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1,506 results for “objects”
zebrafish scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
Thimble-like object with shrine and tree
<p>Thimble-like object cast in a copper alloy; on one side a rectangular panel showing a sacred tree, shrine, altar and standard; pierced. Probably from central India. British Museum number 1995,1018.1</p>
FG-OVD: Fine-grained Open-Vocabulary Object Detection Benchmark Suite
<p>A collection of annotations for PACO images containing free-form fine-grained textual captions of objects, their parts, and their attributes. It also comprises several sets of negative captions that can be used to test and evaluate the fine-grained recognition ability of open-vocabulary models.</p>
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI)
<pre><a href="http://data.pymvpa.org/datasets/haxby2001/">http://data.pymvpa.org/datasets/haxby2001/</a> This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references_ below. Terms Of Use ============ The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/</pre> <pre>References ========== :ref:`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001) <HGF+01>`. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. :ref:`Hanson, S., Matsuka, T., and Haxby, J. (2004) <HMH04>`. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166. :ref:`O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005) <OJA+05>`. Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. :ref:`Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009) <HHS+09b>`. PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.</pre> <p> </p>
Dual energy CT scan of ordinary objects
<p>Dual energy CT scan of ordinary objects: wires, pen, fruits (orange, avocado), pastery, bacon, butter, cheese.</p> <p>The purpose of these scans is to enable experimenting with CT scans using various kernels and iterative reconstructions. For instance, studying metal artifacts at different energies, material identification using dual energy index, examining the relation between reconstruction kernel sharpness, iterative reconstruction strength and noise.</p> <p>The dataset also can be used to set up mock trials, e.g. where readers have to choose the sharpest image, or the one with least disturbing metal artifacts. Similarly, it could serve debug purposes, e.g. testing the workflow, DICOM readers, etc.</p> <p>Zenodo-get ( https://doi.org/10.5281/zenodo.1261812 ) could be used to download the whole record at once.</p>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</p>
Digital models of test objects captured by RFSAT Ltd using 3D photogrammetry
<p>This data set contains a number of digital models produced via 3D photogrammetric scanning as part of the SCAN4RECO project, funded by the European Horizon'2020 program. Scanning and processing of models was done with Pix4D Mapper and Autodesk ReMake software from images captured with Canon 5DS camera in 50 Megapixel image resolution. Example objects include Byzantine icons painted on wood, oil paintings on canvas and painted Venetian carnival paper masks.</p> <p>Second version of the data set includes historical icons of Saint DImitrios and Saint Archangel Michael, an icon of Saint Mary painted specially for testing SCAN4RECO technologies, as well as models of an original high-relief sculpture from OPD and of its 3D printed copy (made by Fraunhofer-IGD and hand painted by RFSAT)..</p> <p>Selected models can be also seen in the SCAN4RECO Virtual Museum developed by CERTH-ITI:<br> http://scan4reco.eu/scan4reco/content/scan4reco-virtual-museum</p>
User Study Data from "Point-and-Shake: Selecting from Levitating Object Displays"
<p>This dataset contains anonymous user study data from the two experiments described in the corresponding CHI 2018 publication.</p>
TensorFlow models for CK object detection
<p>Tarball containing the yolo model for the tensorflow object detection program in CK repositories</p> <p> </p>
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
VCM Dataset for the Classification of Resident Space Objects
<p>The Vector Covariance Message (VCM) data comprise 22,303 RSOs over a period of six months (9/1/2022-2/28/2023). VCM data consist of Resident Space Objects (RSOs) ephemerides from a high-precision special perturbations orbit propagator and estimator using tracking observations. VCMs are issued by the US Space Force (USSF) Space Command (USSPACECOM) and were provided through an Orbital Data Request (ODR) the authors submitted to the 18th Space Defense Squadron (18th SDS). </p> <p>The dataset is organized into subfolders, each containing VCMs for a specific satellite. Filenames correspond to the satellite's NORAD ID (North American Aerospace Defense Catalog Number). A readme file provides details about the VCM content and format. Note that the full covariance matrix has been excluded for public release, whereas the standard deviation of error in satellite's position and velocity is provided.</p> <p>The VCM data have been used in the following work, "Early Classification of Space Objects based on Astrometric Time Series Data", presented at the 25th Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in Maui, Hawaii, United States.</p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
Single objective light-sheet acquired large-scale 3D dataset
<p>This dataset covers Fig. 5 of the following publication:</p> <p>Title: Tilt-invariant scanned oblique plane illumination microscopy for large-scale volumetric imaging<br> Authors: Manish Kumar and Yevgenia Kozorovitskiy <br> Optics Letters Vol. 44, Issue 7, pp. 1706-1709 (2019)<br> https://doi.org/10.1364/OL.44.001706</p> <p>Briefly: The sample imaged is a Thy1GFP expressing transgenic mice brain slice - fixed and coverslipped. No clearing was performed for these.</p> <p>See "readme.txt" for additional info and details about how to use "shearNscaleObliq" file.</p>
mouse scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qui et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Mohammed et al. 2017, Cheng et al. 2019, Pijuan-Sala et al. 2019, Cao et al. 2019 and Qui et al. 2022.</p>
JWST convolutions for a modular set of synthetic SEDs for young stellar objects (Robitaille, 2017)
<p>This is a companion to the models released alongside the publication:</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>The models are convolved with JWST filters taken from the SVO’s filter profile service. Some models with rotationally flattened envelopes (i.e. geometries with<strong> u</strong>)<strong> </strong>not present in the original model grid have since been completed; their convolved SEDs are included here.</p> <p>Files unzip to {geometry}/convolved/JWST/{SVO_filtername}.fits.</p> <p>This is a subset of the information included in https://doi.org/10.5281/zenodo.8114592.</p>
celegans scRNA data set objects
<p>Combined and converted scRNA data from (Packer and Zhu et al. 2019), see a detailed description of the study here: https://www.science.org/doi/full/10.1126/science.aax1971</p> <p>Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Packer and Zhu et al. 2019.</p>
Brown Dwarfs are Violet: Python Tools for the Estimation of Human-eye Colors of Stars and Substellar Objects
<p>The accompanying files include a Python Jupyter notebook (and associated data files read in by the Python code) that carry out the calculations described by Cranmer (2023), talk 246.05 presented at the 241st Meeting of the American Astronomical Society (AAS) in Seattle, Washington. The abstract of the talk is provided here:</p> <p>There has always been interest in the perceived colors of the stars. They were key to the development of the H-R diagram, and they are also used widely in educational and public-outreach imagery. Thus, it is useful to develop software tools to compute these colors, as accurately as possible, from spectral energy distributions. This presentation follows up on an RNAAS paper (<a href="https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..201C/abstract">Cranmer 2021</a>) that presented a collection of objective (CIE coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs. A new empirical method of converting from CIE to RGB values is described, and results for various stellar spectra are presented. Although brown dwarfs over a wide range of effective temperatures (400 to 2000 K) emit most of their flux in the infrared, their visible spectra often exhibit a local maximum around a strong dip in the Na I cross section at 0.4-0.5 microns. Thus, they may appear purple to human eyes. Also, the hottest (O-type) main-sequence stars may appear even "bluer than the blue sky" because of Paschen continuum absorption. This presentation will update earlier stellar and brown-dwarf color estimates using more recently published synthetic spectra, and it will also investigate the effects of atmospheric absorption, over a range of air-mass values, on these perceived colors. Python Jupyter notebooks that carry out these calculations will be uploaded to the Zenodo repository for open-access distribution.</p> <p><strong>NOTE 1: </strong>The algorithms described here, for computing RGB triples, ought to be considered as preliminary results in ongoing research; i.e., they need additional testing and validation by comparing to the results of other more established ways of converting astronomical spectra to perceived colors.</p> <p><strong>NOTE 2:</strong> These files follow on from those provided in another Zenodo upload associated with the 2021 RNAAS paper: <a href="https://doi.org/10.5281/zenodo.5293307">https://doi.org/10.5281/zenodo.5293307</a></p>
Data from: Capacity and selection in immersive visual working memory following naturalistic object disappearance
<p>Trial datasets and timeseries datasets associated with the experiment reported in the manuscript "Capacity and selection in immersive visual working memory following naturalistic object disappearance", by Babak Chawoush, Dejan Draschkow & Freek van Ede</p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</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.