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1,506 results for “objects”
Fluorescence Microscopy Data for Cellular Detection using Object Detection Networks.
<p>This data accompanies work from the paper entitled: </p> <p><strong>Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis. </strong></p> <p>Waithe D1*,2,, Brown JM3, Reglinski K4,6,7, Diez-Sevilla I<sup>5</sup>, Roberts D<sup>5</sup>, Christian Eggeling1,4,6,8</p> <p>1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU.<br> 6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany.<br> 7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany.<br> 8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany.</p> <p>Further details of these datasets can be found in the methods section of the above paper.</p> <p><strong>Erythroblast DAPI (+glycophorin A):</strong> erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone, Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.</p> <p><strong>Neuroblastoma phalloidin (+DAPI): </strong>images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7.</p> <p><strong>Fibroblast nucleopore</strong>: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8.</p> <p><strong>Eukaryote DAPI:</strong> eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9.</p> <p><strong>C127 DAPI:</strong> C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1.</p> <p><strong>HEK peroxisome All</strong>: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9.</p> <p><strong>Erythroid DAPI All: </strong>Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9</p> <p><strong>COS-7 nucleopore. </strong>Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2</p> <p><strong>COS-7 nucleopore 40x</strong>. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11. Average number of cells per image: 31.6.</p> <p><strong>COS-7 nucleopore 10x.</strong> Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6</p> <p><strong>Dataset Annotation</strong></p> <p>Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (<a href="https://www.openmicroscopy.org/omero/">https://www.openmicroscopy.org/omero/</a>). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet. The scripts used to perform this conversion are documented in the repository (<a href="https://github.com/dwaithe/amca">https://github.com/dwaithe/amca</a>/scripts/).</p>
Data from: Wild samango monkeys, Cercopithecus mitis, balance risk and opportunity to interact with novel objects in village gardens
Research suggests that wild animals in urban areas exhibit heightened behavioral flexibility when they encounter novel human-made objects, but most such studies compared responses in urban populations with those from disjunct populations in less disturbed environments. We therefore know little about intrapopulation variation in cognitive or behavioral flexibility under different conditions of anthropogenic exposure. Here, we investigate object exploration and behavioral flexibility in a single group of samango monkeys (Cercopithecus mitis) in an environment where individuals forage in both gardens and natural forest. Over two months, we presented monkeys with novel, human-made objects in both natural and disturbed areas, varying the position of objects in trees to represent exposed or safer foraging zones. We video-recorded and analyzed interactions with these novel objects, assessing interaction times (an indicator of persistence), exploratory diversity (or motor diversity), and the occurrence of foraging innovations. Results from 67 interactions (29 in natural habitats and 38 in disturbed) indicate that samango monkeys exhibited a spatially complex response to novel objects: in contrast to other species, exploration diversity decreased significantly in anthropogenic environments, even as persistence remained largely static across contexts. Monkeys also exhibited foraging innovations by pulling on strings to bring objects closer. This may reduce exposure to danger, as string-pulling was most prevalent in the highest risk condition (ground level in human gardens). Significant intrapopulation variation in behavioral flexibility suggests that samango monkeys adjust the expression of problem-solving behaviors in relation to the degree of human disturbance in their immediate environment.
Data from: Evidence for normal novel object recognition abilities in developmental prosopagnosia
<p>The issue of the face specificity of recognition deficits in developmental prosopagnosia (DP) is fundamental to the organisation of high-level visual memory and has been increasingly debated in recent years. Previous DP investigations have found some evidence of object recognition impairments, but have almost exclusively used familiar objects (e.g., cars), where performance may depend on acquired object-specific experience and related visual expertise. An object recognition test not influenced by experience could provide a better, less contaminated measure of DPs' object recognition abilities. To investigate this, in the current study we tested 30 DPs and 30 matched controls on a novel object memory test (NOMT Ziggerins) and the Cambridge Face Memory Test (CFMT). DPs were impaired on the CFMT but showed no differences in accuracy or reaction times to controls on the NOMT. We found similar results when comparing DPs to a larger sample of 274 web-based controls. Additional individual analyses demonstrated that the rates of object recognition impairment in DPs did not differ from the rate of impairment in either control group. Together, these results demonstrate unimpaired object recognition in DPs for a class of novel objects that serves as a powerful index for broader novel object recognition capacity.</p>
Data from: Treefrogs exploit temporal coherence to form perceptual objects of communication signals
<p>For many animals, navigating their environment requires an ability to organize continuous streams of sensory input into discrete "perceptual objects" that correspond to physical entities in visual and auditory scenes. The human visual and auditory systems follow several Gestalt laws of perceptual organization to bind constituent features into coherent perceptual objects. A largely unexplored question is whether nonhuman animals follow similar Gestalt laws in perceiving behaviorally relevant stimuli, such as communication signals. We used females of Cope's gray treefrog (<em>Hyla chrysoscelis</em>) to test the hypothesis that temporal coherence – a powerful Gestalt principle in human auditory scene analysis – promotes perceptual binding in forming auditory objects of species-typical vocalizations. According to the principle of temporal coherence, sound elements that start and stop at the same time or that modulate coherently over time are likely to become bound together into the same auditory object. We found that the natural temporal coherence between two spectral components of advertisement calls promotes their perceptual binding into auditory objects of advertisement calls. Our findings confirm the broad ecological validity of temporal coherence as a Gestalt law of auditory perceptual organization guiding the formation of biologically relevant perceptual objects in animal behavior.</p>
Dataset: Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
<p>This dataset is the dataset used to train and test the object condensation particle flow approach described in <a href="https://arxiv.org/abs/2002.03605">arxiv:2002.03605</a>.</p> <p>The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore)<br> The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are:</p> <p>isElectron,<br> isGamma,<br> isPositron,<br> true_energy,<br> true_x,<br> true_y</p> <p>The entries in the feature array are of dimension (batch, 200, N_features), with the features being:</p> <p>rechit_energy,<br> rechit_x,<br> rechit_y,<br> rechit_z,<br> rechit_layer,<br> rechit_detid</p> <p>The "train.zip" file contains the training sample<br> The "test.zip" file the test sample</p> <p>The main test sample is identical to the training sample in composition, but statistically independent.<br> Other samples can be found in subfolders:</p> <p>test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle<br> Test/hiNPart: sample with up to 15 particles per event</p>
Investigating Multi and Many-Objective Metaheuristics to Support Software Integration Testing
<p> In spite of the fact that Search-Based Software Testing (SBST) is a very appealing field today, there are few studies that deal with software integration testing and, even so, most of these works are not truly related to the generation of test cases to this testing level. In this paper, we present a method, InMeHy, which aims at investigating the use of metaheuristics to derive integration test cases based on C++ source code. A graph is created based on the code which represents the integration of several classes of the application. Multi and Many-Objective metaheuristics (Evolutionary Algorithms) were considered to generate integration test cases and were assessed via three quality indicators. Results show that the traditional Indicator-Based Evolutionary Algorithm (IBEA) turned out to be the best out of four algorithms evaluated, including newer Many-Objective strategies such as Nondominated Sorting Genetic Algorithm-III (NSGA-III).</p>
Computistical objects and intellectual networks in the Carolingian age
<p>Roughly 400 manuscripts written in the Carolingian period (pre-AD 900) contain a considerable corpus of calendrical knowledge. In most manuscripts, this material amounts to a loosely structured compilation of tables, diagrams, definitions, and algorithms. What is the nightmare of modern cataloguers provides the researcher of intellectual networks with a significant sample size of (often datable) data. The first step is to design a methodology for classifying this data, which is done by providing each item with an unambiguous identifier (termed ‘object oriented cataloguing’). This creates a data set of c. 4000 objects. If network analysis is applied to this data set, this reveals datable patterns for the transmission of ideas and the construction of intellectual networks in the Carolingian age.</p>
Quantitative analysis of subcellular distributions with an open-source, object-based tool
<p>The subcellular localization of objects, such as organelles, proteins, or other molecules, instructs cellular form and function. Understanding the underlying spatial relationships between objects through colocalization analysis of microscopy images is a fundamental approach used to inform biological mechanisms. We generated an automated and customizable computational tool, the SubcellularDistribution pipeline, to facilitate object-based image analysis from 3D fluorescence microcopy images. To test the utility of the SubcellularDistribution pipeline, we examined the subcellular distribution of mRNA relative to centrosomes within <i>Drosophila</i> embryos. Centrosomes are microtubule-organizing centers, and RNA enrichments at centrosomes are of emerging importance. Our open-source and freely available software detected RNA distributions comparably to commercially available image analysis software. The SubcellularDistribution pipeline is designed to guide the user through the complete process of preparing image analysis data for publication, from image segmentation and data processing to visualization.</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Training of 7-month-old infants' manual object exploration skills: Effects of active and observational experience
<p>Dataset representing the data of the paper "Training of 7-month-old infants' manual object exploration skills: Effects of active and observational experience."</p>
A blinded, controlled trial of objective measurement in Parkinson's disease
<p>Medical conditions with effective therapies are usually managed with objective measurement and therapeutic targets. Parkinson's disease has effective therapies, but continuous objective measurement has only recently become available. This blinded, controlled study examined whether management of Parkinson's disease was improved when clinical assessment and therapeutic decisions were aided by objective measurement. The primary endpoint was improvement in the Movement Disorder Society-United Parkinson's Disease Rating Scale's (MDS-UPDRS) Total Score. In one arm, objective measurement assisted doctors to alter therapy over successive visits until objective measurement scores were in target. Patients in the other arm were conventionally assessed and therapies were changed until judged optimal. There were 75 subjects in the objective measurement arm and 79 in the arm with conventional assessment and treatment. There were statistically significant improvements in the moderate clinically meaningful range in the MDS-UPDRS Total, III, IV scales in the arm using objective measurement, but not in the conventionally treated arm. These findings show that global motor and non-motor disability is improved when the management of Parkinson's disease is assisted by objective measurement.</p>
Metal Object, St Mawes Harbour, Cornwall
An iron object affixed onto the rocks at St Mawes, Cornwall. Perhaps once part of a ship. Created in Metashape with 37 photos. Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 4
Høgdur trælutur úr Kirkjubø. *Carved wooden object from Kirkjubø, Faroe Islands.* Created by Petur Hansen, using Artec Space Spider scanner and Artec Studio 16. Dimensions: 157cm x 16.7cm x 10.7cm. Source: Objaverse 1.0 / Sketchfab
Viking Furnishings & Objects
Viking themed furnishings and assorted objects. Untextured Poor UV Wrapping Source: Objaverse 1.0 / Sketchfab
Portola Journey's End (Object Capture)
Nov 1769. Near "El Palo Alto" (the tall tree), the Portola Expedition of 63 men and 200 horses and mules camped. They had traveled from San Diego in search of Monterey but discovered instead the Bay of San Francisco. Finding the bay, too large to go around and deciding that Monterey had been by-passed, they ended the search and returned to San Diego. Erected 1968 by State Department of Parks and Recreation, Portola Expedition Bicentennial Foundation. (Marker Number 2.)  (Note: this is an Ingress portal too) 📍 [Menlo Park, CA](https://scaniver.se/L37.44783,-122.17092) Compare the 3 scans with - [Scaniverse](https://skfb.ly/o6UoL) - [Trnio](https://skfb.ly/o6UoK) - [Apple Object Capture](https://skfb.ly/o6UoM) Scanned with the iPhone12 Pro and Object Capture (https://github.com/eospi/Object-Capture-UI). Please feel free to follow my collections of daily scans ([link](https://skfb.ly/6YuwK)). Follow me on [Twitter](https://twitter.com/emmanuel_2m). Source: Objaverse 1.0 / Sketchfab
Подстаканник СССР | The Object
A cup holder that many are used to seeing in a train carriage. An integral part of the atmosphere of the Soviet train. Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 14
Høgdur trælutur úr Kirkjubø. *Carved wooden object from Kirkjubø, Faroe Islands.* Created by Petur Hansen, using Artec Space Spider scanner and Artec Studio 16. Dimensions: 123cm x 219.3cm x 2cm. Source: Objaverse 1.0 / Sketchfab
Art object
Арт объект на тему русской культуры/ / Art object on Russian culture Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 6
Høgdur trælutur úr Kirkjubø. *Carved wooden object from Kirkjubø, Faroe Islands.* Created by Petur Hansen, using Artec Space Spider scanner and Artec Studio 16. Dimensions: 197cm x 12.5cm x 14.2cm. Source: Objaverse 1.0 / Sketchfab
The belt-like object from Vimose (hinge)
The 3D model presents a digital reconstruction of the hinge of the belt-like object from Vimose (2nd century AD, Nationalmuseet, inv. no. 24223). The copper alloy hinge pivots around an iron pin that has an eyelet with a copper alloy ring (for further details see «Wijnhoven, M.A. (2022, in press) A Roman Iron Age assemblage of mail armour from the weapon deposit of Vimose, Journal of Roman Military Equipment Studies, № 21»). The reconstruction is based upon the measurements of the actual artefact. The object was modelled in 3dsMax and textured in SubstancePainter. The authors of the reconstruction are Aleksei Moskvin (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design) https://independent.academia.edu/MariiaMoskvina Martijn A. Wijnhoven (VU University Amsterdam) https://vu-nl.academia.edu/MartijnAWijnhoven Source: Objaverse 1.0 / Sketchfab
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.