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18 results for “Line Segmentation”
watercourse_100mseg: the Flemish watercourses represented by 100-meter line segments and corresponding downstream endpoints
<p>The data source <code>watercourse_100mseg</code> is derived from the raw data source '<a href="https://doi.org/10.5281/zenodo.4420904">watercourses</a>'. It represents all officially known watercourses of the Flemish Region as line segments of <strong>100 m</strong> (or < 100 m, for the most upstream segment of a watercourse). The data source can be used as a base layer of statistical <strong>population units</strong> (line segments) and corresponding anchor points, in the design of monitoring and research of watercourses.</p> <p>The data source is a GeoPackage with <strong>two spatial layers</strong>:</p> <ul> <li> <p><code>watercourse_100mseg_lines</code>: the line segments;</p> </li> <li> <p><code>watercourse_100mseg_points</code>: the corresponding downstream endpoints ('downstream' as defined in <code>watercourses</code>).</p> </li> </ul> <p>The coordinate reference system is 'Belge 72 / Belgian Lambert 72' (EPSG-code <a href="https://epsg.io/31370">31370</a>). Both layers have the same number of rows, and they share the same <strong>attributes</strong>:</p> <ul> <li><code>rank</code>: a unique, incremental number for each segment/endpoint. It just reflects the downstream-to-upstream order of segments within each original line.</li> <li><code>vhag_code</code>: the VHAG code from the raw <code>watercourses</code> data source. It distinguishes the different watercourses, so it is common to all segments/points that belong to the same watercourse.</li> </ul> <p>This version was derived from version '<code>watercourses_20200807</code>' (<a href="https://doi.org/10.5281/zenodo.4420905">Zenodo DOI</a>) as follows:</p> <ol> <li> <p>each line ('watercourse') of <code>watercourses</code> is split into segments of 100 m, where the remaining segment of < 100 m (per original line) is situated most upstream. For this step, the direction of the lines has been reverted (in <code>watercourses</code> the direction is from upstream to downstream). A unique rank number is assigned to each segment, as well as the VHAG code from the corresponding line in <code>watercourses</code>.</p> </li> <li> <p>the downstream endpoint of each segment is located, and assigned the same attributes (<code>rank</code> and <code>vhag_code</code>).</p> </li> </ol> <p>See R and GRASS code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/6b1d8f7/src/generate_watercourse_100mseg">'n2khab-preprocessing' at commit 6b1d8f7</a> for the creation from the <code>watercourses</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p>
The Monk Line Segmentation (MLS) Dataset
<p>Overview</p> <p>The MLS dataset available from this page consists of 31 handwritten page scans. The dataset contains medieval, historical and contemporary manuscripts, and has the purpose of testing line-segmentation algorithms. The collection contains a wide variation of the common problems in handwriting recognition: lines with overlapping ascenders/descenders, slightly rotated scans and curved base lines. <br> </p> <p>Download</p> <p>The MLS dataset was collected from the Monk system as of Friday May 17 14:15:04 CEST 2013. It was collected by Lambert Schomaker in May 2013 at the Institution of Artificial Intelligence and Cognitive Engineering (ALICE), University of Gronigen. </p> <p>The tar.gz file contains the image dataset for historical manuscripts. For more details please refer to the README file in the tar.gz file. The dataset downloaded for research use only. © 2013 Copyright. <br> </p> <p>@INPROCEEDINGS{Surinta:2014:ICFHR,<br> author = {O. Surinta and M. Holtkamp and M. F. Karaaba and JP. van Oosten and L. R. B. Schomaker and M. A. Wiering},<br> title = {A* Path Planning for Line Segmentation of Handwritten Documents},<br> booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},<br> year = {2014},<br> month = {Sep},<br> pages = {175-180},<br> numpages = {6},<br> isbn = {978-1-4799-4335-7},<br> issn = {2167-6445},<br> publisher = {IEEE},<br> doi = {http://dx.doi.org/10.1109/ICFHR.2014.37},<br> }</p>
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Pecha Line Segmentation Datasets
<p>Training data for line segmentation of Tibetan woodblock prints. This version of dataset contain images randomly picked images from LOC scans of the <a href="https://www.tbrc.org/#!rid=W4CZ5369">derge kangyur</a>.</p> <p>This dataset was trained with Unet Model using fastai library. The result was unsatisfactory due to insufficient data.</p> <p><strong>Datasets contains:</strong></p> <ol> <li>Images/ : Original pecha image</li> <li>Labels/ : Mask of each image</li> <li>valid.txt: list of validation filename</li> <li>code.txt: list of name of object</li> </ol>
WhiteRoadLines: Dataset of 27,025 images (256x256 pixeles at 0.15 m/ pixel) containing representative road lines and markings labelled for multi-class semantic segmentation
<p>The dataset consists of 27,025 PNG images (256x256 pixels) of high resolution aerial orthoimages at 0,15 m/pixel of resolution. The images contain information related to representative road lines and markings found on highway pavement and is labelled for multi-class semantic segmentation with tree classes of white road<br>lines and markings: (1) continuous line (black color), (2) dashed line (dark gray color) and (3) separation of entry and exit lanes (light gray color), together with (4) the background (white color). <br> </p><p>The dataset has been created in the framework of the SROADEX project to train a multiclass semantic segmentation process based on Deep Learning.<br>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the three different types of white road lines. This cartography has been obtained from Spanish official sources (National Geographic Institute) that we have<br>revised and edited in a meticulous and systematic way to verify that the road lines are represented on the cartography according to the orthoimages, available on January 1, 2022 in the download center of the National Center of Geographic Information (CNIG). </p><p>In the digitisation process, 46 homogeneously distributed areas of Spain have been selected. The orthoimages used have been resampled from the original resolution of 0,25m/pixel to 0,15m/pixel, as this is closer to the width of two of the three classes of white lines in the dataset. It resulted in 80% of the images for training (21622), 10% for validation (2702) and 10% for testing (2701). The following table summarises the number of pixels of each category included in each of the three sub-datasets</p><p> </p><p>Set Nº images Class_1 (continuous line) Class_2 (discontinuous line) Class_3 (line defining highway entrance or exit) Class_4 (background)</p><p>Train 21,622 27,633,537 4,543,552 3,284,380 1,381,557,923</p><p>Validation 2,702 3,433,103 570,741 395,646 172,678,782</p><p>Test 2,701 3,435,072 536,838 429,527 172,611,299</p><p>Total 27,025 34,501,712 5,651,131 4,109,553 1,726,848,004</p>
Tango Spacecraft Wireframe Dataset Model for Line Segments Detection
<p><strong>Reference Paper:</strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" dataset here published should be used for line detection and segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the only publicly available dataset of synthetic space-borne images tailored to line detection tasks (up to our knowledge). The label of each image gives the reprojection of a simplified wireframe model of Tango on the image plane split into lines. The labels are written following the Wireframe Model format. The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" is also the largest dataset with wireframe annotations available up to date. More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels in the Wireframe dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> width : 98 # width in pixels (int) of the current image</li> <li> height : 176 # height in pixels (int) of the current image</li> <li> lines : [[line1], [line2], ..., [lineN]] # list of lines in each image</li> <li> filename : tango_img_866.png # string with image name and format</li> </ul> <p>Per each line (line1, ... , lineN) in lines, the format is [x0, y0, x1, y1].</p> <p>(x0, y0) are the coordinates (float) of the line starting point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).<br> (X1, y1) are the coordinates (float) of the line ending point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).</p> <p>Note that the starting point is assumed to be the left-most endpoint (lower x coordinate in image reference frame) of each line. In the case of vertical lines, the starting point is the upper-most endpoint (lower y coordinate in image reference frame) of each line.</p> <p><strong>VERSION CONTROL</strong></p> <ul> <li><strong>v1.0</strong>: All the images (both for train and test) have different resolutions, with Tango always centered in the image. The height of the images is in the range 19 - 352 pixels, while the width is in the range 16 - 336 pixels. The height over width ratio spans from 0.34 to 3.25.</li> <li><strong>v2.0</strong>: This version contains all the images of v1.0 in the .zip folder named <em>Tango_WF.zip</em>, while in the .zip folder named <em>Tango_WF_fullscale.zip</em> there is the dataset (both train and test) of full scale images. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. The labels for the wireframe are in the same format of v1.0.</li> </ul> <p>Note: the dataset in v1.0 is obtained by cropping the fullscale images in v2.0 and by properly rescaling the wireframe annotations.</p> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em><em>"</em> v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6507863">https://doi.org/10.5281/zenodo.6507863</a>) and also "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6499007">https://doi.org/10.5281/zenodo.6499007</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Annotated quantitative phase microscopy cell dataset of various adherent cell lines for segmentation purposes
<p>This dataset contains microscopic images of multiple cell lines captured by quantitative phase microscopy (QPI) without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p>Our data consist of quantitative phase microscopy images:</p> <ul> <li>244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), 25 labelled images of G361, 25 labelled images of A2050 and 25 labelled images of HOB cells , in the paper designated as "<em>labelled"</em>, and</li> <li>1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "<em>unlabelled"</em>.</li> </ul> <ul> <li>See Vicar et al. XXXX 2021 DOI XXX (TBA after publishing)</li> <li>Code using this dataset is available at <a href="https://github.com/tomasvicar/Deep-QPI-Cell-Segmentation">github.com/tomasvicar/Deep-QPI-Cell-Segmentation</a></li> </ul> <p><strong>Materials and methods </strong></p> <p>A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5% CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic microscope (Telight, Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart.</p> <p><strong>Folder structure and file and filename description</strong></p> <p><strong>labelled </strong><em>(labelled)</em>: cells with segmentation labels, e.g.<br> 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values)<br> 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV.</p> <p><strong>unlabelled </strong><em>(unlabelled)</em>: 11 varying cell lines, total 1819 FOVs, 32bit tiff image (in pg/um2 values)</p> <p> </p>
Line Segment in Document Images Datasets
<p>Those datasets are related to the accepted article to ICDAR 2023: "Linear Object Detection in Document Images<br> using Multiple Object Tracking" by Bernet et al.</p> <p>The official github repository is : https://github.com/EPITAResearchLab/bernet.23.icdar</p> <p>.png ground truth are labelled images where white pixels correspond to the background.</p> <p>.csv ground truth are using the following format:<br> ```<br> x,y,w,h # First line is the size of the interest area in the image<br> x1,y1,x2,y2 # Following lines are the coordinates of the extremities of the line<br> ...<br> xn,yn,xn+1,yn+1<br> ```</p>
Data from: Homoploid F1 hybrids and segmental allotetraploids of japonica and indica rice subspecies show similar and enhanced tolerance to N deficiency than parental lines
Open the record for dataset details and reuse information.
Evaluation of the Cone Outer Segment Tips Line After Epiretinal Membrane Surgery
ClinicalTrials.gov study NCT01549249. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Facilitation Through Aggrastat By drOpping or Shortening Infusion Line in Patients With ST-segment Elevation Myocardial Infarction Compared to or on Top of PRasugrel Loading dOse
ClinicalTrials.gov study NCT01336348. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Introgression of chromosome segments from multiple alien species in wheat breeding lines with wheat streak mosaic virus resistance
Pyramiding of alien-derived Wheat streak mosaic virus (WSMV) resistance and resistance enhancing genes in wheat is a cost-effective and environmentally safe strategy for disease control. PCR-based markers and cytogenetic analysis with genomic in situ hybridisation were applied to identify alien chromatin in four genetically diverse populations of wheat (Triticum aestivum) lines incorporating chromosome segments from Thinopyrum intermedium and Secale cereale (rye). Out of 20 experimental lines, 10 carried Th. intermedium chromatin as T4DL*4Ai#2S translocations, while, unexpectedly, 7 lines were positive for alien chromatin (Th. intermedium or rye) on chromosome 1B. The newly described rye 1RS chromatin, transmitted from early in the pedigree, was associated with enhanced WSMV resistance. Under field conditions, the 1RS chromatin alone showed some resistance, while together with the Th. intermedium 4Ai#2S offered superior resistance to that demonstrated by the known resistant cultivar Mace. Most alien wheat lines carry whole chromosome arms, and it is notable that these lines showed intra-arm recombination within the 1BS arm. The translocation breakpoints between 1BS and alien chromatin fell in three categories: (i) at or near to the centromere, (ii) intercalary between markers UL-Thin5 and Xgwm1130 and (iii) towards the telomere between Xgwm0911 and Xbarc194. Labelled genomic Th. intermedium DNA hybridised to the rye 1RS chromatin under high stringency conditions, indicating the presence of shared tandem repeats among the cereals. The novel small alien fragments may explain the difficulty in developing well-adapted lines carrying Wsm1 despite improved tolerance to the virus. The results will facilitate directed chromosome engineering producing agronomically desirable WSMV-resistant germplasm.
Data from: Introgression of chromosome segments from multiple alien species in wheat breeding lines with wheat streak mosaic virus resistance
Open the record for dataset details and reuse information.
Rice stem at early stage growth: Control vs.Chromosome segment substitution lines (SLs) from a cross the Koshihikari (japonica) and Habataki (indica)
GEO Series GSE35762. Oryza sativa; Oryza sativa Japonica Group. 9 samples. Type: Expression profiling by array.
Foveal Cone Outer Segment Tips Line Defect in Macular Pseudohole
ClinicalTrials.gov study NCT01959802. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Recapitulating the Human Segmentation Clock with Pluripotent Stem Cells - RNAseq analysis of healthy control (WT) and knock-out reporter lines of segmentation clock genes HES7, DLL3, LFNG and MESP2.
GEO Series GSE116930. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Recapitulating the Human Segmentation Clock with Pluripotent Stem Cells - RNAseq-based comparison of patient and isogenic rescue/healthy control iPSC lines during in vitro differentiation of human pre
GEO Series GSE116934. Homo sapiens. 58 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic comparative analysis of different resistance to sheath blight in rice chromosome segment substitution lines
GEO Series GSE178395. Oryza sativa. 24 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.