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1,506 results for “objects”
Quotatives Indicate Decline in Objectivity in U.S. Political News
<p>Data to reproduce results for the paper </p> <p>Quotatives Indicate Decline in Objectivity in U.S. Political News (ICWSM 2023)</p> <p>Code: https://github.com/epfl-dlab/quotative_bias</p>
Anndata object of 10x Mouse Brain 5k data set for scDGD training
<p>This data from 10x (5k Adult Mouse Brain Nuclei Isolated with Chromium Nuclei Isolation Kit, Single Cell Gene Expression Dataset by Cell Ranger 7.0.0, 2022) is comprised of 7377 cells from the adult mouse brain with 32285 features. Cell type annotations were approximated using CellTpyist with the `Developing_Mouse_Brain` reference model and majority voting. This resulted in 7 distinct cell types.</p>
Data and code for: Why are biting flies attracted to blue objects?
<p>Diurnal biting flies are strongly attracted to blue objects. This behaviour is widely exploited for fly control, but its functional significance is debated. It is hypothesised that: blue objects resemble animal hosts; blue surfaces resemble shaded resting places; and blue attraction is a by-product of attraction to polarised light. We computed the fly photoreceptor signals elicited by a large sample of leaf and animal integument reflectance spectra, viewed under open/cloudy illumination and under woodland shade. We then trained artificial neural networks (ANNs) to distinguish animals from leaf backgrounds, and shaded from unshaded surfaces, in order to find the optimal means of doing so based upon the sensory information available to a fly. After training, we challenged ANNs to classify blue objects used in fly control. Trained ANNs could make both discriminations with high accuracy. They discriminated animals from leaves based upon blue-green photoreceptor opponency, and commonly misclassified blue objects as animals. Meanwhile, they discriminated shaded from unshaded stimuli using achromatic cues and never misclassified blue objects as shaded. We conclude that blue-green opponency is the most effective means of discriminating animals from leaf backgrounds using a fly's sensory information and that blue objects resemble animal hosts through such mechanisms.</p>
Depth image-based deformation estimation of deformable objects for collaborative mobile transportation
<p>Human-Robot collaborative transportation is a promising technology that combines the strength of humans and robots.<br> The most common approaches rely on methodologies that exploit force-sensing. However, the drawbacks are multiple. First, the magnitude of force applied might be limited to avoid damages. Then, force measurements might be unidirectional according to the material properties; e.g., compression forces are not measurable for fabrics.<br> This paper proposes an approach based on estimating the deformation state of the manipulated object from depth images. Specifically, the segmented depth images of the manipulated object are fed to a Convolutional Neural Network (CNN) model to estimate the current deformation status. Compared with the desired deformation, the current deformation status is used to generate the robot's twist command. The methodology is proved in a mobile robot application, where carbon-fiber fabrics are transported. A comparison with the state-of-the-art is reported proving that the proposed method is more accurate and more repeatable.</p>
Comparison tables for evaluating FAIR Digital Object and Linked Data
<p>RO-Crate and tables from the paper "<em>Evaluating FAIR Digital Object and Linked Data as distributed object systems</em>" <a href="https://doi.org/10.48550/arXiv.2306.07436">https://doi.org/10.48550/arXiv.2306.07436</a></p> <p>We systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself.</p> <ul> <li>table1.html<br> Considering FDO and Web according to the quality levels of the Interoperability Framework for Fast Data (Delgado 2016)</li> <li> <p>table2.html<br> Mapping the Metamodel concepts from the Interoperability Framework for Fast Data (Delgado 2016) to equivalent concepts for FDO and Web.</p> </li> <li> <p>table3.html<br> Checking FDO guidelines (Bonino et al. 2019; Anders et al. 2023) against its current implementations as DOIP (DONA 2018) and Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022), with suggestions for required additions</p> </li> <li> <p>table4.html<br> Comparing FAIR Digital Object (with the DOIP 2.0 protocol (DONA 2018)) and Web technologies (using Linked Data) as middleware infrastructures (Zarras 2004)</p> </li> <li> <p>table5.html</p> <p>Assessing RDA’s FAIR Data Maturity Model (FAIR Data Maturity Model Working Group 2020; Bahim et al. 2020) (first 2 columns) against the FDO guidelines (Bonino et al. 2019), FDO implemented with the protocol DOIPv2 (DONA 2018), Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022) and examples from Linked Data practices in general. (— indicates Unspecified, may be possible with additional conventions)</p> </li> </ul> <p>A web rendering of this RO-Crate is available at <a href="https://w3id.org/ro/doi/10.5281/zenodo.8075229">https://w3id.org/ro/doi/10.5281/zenodo.8075229</a></p>
White Dwarfs Revealed in Gaia's Candidate Compact Object Binaries
<p>Discovery and characterisation of black holes (BHs), neutron stars (NSs), and white dwarfs (WDs) with detached luminous companions (LCs) in wide orbits are exciting because they are important test beds for dark remnant (DR) formation physics as well as binary stellar evolution models. Recently, 187 candidates have been identified from <em>Gaia</em>'s non-single star catalog as wide orbit (P<sub>orb</sub>/day>45), detached binaries hosting DRs. We identify UV counterparts for 49 of these sources in the archival GALEX data. Modeling the observed spectral energy distribution (SED) spanning FUV-NUV to IR for these sources and stellar evolution models, we constrain the LC properties including mass, bolometric luminosity, and effective temperature for these 49 sources. Using the LC masses, and the astrometric mass function constrained by <em>Gaia,</em> we constrain the DR masses for these sources. We find that 9 have masses clearly in the NS or BH mass range. Fifteen sources exhibit significant NUV excess and 4 show excess both in FUV and NUV. The simplest explanation for these excess UV fluxes is that the DRs in these sources are WDs. Using SED modeling we constrain the effective temperature and bolometric luminosity for these 15 sources. Our estimated DR masses for all of these 15 sources are lower than the Chandrasekhar mass limit for WDs. Interestingly, five of these sources had been wrongly identified as neutron stars in literature.</p> <p>MIST_OUTPUT.zip : MIST output file for mass estimations</p> <p>Readme : Description of MIST_output files.</p>
An Extensive Dataset of Geometric Objects Placed in a Watertank for Electrical Impedance Tomography
<p>Open-access experimental Electrical Impedance Tomography (EIT) data set.<br> The measurements were generated using the ScioSpec EIT device from which 16 electrodes were connected to a phantom tank filled with saline. Four 3D-printed cylindrical objects of different sizes were placed in this tank area-wide. Adjacent and opposite injection patterns have been used to measure large datasets with more than 125000 samples.</p> <p>The tutorial can be found here: https://github.com/JacobTh98/sciospeceit/blob/main/tutorial.ipynb</p> <p>Contact: jacob.thoenes@uni-rostock.de</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
ALMA and VLA observations of the Extended Green Object G19.01-0.03
<p>In this repository, we deposit final data products that underlie the following two articles:</p> <p>- ALMA observations of the Extended Green Object G19.01--0.03 - I. A Keplerian disc in a massive protostellar system, Williams et al. 2022, MNRAS, 509, 748</p> <p>- ALMA observations of the Extended Green Object G19.01--0.03 - II. A massive protostar with typical chemical abundances surrounded by four low-mass prestellar core candidates, Williams et al. 2023, MNRAS, 525, 6146</p> <p>The included README.md file describes the data files in detail.</p>
Original MAB Object Set for CASTAway M5 Submission Exploration
<p>The zip file containts the ephemeris of the MAB objects used in the published works of:</p> <p>J.P. Sánchez, A. Gibbings, C. Snodgrass, N. Bowles, S. Green, Asteroid Belt Multiple Flyby Options for M-Class Missions, in: 67th International Astronautical Congress International Astronautical Federation, Guadalajara, Mexico, 26-30 September 2016.</p> <p>M. Di Carlo, M. Vasile, J. Dunlop, Low-thrust tour of the main belt asteroids, Advances in Space Research, 62 (2018) 2026-2045.</p> <p>Currently, a new manuscrip is being considered for publication, which also uses the same ephemeris set to comput asteroid tours in the Main Asteroid Belt. </p> <p>A. Bellome, J.P. Sánchez, J.C. García-Mateas, L. Felicetti and S. Kemble, Modified Dynamic programming for Asteroid Belt exploration, Acta Astronautica, Under Review. </p> <p>The files containt the ephemeris and and some matlab functions to retrieve position and velocity of the asteroids at a given epoch or time. </p> <p> </p>
Roost-dataset: a remote sensing object detection dataset
<p>We release a multi-channel weather radar sensing dataset with roost annotations, for the purpose of ecological analyses and developing visual object detection and tracking models to recognize biological phenomena in radar data. Please refer to https://github.com/darkecology/roost-dataset for more details. Here we upload arrays rendered from weather radar data. Radar scan lists and roost annotations are released in json files of a COCO-like format in the roost-dataset Github repository.</p>
Dataset from 'Klever, L., Islam, J., Võ, M., & Billino, J. (2023). Aging attenuates the memory advantage for unexpected objects in real-world scenes. Heliyon, e20241, doi: 10.1016/j.heliyon.2023.e20241
<p>We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file.</p> <p>___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
20230822-IKEM: Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20230822-IKEM<br> <br> Experiment design: Agents interact by playing games with objects and adapt their ontologies to agree on decision taking. Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.<br> <br> Experiment setting: Agents learn decision trees (transformed into ontologies); choose the objects (and partners) to interact with; adapt by splitting their leaf nodes<br> <br> Hypotheses: 1) Agents will be able to fulfil their motivation in terms of increased exploration (exploratory motivations) and decreased exploration (non-exploratory motivation). 2) Agents with exploratory motivation will be more complete, but less accurate than the baseline and non-exploration. 3) Curious agents will be more accurate and complete, but converge slower in comparison with creativity. 4) In more complex settings (higher number of agents and properties), exploration leads to more completeness, accuracy, distance and faster convergence than the baseline. 5) Agents with non-exploratory motivation will be less accurate and complete than the baseline. 6) The indirect learning models have a higher accuracy and completeness than the direct models. 7) Agents with socially oriented intrinsic motivation will have less diverse knowledge, but agree more and converge faster than agents with individual intrinsic motivation. 8) Heterogeneously motivated agents will have a higher accuracy and completeness, but lower diversity and converge slower than homogeneously motivated agents.<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] <a href="\">https://sake.re/20230822-IKEM</a><br> [2] <a href="\">https://gitlab.inria.fr/moex/lazylav/</a><br> </p>
Sainfoin Fruit Processing - Object Detection Dataset
<p>This dataset consists of 500 images of sainfoin (Onobrychis viciifolia) seed pods, seed, and split seeds. The images were taken as a part of an experiment to determine minimum sample size of seed pods needed to accurately estimate pod threshing trait heritability within sainfoin breeding lines.</p><p>The experiment was a complete factorial design with the following factors:</p><ul><li>Sainfoin named varieties: AAC Mountainview, Delaney, Eski , Rocky Mountain Remont, and Shoshone</li><li>Sample Size: 1, 2, 3, 4, and 5 grams of dried seed pods</li><li>Two different threshing types: Belt thresher processed 3X, Haldrup Impact Thresher (35sec @ Speed 9)</li></ul><p>This makes for a total factorial combination set of 5 varieties X 5 sample sizes X 2 threshing types = 50.</p><p>Each combination was comprised of 10 individual replicates where each replicate in a combination was a unique, random sample of seeds of the same mass (So, 10 random, 2g samples of Eski seed, processed by belt thresher; 10 random, 5g samples of Delaney seed processed by the Haldrup thresher, etc.). This makes for a total of 500 experimental units that comprise the sample set.</p><p>Once the seeds were sampled, weighed, and processed through the threshing equipment, they were weighed again and imaged.</p><p>The threshed seeds were scattered onto an imaging platform with a blue background, lit by 2 LED panels, and photographed with a Sony ILCE-7RM2 at the following settings:</p><ul><li>ISO: 100</li><li>Exposure: 1/40s</li><li>Focal Length: 55mm</li><li>Format: TIFF</li><li>Size: 7968x5320</li></ul><p>The raw images were converted from TIFF files to JPEG format and annotated in image labeling software. The seed objects were annotated with bounding boxes classified as the following classes</p><ol><li>pod: an enclosed seed pod</li><li>seed: a seed which was successfully threshed from the legume pod carpel</li><li>split: a seed threshed from the pod, but which split in two halves during the threshing process</li></ol><p>All image annotations were exported into the convenient <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html">COCO format</a>.</p><p>No further image processing was performed.</p><p>The image set was split into a 80/20 training and validation step using `scikit-learn` in Python 3.11 stratifying the datasets equally over the various experimental factor levels.</p><p>The zip file 'train_val_images.zip' contains a 'train' folder with 400 training images, 'val' containing 100 validation images, an image taken with a color correction card named 'color_test.jpg', and a json file with all the annotations.</p><p>Another file called 'seed_weights.csv' contains the image_name to global-key mapping in tabular format as well as the before and after threshing seed weights for each experimental sample.</p><p><strong>Labeling Metrics:</strong></p><ul><li>Pod (48.58%)<ul><li>36,599 objects</li></ul></li><li>Seed (33.83%)<ul><li>25,488 object</li></ul></li><li>Split (17.59%)<ul><li>13,255 objects</li></ul></li><li><strong>TOTAL (100%)</strong><ul><li><strong>75,342 objects</strong></li></ul></li></ul>
Wooden Object, Kirkjubøur - Træ 5
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 13.2cm x 12.5cm. Source: Objaverse 1.0 / Sketchfab
Mile 0 Sign - Object Capture
Modeled using Apple's Object Capture Description from [Atlas Obscura](https://www.atlasobscura.com/places/mile-zero): > LOCATED IN PICTURESQUE BEACON HILL Park in British Columbia's capital, Mile 0 is one of the symbolic starting points of the Trans-Canada Highway on the Pacific Coast. The highway, conceived in the late 1940s and constructed between 1950 and 1971, was the longest uninterrupted highway in the world at the time of its completion. Spanning a whopping 4,860 miles (7,821 kilometers), it runs across all 10 of Canada's provinces, crossing the likes of the Rocky Mountains and the wide-open prairies of central Canada. There is no officially recognized starting point or "mile zero," St. John's, Newfoundland, also has a "mile zero" of their own, being the Atlantic end of the route. Located nearby is a statue of runner Terry Fox, who attempted to run the length of Trans-Canada Highway to raise money for cancer research, before succumbing to the disease himself at around the midpoint of his trek. Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 13
Høgdur trælutur úr Kirkjubø. *Carved wooden object from Kirkjubø, Faroe Islands.* Created by Petur Hansen, using Artec Eva scanner and Artec Studio 16. Dimensions: 111cm x 33.3cm x 6. Source: Objaverse 1.0 / Sketchfab
Object-Stanley Museum of Art
The University of Iowa Stanley Museum of Art is a visual arts institution affiliated with the University of Iowa. Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 15
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: 109cm x 20.6cm x 2.5cm. Source: Objaverse 1.0 / Sketchfab
The belt-like object from Vimose (2)
The 3D model presents a hypothetical digital reconstruction of the belt-like object from Vimose (2nd century AD, Nationalmuseet, inv. no. 24223). The original artefact consists of a copper alloy hinge that pivots around an iron pin with a copper alloy ring. Two fragments of mail fabric are attached to the left and right sides of the hinge (for further details see «Wijnhoven, M.A. (2022) 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 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.