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opencc-by-4.0Aug 2024View details →

Radiation exposure and shielding effects on the lunar surface - supplementary data

<p>Supplementary data for submission of "Radiation exposure and shielding effects on the lunar surface"</p>

opencc-by-4.0Aug 2024View details →

Supplementary data: ONT for Listeria monocytogenes outbreak detection

<p>Supplementary data for the publication "Oxford Nanopore’s 2024 sequencing technology for Listeria monocytogenes outbreak detection and source attribution: progress and clone-specific challenges"</p> <ol> <li><em>L. monocytogenes </em>assemblies generated from  <ul> <li>Illumina-only data (shovill)</li> <li>ONT-only data (100x coverage, sup@v5.0 + flye + flye polisher + medaka [model r103_min_high_g360])</li> <li>ONT + Illumina data (100x ONT coverage, sup@v5.0 + flye + flye polisher + polypolish)</li> </ul> </li> <li>Raw ONT pod5 data from three<em> L. monocytogenes </em>isolates (N19-1094, N21-2956, N23-1883) with GAAG6mAC / GT4mCTTC methylations, among other methylated motifs</li> </ol>

LLAMAFUZZ real-world programs supplementary experiment data

<p>This is the raw experimental data of LLAMAFUZZ and AFL++ on a set of real-world programs. We choose FuzzBench as running framework to ensure the fairness.</p>

Supplementary data for the doctoral thesis "Graph decomposition techniques for quantum spin systems with multi-spin interactions"

<p>This upload contains data for the doctoral thesis of Matthias Mühlhauser with the title "Graph decomposition techniques for quantum spin systems with multi-spin interactions".  <br>This doctoral thesis has been written at Friedrich-Alexander-Universität Erlangen-Nürnberg under the supervision of Professor Kai P. Schmidt.</p> <p>The given folders contain: </p> <p>XC-HPF.zip:<br>        The series expansion about the high-field limit of the X-Cube model in a general parallel field.<br>        <br>NLCE-TFIM-LF.zip:<br>        The NLCE data for the TFIM in the low-field limit on the triangular and on the square lattice.<br><br>All folders contain a file README.txt explaining the provided data.</p>

Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019)

<p>Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019) by </p> <p><span>Adam R. Mason <sup>1,a</sup>, Tom Bide <sup>2,b</sup>, Junyang Wang <sup>3,c</sup>, John Morley <sup>4,d</sup>, Mohit Arora <sup>5,e</sup>, Alperen Yayla<sup>1,f</sup>, Julia A. Stegemann <sup>5,g</sup>, Rupert J. Myers <sup>1,h,*</sup></span></p> <p><span> </span></p> <p><sup><span>1</span></sup><span> Department of Civil and Environmental Engineering, Imperial College London, UK</span></p> <p><sup><span>2</span></sup><span> British Geological Survey, UK</span></p> <p><sup><span>3 </span></sup><span>Department of Mathematics, Imperial College London, UK</span></p> <p><sup><span>4</span></sup><sub><span> </span></sub><span>Department of Earth Science and Engineering, Imperial College London, UK</span></p> <p><sup><span>5</span></sup><span> School of Engineering, King’s College London, UK</span></p> <p><sup><span>6</span></sup><span> Department of Civil, Environmental and Geomatic Engineering, University College London, UK</span></p> <p><span> </span></p> <p><span>Author e-mails: <sup>a </sup></span><a href="mailto:a.mason19@imperial.ac.uk"><span>a.mason19@imperial.ac.uk</span></a><span>,<sup> b </sup></span><a href="mailto:tode@bgs.ac.uk"><span>tode@bgs.ac.uk</span></a><span>,<sup> c </sup></span><a href="mailto:junyang.wang21@imperial.ac.uk"><span>junyang.wang21@imperial.ac.uk</span></a><span>,<sup> d </sup></span><a href="mailto:john.morley18@imperial.ac.uk"><span>john.morley18@imperial.ac.uk</span></a><span>,<sup> e </sup></span><a href="mailto:mohit.arora@kcl.ac.uk"><span>mohit.arora@kcl.ac.uk</span></a><span>,<sup> f </sup></span><a href="mailto:a.yayla22@imperial.ac.uk"><span>a.yayla22@imperial.ac.uk</span></a><span>,<sup> g </sup></span><a href="mailto:j.stegemann@ucl.ac.uk"><span>j.stegemann@ucl.ac.uk</span></a><span>; *corresponding author:<sup> h</sup> </span><a href="mailto:r.myers@imperial.ac.uk"><span>r.myers@imperial.ac.uk</span></a></p> <p> </p>

Supplementary data for the publication: 'Impact of ice topography, basal channels and subglacial discharge on basal melting under the floating ice tongue of 79N Glacier, northeast Greenland'

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Supplementary data: model results for "Labor market evolution is a key determinant of global agroeconomic and environmental futures"

<p>This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "<strong>Labor market evolution is a key determinant of global agroeconomic and environmental futures</strong>".</p> <p>The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-nc2024-LandBasedCDR-GCAM" target="_blank" rel="noopener">realxinzhao/paper-nc2024-LandBasedCDR-GCAM</a>.</p> <p>In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems" target="_blank" rel="noopener">realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems</a>.</p>

Supplementary data for "Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2"

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Supplementary data for the publication - 79NG & GROCE & GIPM

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Supplementary Material for "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis"

<p>Supplementary material for the paper "Integrating Security-Enriched Data Flow Diagrams Into Architecture-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://dataflowanalysis.org</p>

Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)

<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>

Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)

<p>This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation. </p> <p>The dataset includes:</p> <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>

Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)

<p>This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel. </p> <p>The dataset used for rock parameter statisitcs includes:</p> <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>

Supplementary Information Table S4. Smith Mountain Lake (44PY152) SP-TOF-ICPMS nanoparticle data.

<p>Supplementary information for:</p> <p><a name="_Hlk153447424"></a><strong><span>12,800-year-old</span></strong><span><strong><span> </span></strong></span><span><strong><span>Microspherule Layer useful as a Chronostratigraphic Datum at Smith Mountain Lake (44PY152), USA</span></strong></span></p> <p> </p> <p> </p>

The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling -- Supplementary Data

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Supplementary Data for Provenance, Environments, and Tectonic Signatures of Tanjong-Sandakan Formations, Northeast Sabah, Malaysia: Implications for Sedimentology, Palynology, and Geochemistry

<p>This dataset includes supplementary sedimentary facies tables and geochemical analysis data used in the manuscript titled <strong>Provenance, Environments, and Tectonic Signatures of Tanjong-Sandakan Formations, Northeast Sabah, Malaysia: Implications for Sedimentology, Palynology, and Geochemistry</strong>.</p>

openapache2.0Oct 2024View details →

Supplementary Data "Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization"

<p>Pe-computed data for re-producing the results of "Robustness and Generalization in Quantum<br>Reinforcement Learning via Lipschitz Regularization".</p> <p>See also the GitHub repository https://github.com/nicomeyer96/regularized-qpg </p>

[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.

<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines.  These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems.  The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory.  A brief description of the folders and what they contain are provided below:    </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│   ├── _PM_AI_Models/</div> <div>│   │    ├── Type1/</div> <div>│   │    ├── Type2/</div> <div>│   │    └── Type3/</div> <div>│   │</div> <div>│   ├── _PM_AI_Module/</div> <div>│   │    ├── __init__.py</div> <div>│   │    └── PM_Methods.py</div> <div>│   │</div> <div>│   ├── _PM_JL_Data/</div> <div>│   ├── C1_Model/</div> <div>│   ├── C2_Model/</div> <div>│   ├── C3_Model/</div> <div>│   ├── M1_Model/</div> <div>│   ├── M2_Model/  </div> <div>│   └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│   ├── Sensitivity_Plots/</div> <div>│   ├── Sensitivity_PM_JL_Data/</div> <div>│   ├── get_sensitivity_PMJL_data.py</div> <div>│   └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│   ├── BaseModel_A/</div> <div>│   ├── BaseModel_B/  </div> <div>│   └── BaseModel_C/</div> <div>│  </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1.      </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a)  <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b)   It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2.      </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a)      <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b)       <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3.      </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/:  </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis.  PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders.  </div> <div>   </div> <div><strong>3. _PM_JL_Data/:  </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format.  </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div>   </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/:  </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders:  </div> <div>   - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong>  </strong>- <em>PredictionPlots/</em>: Stores model prediction results.  </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div>   This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div>   </div> <div><strong>2. Scripts:  </strong></div> <div>   - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details).  </div> <div>   - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript.  </div> <div>   </div> <div><strong>3. Subfolders:</strong>  </div> <div>   - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis.  </div> <div>   - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results:  </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript.  The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis.  </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>

Supplementary, raw data, and model for manuscript titled Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model

<p>This file contains 1. raw data simulated by WRF and PALM, and LiDAR and surface station measurements (<a href="https://zenodo.org/api/records/13959541/draft/files/Raw%20data%20PALM_WRF_Obs.zip/content" target="_blank" rel="noopener noreferrer">Raw data PALM_WRF_Obs.zip</a>); 2. supplementary drawing modeled and simulated U10, T2, and RH2 time series at surface stations (<a href="https://zenodo.org/api/records/13959541/draft/files/Supplementary.docx/content" target="_blank" rel="noopener noreferrer">Supplementary.docx</a>); 3. WRF version containing the TKE-ACM2+BEP (<a href="https://zenodo.org/api/records/13959541/draft/files/WRF433_TKE-ACM2+BEP.tar.gz/content" target="_blank" rel="noopener noreferrer">WRF433_TKE-ACM2+BEP.tar.gz</a>).</p>