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1,506 results for “objects”
Object 6-4
<p>Video demonstration of altissimo D to D quarter tone sharp on bass clarinet</p>
Object 6-2
<p>Video demonstration of quarter tones in the throat tone register of the bass clarinet</p>
Object 6-6
<p>Video demonstration showing the change of tongue position required to perform a slap tongue</p>
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invitro ArchR Object)
<p>This repo contains invitro ArchR object</p>
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invivo ArchR Object)
<p>This repo contains invivo ArchR object</p>
Dataset of Bimanual Human-to-Human Object Handovers
<p>We present a multi-sensor dataset of bimanual human-to-human object handovers. The dataset consists of 240 recordings obtained from 12 pairs of participants performing bimanual object handovers with 10 objects, and 120 recordings obtained from the same 12 pairs of participants performing unimanual handovers with 5 of those objects. Each recording includes the giver and receiver's 13 upper-body bone position and orientation trajectories, position trajectories for the 27 markers placed on their upper bodies, object position and orientation trajectories, and two RGB-D data streams. The motion trajectories are recorded at 120Hz and the RGB-D streams are recorded at 30Hz. The recordings are annotated with the three handover phases: reach, transfer, and retreat. The dataset also includes four anthropometric measurements of the participants: height, waistline height, arm span, and weight. Our dataset could help investigations of the bimanual reaching motions and grasps utilized by humans while performing handovers. Also, it can be used to train robots to perform bimanual object handovers with humans.</p> <p>More details about the dataset can be found in this paper: https://doi.org/10.1016/j.dib.2023.109277</p>
EUinLAC Lecture 2 Video: Autonomy as a foreign policy objective: Perspectives from Europe and Latin America
<p>The project “The reconfiguration of the European Union presence in Latin America and the Caribbean” (EUinLAC) was conceived and launched between 2019 and 2020. The European Union Jean Monnet programme generously financed it in 2020 and the project activities were carried out between 2020 and 2023. The project was designed to promote both research and teaching on European Union (EU)-Latin America and the Caribbean (LAC) relations. The first mission was accomplished with the publication of the volume named after the project “The Redefinition of EU presence in Latin America and the Caribbean”, which brings together 18 original contributions on the topic and was published by Peter Lang in 2023. The second objective was met by the release of this innovative open educational resource, or teaching tool, an undertaking that Friedrich-Alexander University Press enthusiastically embraced and published in full open access to reach as wide an audience as possible.</p>
Collection of Object-Centric Event Logs (OCEL 2.0 format; SQLite specification)
<p>We provide some realistic examples of logs in the OCEL 2.0 standard.</p>
Collection of Object-Centric Event Logs (OCEL 2.0 format; XML specification)
<p>We provide some realistic examples of logs in the OCEL 2.0 standard.</p>
TRIBE AGROset - An agricola visual dataset for object detection
<p>This a computer vision dataset for object detection of fruits and trunks in agriculture. In its current stage, the dataset has 22 classes, such as:</p> <ul> <li>background</li> <li>arugula_minus9 (arugula in an early stage of development with less than 9 leaves)</li> <li>arugula_plus9 (arugula in an advanced stage of development with more than 9 leaves)</li> <li>carrot</li> <li>carrot_small_leaves</li> <li>coriander</li> <li>coriander_small_leaves</li> <li>coty (lettuce in the starting stage of development, with about two very small leaves)</li> <li>grape_bunch (bunches of full-size grapes)</li> <li>grape_bunch_small (bunches of pee-size grapes)</li> <li>lettuce_minus9 (lettuce in an early stage of development with less than 9 leaves)</li> <li>lettuce_plus9 (lettuce in an advanced stage of development with more than 9 leaves)</li> <li>lettuce_ready (lettuce ready for being harvested)</li> <li>radish_root</li> <li>radish_small_leaves</li> <li>radish_big_leaves</li> <li>spinach</li> <li>spinach_big</li> <li>swiss_chard</li> <li>tomato</li> <li>trunk (vine and forestry trunk)</li> <li>turnip</li> <li>turnip_small_leaves</li> </ul> <p>All the images were self-acquired in other datasets, under real-world conditions and using typical visual sensors for agricultural robots and IoT. This dataset is the result of the merging of some other datasets after fixing some annotations.</p> <p>Magalhães, Sandro A. ‘Dataset of Tomato inside Greenhouses for Object Detection in Pascal VOC’. INESC TEC, 2021. https://doi.org/10.25747/pc1e-nk92.</p> <p>Aguiar, André Silva, and Sandro Magalhães. ‘Grape Bunch and Vine Trunk Dataset for Deep Learning Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5139598.</p> <p>Rodrigues, Leandro, Francisco Terra, Sandro Magalhães, Filipe Santos, Pedro Moura, and Mário Cunha. ‘PixelCropRobot Dataset: Images of Vegetables Crops in Different Phenological Stages Taken in Greenhouses’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.7433286.</p> <p>Moreira, Germano, Sandro Augusto Magalhães, Tiago Padilha, Filipe Neves dos Santos, and Mário Cunha. ‘RpiTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596363.</p> <p>Sarmento, José, Filipe Neves Dos Santos, and André Silva Aguiar. ‘VinePoint’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5038646.</p> <p>Magalhães, Sandro Augusto, Germano Moreira, Filipe Neves dos Santos, and Mário Cunha. ‘AgRobTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596799.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDet -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5213825.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDetV2 -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection (Augmented Version)’. Zenodo, 2022. https://doi.org/10.5281/ZENODO.7186052.</p> <p> </p>
Data for The eyes anticipate where objects will move based on their shape
<p>Raw data for publication "The eyes anticipate where objects will move based on their shape."</p> <p>Stewart and Fleming (2023). The eyes anticipate where an object will move based on its shape. <em>Current Biology,</em> 33(17), R894-R895.</p>
Keynote speaker Simone Natale What is life to digital objects Luxembourg meeting 2020
<p>Keynote speaker Simone Natale What is life to digital objects Luxembourg meeting 2020</p>
Container Logistics Object-centric Event Log
<p><strong>General Description</strong></p> <p>Our company sells goods overseas. After receiving an order, the shipment of goods is scheduled. According to this schedule, the goods are picked up from the local production site and brought to a terminal where a logistics service provider receives and ships them.</p> <p>This is an artificial event log according to the <a href="https://www.ocel-standard.org/">OCEL 2.0 Standard</a> simulated using CPN-Tools. Both the CPN and the SQLite can be downloaded. </p> <p><strong>Process Overview</strong></p> <p>From a <strong>customer order</strong> perspective, the process begins when the order is registered at our company <em>(register customer order)</em>. After registration, a <strong>transport document</strong> is created in which details of the further process are recorded <em>(create transport document)</em>.</p> <p>Using this information, the logistics service provider is contacted to coordinate the transport of the ordered goods to the seaport. Twice a week, that provider sends a <strong>vehicle</strong> to a terminal, with a limited capacity for containers of ordered goods to be transported from the terminal to a seaport. For our company, available capacties vary from vehicle to vehicle, as we are not the only company booking spots. Once the logistics service provider receives our transport documents, they book capacities according to availability and container prioritizations in the upcoming weeks <em>(book vehicles)</em>. Once the dates for transporting the goods to the terminal are set, our company contacts a <strong>container</strong> depot to reserve the required containers <em>(order empty containers)</em>.</p> <p>When a container’s vehicle departure approaches, the goods are prepared, packed and shipped to the terminal. For this purpose, a <strong>truck</strong> is sent to the container depot <em>(pick up empty container)</em>. Meanwhile, the ordered goods to be shipped are packed into <strong>handling units</strong> at the production site. After loading the handling units <em>(load truck)</em>, the truck drives the full container to the terminal <em>(drive to terminal)</em>.</p> <p>At the terminal, the container is picked up by a free <strong>forklift</strong> and weighed <em>(weigh)</em>. Unless the vehicle departure is imminent, the container is placed in the storage location at the terminal <em>(place in stock)</em>. Finally, it is moved to the vehicle <em>(bring to loading bay, load to vehicle)</em> which departs at a fixed time <em>(depart)</em>.</p> <p>Despite careful planning, containers sometimes miss a vehicle’s departure. In this case, the container is rescheduled to the next possible vehicle <em>(reschedule container)</em> and kept near the loading ramp until then.</p> <p>Further information can be found at: <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/">https://www.ocel-standard.org/beta/event-logs/simulations/logistics/</a></p> <p><strong>General Properties </strong></p> <p>An overview of log properties is given below.</p> <table> <thead> <tr> <th>Property</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Event Types</td> <td>14</td> </tr> <tr> <td>Object Types</td> <td>7</td> </tr> <tr> <td>Events</td> <td>35761</td> </tr> <tr> <td>Objects</td> <td>14013</td> </tr> </tbody> </table> <p><strong>Control-Flow Behavior </strong></p> <p>The behavior of the log is described by a <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/full-ocpn.svg">respective object-centric Petri net</a>. Also, individual object types exhibit behavior that can be described by simpler Petri nets. See below.</p> <table> <tbody> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Container-ocpn.svg">Container</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Transport%20Document-ocpn.svg">Transport Documents</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Customer%20Order-ocpn.svg">Customer Order</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Truck-ocpn.svg">Truck</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Forklift-ocpn.svg">Forklift</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Vehicle-ocpn.svg">Vehicle</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Handling%20Unit-ocpn.svg">Handling Unit</a></td> <td> </td> </tr> </tbody> </table> <p> </p> <p><strong>Object Relationships </strong></p> <p> </p> <p>During the process, object-to-object relations can emerge at activity occurrences as follows.</p> <table> <thead> <tr> <th>Activity</th> <th>Source Object Type</th> <th>Target Object Type</th> <th>Qualifier</th> </tr> </thead> <tbody> <tr> <td>Create Transport<br> Document</td> <td>Customer Order</td> <td>Transport Document</td> <td>TD for CO</td> </tr> <tr> <td>Book Vehicle</td> <td>Transport Document</td> <td>Vehicle</td> <td>Regular VH for TD</td> </tr> <tr> <td>Book Vehicle</td> <td>Transport Document</td> <td>Vehicle</td> <td>High-Prio VH for TD</td> </tr> <tr> <td>Order Empty<br> Containers</td> <td>Transport Document</td> <td>Container</td> <td>CR for TD</td> </tr> <tr> <td>Pick Empty<br> Container</td> <td>Truck</td> <td>Container</td> <td>TR loads CR</td> </tr> <tr> <td>Load Truck</td> <td>Container</td> <td>Handling Unit</td> <td>CR contains HU</td> </tr> <tr> <td>Reschedule<br> Container</td> <td>Transport Document</td> <td>Vehicle</td> <td>Substitute VH for TD</td> </tr> </tbody> </table> <p><strong>Simulation Model </strong></p> <p>The CPN used to create this event log can also be downloaded.To obtain simulated data, extract the linked ZIP file and play out the CPN therein, e.g., by using <a href="https://cpntools.org/">CPN Tools</a>.</p> <p>The play-out produces CSV files according to the schema of OCEL2.0. <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/data/csv_to_sql.ipynb">This Python notebook</a> can be used to convert these files to an SQLite dump.</p> <p>For a technical documentation of the simulation model, please open the attached CPN with CPN Tools and see the annotations therein.</p> <p><strong>Acknowledgements</strong></p> <p>Funded under the Excellence Strategy of the Federal Government and the Länder<em>. </em>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
Figure 5 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 5. – Mean values of STM and STA in Engraulis encrasicolus from October 2007 to May 2008. A: Seminiferous tubules number (counted under 10x magnification). B: Seminiferous tubules area (mm2) (measured on the basis of 25 tubules). Data are represented as Mean ± SEM (values indicate the monthly average number of repetitions).
Figure 2 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 2. – Mean values of Gonadosomatic Index percentage (GSI) in Engraulis encrasicolus male at different gonadal maturity stages (I to V). Data are represented as Mean ± SEM. Different letters indicate significant differences (ANOVA with Tukey's post-hoc test, p <0.05).
Figure 3 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 3. – Mean values of STM and STA in Engraulis encrasicolus according to the gonad maturity stages (I to V). A: Seminiferous tubules number (counted under 10x magnification). B: Seminiferous tubules area (mm2) (measured on the basis of 25 tubules). Data are represented as Mean ± SEM. Different letters indicate significant differences (ANOVA with Tukey's post-hoc test, p <0.05).
Combining Objective and Subjective Sedation Assessment Tools
ClinicalTrials.gov study NCT00734409. IPD Sharing: Not stated. Countries: 1. Publications: 0.
NAVITAS and ENVISION - Study to Characterize Select Objective Metrics & Clinical Outcomes in Chronic Pain Patients With Boston Scientific Neurostimulation Systems
ClinicalTrials.gov study NCT03240588. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Objective Verification of Physiologic Changes During Accommodation Under Binocular, Monocular, and Pinhole Conditions
ClinicalTrials.gov study NCT03557346. IPD Sharing: YES. Countries: 0. Publications: 1.
Feasibility Of Objective Measures and Outpatient Washout in Disease Modifying Trials for Parkinson's Disease
ClinicalTrials.gov study NCT06192823. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.