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1,167 results for “Real-world”
Sionna ray tracing vs. real-world measurements: Signal strength comparison in rural scenarios with foliage
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Figure 1 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 1 Zooming into phase C: the process of knowledge re-integration into societal practice in a TDR process.
raw data - Old Questions, New Answers: Real-World, Long-Term Efficacy of Hymenoptera Venom Immunotherapy: Prevalence of Venom-Induced Anaphylaxis, Risk Factors, Field Sting Reactions
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Evaluation of real-world high-degree graphs
<p>Evaluation of real-world high-degree graphs</p>
Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</p>
DoH -- Real-World
<p> </p> <p><strong>Please refer to the original data article for further data description:</strong> <a href="https://doi.org/10.1016/j.dib.2022.108310">Jeřábek & Hynek et al., Collection of datasets with DNS over HTTPS traffic In: Data in Brief Journal</a> ,DOI:<a href="https://doi.org/10.1016/j.dib.2022.108310">10.1016/j.dib.2022.108310</a></p> <p>The collection of datasets contains DoH and HTTPS traffic that was captured in a real large ISP network. The data are provided in the form of PCAP files. However, since we needed to anonymize the real captures, we also provided TLS enriched flow data that are generated with opensource <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a> flow exporter. Other than TLS related information is not relevant since the dataset comprises only encrypted TLS traffic. The TLS enriched flow data are provided in the form of CSV files with the following columns:</p> <table> <thead> <tr> <th>Column Name</th> <th>Column Description</th> </tr> </thead> <tbody> <tr> <td>DST_IP</td> <td>Destination IP address</td> </tr> <tr> <td>SRC_IP</td> <td>Source IP address</td> </tr> <tr> <td>BYTES</td> <td>The number of transmitted bytes from Source to Destination</td> </tr> <tr> <td>BYTES_REV</td> <td>The number of transmitted bytes from Destination to Source</td> </tr> <tr> <td>TIME_FIRST</td> <td>Timestamp of the first packet in the flow in format YYYY-MM-DDTHH-MM-SS</td> </tr> <tr> <td>TIME_LAST</td> <td>Timestamp of the last packet in the flow in format YYYY-MM-DDTHH-MM-SS</td> </tr> <tr> <td>PACKETS</td> <td>The number of packets transmitted from Source to Destination</td> </tr> <tr> <td>PACKETS_REV</td> <td>The number of packets transmitted from Destination to Source</td> </tr> <tr> <td>DST_PORT</td> <td>Destination port</td> </tr> <tr> <td>SRC_PORT</td> <td>Source port</td> </tr> <tr> <td>PROTOCOL</td> <td>The number of transport protocol</td> </tr> <tr> <td>TCP_FLAGS</td> <td>Logic OR across all TCP flags in the packets transmitted from Source to Destination</td> </tr> <tr> <td>TCP_FLAGS_REV</td> <td>Logic OR across all TCP flags in the packets transmitted from Destination to Source</td> </tr> <tr> <td>TLS_ALPN</td> <td>The Value of Application Protocol Negotiation Extension sent from Server</td> </tr> <tr> <td>TLS_JA3</td> <td>The JA3 fingerprint</td> </tr> <tr> <td>TLS_SNI</td> <td>The value of Server Name Indication Extension sent by Client</td> </tr> </tbody> </table> <p>The DoH resolvers in the dataset can be identified by IP addresses written in <strong><em>doh_resolver_ip.csv </em></strong>file.</p> <p>The main part of the dataset is located in <strong><em>DoH-Real-World.tar.gz</em></strong> and has the following structure:</p> <pre><code>. └── data | - Main directory with data └── captured | - Directory with data captured on ISP backbone lines ├── pcap | - ISP backbone PCAPS └── tls-flow-csv | - ISP backbone CSV flow data </code></pre> <p> </p> <p><strong>Dataset collection statistics:</strong></p> <table> <thead> <tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Total Data Size</td> <td>179 GB</td> </tr> <tr> <td>Total Time</td> <td>~10 Days</td> </tr> <tr> <td>Connections</td> <td>~420 M</td> </tr> <tr> <td>Number of unique Client IP addresses</td> <td>116,263</td> </tr> <tr> <td>Number of unique Server IP addresses</td> <td>9343</td> </tr> <tr> <td>Number of unique DoH Resolver's IP addresses</td> <td>142</td> </tr> </tbody> </table> <p> </p> <p>Please cite the original article:</p> <pre><code>@article{Jerabek2022, title = {Collection of datasets with DNS over HTTPS traffic}, journal = {Data in Brief}, volume = {42}, pages = {108310}, year = {2022}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2022.108310}, url = {https://www.sciencedirect.com/science/article/pii/S2352340922005121}, author = {Kamil Jeřábek and Karel Hynek and Tomáš Čejka and Ondřej Ryšavý} }</code></pre> <p> </p>
Audio clips of real-world experiments (DeFTAN-II)
<p>Noisy, enhanced, clean speech of real-world experiments using DeFTAN-II (100 audio clips)</p>
Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects
<p><strong>Contacts:</strong></p> <p><strong>website:</strong> https://labsoft-ufmg.github.io/</p> <p><strong>email:</strong> henrique.mg.bh@gmail.com</p>
Dataset for the ARON-1 trial, paper "Real-World Impact of Upfront Cytoreductive Nephrectomy in Metastatic Non-Clear Cell Renal Cell CarcinomaTreated with First-Line Immunotherapy Combinations or Tyrosine Kinase Inhibitors (A Sub-Analysis from theARON-1 Retrospective Study)"
<p>Dataset for the ARON-1 trial</p>
Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects
<p><strong>Contact</strong></p> <p><strong>website:</strong> https://labsoft-ufmg.github.io/</p> <p><strong>email:</strong> henrique.mg.bh@gmail.com</p>
ESMO-GROW checklist for the publication PD-1/PD-L1 blockade plus abnobaViscum® therapy is associated with improved survival in advanced or metastasized NSCLC patients, a real-world data registry study according to the ESMO-GROW criteria
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Dataset for Phase-Diverse Imaging and Models for Real-World Aberration Correction
<p>The work is currently submitted for publication.</p>
Real-World Predator and Prey Experimentation Videos
<p>These are the videos of each run made to evaluate the policy of predators and a prey in the real world.</p>
CARE Initiative: Real-world Emulation of the KEYNOTE-189 Trial [DS4]
ClinicalTrials.gov study NCT06585189. IPD Sharing: NO. Countries: 0. Publications: 1.
Characterizing Outcomes and Real-World Experience of Cardiac Physiologic Pacing (The CORE-CPP Study)
ClinicalTrials.gov study NCT06606288. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Evaluate the Quality of Life in Male and Female Adult Participants With Severe Asthma Treated With Dupilumab in a Real-world Setting
ClinicalTrials.gov study NCT06018441. IPD Sharing: YES. Countries: 1. Publications: 0.
To Evaluate Real-world Safety, Effectiveness and Appropriate Use of Micatrio® Combination Tablets Treatment in Patients With Hypertension
ClinicalTrials.gov study NCT03021265. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Nicotine Withdrawal and Reward Processing: Connecting Neurobiology to Real-world Behavior
ClinicalTrials.gov study NCT03840694. IPD Sharing: YES. Countries: 1. Publications: 0.
Real-World Effectiveness of Afatinib (Gilotrif) Following Immunotherapy in the Treatment of Metastatic, Squamous Cell Carcinoma of the Lung: A Multi-Site Retrospective Chart Review Study in the U.S.
ClinicalTrials.gov study NCT04750824. IPD Sharing: NO. Countries: 1. Publications: 0.
Real-world Effectiveness Evaluation of Clinical Decision Support System Based on Artificial Intelligence (AI-CDSS)
ClinicalTrials.gov study NCT05065931. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
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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.