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1,167 results for “Real-world”
Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade
<p>Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO<sub>2</sub> emissions in this study. CO<sub>2</sub> model results using VSP computed with the measured real-world road grade (R<sup>2</sup> = 0.86) gave improved fits over the no-grade VSP model (R<sup>2</sup> = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.</p>
Dataset of real-world WebXR bugs
<p>This is the released dataset of paper <em>An Exploratory Study of Bugs in Extended Reality Applications on the Web </em> to appear in ISSRE 2020.</p> <p>The "WebXR-Bugs-Dataset.zip" includes:</p> <p>(1) A WebXR bug dataset. The dataset contains 368 real bugs from 33 WebXR projects hosted on GitHub, with the final taxonomy with respect to their symptoms and root causes.</p> <p>(2) Materials of the manual analysis process of each round before the final taxonomy was built. The whole process contains 7 rounds.</p> <p>Our dataset is also released at this website: <a href="https://sites.google.com/view/webxr-bug-study/home">https://sites.google.com/view/webxr-bug-study/</a>.</p>
CARA: Benchmarking Compound Activity Prediction for Real-World Drug Discovery Applications
<p>Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a benchmark, named CARA.Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that current models can make successful predictions for certain proportions of assays, while the performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
ReCANVo: A Dataset of Real-World Communicative and Affective Nonverbal Vocalizations
<p>A dataset of 7077 labeled vocalizations made by non-speaking individuals. Each vocalization lasts approximately 0.5-4 seconds and is labeled with its affective or communicative meaning. Data were acquired in real-world settings (homes, schools, etc.) and were labeled in real-time by parents or caregivers who knew the non-speaking communicator well. </p> <p>dataset_file_directory.csv provides the name of each vocalization file, the corresponding participant ID, and the vocalization meaning or label (delighted, frustrated, request, etc.).</p> <p>If you use this dataset, please <strong>cite Johnson & Narain et al., "ReCANVo: A Database of Real-World Communicative and Affective Nonverbal Vocalizations"</strong>. The authors are Jaya Narain, Kristina T. Johnson, Thomas Quatieri, Pattie Maes, and Rosalind Picard. This paper provides more information about the dataset, including data acquisition methodology, pre-processing procedures, and participant demographics. </p> <p>**J.N. and K.T.J. are joint first authors on this project. Please include both names in attribution when possible (e.g., Johnson & Narain et al.).</p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions
<p>This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China.</p> <p>See our published paper for more information: </p> <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>
DroidLeaks: A Large Collection of Resource Leak Bugs in Real-World Android Apps
<p>DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:<br> 1. the code repository of the app subject<br> 2. the concerned resource class<br> 3. the buggy code revision (and buggy file and method names)<br> 4. the bug-fixing code revision (i.e., link to the patch)<br> 5. the bug report or the corresponding pull request for patches (if located)</p>
Fully synthetic longitudinal real-world data from hearing aid wearers for public health policy modeling
<p>Real-world data from hearing aids and Bluetooth connected smartphones. The associated data report can be found here: <a href="https://doi.org/10.3389/fnins.2019.00850">https://doi.org/10.3389/fnins.2019.00850</a></p>
The Real-World Benchmark of Android App Vulnerability from CVE
<p>The Real-World Benchmark of Android App Vulnerability from CVE named CVE-based benchmark.</p> <p>For more information about this benchmark, please refer to <a href="https://github.com/android-app-sast/VulsTotal">android-app-sast/VulsTotal: A Unified Platform for Evaluating SAST Tools for Android (github.com)</a></p>
Comprehensive Dataset for Detecting Road Anomalies in Diverse Real-World Situations
<p>In Smart Cities, technologies are playing an important role in efficiently managing the rapid growth of the world's industrialization today. The deployment of surveillance cameras has proliferated to improve public safety and security. Many Closed-Circuit Television (CCTV) cameras have been installed to monitor and safeguard public spaces efficiently within the cities. Despite advancements in technology, video and image processing still largely rely on manual observation. This manual analysis is time-consuming, prone to missing critical details, and costly in terms of labor and resources. Nevertheless, monitoring large video feeds for long periods indicates fatigue, demise of focus, and errors, particularly when video surveillance is a necessity. <br>Road anomaly detection is one of the prominent computer vision issues that researchers have investigated to guarantee public safety. Road anomaly identification is increasingly difficult and complex due to the variety and complexity of abnormalities. <br>Deep learning algorithms must be efficient but also need a large dataset to train to recognize road anomalies in different environments. We proposed a custom real-world data set containing road anomaly images and videos that are made available to the public and private surveillance systems. Primary data were collected from diverse sites in Pakistan, and the data were gathered by recording videos and capturing images by using mobile and surveillance cameras The dataset encompasses five major categories of road anomaly effects.: vehicle accidents, vehicle fire, fighting, snatching(gunpoint), and potholes that classification modeling while promoting improvement in both scientific research and realistic application. The dataset also encompasses annotations with You Only Look Once (YOLO) based bounding boxes and class label files in text format for every image. <br>The researchers can utilize data to train and validate their anomaly detection algorithms and models, thus increasing public security and safety. This dataset focuses on natural environment scenes with a detailed examination of safe transportation and impacts on broader environmental knowledge. Data can give to the liable and ethical arrangement of Artificial Intelligence technologies in surveillance security system</p>
Invention and application of epoch of Kifilideen's Sum Formula for Kifilideen's General Matrix Progression Series of infinite terms in solving real-world problems
<p>*Sharing a Notable Achievement* I am honored to announce that my research paper, titled 'Invention and Application of Epoch of Kifilideen’s Sum Formula for Kifilideen’s General Matrix Progression Series of Infinite Terms in Solving Real-World Problems,' was among the three papers selected by the College of Engineering (COLENG) for presentation at the recent Science and Technology Fair/Exhibition organized by Bells University of Technology, Ota, Ogun State, Nigeria, on October 28, 2024. I am delighted to share that COLENG secured the first position in the Inter-Collegiate poster competition, a testament to the outstanding collective efforts of our researchers, staff, and team members who contributed to the success. I extend my gratitude to the Dean of Engineering for his exceptional leadership and guidance, which played a pivotal role in this achievement. Congratulations to my colleagues whose papers were also presented, and to everyone who contributed to this success. Award Recognition Sincerely, Engr. Dr. Kifilideen Lekan OSANYINPEJU Agricultural and Bio-System Engineering College of Engineering Bells University of Technology, Ota, Ogun State, Nigeria</p>
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
CLEAR: Continual LEArning on Real-World Imagery
<p>Continual learning (CL) is considered as one of the next big challenges in AI. However, the existing CL benchmarks, e.g. Permuted-MNIST and Split-CIFAR, are artificially designed to be made continual and do not align with or generalize to real-world. In this paper, we introduce CLEAR, the first continual image recognition benchmark dataset with a natural temporal evolution of visual concepts in the real world that spans a decade (2004-2014). We build CLEAR from existing image collection (YFCC100M) by proposing a novel low-cost visio-linguistic dataset curation approach. It involves using pretrained vision-language models (e.g. CLIP) to quickly build high-quality labeled datasets on a tight budget. Finally, we post-process CLEAR via crowd-sourcing to remove errors and even inappropriate images hidden in original YFCC100M. The major strengths of CLEAR over prior CL benchmarks include (1) smooth and realistic temporal evolution of visual concepts with real-world imagery, enabling a more practical "online" (i.e., train on past, test on future) evaluation protocol (2) high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised and unsupervised learning. Our extensive experiments reveal that mainstream "offline" evaluation protocols, which train and test on iid data, artificially inflate performance of CL systems, stressing the need for our "online" protocol since the models we train today will always be tested in future. Moreover, we find that state-of-the-arts CL algorithms that only utilize fully-supervised data fall short whereas unsupervised pretraining provides significant boost. Lastly, we introduce a biased reservoir-sampling algorithm that dynamically caches more recent training data, achieving the new state-of-the-arts while still leaving large room for improvement.</p>
Parcel2D Real - A real-world image dataset of cuboid-shaped parcels with 2D and 3D annotations
<p>Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>.</p> <p>Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction (.binvox files)</li> <li>3D reconstruction (.obj files)</li> </ul> <p>For details, see our <a href="https://ieeexplore.ieee.org/abstract/document/10069342">paper</a> and <a href="https://a-nau.github.io/parcel2d/">project page</a>.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannScrapeCutPasteLearn2022, title = {Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics}, author = {Naumann, Alexander and Hertlein, Felix and Zhou, Benchun and Dörr, Laura and Furmans, Kai}, booktitle = {{{IEEE Conference}} on {{Machine Learning}} and Applications ({{ICMLA}})}, date = 2022 }</code></pre> <p> </p>
Dataset from 'Klever, L., Islam, J., Võ, M., & Billino, J. (2023). Aging attenuates the memory advantage for unexpected objects in real-world scenes. Heliyon, e20241, doi: 10.1016/j.heliyon.2023.e20241
<p>We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file.</p> <p>___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Real-World AR101 Market-Supporting Experience Study in Peanut-Allergic Children (RAMSES)
ClinicalTrials.gov study NCT03126227. IPD Sharing: NO. Countries: 2. Publications: 1.
Improving Negative Symptoms of Psychosis In Real-world Environments
ClinicalTrials.gov study NCT02170051. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Real-World Outcomes of People With Crohn's Disease (CD)
ClinicalTrials.gov study NCT05056441. IPD Sharing: YES. Countries: 3. Publications: 1.
Otrivine: Quality of Life (QoL) Impact in a Real-World Setting
ClinicalTrials.gov study NCT05556148. IPD Sharing: YES. Countries: 1. Publications: 1.
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.