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PyQBF: A Python Framework for Solving Quantified Boolean Formulas
<h1>PyQBF: A Python Framework for Solving Quantified Boolean Formulas</h1> <h2>Introduction</h2> <p>This artifact was submitted by Mark Peyrer, Maximilian Heisinger and Martina Seidl for the iFM 2024 with the submission number 2625 aiming for Available, Functional and Reusable badges. It contains everything necessary to reproduce the experiments shown in the corresponding paper. In the following sections you will learn everything you need to know about this artifact and how to reproduce the experiments.</p> <p>In order to make this artifact as accessible as possible, we divided the experiments into three tracks:</p> <ul> <li>The <strong>small</strong> track contains only a fraction of the experiments. It requires little time and memory usage compared to the whole track, producing only a rough overview of the desired data accordingly.</li> <li>The <strong>medium</strong> track gives a good overview and trend on how the data will look while remaining somewhere reasonable with time resources.</li> <li>The <strong>all</strong> track fully reproduces the experiment as shown in the original paper. Note that this track <strong>does not include formulas which ran out of resources for both solver and pyqbf in the original experiment</strong>.</li> </ul> <p>The artifact was published under the following DOI: <a href="../qbf/pyqbf-artifact/-/blob/master/10.5281/zenodo.13341211">10.5281/zenodo.13341211</a></p> <h2>Artifact Requirements</h2> <p>The following requirements were measured on a host machine for the docker container with the following specifications:</p> <ul> <li><a href="https://www.intel.de/content/www/de/de/products/sku/232160/intel-core-i71355u-processor-12m-cache-up-to-5-00-ghz/specifications.html" target="_blank" rel="nofollow noreferrer noopener">Intel Core i7-1355U Prozessor</a></li> <li>32 GB RAM</li> <li>Intel Iris Xe GPU</li> </ul> <p>Resource consumption in the original experiment was restricted (as it also is in this artifact) on 1hr as well as 8000MB per instance.</p> <p><strong>Outer-Counter Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min30s with a maximum memory usage of 65MB</li> <li><strong>medium</strong>: Approximately 1h with a maximum memory usage of 270MB</li> <li><strong>all</strong>: Approximately 7hrs with a maximum memory usage of 425MB</li> </ul> <p><strong>Caqe Comparison Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min with a maximum memory usage of 400MB</li> <li><strong>medium</strong>: Approximately 1h15min with a maximum memory usage of 2500MB</li> <li><strong>all</strong>: Approximately 12hrs30min with a maximum memory usage of 4000MB</li> </ul> <p><strong>DepQBF Comparison Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min45s with a maximum memory usage of 500MB</li> <li><strong>medium</strong>: Approximately 40min with a maximum memory usage of 2500MB</li> <li><strong>all</strong>: Approximately 25hrs30min with a maximum memory usage of 8000MB</li> </ul> <p><strong>QFun Comparison Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min with a maximum memory usage of 325MB</li> <li><strong>medium</strong>: Approximately 1h15min with a maximum memory usage of 820MB</li> <li><strong>all</strong>: Approximately 12hrs with a maximum memory usage of 4000MB</li> </ul> <p><strong>Qute Comparison Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min with a maximum memory usage of 90MB</li> <li><strong>medium</strong>: Approximately 2hrs with a maximum memory usage of 425MB</li> <li><strong>all</strong>: Approximately 22hrs with a maximum memory usage of 3200MB</li> </ul> <p><strong>RAReQS Comparison Experiment</strong></p> <ul> <li><strong>small</strong>: Approximately 1min with a maximum memory usage of 150MB</li> <li><strong>medium</strong>: Approximately 2hrs with a maximum memory usage of 7500MB</li> <li><strong>all</strong>: Approximately 17hrs with a maximum memory usage of 8000MB</li> </ul> <h2>Structure and Content</h2> <p>For running the artifact, the following structure is relevant for the experiments:</p> <div> <pre><code>/ |-- expected |-- experiments | |-- outer-counter | |-- compare-caqe | |-- compare-depqbf | |-- compare-qfun | |-- compare-qute | |-- compare-rareqs |-- gallery23 |-- output | |-- plots |-- scripts</code></pre> </div> <ul> <li><code>expected</code> contains the runs of the experiments conducted with this artifact on our machine for reference and the long runs</li> <li><code>experiments</code> contains the scripts and runtime data when running the experiments. Each experiment has their own folder</li> <li><code>gallery23</code> contains the complete testset of our experiments, i.e. the <a href="https://qbf23.pages.sai.jku.at/gallery/" target="_blank" rel="nofollow noreferrer noopener">QBF-Gallery 2023</a> QDIMACS track. A complete list of the instances with absolute paths can be found in <code>/experiments/targets.txt</code></li> <li><code>output</code> is the target of the results from the experiments, thus containing plots and sqlite-databases once executed. Furthermore, this folder is mounted by the host machine for comfortable access</li> <li><code>scripts</code> is a collection of python-scripts used by the experiments</li> </ul> <p>Furthermore, there are folders containing the necessary tools used by the experiments. Those are not relevant when running the experiments but for extending the artifact.</p> <div> <pre><code>/ |-- executables |-- outer-count |-- pyqbf |-- runlim |-- simsala |-- solvers | |-- caqe | |-- depqbf | |-- qfun | |-- qute | |-- rareqs</code></pre> </div> <ul> <li><code>executables</code> contains the executables of the solvers used in the experiments. They were built from the sources (except caqe) during the building-step of the docker image</li> <li><code>outer-count</code> is the <a href="https://github.com/marseidl/outer-count" target="_blank" rel="nofollow noreferrer noopener">cloned repository</a> containing the outer-count tool for counting models</li> <li><code>pyqbf</code> contains all sources of our framework. Note that it is already installed into Python as a module such that <code>import pyqbf</code> is sufficient for any python script in the docker container</li> <li><code>runlim</code> is the <a href="https://github.com/arminbiere/runlim" target="_blank" rel="nofollow noreferrer noopener">cloned repository</a> containing the runlim tool for benchmarking</li> <li><code>simsala</code> is the <a href="https://gitlab.sai.jku.at/simsala/simsala">cloned repository</a> containing the perl-scripts used for working with slurm. This was used to run the original experiments of the paper.</li> <li><code>solvers</code> contains the sources of the solvers their executables are build from. You can furthermore find their licences in there</li> </ul> <h2>How to use this artifact</h2> <h3>1. Setup Docker</h3> <p>We require you to have a working docker environment installed on your machine. In order to set-up the docker container, run the following commands</p> <div> <pre><code># download pyqbf-artifact.zst from zenodo docker load < pyqbf-artifact.zst docker run -v `pwd`/output:/output -itd --network none --name pyqbf_artifact1 pyqbf-artifact docker exec -it pyqbf_artifact1 bash</code></pre> </div> <p>The first line loads the docker-image from the the export, the second will create a container using the image and the last connects your current terminal to the container.</p> <p>In order to leave the container, just execute <code>exit</code>. Furthermore, you can call</p> <div> <pre><code>docker stop pyqbf_artifact1 docker rm pyqbf_artifact1</code></pre> </div> <p>to stop and delete the running container.</p> <p><strong>Important:</strong> Please ensure to have a working docker setup, with your user added to the <code>docker</code> group. This can be verified using <code>docker ps</code>. If there are no errors, your environment should be in working order.</p> <h3>2. Run the desired experiments</h3> <p>Reproduces figure 4, partwise.</p> <p><strong>Outer Counter</strong> (right part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/outer-counter/run-outer-small-seq.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/outer-counter/run-outer-medium-seq.sh</code></li> <li><strong>all:</strong> <code>/experiments/outer-counter/run-outer-all-seq.sh</code></li> </ul> <p><strong>Comparison of Caqe executable with its PyQBF version</strong> (partial left part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/compare-caqe/run-caqe-small.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/compare-caqe/run-caqe-medium.sh</code></li> <li><strong>all:</strong> <code>/experiments/compare-caqe/run-caqe-all.sh</code></li> </ul> <p><strong>Comparison of DepQBF executable with its PyQBF version</strong> (partial left part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/compare-depqbf/run-depqbf-small.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/compare-depqbf/run-depqbf-medium.sh</code></li> <li><strong>all:</strong> <code>/experiments/compare-depqbf/run-depqbf-all.sh</code></li> </ul> <p><strong>Comparison of QFun executable with its PyQBF version</strong> (partial left part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/compare-qfun/run-qfun-small.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/compare-qfun/run-qfun-medium.sh</code></li> <li><strong>all:</strong> <code>/experiments/compare-qfun/run-qfun-all.sh</code></li> </ul> <p><strong>Comparison of Qute executable with its PyQBF version</strong> (partial left part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/compare-qute/run-qute-small.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/compare-qute/run-qute-medium.sh</code></li> <li><strong>all:</strong> <code>/experiments/compare-qute/run-qute-all.sh</code></li> </ul> <p><strong>Comparison of RAReQS executable with its PyQBF version</strong> (partial left part of figure 4)</p> <ul> <li><strong>small:</strong> <code>/experiments/compare-rareqs/run-rareqs-small.sh</code></li> <li><strong>medium:</strong>: <code>/experiments/compare-rareqs/run-rareqs-medium.sh</code></li> <li><strong>all:</strong> <code>/experiments/compare-rareqs/run-rareqs-all.sh</code></li> </ul> <p>All of the scripts will show verbose output when called.</p> <h3>3. Check the results</h3> <p>The results are copied automatically to the <code>/output</code> folder, which is mounted by a local <code>./output</code> folder, such that after running the scripts the results will be available on your host machine.</p> <p>Furthermore, the results produced by us running the artifact can be found in the <code>/expected</code> folder in the docker container.</p> <p>In <code>/output/plots</code> (or <code>/expected/plots</code> respectively) you can find a logarithmic scatter plot with a corresponding name to the experiment produced by the data retrieved from the current run.</p> <p>Using the script <code>/scripts/diff-all.sh</code> you can check all available databases in the <code>/output</code> folder for differences in the result-code.</p> <p>Also the raw data is available directly in the <code>/output</code> folder as sqlite-databases. Those can either be looked at by a tool (we recommend <a href="https://sqlitebrowser.org/dl/" target="_blank" rel="nofollow noreferrer noopener">DB Browser for sqlite</a>) or by the command <code>sqlite3 <name>.db</code>. The latter will open the database and allows queries on the data. <strong>Note:</strong> Both variants require a working sqlite installation on your host machine (e.g. <code>apt-get install sqlite3</code>)</p> <h2>Functional Badge</h2> <p>In the paper, we claimed that using our framework only comes with small overhead in comparison to using the executables. In particular, we showed a state-of-the-art implementation of an outer-model counter and compared it to a scripted version using PyQBF.</p> <p>These claims can be replicated using this artifact. The <strong>all</strong>-track of each experiment will produce the necessary data to check this claim on the current machine on the same instances as seen in the paper. The other tracks (<strong>small</strong> and <strong>medium</strong>) will show a trend by evaluating a subset. By producing a scatter plot comparing a state-of-the-art instance to a scripted instance using PyQBF, it is trivial to see whether the claim holds.</p> <p>There is no direct evaluation script as the datapoints are too dependent on external factors to produce the same results. However, the result codes of the solver can be compared using the <code>/scripts/diff-all.sh</code> script.</p> <p>In order to check the code, we refer to the verbose of each of the experiments, where it is logged which executable is compared to which script. All sources of used tools are available within the artifact. Please check the <em>Structure and Content</em> section for further details.</p> <h2>Reusable badge</h2> <p>PyQBF is released under the GPLv3 licence and can be found publicly <a href="https://gitlab.sai.jku.at/qbf/pyqbf/">here</a>. Furthermore, an extensive documentation can be found <a href="https://gitlab.sai.jku.at/qbf/pyqbf/">here</a>.</p> <p>This artifact can be used in numberous ways beyond the paper. The full potential of PyQBF is at one's disposal with a simple <code>import pyqbf</code>. Let it be a new algorithm using QBF-solvers or simple tasks like negating a QCNF formula - the framework allows easy prototyping with minimal overhead. Another example is the comparison of solvers against each other. The script <code>/scripts/produce_scatter.py</code> can be used to compare the data of any two sqlite-databases generated by such experiments and plot an overview of by linking the problems by their names. Finally, also PyQBF itself can be extended by e.g. more solvers. While this requires some particular knowledge about the architecture and building-pipeline, the already existing solvers will serve as a good example on how to implement such task - no matter if used by an interface or as a library. If just using a solver is sufficient, it can be directly bound into the PyQBF framework using <a href="https://github.com/maximaximal/QuAPI" target="_blank" rel="nofollow noreferrer noopener">QuAPI</a> without needing to extend PyQBF (this only works if the command line interface is compatible with QuAPI).</p> <p>At the current state, we support PyQBF for linux-based operating systems. Windows is not and will not be supported. Support for MacOS is currently in development.</p> <h2>Dependencies</h2> <p>The following core-dependencies are required by this artifact. Note that this is <strong>soley for information purpose, those do not have to be installed manually</strong>. For a more detailed list we want to refer to the <a href="../qbf/pyqbf-artifact/-/blob/master/Dockerfile">Dockerfile</a>, where the whole artifact can be reproduced from scratch.</p> <p><strong>PyQBF (<a href="https://gitlab.sai.jku.at/qbf/pyqbf/-/tree/6a6cd1b535597d60895efd4971c7725f16e66eef">Commit 6a6cd1b535597d60895efd4971c7725f16e66eef</a>):</strong></p> <ul> <li><a href="https://pysathq.github.io/" target="_blank" rel="nofollow noreferrer noopener">PySAT</a></li> <li><a href="https://github.com/ltentrup/caqe" target="_blank" rel="nofollow noreferrer noopener">Caqe v4.0.1</a></li> <li><a href="https://github.com/lonsing/depqbf" target="_blank" rel="nofollow noreferrer noopener">DepQBF v6.03</a></li> <li><a href="https://github.com/MikolasJanota/qfun" target="_blank" rel="nofollow noreferrer noopener">QFun v1.0</a></li> <li><a href="https://github.com/fslivovsky/qute" target="_blank" rel="nofollow noreferrer noopener">Qute v1.1</a></li> <li><a href="https://sat.inesc-id.pt/~mikolas/sw/areqs/" target="_blank" rel="nofollow noreferrer noopener">RAReQS v1.1</a></li> <li><a href="https://github.com/maximaximal/QuAPI" target="_blank" rel="nofollow noreferrer noopener">QuAPI</a></li> <li><a href="https://fmv.jku.at/bloqqer/" target="_blank" rel="nofollow noreferrer noopener">Bloqqer</a></li> </ul> <p><strong>Needed for building PyQBF:</strong></p> <ul> <li>GCC for C++ (<a href="https://gcc.gnu.org/" target="_blank" rel="nofollow noreferrer noopener">g++</a>) compiler</li> <li><a href="https://zeromq.org/" target="_blank" rel="nofollow noreferrer noopener">ZeroMQ</a></li> <li><a href="https://www.zlib.net/" target="_blank" rel="nofollow noreferrer noopener">ZLib</a></li> <li><a href="https://cmake.org/" target="_blank" rel="nofollow noreferrer noopener">CMake</a></li> <li><a href="https://www.python.org/downloads/" target="_blank" rel="nofollow noreferrer noopener">Python3</a> and Pip</li> </ul> <p><strong>Needed for the Test-Environment:</strong></p> <ul> <li><a href="https://github.com/marseidl/outer-count" target="_blank" rel="nofollow noreferrer noopener">outer-count</a></li> <li><a href="https://www.sqlite.org/" target="_blank" rel="nofollow noreferrer noopener">Sqlite3</a></li> <li><a href="https://github.com/arminbiere/runlim" target="_blank" rel="nofollow noreferrer noopener">Runlim</a></li> <li><a href="https://gitlab.sai.jku.at/simsala/simsala/-/tree/main">Simsala</a></li> <li><a href="http://www.gnuplot.info/" target="_blank" rel="nofollow noreferrer noopener">Gnuplot</a></li> <li><a href="https://qbf23.pages.sai.jku.at/gallery/" target="_blank" rel="nofollow noreferrer noopener">QBF Gallery 2023</a></li> </ul>
Metadata - Tetrathiafulvalene-based covalent organic framework as high-voltage organic cathodes for lithium batteries
Open the record for dataset details and reuse information.
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
Hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity
<p>Entitled “A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity” for possible publication in Water Resources Research.</p> <p>Data:</p> <p>The Salinized Farmland Flux Station sites are located in the typical irrigated agricultural area of the arid continental monsoon region in northwest China.In this study, we used data from four flux tower located in saline farmland: two for maize (MZ1, MZ2) and two for sunflower (SF1, SF2).</p> <p>For each site, we collected the following variables at half-hourly temporal resolution: (i) latent heat (<em>LE</em>, W m<sup>-2</sup>) fluxes, serving as a direct measure representing the energy component of <em>ET</em> (mm h<sup>-1</sup>), (ii) net radiation (<em>R<sub>n</sub></em>, W m<sup>-2</sup>), (iii) ground heat flux (<em>G</em>, W m<sup>-2</sup>), (iv) solar irradiance (<em>R<sub>s</sub></em>, W m<sup>-2</sup>), (v) air temperature (<em>T</em><sub>a</sub>, °C), (vi) vapor pressure deficit (<em>VPD</em>, KP<sub>a</sub>), (vii) wind speed (<em>U<sub>s</sub></em>, m s<sup>-1</sup>), (viii) relative humidity (<em>RH,</em> %), and (ix) atmospheric carbon dioxide concentration (<em>C<sub>a</sub></em>, mg m<sup>-3</sup>). During the crop growth period, field in-situ measurements of soil and vegetation data from these farmlands are conducted approximately every 10 days. </p> <p>Code:</p> <p>All the codes were executed in Python. The provided hybrid deep learning model code can be run on Jupyter Notebook.</p>
FRAMEWORK FOR A VOLUME MODEL FOR MONOCLINIC AMPHIBOLE - Data and Code
<p>This upload includes the data and code I used to calibrate a volume model for monoclinic amphoboles.</p>
Evaluating Grid Affordability Across Multi-Tier Framework in 48 Sub-Saharan African Countries
<p>This folder supports a master's thesis titled 'Bridging the Energy Divide: Evaluating Grid Affordability and Expansion Feasibility Using a Multi-Tier Framework Across 48 Sub-Saharan African Countries.' It includes all relevant documents, input data, and code for analysing grid electricity affordability in these 48 SSA countries.</p>
Process Integration Multi-Actor Multi-Criteria Optimization (PI-MAMCO) Framework: Case Study on Palm Oil Based Complex (POBC)
<p>The dataset includes the complete workflow for the PI-MAMCO method implementation, demonstrated on a POBC integration case study. The files are structured as follows:</p> <ol> <li> <p><strong>NOTEBOOKS Folder</strong>: This folder contains the Jupyter notebooks that constitute the entire workflow of our case study. These notebooks import necessary model equations, data, and functions from the "MODEL" folder. Running these notebooks with the correct Python package versions will reproduce the exact results presented in our submitted article.</p> </li> <li> <p><strong>MODEL Folder</strong>: This folder includes all model equations, data, and functions required by the Jupyter notebooks. The scripts in this folder are integral to executing the PI-MAMCO method as outlined in our research.</p> </li> <li> <p><strong>OUTPUTS Folder</strong>: The results generated by running the notebooks are saved in this folder. This includes all outputs reported in our manuscript as well as additional results not included in the paper.</p> </li> <li><strong>OUTPUTS_GENERATED Folder</strong>: The files contained in this folder are the expected results from executing the case study notebook.</li> </ol> <p><em>Reproducibility</em>: To ensure the reproducibility of our results, please ensure that you are using the correct versions of the required Python packages. Detailed instructions for setting up the Python environment are provided within the notebook and the readme.txt.</p> <p>Contained are also outputs from the <a href="https://www.sciencedirect.com/science/article/pii/S0957417422003931" target="_blank" rel="noopener">IRenE procedure</a>, used for the literature review (<em>01_extracted_keywords.xlsx</em> ; <em>02_sampling_results.csv</em>)</p>
Spin density studies of tetrahedral Cu(II) ions doped into porous zeolitic imidazolate frameworks
<p><strong>Description of the dataset:</strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements</li> <li>Files are with filename extensions: <strong>spc, par, dsc, dta, out, and oof</strong></li> <li>Information on <strong>origin of the data</strong>:</li> <li>The X-band continuous wave (CW) EPR spectroscopic measurements were generated by EMX spectrometer equipped with ER 4119 HS Resonator. The raw data extensions: spc and par.</li> <li>The Q-band pulsed EPR experiments (Davies and Mims ENDOR) were conducted using a Bruker Elexsys E580 spectrometer with a SuperQ-FT microwave bridge. The raw data extensions: dsc and dta.</li> <li>Periodic geometry optimizations were carried out by using the replicated data-parallel version of CRYSTAL17 code within the framework of Density Functional Theory (DFT) exploiting the hybrid B3LYP method. Molecular cluster calculations were carried out with ORCA (v5.0.2) code.</li> </ul> <ul> <li><strong>Folders are classified based on the type of measurement conducted.</strong></li> </ul> <ul> <li><strong>Additional Information </strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>ZIF</strong> – Zeolitic imidazolate frameworks, <strong>HYSCORE</strong>- Hyperfine sublevel correlation, <strong>ENDOR </strong>- Electron nuclear double resonance, <strong>EDFS </strong>- Echo-detected field sweep, <strong>ESEEM- </strong>electron spin echo envelope modulation, <strong>Act - </strong>activated.</li> <li>definitions of variables: <strong>magnetic field, pulse sequence, microwave power.</strong></li> <li>units of measurement: <strong>Gauss (G), milliTesla (mT), nanosecond(ns), microwave power (dB), Kelvin (K), Cu dopant percentage (%)</strong>.</li> <li>abbreviations on the data filename for pulsed EPR results: Dates, sample name, sample state, type of experiments (including the pulse delay tau or d1), temperature, number of scan, baseline correction if indicated.</li> </ul> </li> </ul>
Sulfidation of a metal-organic framework via heterolytic splitting of organo sulfides at distorted Zr-nodes
<p>Raw Dataset containing all calculation data of the publication "Sulfidation of a metal-organic framework via heterolytic splitting of organo sulfides at distorted Zr-nodes".</p>
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Supporting Material of D2.1: Analysis of the OSTrails Interoperability Framework Tools Compliance
<p>This folder contains <span>the per-tool analyses, tool flashcards, master table, and tool onboarding document that support D2.1 findings.</span></p>
Table II in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
<p>Table II. – AfriBasin size, species and endemics per AfriBasin and different proxies of sampling effort based on 42022 georeferenced records from the RMCA fish collection and GBIF.</p><table><tbody><tr><th>Subbasin</th><th>Size (km 2)</th><th>Species</th><th>Species / 1000 km 2</th><th>Endemics</th><th>Endemics / 1000 km 2</th><th>Records</th><th>Records / 1000 km 2</th><th>Sampling localities</th><th>Sampling loc. / 1000 km 2</th><th>Sampling days</th><th>Sampling days / 1000 km 2</th><th>Expeditions</th><th>Expeditions / 1000 km 2</th></tr></tbody><tbody><tr><th>Middle Congo</th><td>75000</td><td>344</td><td>4.59</td><td>3</td><td>0.04</td><td>3622</td><td>48.29</td><td>172</td><td>2.29</td><td>495</td><td>6.60</td><td>154</td><td>2.05</td></tr><tr><th>Kasai</th><td>290000</td><td>259</td><td>0.89</td><td>23</td><td>0.08</td><td>1630</td><td>5.62</td><td>121</td><td>0.42</td><td>239</td><td>0.82</td><td>138</td><td>0.48</td></tr><tr><th>Pool Malebo</th><td>9500</td><td>254</td><td>26.74</td><td>10</td><td>1.05</td><td>4455</td><td>468.95</td><td>101</td><td>10.63</td><td>518</td><td>54.53</td><td>200</td><td>21.05</td></tr><tr><th>Ubangi</th><td>240000</td><td>248</td><td>1.03</td><td>19</td><td>0.08</td><td>1705</td><td>7.10</td><td>119</td><td>0.50</td><td>164</td><td>0.68</td><td>61</td><td>0.25</td></tr><tr><th>Lower Congo</th><td>50000</td><td>248</td><td>4.96</td><td>53</td><td>1.06</td><td>2233</td><td>44.66</td><td>264</td><td>5.28</td><td>366</td><td>7.32</td><td>131</td><td>2.62</td></tr><tr><th>Lualaba</th><td>323000</td><td>247</td><td>0.76</td><td>18</td><td>0.06</td><td>4354</td><td>13.48</td><td>388</td><td>1.20</td><td>645</td><td>2.00</td><td>226</td><td>0.70</td></tr><tr><th>Aruwimi</th><td>127500</td><td>233</td><td>1.83</td><td>12</td><td>0.09</td><td>1455</td><td>11.41</td><td>150</td><td>1.18</td><td>213</td><td>1.67</td><td>69</td><td>0.54</td></tr><tr><th>Ruki</th><td>177000</td><td>231</td><td>1.31</td><td>9</td><td>0.05</td><td>2211</td><td>12.49</td><td>81</td><td>0.46</td><td>328</td><td>1.85</td><td>103</td><td>0.58</td></tr><tr><th>Itimbiri</th><td>55000</td><td>228</td><td>4.15</td><td>4</td><td>0.07</td><td>1548</td><td>28.15</td><td>26</td><td>0.47</td><td>108</td><td>1.96</td><td>50</td><td>0.91</td></tr><tr><th>Upper Lualaba</th><td>144000</td><td>219</td><td>1.52</td><td>28</td><td>0.19</td><td>3449</td><td>23.95</td><td>371</td><td>2.58</td><td>644</td><td>4.47</td><td>160</td><td>1.11</td></tr><tr><th>Sangha</th><td>180000</td><td>215</td><td>1.19</td><td>8</td><td>0.04</td><td>2012</td><td>11.18</td><td>181</td><td>1.01</td><td>195</td><td>1.08</td><td>72</td><td>0.40</td></tr><tr><th>Uélé</th><td>122000</td><td>195</td><td>1.60</td><td>7</td><td>0.06</td><td>1104</td><td>9.05</td><td>85</td><td>0.70</td><td>184</td><td>1.51</td><td>83</td><td>0.68</td></tr><tr><th>Lindi-Tshopo</th><td>54000</td><td>182</td><td>3.37</td><td>1</td><td>0.02</td><td>931</td><td>17.24</td><td>86</td><td>1.59</td><td>128</td><td>2.37</td><td>47</td><td>0.87</td></tr><tr><th>Lomami</th><td>74000</td><td>181</td><td>2.45</td><td>1</td><td>0.01</td><td>738</td><td>9.97</td><td>151</td><td>2.04</td><td>161</td><td>2.18</td><td>44</td><td>0.59</td></tr><tr><th>Marine Lower Congo</th><td>10000</td><td>164</td><td>16.40</td><td>8</td><td>0.80</td><td>1421</td><td>142.10</td><td>104</td><td>10.40</td><td>192</td><td>19.20</td><td>123</td><td>12.30</td></tr><tr><th>Mweru</th><td>94000</td><td>163</td><td>1.73</td><td>29</td><td>0.31</td><td>3376</td><td>35.91</td><td>327</td><td>3.48</td><td>569</td><td>6.05</td><td>155</td><td>1.65</td></tr><tr><th>Léfini-Likouala</th><td>110000</td><td>140</td><td>1.27</td><td>10</td><td>0.09</td><td>2278</td><td>20.71</td><td>128</td><td>1.16</td><td>195</td><td>1.77</td><td>50</td><td>0.45</td></tr><tr><th>Sankuru</th><td>130000</td><td>119</td><td>0.92</td><td>10</td><td>0.08</td><td>659</td><td>5.07</td><td>33</td><td>0.25</td><td>80</td><td>0.62</td><td>34</td><td>0.26</td></tr><tr><th>Tumba</th><td>6500</td><td>110</td><td>16.92</td><td>3</td><td>0.46</td><td>357</td><td>54.92</td><td>12</td><td>1.85</td><td>67</td><td>10.31</td><td>23</td><td>3.54</td></tr><tr><th>Bangweulu</th><td>110000</td><td>103</td><td>0.94</td><td>1</td><td>0.01</td><td>1325</td><td>12.05</td><td>172</td><td>1.56</td><td>190</td><td>1.73</td><td>70</td><td>0.64</td></tr><tr><th>Kwilu</th><td>85000</td><td>86</td><td>1.01</td><td>5</td><td>0.06</td><td>130</td><td>1.53</td><td>19</td><td>0.22</td><td>34</td><td>0.40</td><td>26</td><td>0.31</td></tr><tr><th>Kwango</th><td>195000</td><td>84</td><td>0.43</td><td>10</td><td>0.05</td><td>299</td><td>1.53</td><td>24</td><td>0.12</td><td>33</td><td>0.17</td><td>18</td><td>0.09</td></tr><tr><th>Mai-Ndombe</th><td>40000</td><td>72</td><td>1.80</td><td>8</td><td>0.20</td><td>89</td><td>2.23</td><td>27</td><td>0.68</td><td>16</td><td>0.40</td><td>25</td><td>0.63</td></tr><tr><th>Lukenie</th><td>76500</td><td>51</td><td>0.67</td><td>2</td><td>0.03</td><td>275</td><td>3.59</td><td>18</td><td>0.24</td><td>32</td><td>0.42</td><td>21</td><td>0.27</td></tr><tr><th>Mongala</th><td>45000</td><td>41</td><td>0.91</td><td>0</td><td>0</td><td>93</td><td>2.07</td><td>9</td><td>0.20</td><td>17</td><td>0.38</td><td>14</td><td>0.31</td></tr><tr><th>Lulonga</th><td>67000</td><td>20</td><td>0.30</td><td>0</td><td>0</td><td>80</td><td>1.19</td><td>13</td><td>0.19</td><td>17</td><td>0.25</td><td>12</td><td>0.18</td></tr><tr><th>Kotto</th><td>71000</td><td>19</td><td>0.27</td><td>1</td><td>0.01</td><td>126</td><td>1.77</td><td>15</td><td>0.21</td><td>12</td><td>0.17</td><td>16</td><td>0.23</td></tr><tr><th>Bomu</th><td>130000</td><td>14</td><td>0.11</td><td>1</td><td>0.01</td><td>67</td><td>0.52</td><td>11</td><td>0.08</td><td>14</td><td>0.11</td><td>9</td><td>0.07</td></tr></tbody></table>
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
Underlying data for "Development of a framework of potential adverse effects of interventions to improve critical thinking about health choices: A mixed methods study."
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Supporting data for 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator'
<p>Supporting data for the preprint 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator' published at ChemRxiv (doi://10.26434/chemrxiv-2024-x62zz)</p> <p> </p>
Experimental and theoretical data generated on optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size
<p>Open access to experimental data generated by the project <a href="https://www.electro-intrusion.eu/en">Electro-Intrusion</a> (101017858, Horizon 2020, European Union) along with the research on the mechanical behaviour of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2).</p> <p>Underlying data for the publication Johnson, L. J. W. et al. Optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size: The role of hydrogen bonding between intruded and bulk liquid. Journal of Colloids and Interface Science 2023, 645, 775-783. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jcis.2023.04.059" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jcis.2023.04.059</span></span></a>. Data related to Figures 2, 4, 5 and S1 in the article.</p>
Theoretical and experimental data on supercritical water confined in hydrophobic metal organic frameworks.
<p>/* **********<br>/* This work is licensed under a Creative Commons Attribution 4.0 International License.<br>/* **********<br> <br>Open access to experimental and theroretical data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). <br>Underlying data for the publication Merchiori, S. et al. Mild-Temperature Supercritical Water Confined in Hydrophobic Metal Organic Frameworks. The Journal of American Chemistry Society 2024, 146, 19, 13236. doi: <a title="DOI URL" href="https://doi.org/10.1021/jacs.4c01226">https://doi.org/10.1021/jacs.4c01226</a></p> <p> Data related to Figures 1, 2, 3, 4 in the article and figure S13 and S18 ins the Supporting Information.<br> </p>
RatXcan: A framework for cross-species integration of genome-wide association and gene expression data
<p>Data for paper</p> <p><span>RatXcan: A framework for cross-species integration of genome-wide association and gene expression data</span></p> <p><span>Natasha Santhanam</span><span><span>1</span></span><span><span>†</span></span><span>, Sandra Sanchez-Roige</span><span><span>2,3,4</span></span><span><span>†</span></span><span>, Sabrina Mi</span><span><span>2</span></span><span>, Yanyu Liang</span><span><span>1</span></span><span>, Apurva S. Chitre</span><span><span>2</span></span><span>, Daniel Munro</span><span><span>2</span></span><span>, Denghui Chen</span><span><span>2</span></span><span>, Riyan Cheng</span><span><span>2</span></span><span>, Jianjun Gao</span><span><span>2</span></span><span>, Angel Garcia-Martinez</span><span><span>6</span></span><span>, Anthony M. George</span><span><span>5</span></span><span>, Alexander F. Gileta</span><span><span>2</span></span><span>, Wenyan Han</span><span><span>6</span></span><span>, Katie Holl</span><span><span>7</span></span><span>, Alesa Hughson</span><span><span>8</span></span><span>, Christopher P. King</span><span><span>9</span></span><span>, Alexander C. Lamparelli</span><span><span>9</span></span><span>, Connor D. Martin</span><span><span>5</span></span><span>, Festus Nyasimi</span><span><span>1</span></span><span>, Celine L. St. Pierre</span><span><span>2</span></span><span>, Sarah Sumner</span><span><span>1</span></span><span>, Jordan Tripi</span><span><span>9</span></span><span>, Tengfei Wang</span><span><span>6</span></span><span>, Hao Chen</span><span><span>6</span></span><span>, Shelly Flagel</span><span><span>8</span></span><span>, Keita Ishiwari</span><span><span>5,10</span></span><span>, Paul Meyer</span><span><span>5,9</span></span><span>, Oksana Polesskaya</span><span><span>2</span></span><span>, Laura Saba</span><span><span>11</span></span><span>, Leah C. Solberg Woods</span><span><span>12</span></span><span>, Abraham A. Palmer</span><span><span>2,3</span></span><span>*, Hae Kyung Im</span><span><span>1</span></span><span>*</span></p> <p><span> </span></p> <p><span>[1] Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA</span></p> <p><span>[2] Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[3] Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[4] Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA</span></p> <p><span>[5] University at Buffalo, Clinical and Research Institute on Addictions University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[6] University of Tennessee Health Science Center, Department of Pharmacology, Addiction Science and Toxicology, Memphis, TN, 38120, USA</span></p> <p><span>[7] Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, 53226, USA</span></p> <p><span>[8] University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA</span></p> <p><span>[9] University at Buffalo, Department of Psychology, Buffalo, NY, 14260, USA</span></p> <p><span>[10] University at Buffalo, Pharmacology and Toxicology University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[11] University of Colorado Anschutz Medical Campus, Department of Pharmaceutical Sciences, Aurora, CO 80045, USA</span></p> <p><span>[12] Wake Forest University School of Medicine, Department of Internal Medicine, Winston-Salem, NC, 27157, USA</span></p>
Dataset: Towards a Knowledge Management Framework for LLM-Generated Personas in Collaborative Systems
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Theoretical Design and Synthesis of Metal-Inorganic Frameworks Using Host Atom-Centered Building Blocks for Efficient Catalysis with Diverse Reactive Sites
<p><strong>ML_ABN</strong> is a necessary file for training machine learning potentials using VASP. It contains the structural details and energies of the training set. <strong>ML_FFN</strong> is the force field file obtained after training. <strong>Test_DataSet.zip</strong> contains the test set with 900 structures, and <strong>Test_DataSet_Energy.xlsx</strong> stores the energies of these structures after being evaluated by DFT.</p>
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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.