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Dataset results
131 results for “queries”
Preprocessed Datasets for "Synthesizing Trajectory Queries from Examples" at CAV 2023
<p>Preprocessed Datasets for "Synthesizing Trajectory Queries from Examples" at CAV 2023</p>
GOAL International QuERI - Protocol CHRC 2019 GOAL INTERNATIONAL
ClinicalTrials.gov study NCT03861533. IPD Sharing: Not stated. Countries: 4. Publications: 0.
Querying Stroke Unit Nursing Interventions in the Emergency Department
ClinicalTrials.gov study NCT04011514. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction of Recruitment Potential of Participating Centers in Clinical Trials by Standardized Translation of Selection Criteria and Queries From DRG Database
ClinicalTrials.gov study NCT03019068. IPD Sharing: NO. Countries: 1. Publications: 0.
ICARE-LCS QUERI Partnered Evaluation Initiative: Integrating Comprehensive Tobacco Treatment to Enhance Lung Cancer Screening
ClinicalTrials.gov study NCT06745258. IPD Sharing: NO. Countries: 1. Publications: 0.
Abstractive Snippet Generation (query-biased DMOZ descriptions)
<p>Abstractive Snippet Generation (query-biased DMOZ descriptions).</p>
Retrieving API knowledge from Tutorials and Stack Overflow based on Natural Language Queries
<p>The replication package of PLAN</p>
Retrieving API knowledge from Tutorials and Stack Overflow based on Natural Language Queries
<p>The replication package of PLAN</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact
<div> <h1>Artefact for CAV 2024</h1> <a href="https://github.com/cxlvinchau/cav2024-experiments#artefact-for-cav-2024"></a></div> <p>This artefact accompanies the submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes".</p> <div> <h2>Contents of this artefact</h2> <a href="https://github.com/cxlvinchau/cav2024-experiments#contents-of-this-artefact"></a></div> <p>This artefact consists of two different folders, namely <code>data</code> and <code>software</code>.</p> <div> <h3>The <code>data</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-data-folder"></a></div> <p>The <code>data</code> folder contains data and results presented in the experimental section, along with models and properties that have been used. For every model and type of query (mean-payoff or reachability), there is a separate folder, containing the following types of files:</p> <ul> <li><code>data.csv</code> - A csv file where each row corresponds to a query and a model the query was considered for. Every row then contains information on the runtimes and model sizes. A detailed explanation of the columns can be found in the <code>README.md</code> of the data folder.</li> <li>PRISM model files with <code>.nm</code> or <code>.prism</code> extension, corresponding to the models used for the experiments.</li> <li>PRISM property files with <code>.props</code> extension, corresponding to the queries used for the experiments.</li> <li>A <code>README.md</code> with notes on the origins of the models.</li> </ul> <div> <h3>The <code>software</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-software-folder"></a></div> <p>The <code>software</code> directory contains our implementation of the presented techniques, including computation of the certificates (aka certification), computation of witnessing schedulers and the computation of minimal witnessing subsystems. It consists of the following components:</p> <ul> <li><code>cpmc</code> contains the Python implementation of the techniques and consists of several submodules: <ul> <li><code>cpmc/core</code> implements classes for representing MDPs and other modeling components</li> <li><code>cpmc/mean_payoff</code> implements the techniques for working with multi-objective mean-payoff queries</li> <li><code>cpmc/reachability</code> implements the techniques for working with multi-objective reachability queries</li> <li><code>cpmc/prism</code> implements the translation of subsystems to PRISM code</li> <li><code>cpmc/test</code> contains unit tests that can be run by navigating into the folder and running <code>pytest .</code></li> </ul> </li> <li><code>experiments</code> contains scripts and utility files for running the experiments: <ul> <li><code>experiments/phil.py</code> Script for running the dining philosophers experiment</li> <li><code>experiments/csn.py</code> Script for running the csn mean-payoff experiment</li> <li><code>experiments/csn_reachability.py</code> Script for running the csn reachability experiment</li> <li><code>experiments/sensors.py</code> Script for running the sensors experiment</li> <li><code>experiments/consensus.py</code> Script for running the consensus (coin) experiment</li> <li><code>experiments/firewire.py</code> Script for running the firewire experiment</li> <li><code>experiments/main.py</code> Command line interface for runnign the experiments</li> </ul> </li> </ul>
Restricted corpora used in the benchmarks of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora"
<p>Corpora used in the evaluation of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora" (Thomas Krause 2018, Humboldt-Universität zu Berlin) which access is limited to academic research purposes. This includes the following corpora in the relANNIS format:</p> <ul> <li> <p>Kobalt-DaF-Korpus (not published under an open license yet, https://www.linguistik.hu-berlin.de/de/institut/professuren/korpuslinguistik/forschung/kobalt-daf)</p> </li> <li> <p>TIGER (http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html)</p> </li> <li> <p>TüBa-D/Z (http://www.sfs.uni-tuebingen.de/ascl/ressourcen/corpora/tueba-dz.html)</p> </li> </ul> <p>The original TIGER corpus can be downloaded from http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/license/htmlicense.html and the TüBa-D/Z is available after manually requesting a download-link at http://www.sfs.uni-tuebingen.de/ascl/ressourcen/corpora/tueba-dz.html</p> <p>Access to both the TIGER and TüBa-D/Z corpora is restricted by the original authors and you need to accept licenses that you are solely using the corpus for academic research. Since the original providers don't include versions in the relANNIS format, it has been made available as restricted download here.</p> <p> </p> <p> </p>
Corpora used in the benchmarks of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora"
<p>Corpora used in the evaluation of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora" (Thomas Krause 2018, Humboldt-Universität zu Berlin) which have some form of license that allows their redistribution. See LICENSE.txt in each folder for the actual license of each corpus and the original authors.</p> <p>Since each corpus has a different license, the access is restricted.</p> <p><strong>If using one of the corpora please cite the original authors mentioned in the LICENSE.txt!</strong></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.
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DANDI Archive for NWB datasets
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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
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