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31 results for “python scripts”
E3SM simulation results and associated python analysis scripts
<p>This archive contains E3SM Land Model simulation results associated with the <em>Journal of Advances in Modeling Earth Systems (JAMES)</em><em> </em>article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.</p>
HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"
<p>HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"</p> <p>RBW simulation datasets in HDF5 format:</p> <ul> <li>300pT.h5 The particle dataset to generate the figures.</li> </ul> <p>Python scripts to generate figures in the manuscript.</p> <p>- Environment: Python 3.6.7 :: Anaconda 4.4.0 (64-bit)</p> <p>- Required modules: matplotlib, numpy, h5py</p> <ul> <li>Figure1.py Generate figure 1.</li> <li>Figure2.py Generate figure 2.</li> <li>Figure3.py Generate figure 3.</li> <li>Figure4.py Generate figure 4.</li> <li>QLDe.py Calculate bounce averaged diffusion coefficients according to Shprits et al. (2006) (doi: https://doi.org/10.1029/ 2006JA011725).</li> </ul> <p> </p>
Dataset of Jupyter Notebooks from the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<pre>This archive contains the dataset of properly-licensed Jupyter notebooks from the MSR'22 paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts". The dataset contains 847,881 notebooks stored in the PostgreSQL dump file. You can find the details about the database in the README file. To transform the notebooks into this convenient format and to calcuate the structural metrics, we used our library called Matroskin, which can be found here: <a href="https://github.com/JetBrains-Research/Matroskin">https://github.com/JetBrains-Research/Matroskin</a>. </pre>
Python scripts / Jupyter Notebooks and data for training segmentation models on slide scans of diatom preparations from river Menne
<p>This archive contains the Jupyter Notebooks and data used for the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>The notebooks are numbered according to the order in which they are to execute. Please refer to the comments and documentation within the notebooks as well as to the manuscript for details. The data (image data, mask data & segmentation ground truth in COCO format for several different tiling strategies) is stored in separate subfolders corresponding with data usage (model training, validation, test) and tiling strategy. Please refer to the "readme" files for detailed information.</p> <p> </p> <p> </p>
Python scripts and datasets used in the article "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients"
<p>This package includes datasets and python scripts used in the analysis and creation of figures in the A&A paper "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients" (Wichern et al. 2024).</p>
Data and Python scripts for "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic"
<p>Data and Python scripts for reproducing the figures and results included in the manuscript "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic".</p> <p>Figures 1-4 are produced via respective Python codes. Heatwave magnitude index daily (HWMId) for ERA5-Land is available from. hw_era5land.nc file. HWMId fields for CMIP6 models are included in cmip6_hwmid.zip. The underlying data behind the figures 1-4 are included in data_to_produce_figs.zip.</p> <p>The paper is published in Rantanen, M., Kämäräinen, M., Luoto, M. <em>et al.</em> Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic. <em>Commun Earth Environ</em> <strong>5</strong>, 570 (2024). https://doi.org/10.1038/s43247-024-01750-8</p>
3D CAD models exemples to run "ArtificialReef_Complexity" Python script (STL files)
<p>Here you will find 3D CAD models in STL format.</p> <p>These are 3D CAD models of fractal pyramid.</p> <p>These STL files can be used as an example to run the Python script "ArtificialReef_Complexity: v.1.3" available on GitHub (<a href="https://github.com/ELI-RIERA/ArtificialReef_Complexity/tree/V1.3">https://github.com/ELI-RIERA/ArtificialReef_Complexity/tree/V1.3</a>)</p>
Custom made python script using network assignment and scoring to estimate the impact of biological processes.
Open the record for dataset details and reuse information.
Type Ia supernovae from non-accreting progenitors: data, python scripts and mesa inlists
<p>This release contains the inlists and final profiles described in: Antoniadis et al., "Type Ia supernovae from non-accreting progenitors" Mesa v. 10398</p>
Data and Python script for article "A text mining analysis of the climate change literature in industrial ecology'
<p>The data and Python script are part of the forum article "A text mining analysis of the climate change literature in industrial ecology" authored by Dayeen, F.R., Sharma, A.S., and Derrible, S., and published in the <em>Journal of Industrial Ecology</em> in 2020.</p> <p>The Python script and instructions are included in the LiTCoF_v1.00-py.zip file. The original data is available in two formats: .csv and .pkl.</p> <p>Updates of the script will be posted at https://github.com/csunlab/LiTCoF and at https://csun.uic.edu/codes/LiTCoF.html. The data is also available at https://csun.uic.edu/datasets.html#AbstractsIE.</p> <p>Feel free to contact any of the authors for information and questions about the data and code.</p>
Python scripts for input and post-processing of fuzz sputtering TRI3DYN simulations
<p>The influence of a fuzzy surface on the physical sputtering of Mo in He plasmas has been studied with hyperspectral imaging (HSI) measurements and simulations that couple the TRI3DYN code with an impurity transport code. The 2D profiles of the Mo I line emission intensity from HSI images reveal that the sputtering yield, Y, is reduced to ~40% of the smooth-surface value due to the presence of a fuzz layer, while the angular distribution of the sputtered Mo atoms might not change significantly. The simulations reproduce the Y reduction successfully, but indicate that fuzz causes an increase in the small-angle distribution of sputtered atoms. However, the increase is too small to produce an observable change in the Mo I emission profiles. A simple analytical model that assumes a single collision mean free path for a fuzz layer and considers only the primary sputtering events qualitatively reproduces the Y reduction and the small-angle distribution enhancement, explaining the geometrical effect of fuzz on physical sputtering.</p>
Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"
<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al. (2022): "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length".</p>
Eurasian cooling paper datasets and python scripts
<div>This repository contains model output datasets and python scripts that I used tp produce figures those presented in this study.</div> <div> <div> </div> </div>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere".</p> <p>Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p> </p> <ul> <li>Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</li> <li>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt.</li> <li>Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt.</li> <li>Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from <a href="https://swarm-diss.eo.esa.int/">https://swarm-diss.eo.esa.int/</a> and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].</li> </ul>
Python scripts for input and post-processing of fuzz sputtering TRI3DYN simulations
Open the record for dataset details and reuse information.
Python Script used in: Evolution of nuptial gifts and its coevolutionary dynamics with male-like persistence traits of females for multiple mating
<p>Many male animals donate nutritive materials during courtship or mating to their female mates. Donation of large-sized gifts, though costly to prepare, can result in increased sperm transfer during mating and delayed remating of the females, resulting in a higher paternity. Nuptial gifting sometimes causes severe female-female competition for obtaining gifts (i.e., sex-role reversal in mate competition) and female polyandry, changing the intensity of sperm competition and the resultant paternity gains. We built a theoretical model to analyze such coevolutionary feedbacks between nuptial gift size (male trait) and propensity for multiple mating (female trait). Our genetically explicit, individual-based computer simulations demonstrate that a positive correlation between donated gift size and the resultant paternity gain is a requisite for the co-occurrence of large-sized gifts and females' competitive multiple mating for the gifts. When gift donation imposes monandry, exaggeration of nuptial gift size also occurs under the assumption that the last male monopolizes paternity, although it reduces mating opportunities, also occurs under the assumption that the last male monopolizes paternity. We also analyzed the causes and consequences of the evolution of a female persistence trait in trading of nuptial gifts, that is, double receptacles for nuptial gifts known to occur in an insect group with a "female penis" (<em>Neotrogla</em> spp.).</p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
Optical constants, cross-sections, and supporting Python scripts for Fe L shell XAFS compounds in Corrales et al (2024), accepted to AAS Journals
<p>This Zenodo repository contains the data products and calculations of Corrales et al. (2024), https://arxiv.org/abs/2402.06726 (accepted to AAS Journals)</p> <p> </p> <p><strong>A WORD OF CAUTION</strong></p> <p>The cross-sections presented here have not been shifted in absolute energy scale. One of the key results from Corrales et al. (2024) is that the energy scale calibration for these compounds needs to be revisited. Please proceed with caution when using this information.</p> <p> </p> <h2>Optical Constants</h2> <p>kkcalc_products/ - This folder contains optical constants for the various compounds</p> <p>kkcalc_products/*_input.dat files contain the absorption as measured in Lee et al. (2009) https://ui.adsabs.harvard.edu/abs/2009ApJ...702..970L/abstract). These values are supplied as input to kkcalc (https://ui.adsabs.harvard.edu/abs/2014OExpr..2223628W/abstract, available at https://github.com/benajamin/kkcalc), along with the stoichiometric formula and material density for the compound of interest.</p> <p>kkcalc_products/*_refrac.dat files contain the kkcalc output, i.e., the real and imaginary parts of the complex index of refraction (m). The "Delta" column equals Re(1-m) and the "Beta" column equals Im(m).</p> <p> </p> <h2>Python Scripts</h2> <p>extinction_xsects.py - Calculates the extinction cross-section for an MRN distribution of dust</p> <p>These Python files from github.com/eblur/gastronomy are used by extinction_xsects.py in order to properly scale the mass column density to Fe abundance:</p> <ul> <li>abundances.py</li> <li>molecules.py</li> <li>minerals.py</li> </ul> <p> </p> <h2>Extinction Cross-sections</h2> <p>extinction_xsects/ - This folder contains the results of extinction_xsects.py</p> <p>extinction_xsects/*_FeL.pdf - A plot of the high resolution Fe L shell features</p> <p>extinction_xsects/*_broad.pdf - A plot of the broad band (0.3 - 10 keV), lower resolution cross-sections with 50 eV spacing. These cross-sections include extrapolations for the K and L shell features for other elements in the compounds based on Henke tables (see Watts et al. 2014)</p> <p>extinction_xsects/*_final.pdf - A plot of the consolidated (low resolution broad band and high resolution Fe L shell) extinction cross-sections</p> <p>extinction_xsects/*_xsect.fits - The final cross-section information for each compound, stored as fits file table. The table columns are energy, absorption optical depth, scattering optical depth, and extinction optical depth. All optical depths are scaled to have a total dust mass column of 1e-4 g cm^-2.</p>
APL synapses distribution on PN and KC meshes, primary data, calcium imaging macros and python scripts from Prisco et al.
<p class="MsoBodyText">To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells of the <i>Drosophila </i>adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, we investigated the role of the inhibitory anterior paired lateral neuron (APL) in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic Kenyon cells (KCs). Combining EM data analysis and <i>in vivo</i> calcium imaging, we show that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. Our data indicate that the formation of a sparse code by the Kenyon cells requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation.</p>
Additional 1000 notebooks for the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<p>Additional 1000 notebooks for the review of the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts".</p> <p>The notebooks can be processed using Matroskin tool from the supplementary materials. To do that, place:</p> <p>- the folder "1k_notebooks_dataset" into the folder ".../databases/datasets"</p> <p>- the file "mapping_of_1k_notebooks.json" into the folder ".../databases/mappings"</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.