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4,325 results for “real”
Decorative bronze piece, Alarcos, Ciudad Real
"Decorative bronze piece from the site of Alarcos, Ciudad Real, Castilla-La Mancha, Spain. Iron Age. In the collection of the Ciudad Real Museum. Catalog number DO000475. Processed in Reality Capture from 461 images. Alarcos 85 sector IV, cuadrícula 19 Nº 1804. This work is part of the collaboration agreement signed in 2020 between the Junta de Comunidades de Castilla-La Mancha and Global Digital Heritage for the digitization of the Cultural Heritage of Castilla-La Mancha region. We want to publicly thank the collaboration of the Museum of Ciudad Real, especially by its director José Ignacio de la Torre. Source: Objaverse 1.0 / Sketchfab
Medieval Arch, the Synagogue, Ciudad Real
"Medieval Arch from the site of the Synagogue, Ciudad Real, Castilla-La Mancha, Spain. Middle Ages . In the collection of the Ciudad Real Museum. Catalog number CE020563. Processed in Reality Capture from 939 images. This work is part of the collaboration agreement signed in 2020 between the Junta de Comunidades de Castilla-La Mancha and Global Digital Heritage for the digitization of the Cultural Heritage of Castilla-La Mancha region. We want to publicly thank the collaboration of the Museum of Ciudad Real, especially by its director José Ignacio de la Torre. Source: Objaverse 1.0 / Sketchfab
Supplemental Information: Cooperative Coevolution of Control for a Real Multirobot System
<p>Supplemental information for the paper "Cooperative Coevolution of Control for a Real Multirobot System".</p> <p>[real_logs.csv] Position (from GPS) and orientation (from compass) logs of the robots in the real-robot experiments.</p> <p>[camera_video.mp4] Video recorded in one of the experimental trials, showing the real robots and the environment.</p> <p>[traces_all_trials.pdf] Images with the traces of the robots in all real-robot experiments.</p> <p>[video_CONTROLLER_TRIAL] Videos of the traces of the robots in the real-robot experiments.</p> <p> </p> <p>The source code for the evolutionary process can be found here: https://github.com/jorgemcgomes/mase</p> <p>And the simulation environment here: https://github.com/BioMachinesLab/drones</p>
FIGURE 6 in A new species of Psychrophrynella (Anura: Craugastoridae) from the Cordillera Real, Department La Paz, Bolivia
FIGURE 6. Advertisement call of Psychrophrynella teqta sp. nov. Air temperature at the time of recording, 10.8ºC. Above, oscillogram and power spectra (power spectra analysis bandwidth: 15 Hz). Below, spectrogram and oscillogram (spectrogram analysis bandwidth: 94 Hz).
FIGURE 2 in A new species of Psychrophrynella (Anura: Craugastoridae) from the Cordillera Real, Department La Paz, Bolivia
FIGURE 2. Variation in color pattern of Psychrophrynella teqta sp. nov. Dorsolateral and ventral views of A: MNCN 45704 (female; SVL 27.8 mm); B: CBF 6726 (male, 23.6 mm SVL); C: CBF 6727 (female, 27.2 mm SVL); D: CBF 6728 (female, 25.8 mm SVL).
FIGURE 1 in A new species of Psychrophrynella (Anura: Craugastoridae) from the Cordillera Real, Department La Paz, Bolivia
FIGURE 1. Dorsolateral and ventral view of the living holotype of Psychrophrynella teqta sp. nov. (CBF 6725; SVL 26.1 mm).
FIGURE 4 in A new species of Psychrophrynella (Anura: Craugastoridae) from the Cordillera Real, Department La Paz, Bolivia
FIGURE 4. Ruins of old local constructions, which create an optimal microhabitat for Psychrophrynella teqta sp. nov.
FIGURE 3 in A new species of Psychrophrynella (Anura: Craugastoridae) from the Cordillera Real, Department La Paz, Bolivia
FIGURE 3. Map of northern Bolivian Andes showing the distribution of Psychrophrynella teqta sp. nov. (star), and general aspect of the type locality.
Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics - datasets and results
<p>This directory contains the data and results that we used and obtained during the realization of the following paper:</p> <blockquote> <p>Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017).</p> </blockquote> <p>The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130.</p> <p>This repository contains the following files:</p> <ul> <li>evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment.</li> <li>JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760</li> <li>realtime.zip (expands to 913.5 MB) A zip file containing the main results</li> <li>dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments.</li> <li>overview.zip (expands to 42 MB) Contains the following files: <ul> <li>overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper.</li> <li>overview/heuristics/ - folder with a tree visualization for every evolved heuristic.</li> <li>overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/.</li> <li>readme.txt - this file.</li> <li>overview/results/ - contains the scripts</li> <li>overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.</li> </ul> </li> </ul>
Concentrated Hybrid Solar Panel Real Measurements in Switzerland
<h3>Measurements of concentrated hybrid solar panel</h3> <p>Datasheet of the hybrid PV panel (<a title="Datasheet" href="https://cdn.enfsolar.com/Product/pdf/Crystalline/55adc587c2506.pdf" target="_blank" rel="noopener">Here</a>)</p> <p>These measurements have taken place in Granges, Valais, Switzerland in longitude: 7.4649965° and latitude: 46.2647793° with three flat mirrors that concentrated the sun's reflection on one hybrid panel.</p> <p>The PV-T and each mirror length (L) : 1 m</p> <p>The distance between the mirrors (D) : 1.5 m</p> <p>The distance between the mirrors and PV-T (F) : 10 m</p>
Dataset for 'Assessing Golang Static Analysis Tools on Real-World Issues'
<p>Go Linter Evaluation Dataset</p> <p>This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.</p> <p>Authors: Jianwei Wu, James Clause</p> <p>Collected Survey Data:<br>- This Excel file contains the collected survey data for the empirical study in details.</p> <p>R Scripts and Raw Data:<br>- These scripts are used for data analysis and processing.<br>- This is the initial data collected from surveys or other sources before any processing or analysis.</p> <p>Surveys for External Participants:<br>- This Excel file contains survey data collected for the evaluation of Go linters.<br>- This folder contains the surveys sent to external participants for collecting their feedback or data.</p> <p>Recruitment Letter.pdf:<br>- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.</p> <p>Outputs from Existing Go Linters and Summarized Categories.xlsx:<br>- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.</p> <p>Selection of Go Linters.xlsx:<br>- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.</p> <p>UD IRB Exempt Letter.pdf:<br>- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.</p> <p>Survey Template.pdf:<br>- This PDF contains an example of the survey sent to the participants.</p> <p>govet issues.pdf:<br>- This PDF contains a list of reported issues about govet. Collected from various pull requests.</p> <p>Approved linters:<br>- staticcheck gofmt govet revive gosec deadcode errcheck.</p> <p>Table 2.jpg:<br>- A detailed figure to show the technical data in Table 2 of the paper.</p> <p>PR-summary-standalone:<br>- This is the AI tool we used to summarize the content of pull requests. Readme file is avaliable in this folder.</p>
Real-World Gait Speed Estimation: Laboratory and Real-Life Patient Data from TOLIFE Wearables
<div><strong>TOLIFE Wearable Dataset Collection</strong></div> <p>This data collection was carried out in the context of the TOLIFE Horizon Europe project, which aims to develop and validate artificial intelligence (AI) solutions for processing patient data collected through non-intrusive wearable sensors. The ultimate goal is to enable personalized treatment, monitor health outcomes, and improve the quality of life of patients with Chronic Obstructive Pulmonary Disease (COPD).</p> <p>The dataset is composed of four distinct subsets, each acquired under different conditions and with specific goals:</p> <div><strong>1. </strong>Gait Speed Dataset</div> <div><strong>2.</strong> Activity Recognition Dataset</div> <div><strong>3. </strong>Validation Dataset</div> <div><strong>4. </strong>Daily Life Dataset</div> <div> </div> <p><strong>1. Gait Speed Dataset:</strong></p> <div><strong>Objective:</strong> To collect reference gait speed data under controlled conditions using wearable sensors, with a focus on validating gait speed estimation algorithms.</div> <div> </div> <div><strong>Materials:</strong></div> <div>Smartphone: Samsung Galaxy A14</div> <div>Smartwatch: Samsung Galaxy Watch 5</div> <div>Smart shoes (custom prototype)</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 25 healthy individuals (10 M, 15 F), age: 28.9 ± 4.8 years</div> <div>Test: Six Minute Walking Test (6MWT) along a 10-meter path</div> <div>Paces: Slow, Medium, Fast (self-selected)</div> <div> </div> <div>Device Placement:</div> <div>- Watch: left </div> <div>- Phone: left front pocket (screen facing thigh, Y-axis upward)</div> <div>- Reference System: Xsens Awinda (17 wireless IMUs) with MVN Analyze software</div> <div>- Reference gait speed = horizontal velocity of Center of Mass (CoM)</div> <p><strong>Folder Content:</strong></p> <div>Wearable folder: individual .csv files for each sensor (phone, watch, shoes)</div> <div>Reference folder: CoM speed from Xsens + walked distances in smwd.csv</div> <div> </div> <p><strong>2. Activity Recognition Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To build a dataset for training AI models to recognize different daily activities (e.g., resting, walking, stair climbing) using wearable sensor data.</div> <p><strong>Materials:</strong></p> <div>Same TOLIFE wearable platform as above (phone, watch, smart shoes).</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 15 healthy individuals (7 F, 8 M), age: 27.3 ± 3 years</div> <div>Activities:</div> <div>- Resting: lying, sitting, standing (max 5 sec walking allowed)</div> <div>- Walking: 3 sessions (2 x 180 m + 1 x 450 m with directional changes)</div> <div>- Stair climbing: continuous ascent and descent for ~2 minutes</div> <div>Smartwatch placement: user's preferred wrist</div> <p><strong>Folder Content:</strong></p> <div>Data folder: wearable sensor data for each recording session</div> <div>Timestamps folder: .csv files with annotated start/stop times and activity labels</div> <div> </div> <p><strong>3. Validation Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To test the robustness of trained activity and gait estimation models in less controlled, real-world environments.</div> <p><strong>Materials: </strong>Same TOLIFE wearable platform.</p> <p><strong>Acquisition protocol:<br></strong>Participants: 10 healthy adults (5 M, 5 F), age: 43.8 ± 12.1 years<br>Protocol: free walking outdoors, with mixed activities:<br>- Resting, level walking, stair climbing<br>- 4 walking paths of known length (100–280)<br>- Device placement: user’s preferred side (watch), front pants pocket (phone)</p> <p><strong>Folder Content:<br></strong>Data folder: continuous sensor data recordings<br>Timestamps folder: annotated transitions between activities</p> <p> </p> <div><strong>4. Daily Life Dataset:</strong></div> <div> </div> <div><strong>Objective: </strong>To capture real-world data from COPD patients in daily life conditions for long-term monitoring of mobility and gait speed.</div> <p><strong>Materials: </strong>TOLIFE wearable platform (smartphone, smartwatch, smart shoes)</p> <p><strong>Acquisition Protocol:<br></strong>Participants: 38 COPD patients (23 M, 15 F), age: 63.3 ± 5.8 years<br>Duration: continuous data collection over two weeks<br>Initial Reference Visit (RV):<br>- 2 clinical 6MWTs performed by a pulmonologist, average distance used as a reference value<br>- Instructions: participants used the devices freely (indoors/outdoors) in comfortable conditions</p> <p><strong>Folder Content:<br></strong>Data folder: two weeks of wearable sensor recordings per patient<br>Clinical Six_Minute_Walking_Distance.csv: distances from RV</p> <p> </p> <p>Citation:</p> <p>When using any portion of this dataset collection, please cite:</p> <p>Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205</p>
Dataset for the manuscript 'Orbital-optimized Density Functional Calculations of Molecular Rydberg Excited States with Real Space Grid Representation and Self-Interaction Correction'
<p>Dataset for the manuscript 'Orbital-optimized Density Functional Calculations of Molecular Rydberg Excited States with Real Space Grid Representation and Self-Interaction Correction'</p>
Coulomb Integral Solution in Discretized Real Space
<p>This File contains the values of the dimensionless tensor a_{ijk}, as described in the publication.</p>
Mapping Enzyme Activity in Living Systems by Real-Time Mid-Infrared Photothermal Imaging of Nitrile Chameleons
Open the record for dataset details and reuse information.
A Machine Learning Approach for Real-time Cortical State Estimation
<p>Data and code accompanying the following publication: Weiss, D. A., Borsa, A. M., Pala, A., Sederberg, A. J., & Stanley, G. B. (2024). A machine learning approach for real-time cortical state estimation. <em>Journal of neural engineering</em>, <em>21</em>(1), 10.1088/1741-2552/ad1f7b. https://doi.org/10.1088/1741-2552/ad1f7b</p>
MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
Results of the Claude-based real estate ontology population
Open the record for dataset details and reuse information.
Clay spindle whorl, Alarcos, Ciudad Real
"Clay spindle whorl from the site of Alarcos, Ciudad Real, Castilla-La Mancha, Spain. Iron Age. In the collection of the Ciudad Real Museum. Catalog number DO000444. Processed in Reality Capture from 490 images. Alarcos 86 sector IV, cuadrícula 16 Nº 1505. This work is part of the collaboration agreement signed in 2020 between the Junta de Comunidades de Castilla-La Mancha and Global Digital Heritage for the digitization of the Cultural Heritage of Castilla-La Mancha region. We want to publicly thank the collaboration of the Museum of Ciudad Real, especially by its director José Ignacio de la Torre. Source: Objaverse 1.0 / Sketchfab
Ciudad Real Museum DO000077
Protohistoric pottery from the important Iberian fortified town (oppida) of Alarcos. The archaeological site of Alarcos is located 10 kilometers from Ciudad Real (Spain) in the Archaeological Park of Alarcos, next to the Guadiana river. This piece is currently deposited in the Ciudad Real Museum. 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.