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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>
Catalogue of artefacts from the Nilgiri Hills
<p>Catalogue of artefacts from the Nilgiri Hills, collection donated by James W. Breeks and Sir Walter Elliot.</p>
Artefact for ``Addressing Combinatorial Experiments and Scarcity1 of Subjects by Provably Orthogonal and Crossover2 Experimental Designs''
Open the record for dataset details and reuse information.
Research Artefact: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types
<p>This is a research artefact for the paper: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types. This artefact is a repository consisting of the collected dataset including 447,542 React-related SO question posts and 384 representative samples that were obtained randomly. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
Research Artefact: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types
<p>This is a research artefact for the paper: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types. This artefact is a repository consisting of the collected dataset including 447,542 React-related SO question posts and 384 representative samples that were obtained randomly. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
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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.