01Z-00-DX1 TCGA-GN-A268-01Z-00-DX1 TCGA-GN-A26A-01Z-00-DX1 TCGA-XV-AB01-01Z-00-DX1 TCGA-AJ-A23O-01Z-00-DX1 TCGA-AP-A056-01Z-00-DX1 TCGA-BK-A139-01Z-00-DX1 TCGA-E6-A1M0-01Z-00-DX1</code></pre> </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>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-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> <h3>References</h3> </div> </div> <div>[1] 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).</div> <div> </div> <div>[2] Hou, L., Gupta, R., Van Arnam, J. S., Zhang, Y., Sivalenka, K., Samaras, D., Kurc, T., & Saltz, J. H. (2019). Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.4A4DKP9U</div>
The processing results, dataset and original codes of Coral reef PtCloud segmentation model (based on proposed underwater camera systems)
<p><strong># Data Introduction</strong><br>The proposed camera system includes three operation modes: Surface Mode, Horizontal Mode, and Curved Mode.</p> <ul> <li><strong>Surface Mode</strong>: Provides 3D reconstructions (point cloud), DEM, and orthophoto maps of the seabed (covering a line of 70m in length).</li> <li><strong>Horizontal Mode</strong>: Provides 3D reconstructions (point cloud and mesh) and orthophoto maps of a single transect line.</li> <li><strong>Curved Mode</strong>: Offers 3D reconstructions (mesh) of various targets, including artificial coral reefs, coral reefs with snails, and coral reefs with starfish.</li> </ul> <p>All mesh results are saved in <code>.FBX</code> format, and point cloud results are saved in <code>.TXT</code> format.</p> <p><strong># Code Introduction</strong><br>The backbone of our point cloud segmentation model is the KPConv model.<br>If you encounter any issues during code deployment, please refer to the original KPConv repository (<a href="https://github.com/HuguesTHOMAS/KPConv" target="_new" rel="noopener">Original Code: https://github.com/HuguesTHOMAS/KPConv</a>).<br>We provide our modified code (customized for our task) along with the complete point cloud dataset.</p>
A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images
<p>We have constructed a SAR (Synthetic Aperture Radar) image semantic segmentation dataset that includes 12 oceanic and atmospheric phenomena: Atmospheric Front (AF), Oceanic Front (OF), Rainfall (RF), Iceberg (IC), Sea Ice (SI), Pure Ocean Wave (POW), Wind Streak (WS), Low Wind Area (LWA), Biological Slick (BS), Micro Convective Cells (MCC), Internal Wave (IW), and Eddy.</p> <p>This dataset is built using Sentinel-1 IW and WV mode images. For WV mode data, we referenced TenGeoP-SARwv and SAR_WV_SemanticSegmentation and selected 2,383 images for semantic segmentation and annotation. For IW mode images, we incorporated some images from Tao et al.'s internal wave detection dataset. We selected 484 Sentinel-1 IW mode images obtained from 2015 to 2022 and divided them into 2,628 sub-images.</p> <p>The dataset contains a total of 5,011 image slices, with approximately 400 images for each phenomenon. All images are 16-bit .tiff files with a resolution of 100m and a size of 256x256 pixels. The images were manually annotated using the Labelme software, generating corresponding JSON files, which were then used to create the related annotation .png files.</p> <p>The updated version(V2) provides geographic information for each image.</p> <p>Thank you for your interest in our dataset. Here are the meanings of each label:</p> <p>1. BG: The unlabelled parts in JSON files are "BG" (Background)<br>2. AF: Atmospheric Front<br>3. BS: Biological Slick<br>4. I: “I” is equivalent to “IB”, representing icebergs<br>5. LWA: Low Wind Area<br>6. MCC: Micro Convective Cells<br>7. OF: Oceanic Front<br>8. POW: Pure Ocean Wave<br>9. RC: “RC” (Rain Cells) is equivalent to “RF” (Rainfall), both representing the rainfall phenomenon in the SAR image. <br>10. SI: Sea Ice<br>11. WS: Wind Streak<br>12. Eddy<br>13. IW: Internal Wave<br><em>14. HM: Represents the artificial objects appearing in the image, such as ships, aquaculture floating rafts, wind power facilities, etc.</em><br><em>15. OS: Unlike “BS”,“OS” represents mineral oil spills appearing in the SAR image (currently, there is insufficient data available for training, which will be supplemented in the future).</em></p>
Dataset for 'Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging'
<p>This is the dataset used in our paper '<em>Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging</em>', which mainly includes data from <strong>40Ah batteries</strong> at <strong>0.3C, 1C, 2C</strong>, and two <strong>280Ah batteries</strong> at <strong>20%, 60%, and 100% DOD</strong>, respectively. For specific details, please refer to Readme.text</p> <p>The data in<strong> LISHEN</strong> is the cycle test data of 40Ah battery at different discharge depths under 100%,60%,20%DOD working condition. The data in <strong>CATL</strong> and<strong> EVE </strong>is the cyclic test data of 280Ah battery at different discharge depths under 100%,60%,20%DOD conditions. <br>The CSV file has 21 or 17 columns, (17 columns for, data sequence number, good cycling, step number, step type, time (s) , total time (s) , current (a) , voltage (V) , capacity (AH) , charging capacity (AH) , discharge capacity (AH) , energy (Wh) , charging energy (Wh) , discharge energy (Wh) , absolute time (s) , power (W) , temperature (° C-RRB-) , the 21-column CSV adds four metrics (DQ/DV (AH/V) in column 17, dQm/DV (mAh/V) in column 18, contact resistance (m ω) in column 19, and module start-stop status in column 20) , More detailed files can be found in the zip file.</p>
TotalSegmentator MRI dataset: 616 MRI images with segmentations for 50 anatomical regions
<p>In 616 MR images we segmented 50 anatomical structures covering a majority of relevant classes for most use cases. The MR images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. Moreover, it contains some images from <a href="https://portal.imaging.datacommons.cancer.gov/" target="_blank" rel="noopener">IDC</a> for further data diversity (see column "source" in meta.csv).</p> <p>Link to a copy of this dataset on Dropbox for much quicker download: <a href="https://www.dropbox.com/scl/fi/gskhmz2n9mmlt7rcn77wo/TotalsegmentatorMRI_dataset_v200.zip?rlkey=kf1eocb6xjmqwzafuohhv19by&st=ka3m6wae&dl=0" target="_blank" rel="noopener">Dropbox Link</a></p> <p>You can find a segmentation model trained on this dataset <a href="https://github.com/wasserth/TotalSegmentator">here</a>.<br><br>More details about the dataset can be found in the corresponding <a href="https://arxiv.org/abs/2405.19492">paper</a>. Please cite this paper if you use the dataset. The CT images described in the paper can be found <a href="../doi/10.5281/zenodo.6802613" target="_blank" rel="noopener">here</a>.</p> <p>This dataset contains all 50 structures from the TotalSegmentator "total" task. It does not contain the structures of other TotalSegmentator MRI subtasks.</p> <p>This dataset was created by the department of <a href="https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin">Research and Analysis at University Hospital Basel</a>.</p> <p><strong>UPDATE</strong>: on 2025-01-21 we uploaded version 2.0.0 which increases the number of images from 298 to 616. It also contains slightly different structures.</p>
Conifer Lumen Segmentation (CoLuS) Dataset
<p>Conifer Lumen Segmentation (CoLuS) Dataset used in the publication "Towards ROXAS AI: Deep learning for faster and more accurate conifer cell analysis". More details found in the Github repository: https://marckatzenmaier.github.io/TowardsRoxasAI</p>
DeepBacs – Mixed segmentation dataset and StarDist model
<p>Mixed training and test images of <em>S. aureus</em>, <em>E. coli</em> and <em>B. subtilis </em>for cell segmentation using StarDist, as well as the trained StarDist model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field / fluorescence and segmented mask images</p> <p><strong>Microscopy data type</strong>: 2D widefield images; DIC and fluorescence for <em>S. aureus</em>, bright field images for <em>E. coli</em>, and fluorescence images for <em>B. subtilis</em></p> <p><strong>Microscopes</strong>: </p> <p><em>S. aureus</em>: </p> <p>GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)</p> <p><em>E.coli</em>:</p> <p>Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><em>B. subtilis</em>:</p> <p>Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)</p> <p> </p> <p><strong>Cell types</strong>: <em>S. aureus</em> strain JE2, <em>E. coli </em>MG1655 (CGSC #6300) and <em>B. subtilis</em> strain SH130; all grown under agarose pads</p> <p><strong>File format</strong>: .tif (8-bit and 16-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² @ 80 nm pixel size (S. aureus); 1024 x 1024 px² @ 79 nm pixel size (E. coli); 1024 x 1024 px² @ 65 nm pixel size (B. subtilis)</p> <p><strong>Image preprocessing</strong>: </p> <p><em>S. aureus:</em></p> <p>Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches.</p> <p><em>E. coli:</em></p> <p>Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks.</p> <p><em>B. subtilis</em>:</p> <p>Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin</p> <p> </p> <p><strong>StarDist model:</strong></p> <p>The StarDist 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 200 epochs (120 steps/epoch) on 155 paired image patches (image dimensions: (1024, 1024), patch size: (256,256)) with a batch size of 4, 10% validation data, 64 rays on grid 2, a learning rate of 0.0003 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.2). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The dataset was augmented by a factor of 3.</p> <p> </p> <p>The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook, the StarDist Fiji plugin or the TrackMate Fiji plugin (v7+).</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup>, Mia Conduit<sup>4</sup>, Séamus Holden<sup>4,5</sup>, Pedro Matos Pereira<sup>6,7</sup>, Mariana Pinho<sup>6,8</sup></p> <p><strong>Contact email</strong>: <a href="mailto:christoph.spahn@mpi-marburg.mpg.de">christoph.spahn@mpi-marburg.mpg.de</a>, <a href="mailto:Seamus.Holden@newcastle.ac.uk">Seamus.Holden@newcastle.ac.uk</a>, <a href="mailto:pmatos@itqb.unl.pt">pmatos@itqb.unl.pt</a> and <a href="mailto:mgpinho@itqb.unl.pt">mgpinho@itqb.unl.pt</a></p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p> <p>4) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK</p> <p>5) ORCID: 0000-0002-7169-907X</p> <p>6) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal</p> <p>7) ORCID: 0000-0002-1426-9540</p> <p>8) ORCID: 0000-0002-7132-8842</p>
KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells
<p><span>Mechanobiology requires precise quantitative spatial information on processes taking place in specific 3D microenvironments. Connecting microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps is particular challenging for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. Here we present raw data and processed segmented data of fluorescence-tagged keratin intermediate filaments from MDCK, HaCaT and RPE epithelial cells. The 30 datasets contain unprocessed airyscan superresolution 3D image data and numerical network data of the 3D arrangement of the keratin intermediate filament cytoskeleton. Information is provided on network organization at the subcellular level, including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. The datasets contain data in exchange format for 3d software packages. Comparison of the resulting network parameters helps to identify similarities and differences of keratin network organization in epithelial cell types. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested. </span></p>
Root tissue segmentation dataset
<p>The PHDFM dataset is composed of fluorescence microscopy images of root tissue samples from <em>A. thaliana</em>, using the ratiometric fluorescent indicator 8‐hydroxypyrene‐1,3,6‐trisulfonic acid trisodium salt (HPTS). This semantic segmentation training dataset consists of 2D microscopy images (the brightfield channel for excitation at 405 nm), each containing a segmentation mask as an additional image channel (manually annotated by plant biologists). The segmentation masks classify pixels into the following 5 labels with the corresponding IDs: background (0), root tissue (1), early elongation zone (2), late elongation zone (3), and meristematic zone (4).</p>
Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
<p>The datasets used in this study are provided in 7 folders:</p> <ol> <li> <p>"dataset A", containing data from Sandwich Town Neck Beach on Cape Cod, Massachusetts. These images are published as a USGS data series (Sherwood et al., 2021) are publicly available at <a href="https://doi.org/10.5066/P9BFD3YH">https://doi.org/10.5066/P9BFD3YH</a></p> </li> <li> <p>"dataset B", containing data from North and South Carolina collected immediately after Hurricane Florence in October 2018. National Geodetic Survey emergency response imagery courtesy of the National Oceanic and Atmospheric Administration, available at <a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov</a></p> </li> <li> <p>"dataset C", containing some examples of shoreline environments captured by a low-altitude aircraft. These images are published as a USGS data series (Kranenburg et al., 2020) are publicly available at <a href="https://doi.org/10.5066/P9CA3D8P">https://doi.org/10.5066/P9CA3D8P</a></p> </li> <li> <p>"dataset D", containing data collected from the Pearl River and its tributary the Bogue Chitto, and from the Chickasawhay, Buoy and Leaf tributaries of the Pascagoula River, in spring 2021. Used with permission from U.S. Fish and Wildlife Service</p> </li> <li> <p>"dataset E", containing Sentinel-2 satellite images of coastal lagoon environments in Salinas Rivermouth Natural Preserve and National Wildlife Refuge in Monterey, California. Sentinel-2 imagery courtesy of European Space Agency (ESA)</p> </li> <li> <p>"dataset F", containing Landsat-8 of Cape Hatteras, Cape Hatteras National Seashore, North Carolina. Landsat-8 imagery is courtesy of U.S. Geological Surve</p> </li> <li> <p>"code", containing a version of the code used to generate the results contained in this data repository. Full details about this code can be obtained from the github code repository (<a href="https://github.com/dbuscombe-usgs/dash_doodler">https://github.com/dbuscombe-usgs/dash_doodler</a>) and website (<a href="https://dbuscombe-usgs.github.io/dash_doodler/">https://dbuscombe-usgs.github.io/dash_doodler/</a>).</p> </li> </ol>
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.