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306 results for “prototypes”
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 4. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Source Data for the paper: "Quantum-classical simulations reveal the photoisomerization mechanism of a prototypical first-generation molecular motor"
<p>This dataset contains the raw data for the results shown in the paper.</p> <p>For each figure of the paper (main text), one directory with data file(s) is provided.</p>
Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations: video supplements
<p>These are video supplements cited in the manuscript "Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations" that has been submitted to Geoscientific Model Development (GMD; MS No.: gmd-2018-192).</p> <p>The animations (with a 10yr running window) show the development of temperature biases in the deep ocean over a period of 100 years in two pre-industrial configurations with the AWI Climate Model: AWI-CM-LR and AWI-CM-HR. Meridional biases along the 30.5°W transect through the Atlantic Ocean (S3 and S4; animated version of Fig.8 for LR and HR) and maps of along-isopycnal biases (sigma_1=31.8) are shown (S1 and S2). Time axes have been added compared to version 1.</p>
SCoRe - Prototyp 2.2 - Erprobung des Forschungsszenarios "Urbane Grünflächen" - UGF-2
<p>Dieses Datenset enthält Materialien (Videos, Protokolle und Fallbeschreibungen) aus der zweiten prototypischen Durchführung des Forschungsszenarios "Urbane Grünflächen" im Teilprojekt <a href="http://www.360total.de/score/">SCoRe-VideoLearning</a> des <a href="https://scoreforschung.com/ueber/">Score-Projektes</a> ..</p> <p>Hierin finden sich drei exemplarische Fälle von Studierenden, welche sich videografisch forschend mit urbanen Grünflächen auseinandersetzten und dabei die Merkmale der Grünfläche hinsichtlich urbanen Nutzungsmöglichkeiten und der biologischen Vielfalt untersuchten. Dazu wurden die Grünflächen zunächst ausgewählt und in Bezug auf verschiedene vorgegebene Ordnungskriterien beschrieben und bewertet (Fallbeschreibung). Zur Produktion der Videoforschungsdaten - als Basismaterial der empirischen Untersuchung - waren die Studierenden angehalten ein Produktionsprotokoll während aller drei Produktionsphasen der Videografie (Vorproduktion, Produktion im Feld sowie Nachproduktion) auszufüllen und somit für sich sowie andere analysierende Studierende die Entscheidungsprozesse zur Gestaltung der Videoforschungsdaten zu explizieren und zu dokumentieren.. Diese Protokolle bilden entsprechend die Grundlagen für Gütekriterien qualitativer Forschungsdaten: Transparenz und intersubjektive Nachvollziehbarkeit (vgl. <a href="https://scoreforschung.files.wordpress.com/2022/03/score-vl-wirkungsbericht-3-zur-summativen-evaluation-des-prototypen-3_mhh-2.pdf">Wirkungsbericht 3</a>).</p>
Particle Tracking Data: Bergen DTC Prototype
<p><strong>Dataset Description: </strong>Proton computing tomography is an imaging modality promising improved treatment planning for proton therapy. For this application, the <em><a href="https://www.uib.no/en/ift/142356/medical-physics-bergen-pct-project">Bergen pCT collaboration</a> is </em>developing a high granularity<em> </em>Digital Tracking Calorimeter (DTC), capable of measuring high multiplicities of particles in parallel [1].<strong> </strong>In this dataset, we include various Monte Carlo (MC) simulations (generated using the Gate 9.2 simulation toolkit [2, 3] built upon Geant4 [4,5,6]) with different setups and phantom materials for evaluating and comparing particle reconstruction algorithms on the Bergen DTC.</p> <p><strong>Files: </strong>We provide multiple simulations for different phantom geometries and simulation setups, each generated with a mono-energetic pencil beam (230 MeV, 2 sigma). The supplied files include a spot scanning dataset generated for a pediatric head phantom [7], spot scanning on water phantoms (100, 150 and 200 mm) as well as single beam spots for water phantoms of various thicknesses (0, 100, 150 and 200 mm):</p> <ul> <li>head_filtered_2k_spot.npz</li> <li>water_{100,150,200}_2k_spot.npz</li> <li>water_{100,150,200}_10k.npz</li> <li>no_phantom_10k.npz</li> </ul> <p><strong>Columns:</strong> All above-mentioned simulation files contain MC simulated data of a single simulation run in tabular form, where each row represents a single particle hit inside the detector. Furthermore, each particle hit is parametrized by the following columns:</p> <ul> <li><strong>posX, posY, posZ: </strong>Measured x, y, z position (in millimeter) of the particle hit relative to the simulation origin defined by the center of the phantom.</li> <li><strong>edep: </strong>Amount of energy (in MeV) deposited by a particle while interacting with the sensitive area of the detector.</li> <li><strong>eventID</strong>: Each primary is simulated in its own isolated "event" and gets an incremental ID. Everything that happens during the simulation of said primary is grouped under the same eventID. Events are simulated independent of each other. trackIDs are only unique within their respective event.</li> <li><strong>trackID:</strong> A track describes a single particle throughout its entire lifetime in the simulation. In any given event, the first track (trackID = 1) is always associated with the primary particle. Every subsequently produced secondary particle has an incremental trackID.</li> <li><strong>parentID: </strong>The parentID specifies the trackID in the current event that caused this track to exist. If the parentID is 0, the particle is a primary, i.e., generated by the particle beam. Otherwise, the row describes a secondary which was generated through interactions of a primary with the traversed matter.</li> <li><strong>volumeID[2]: </strong>Incremental numerical identifier of layer containing particle hit inside GATE volume 2 defined within the detector geometry. 0 for tracking layers, 1 for calorimeter layers.</li> <li><strong>volumeID[3]: </strong>Incremental<strong> </strong>numerical identifier of layer containing particle hit inside GATE volume 3 defined within the detector geometry. Unique identifiers (starting from zero) for tracking layer (0, 1) and calorimeter layer (0, 1, …, 40).</li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] J. Alme, G. G. Barnafoldi, R. Barthel et al., “A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT, ”Frontiers in Physics, vol. 8, no. October, pp. 1–20, 2020.</p> <p>[2] S. Jan, G. Santin, D. Strul et al., “GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,”Phys Med Biol. Phys Med Biol, vol. 49, no. 19, pp. 4543–4561, 2004.</p> <p>[3] S. Jan, D. Benoit, E. Becheva et al., “GATE V6: A major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, ”Physics in Medicine and Biology, vol. 56, no. 4,pp. 881–901, 2011.</p> <p>[4] S. Agostinelli, J. Allison, K. Amako et al., “GEANT4 - A simulation toolkit, ”Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250–303, 2003.</p> <p>[5] J. Allison, K. Amako, J. Apostolakis et al., “Geant4 developments and applications, ”IEEE Transactions on Nuclear Science, vol. 53, no. 1, pp. 270–278, 2006.</p> <p>[6] J. Allison, K. Amako, J. Apostolakis et al., “Recent developments in geant4”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186–225, 201</p> <p>[7] V. Giacometti, S. Guatelli, M. Bazalova-Carter et al., “Development of a high resolution voxelised head phantom for medical physics applications. ”Physica Medica, vol. 33, pp. 182–188, 2017.</p>
Case study of a rapid prototyping method for optimizing soft gripper structures with integrated piezoresistive sensors
<p>Closed-loop control systems and monitoring the activities of soft robots in the natural environment require sensing elements in soft actuator modules. In this study, additive manufacturing is used for sensorized soft actuator modules to investigate the influence of the Shore hardness and design aspects of an open-source tendon-based gripper structure, in a time-efficient way. Additionally, the placement of the piezoresistive sensing element (tension or compression side on the bending soft gripper) was investigated. A user-friendly method, based on thermoplastic material extrusion, has been explored to improve the future design optimization in of active soft robotic structures successfully. A higher Shore hardness resulted in a higher total deflection and a higher force to bend the gripper structure. By increasing the geometrical stiffness of the gripper printed with low Shore hardness, the total deflection was increased, but the force needed to activate the movement was higher in comparison to high Shore hardness and low geometrical stiffness. Moreover, the sensing element on the substrate of higher Shore hardness, leads to low drift, monotonic response, with good sensitivity, independent of the sampling rate. The gripper of higher Shore hardness had a larger functional range, being capable of gripping small and larger objects.</p>
FastKGQA: A modified knowledge base of the MoviesQA dataset for prototyping
<p><strong>Full Changelog</strong>: <a href="https://github.com/d1egoprog/FastKGQA/commits/1.0">https://github.com/d1egoprog/FastKGQA/commits/1.0</a></p>
Data for the paper 'Design and Implementation of a prototype infrared video bolometer (IRVB) in MAST Upgrade'
<p>Raw and processed data from the MASTU IRVB infrared camera for the calibration of the diagnostic and from the first experimental campaign.</p>
WHOI prototype Vertical Temperature Profiler data from Quashnet River site QRSP28
<p>Vertical Temperature Profiler data from the WHOI prototype instrument, acquired at the Quashnet River site QRSP28 from 17 June, 2022 to 11 July, 2022. Data sampled at 5 minute intervals. Eighteen total temperature records, with the measurement depth listed in the first (header) row of the file. Depths are in cm (e.g., T8 is the temperature record from 8 cm depth). Note that absolute depths could be in error by as much as 2 cm due to uncertainty introduced by insertion process, but relative depths are highly accurate. Data have been calibrated based on water bath tests.</p>
A Study to Evaluate the Effect of Single-Dose Intravenous Rifampin as a Prototypic Inhibitor of Organic Anion Transporting Polypeptide (OATP) 1B1 and OATP1B3 on the Single-Dose Pharmacokinetics (PK) o
ClinicalTrials.gov study NCT04121078. IPD Sharing: YES. Countries: 1. Publications: 1.
Supplementary data for: Comparison of phenotypic and transcriptomic profiles between HFPO-DA and prototypical PPARα, PPARγ, and cytotoxic agents in wild-type and Ppara-null mouse livers
Open the record for dataset details and reuse information.
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in wild-type and PPARa knockout mouse hepatocytes
Open the record for dataset details and reuse information.
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
Open the record for dataset details and reuse information.
Prototyping Modes of Interactive Mapping
<p>3<sup>rd</sup> Project Presentation</p>
Paper Prototype - Example of LearnIn's Personal Learning Record Store as User Story
<p>This artefact illustrates a possible user journey and very simplified, the support that LearnIn's digital ecosystem can provide. <br> The steps and stages show - in a paper prototype style - MIZO's learning pathway. </p>
Juha Sulkakoski's Snowboard Prototype
Plywood snowboard built in 1984 by Juha Sulkakoski, who found inspiration from pictures in the American magazine Skiing. Sulkakoski made his first boards in his garage in Jyväskylä and sold some of them to his friends. All that was needed was plywood, paint and a couple of sturdy cloth ribbons for strap bindings. Plywood boards could only be used on soft snow – the fresher the better. Pointy nose and V-shaped tail makes this board markedly different from modern snowboards. In 1993 Juha Sulkakoski launched the coaching and national team program of the Finnish Snowboarding Association. Source: Objaverse 1.0 / Sketchfab
Jacdac: Service-based Prototyping of Embedded Systems (Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
SDMT Prototype
<p>A Prototype for Shared Decision-Making for Type 2 Diabetes Prevention</p>
A video demonstrating a prototype tool for iStar modeling
<p>A video demonstrating a prototype tool for iStar modeling, additional material for the paper A tool-supported modeling process for improving iStar modeling practice based on empirical evidence.</p>
REIP: a Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping - use case dataset
<p>Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We show the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.</p> <p>This dataset was created for the case study in Section 5 of the paper.</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.