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1,506 results for “objects”
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
<p><strong>Description: </strong>This MOCAS is a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin, which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.</p> <p><strong>Data Access:</strong> In order to protect the sensitive data and privacy of human subjects (e.g., physiological signals and facial views), only authorized researchers who consent to the End User License Agreement (EULA) are allowed to download the MOCAS. The researchers who want to access the MOCAS should visit our website and download the ELUA document;<a href="https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing"> https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing</a> After reviewing and filling the document up, they should email the signed ELUA document to <a href="mailto:info@smart-laboratory.org">info@smart-laboratory.org</a> and <a href="mailto:wonsu0513@gmail.com">wonsu0513@gmail.com </a>with Zenodo account and Reqeust Access via this repository (see the below file section). Then, our research group will review and grant their access to our Zenodo repository having the downsampled MOCAS dataset, subjective information, and supplementary codes used in this paper. For sharing the raw dataset, we will sequentially invite their email address used in the Zenodo and the EULA document to access raw dataset uploaded on an additional repository (Purdue BOX, https://purdue.box.com/v/mocas-dataset), due to huge size of the raw dataset.</p> <p><strong><a title="BibTeX">BibTeX </a>Citation:</strong></p> <div>Jo, W., Wang, R., Cha, G. E., Sun, S., Senthilkumaran, R. K., Foti, D., & Min, B. C. (2024). MOCAS: A multimodal dataset for objective cognitive workload assessment on simultaneous tasks. <em>IEEE Transactions on Affective Computing</em>.</div> <pre>or<br><br>@ARTICLE{jo2024mocas, author={Jo, Wonse and Wang, Ruiqi and Cha, Go-Eum and Sun, Su and Senthilkumaran, Revanth Krishna and Foti, Daniel and Min, Byung-Cheol}, journal={IEEE Transactions on Affective Computing}, title={MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks}, year={2025}, volume={16}, number={1}, pages={116-132}, doi={10.1109/TAFFC.2024.3414330}}</pre>
Wooden Object, Kirkjubø - Træ 1
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: 84.5cm x 32.7cm x 20.7cm. Source: Objaverse 1.0 / Sketchfab
Neo-Normcore - Unknown Object
A nostalgic movement during a period of seamless VR, a reactionary return to a past time, when virtual felt unreal. Simpler times. When blending in was the new standing out. This example was taken from the well known future exhibition, 'Live in Your Head 2: When Forms had Attitude'. ********************************* 2B3D FULL BODY 3D DIGITAL AVATAR PORTAL by 2B3DGLOBAL is licensed under CC Attribution Source: Objaverse 1.0 / Sketchfab
Figure 4 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 4 Excel Worksheet Change event macro to copy result from each solver iteration.
Figure 2 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 2 Solver options tab.
Figure 3 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
Figure 3 Solver message box showing iteration result message.
Data from: Towards a Methodology for Technoscientific Objects Extraction (Short Paper)
<p>This dataset includes data adopted in the paper "Towards a Methodology for Technoscientific Objects Extraction (Short Paper)". </p> <p>The file "articles_urls_2022.txt.zip" is an archive containing a txt file; each line of the txt file is an article URL. </p> <p>The libraries adopted to process the articles are reported in the paper or in the paper references. </p>
VariCity: Visualizing Object-Oriented Variability Implementations as Cities — Scenarios projects
<p>This archive contains the sources for the three projects used in the scenarios presented in the paper "VariCity: Visualizing Object-Oriented Variability Implementations as Cities", along with the plugin configurations.</p> <pre>- <a href="https://github.com/jfree/jfreechart/tree/v1.5.0/">JFreeChart 1.5.0</a> - <a href="https://github.com/apache/netbeans/tree/12.2/">NetBeans 12.2</a> - <a href="https://github.com/NationalSecurityAgency/ghidra/tree/Ghidra_10.0.4_build/">Ghidra 10.0.4</a> </pre> <p>The plugin configuration consists in a hidden .varicity directory, placed at the root of each project.<br> It contains a JSON being the variation points and variants identified by symfinder, as well as the VariCity configuration to display the city with the parameters given in the paper.</p>
Stories that an archaeological object may tell: a use case
<p>Demo accompanying the poster.</p>
Figure 2 in How jumping spiders (Araneae: Salticidae) find and use indirect routes to reach their sighted objectives
Figure 2. Symbolic description of pursuit shown in Figure 1. In this and in subsequent diagrams the primary objective (A) is represented by a black circle, secondary or intermediate objectives by grey circles (B, C, D), and positions assumed by the spider by white circles (S). Vectors representing either the original orientation to the prey position (1) or subsequent reorientation to that position (3) are highlighted in red. Movement by the spider (2) is indicated as a dashed line. Not shown here, vectors representing turns to face secondary objectives or plant positions are shown in black. Here the spider faced its prey (1), turned and ran (2) up to a higher position with a closer map (horizontal) distance to that prey, and then reoriented to face the expected position of that prey (3).
Wooden Object, Kirkjubø - Træ 10
Høgdur trælutur úr Kirkjubø. *Carved wooden object from Kirkjubø, Faroe Islands.* Created by Petur Hansen, using Artec Eva scanner and Artec Studio 16. Dimensions: 175cm x 38cm x 4.5cm. Source: Objaverse 1.0 / Sketchfab
Wooden Object, Kirkjubø - Træ 8
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: 120cm x 17cm x 2cm Source: Objaverse 1.0 / Sketchfab
Iron Object
This rivetted wrought-iron object appeared on the foreshore at Newlyn recently. It is 3'6" long. Suggestions for its function on social media have included a diver's helmet, a bucket, a UFO and a pisky submarine. It is in fact part of a 19th century marine donkey boiler. Source: Objaverse 1.0 / Sketchfab
Carved Bone Object
Carved bone object currently housed at Rollins College Department of Anthropology. Source: Objaverse 1.0 / Sketchfab
[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of pathological entities (brain tumors and brain vascular malformations)
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of pathological entities (brain tumors and brain vascular malformations):<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><br>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>- Overall merge of pathological sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from pathological entities including brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). </p>
seurat objects of unsorted endothelial and perivascular cells - individual entities
<p><strong>Seurat objects of individual entities of unsorted endothelial and perivascular cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from fetal brain (Fetal CNS).</p> <p><br><em>-ii) Fetal periphery unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from fetal periphery (Fetal peripheral organs).</p> <p><br><em>- iii) Adult control brain (temporal lobe) unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from adult/control brains (temporal lobes).<br><br><em>- iv) AVM unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- v) LGG unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from lower-grade gliomas (LGG) (a brain tumor).<br><br><em>- vi) GBM unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vii) MET unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- viii) MEN unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain meningioma (MEN) (a brain tumor).</p>
Synth-Yard-MCMOT-1 – A synthetic multi camera multi object dataset in the context of yard logistics
<p>The dataset "Synth-Yard-MCMOT-1" is a novel image dataset for multi-camera multi-object tracking (MCMOT). It is the first of its kind to be generated in a virtual environment with the main focus on the tracking of trucks in yard logistics environments. The dataset consists of a total of 12,008 images generated by eight different cameras. The images contain 44,232 bounding boxes and segmentation masks and 52 individual tracks. Additionally, we provide a ninth camera, which is used to generate unified ground-truth information for the whole scene from an orthographic, top-down perspective comparable to a bird’s eye or map-view. The purpose of this dataset is to provide yard management systems with relevant data, which can be employed when aiming to determine the exact position of a truck and specifically identifying which gateway or designated parking spot it is located in. <br>Additionally in this dataset we added the annotations in the COCO-style format as well as the raw image-data.<br>Initial benchmarks for single-camera tracking demonstrate a mean IDF1 score of 0.96 and a mean MOTA score of 0.94, laying the baseline for computing world coordinates via MCMOT.<br><br><span>This work is part of the project “Silicon Economy Logistics </span><span>Ecosystem” which is funded by the German Federal Ministry </span><span>of Transport and Digital Infrastructure. <br></span><span>This research has partly been funded by the Federal Ministry </span><span>of Education and Research of Germany and the state of North-</span><span>Rhine Westphalia as part of the Lamarr-Institute for Machine </span><span>Learning and Artificial Intelligence. <br></span><span>We would like to thank our colleagues Julian Hinxlage, </span><span>Oleg Belov, Habib Kilic, Nicole Wagner-Hanl, Leona Cerimi, </span><span>Antonia Ponikarov and Nico Lindemann for their support.</span></p> <p><span>The code to create this dataset can be found here: </span><span>https://git.openlogisticsfoundation.org/silicon-economy/base/ml-toolbox/</span><span>synth-data-mcmot</span></p> <div> <div> </div> <div> </div> </div> <p> </p>
Design of NFF antenna by multi-objective algorithm
<p><span>提出了一种多目标方法来优化反射超表面阵列的相位分布</span></p>
DEIMOS (DEep Imaging Multi-Object Spectrograph)
<p>Keck/DEIMOS observations of z~1 galaxies with associated images from Hubble/ACS</p>
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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.