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1,506 results for “objects”
Wooden Object, Kirkjubø - Træ 16
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: 124cm x 20cm x 2.3cm. Source: Objaverse 1.0 / Sketchfab
Poverty Point Object
Baked earth cooking ball. Poverty Point culture. 1700-1200 BCE 35mm x 32mm x 35mm, 33.8g From the collection of Billy Guthrie PPO_02 Source: Objaverse 1.0 / Sketchfab
Motivational Objects in Natural Scenes (MONS): A database of >800 objects
<p>Dataset for the publication: Schomaker, J., Rau, E.M., Einhäuser, W., & Wittmann, B.C. (2017) Motivational Objects in Natural Scenes (MONS): A database of >800 objects. Front. Psychol. 8:1669. doi: 10.3389/fpsyg.2017.01669</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation
<p>Data Package for "Exploring the Interaction of Code Coverage and Non-Coverage Objectives in Search-Based Test Generation"</p> <p>This package contains data generated as part of our experiments on EvoSuite and Defects4J.</p> <p>This paper is currently under submission.</p> <p>This package contains experimental data and the algorithms that generated them in folder "framework\test\". Specifically, in the folder "framework\test\Experiments", the data folders contain the suites generated by each technique for each project and fault ID. </p> <p>If you have questions, please contact Afonso Fontes at afonsohfontes@gmail.com.</p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Inlists for "Rethinking Thorne-Żytkow Object Formation: Assembly via Common Envelope in Field Binaries"
<p>Inlists used in the models of Everson et al. (2023, submitted to ApJ, arXiv:2310.08658) ("Rethinking Thorne-Żytkow Object Formation: Assembly via Common Envelope in Field Binaries").</p> <p> </p> <p>Version: MESA r22.05.1, mesasdk 22.6.1</p>
Urban and rural chickadee response to novel object and simulated predator
<p>Urbanization is changing natural landscapes worldwide, pushing species to quickly acclimate or adapt if they are to survive in urban environments. Mountain chickadees (<em>Poecile gambeli</em>) readily nest in both urban and rural environments without suffering apparent reproductive costs. However, whether urban-nesting chickadees are successful in these environments due to differences in behaviour between urban and rural birds remains untested. We examined the behavioural responses of urban and rural nesting mountain chickadee females when presented with a novel object (red plastic cup) or simulated predator (imitation squirrel model) at the nest. Behavioural responses depended on both the type of model and the habitat. As expected, mountain chickadees responded more strongly to squirrel models than novel objects; however, the magnitude of the difference in response depended on habitat. Urban birds seemingly ignored the novel object, spending little time investigating, and re-entering the nest box quickly. In contrast, rural birds spent more time reacting to the novel object and alarm calling within 5 m of the nest. When presented with a predator model, the urban birds reacted relatively more strongly (compared to the novel stimulus) than rural birds, spending more time within 5 m of the nest and alarm calling. These results suggest that either mountain chickadees in urban environments quickly acclimatize to the presence of novel objects or, potentially, that less neophobic birds disproportionately settle in urban environments or experience positive selection in urban areas. Either way, reduced neophobia may aid in mountain chickadees' ability to readily and successfully nest in such habitats.</p>
Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.
Dataset for Moving Object Tracking based on RCNNshift
<p>The dataset contains 50 videos from OTB2013, annotated by 11 types of image features. The videos are in mp4 format and can be read directly using the VideoCapture function of the Opencv library. The original dataset link is: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html.</p>
Anisotropic sphere object
<p>This dataset focuses on a single, highly complex object created in Blender. It comprises 100 high-resolution images, each 1080 × 1080 pixels, divided into 67 training and 33 testing samples. The object features textured anisotropic reflections and incorporates a distinctive strip with higher roughness. This dataset is specifically designed to challenge and enhance novel view synthesis techniques.</p> <p> </p> <p>To train a model, set near and far parameters to [0.5, 4.5].</p>
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors, dataset
<p>This is the dataset used in the 'Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors.' It contains 2 folders:</p> <ol> <li><strong>train_diffusion</strong>: a set of 5,000 images used to train the diffusion model.</li> <li><strong>translated</strong>: a set of approximately 50,000 images converted via a diffusion model pre-trained on a small set of real images.</li> </ol> <p>The 'translated' folder contains a <code>labels.csv</code> file, providing the corresponding label for every image.</p>
Fig.5 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.5. Projections of harvest rate in the "business as usual" scenario (Source: LFSRI Silava).
Fig.4 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.4. Projections of harvest rate in the Latvia's FRL scenario (Source: LFSRI Silava).
Fig.3 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.3. Net GHG emissions in forest lands, including afforestation (Source: LFSRI Silava).
Object Shape Recognition and Targeting Using 4x4 Ultrasonic Sensor Array
Open the record for dataset details and reuse information.
Figure 1 in Objective record of Epinephelus marginatus (Serranidae: Epinephelinae) from the Sultanate of Oman (Arabian Sea)
Figure 1. – Map of the Arabian Sea with the location of Salalah, Dhofar, in the Sultanate of Oman.
Object A-5
<p>Object A-5. Average nucleotide identity comparisons between groups of cucurbit-associated strains collected in Florida during 2013-14 and 2020-21. Kiersten Fullem, Dissertation, University of Florida 2024.</p>
Object A-4
<p>Object A-4. DNA-DNA hybridization (DDH) comparison of Pssc reference strains and phylogroup 2A and 2B cucurbit-associated strains to type strain genomes of <em>P. syringae</em> and <em>P. cerasi</em>. Kiersten Fullem, Dissertation, University of Florida 2024.</p>
Object A-2
<p>Object A-2. Average nucleotide identity scores between putative <em>P. syringae </em>strains and <em>P. syringae</em> type strain ICMP 3023T. Kiersten Fullem, Dissertation, University of Florida 2024.</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.