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Dataset and Model for Data Science Projects

This zenodo is for the data science projects hosted in this GitHub repository: https://github.com/oathaha/data-science-portfolio/tree/main

opengpl-3.0-or-laterSep 2024View details →

Codes and data upload for Mesocircuit Model project

<h3>Information</h3> This resource contains source codes and data required to produce the figures from: Senk, J., Hagen, E., van Albada, S. J., & Diesmann, M. (2024). Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space. arXiv preprint arXiv:1805.10235v3 For only source codes, see: Senk, J., & Hagen, E. (2024). Mesocircuit Model (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.13798936" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13798936</a> <div> <h3>Instructions</h3> To use this resource, download the mesocircuit-model-1.0.0.tar.gz file. Then, extract the contents of the archive and run the following command in the extracted directory: <div> </div> <div>$ # unzip the archive</div> <div>$ tar -xzf mesocircuit-model-1.0.0.tar.gz</div> <div>$ # create and activate the conda environment</div> <div>$ cd mesocircuit-model</div> <div>$ conda env create -f environment.yml</div> <div>$ conda activate mesocircuit</div> <div>$ # run the figure-generation scripts</div> <div>$ cd scripts</div> <div>$ python ms_figures_simulations.py</div> <div>$ python run_mesocircuit_lfps.py</div> <div> </div> <div>$ # deactivate environment</div> <div>$ conda deactivate </div> </div> <div> </div> <div>Installers for conda may be obtained e.g., from https://github.com/conda-forge/miniforge.  <div>The figures produced by ms_figures_simulations.py will be saved in the `ms_figures` directory.</div> <div>The figures produced by run_mesocircuit_lfps.py will be saved in mesocircuit_data/mesocircuit_MAMV1/dd1dcbd4034fbd4f689fbdd2ff7abb3b/lfp/figures.</div> </div>

opencc-by-4.0May 2024View details →

Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."

Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. 10 272 (2024). Version 2 contains additional results relating to Ni-rich systems. Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi. Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.

Data used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024)

This dataset contains the processed EMAC simulation used as input to the clustering algorithm and the resulting regimes discussed in Li et al. (Atmos. Chem. Phys., 2024).

opencc-by-4.0Feb 2024View details →

Data and model output for "Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation"

Data, model output, and scripts supporting the manuscript: Litwin, D. G., & Harman, C. J. (2024) Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation. Water Resources Research, 60, e2024WR037301. https://doi.org/10.1029/2024WR037301

Training data for IoNNo model

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Prosit-Cit Model Data

Training data used to train retention time and fragment ion intensity Prosit-Cit models.

Dataset from TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]

TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science] Pre-publicatoin upload for VLDB review. Code is at https://github.com/RelationalAI/annotated-tables/. The Github repository name is from a previous draft version and cannot be changed. It is code for TableLabler.

Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS

This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.).

Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"

Data and codes for the manuscript entitled: "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"   Please see Readme.txt for details

Simulation data of Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc

This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc". Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations. The data is provided in a zip archive. After extracting you find a short README.txt with further hints on the structure and available data.

Model results and data associated with "Antecedent effect models as an exploratory tool to link climate drivers to herbaceous perennial population dynamics data"

Model results and data (including Bayesian posteriors) associated with "Antecedent effect models as an exploratory tool to link climate drivers to 3 herbaceous perennial population dynamics data". This is a repository created to store the posteriors of the models fit within this project. Because these occupy so much space, it makes sense to store them in a separate repository. There are two directories: <ul> <li>model_results/ contains all of the posteriors (files with character pattern main_posterior_#.csv). The three types of files contained in this directory are described in metadata_model_results.xlsx. The number # corresponds to column "index" in file raw_data/design_insample.csv.</li> <li>raw_data/ is mostly not essential: it contains the raw data to fit models, and it replicates folder data/ in repository https://dx.doi.org/10.5281/zenodo.13909628.</li> </ul>

PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"

The dataset contains 4 different files:  <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>

openodc-byMar 2022View details →

Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion

Data from: Data-driven analysis of oscillations in Hall thruster simulations   - Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino - Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a> - Date: 2022-03-24 - Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis - Version: 1.0.4 - Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a> - License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a>   Abstract   This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 Plasma Sources Sci. Technol. 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>. Additionally, the raw simulation data is also employed in the following journal publication: Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 Mach. Learn.: Sci. Technol. 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a>   Dataset description   The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>. Please refer to the relative article for further details regarding any of the parameters and/or configurations.   Data files   The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended. The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as: <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables. The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.   Citation   Works using this dataset or any part of it in any form shall cite it as follows. The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available. Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.   Acknowledgments   This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

opencc-by-4.0Dec 2023View details →

Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models

This repository contains data and models used in the following paper. Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. Nature Machine Intelligence, 2024. The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>. The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1). Data   1_training_data: The Acinetobacter baumannii inhibition data used to train antibiotic property prediction models.   2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.   4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).   5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.   6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict A. baumannii inhibition.   7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict A. baumannii inhibition.   8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict A. baumannii inhibition.   9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine. The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.

Data and models for Project 2 funded by FERSC in year 2023

Data for FY23~24 FERSC Project 2: Intermodal solutions for freight flows in Southwest U.S

Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)

This dataset contains <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis. The corresponding publication is: Holthusen, H., Brepols, T., Linka, K., & Kuhl, E.. Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.   Standalone_Materialroutine <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul>   TensorFlow <ul> <li> iCANN: <ul> <li> 01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization </li> <li> 01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization </li> <li> 02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function </li> <li> installed_packages: File containing a list of installed Python modules used to implement the iCANN </li> </ul> The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same. The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.   The experimental data for the cross and stripe specimen are taken from the literature: Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020). Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. Journal of biomechanical engineering, 142(7), 071011. <a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a>

Data: Social tolerance and role model diversity increase tool use learning opportunities across chimpanzees ontogeny.

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opencc-by-4.0Jul 2024View details →

Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés

The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.

How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations: Data and Analysis

Dataset includes simulation output produced by a TERRA simulation for `How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations'.    <h3>Description of data file contents</h3> <ul> <li>NC*comp.tar.gz - compressed archives containing NetCDF files (file-per-process) with TERRA grid data including temperature, velocity, interpolated bulk composition, denisty, and voscosity fields. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>. *dump number</li> <li>NC_seis_037.tar.gz - compressed archive containing NetCDF files (file-per-process) with predicted seismic properties at the resolution of the TERRA grid generated from the present day state of the simulated mantle, including elastic and anelastic Vs and Vp, bulk sound velocity, and predicted density from mineral phyiscs tables. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li>NC_hpes_037.tar.gz - compressed archive containing NetCDF files (file-per-process) with interpolated abundances at the resolution of the TERRA grid for isotopes including the heat-producing elements ^40^K, ^232^Th, ^235^U and ^238^U. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li>P_files_037.tar.gz - compressed archive of TERRA P-files (particle files).</li> <li>C_files_037.tar.gz - compressed archive of TERRA C-files (grid state files) - together with the P-files describe the full present day state of the simulation. </li> <li>seis_filtered_037.tar.gz - compressed archive (file-per-layer) with reparameterised and seismically filtered (against S40RTS) present day Vs field.</li> <li>seis_tables.tar.gz - compressed archive contianing lookup tables of seismic properties for the 3 principal lithologies assumed in the TERRA simulation (harzburgite, lherzolite and basaltic crust).</li> <li>density_037.sph - Spherical harmonic coefficients for the density field in format to be read by the <a title="propagator" href="https://zenodo.org/records/12696774" target="_blank" rel="noopener">propagator matrix code.</a></li> <li>plumes.pkl, ridges.pkl - Files containing tracer particle information for particles associated with plumes and ridges. </li> <li>plumes_ridges.py - Python script containing example code for reading and plotting plumes.pkl and ridges.pkl files. </li> <li>ptcls_rdgs_plms.py, interrogate_particles.py - Python script and module containing required functions for carrying out post processing routine generating the plumes.pkl and ridges.pkl files. Requires <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li>hst.dat - Time series of key simulation properties including mantle temperature profile used for calcualting CMB heat flux. </li> <li>terra, interra - TERRA executable and input parameter file.</li> <li>pyflowng.zip - Compressed directory containing version of the `pyflowng` code used in this work.</li> <li>mode_splitting_methods.zip - Compressed directory contianing synthetic splitting function predictions and maps.</li> </ul>   <h3>Dump Numbers</h3> Below is a table of dump numbers (final three digits of file names) and the corresponding model times. <table> <tbody> <tr> <td>Dump number </td> <td>Model time (Ma)</td> </tr> <tr> <td>037</td> <td>0 (present day)</td> </tr> <tr> <td>027</td> <td>10</td> </tr> <tr> <td>026</td> <td>20</td> </tr> <tr> <td>025</td> <td>30</td> </tr> <tr> <td>024</td> <td>40</td> </tr> <tr> <td>023</td> <td>50</td> </tr> <tr> <td>022</td> <td>60</td> </tr> <tr> <td>021</td> <td>70 </td> </tr> <tr> <td>020</td> <td>80</td> </tr> <tr> <td>019</td> <td>90</td> </tr> <tr> <td>018</td> <td>100</td> </tr> </tbody> </table>