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334 results for “python”
Documentation data for the Python Apecosm package
<p>This dataset contains NEMO/Pisces and Apecosm output files that are used in the documentation of the Apecosm python package.</p>
Extracting Educational Code Scenarios from Python Textbooks
<p><strong>Dataset Overview:</strong> This dataset complements the research project titled "Extracting Learning Scenarios from Python Textbooks." It consists of 1,017 chapter titles collected from 76 Python textbooks.</p><p><strong>Research Findings:</strong> Our analysis revealed that learning scenarios (referred to as "Scenarios") are a prevalent theme, constituting approximately 39.5% of the total chapter titles in comparison to other content categories. We further categorized these scenarios into four types, including Application Programming Interfaces, Data and Processing, Graphical User Interfaces, and other scenarios. Additionally, we identified a list of 19 Python modules commonly used within these scenarios.</p><p><strong>Purpose:</strong> We envision that this work and its insights can serve as stepping stones and lay the groundwork for further extraction and the effective application of how Python can be utilized for its diverse audience.</p>
2004.30.c Apollo the Python-Slayer
You can copy, modify, and distribute this work, even for commercial purposes, all without asking permission. Learn more about The Cleveland Museum of Art's Open Access initiative: http://www.clevelandart.org/open-access-faqs Apollo the Python-Slayer, c. 350 BC. Attributed to Praxiteles (Greek, c. 400BC-c. 330BC). Bronze, copper and stone inlay; overall: 14.8 x 9.4 x 3.6 cm (5 13/16 x 3 11/16 x 1 7/16 in.). The Cleveland Museum of Art, Severance and Greta Millikin Purchase Fund 2004.30.c Learn more on The Cleveland Museum of Art's Collection Online: https://www.clevelandart.org/art/2004.30.c Source: Objaverse 1.0 / Sketchfab
Data from: Improving access and use of climate projections for ecological research through the use of a new Python tool
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Pythons in the Eocene of Europe reveal a much older divergence of the group in sympatry with boas
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Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
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Python scripts for input and post-processing of fuzz sputtering TRI3DYN simulations
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Python code generating the data of figures 2, 3, 4, 5 and 6 of the manuscript: The evolution of cooperation in the unidirectional linear division of labour of finite roles
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Python and Jupyter Notebook for Medical Image Analysis - OpenMRBenelux 2020
<p>Dataset for the workshop "Python and Jupyter Notebook for Medical Image Analysis" at OpenMRBenelux - January 22, 2020 - Nijmegen (The Netherlands)</p>
Ball Python (Python regius) snake brain illustration
<p>3D model of the Ball Python snake brain highlighting the anatomy and the spatial arrangement of its major subdivisions.</p> <p>The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0.</p> <p>Other illustrations can be found <strong><a href="https://zenodo.org/search?page=1&size=20&q=keywords:%22squamate%20brain%22">here</a></strong>.</p> <p><em>If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication:</em></p> <p><a href="https://www.nature.com/articles/s41467-019-13405-w"><em><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong></em></a></p> <p><strong>Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï</strong></p> <p><em>Nature Communications</em> <strong>10, </strong>5560 (2019)</p> <p> </p> <p><em>Check out also our *4K* video collection of various snake and lizard 3D brains:</em></p> <p><strong><a href="https://www.youtube.com/playlist?list=PLgx4vtT32C8hqxG_icKiuXGtZVLVX-oG1">Snake and Lizard brain reconstructions video collection</a></strong></p> <p> </p> <p>For any inquiries or additional information, please, refer to the contacts provided in the <strong><a href="https://www.nature.com/articles/s41467-019-13405-w">article</a></strong>.</p>
Dataset for interactive course on BioImage Analysis with Python (BIAPy)
<p>This dataset can be used to run the course on image processing with Python available here: <a href="https://github.com/guiwitz/neubias_academy_biapy">https://github.com/guiwitz/neubias_academy_biapy</a></p> <p>It combines microscopy images from different publicly available sources. All files are either in the Public Domain (PD) or released with a CC-BY license. The list of the original location of the data as well as their licenses can be found in the LICENSE file.</p>
Data from: Phylogenomics, biogeography and morphometrics reveal rapid phenotypic evolution in pythons after crossing Wallace's line
<p>Ecological opportunities can be provided to organisms that cross stringent biogeographic barriers towards environments with new ecological niches. Wallace's and Lyddeker's lines are arguably the most famous biogeographic barriers, separating the Asian and Australo-Papuan biotas. One of the most ecomorphologically diverse groups of reptiles, the pythons, is distributed across these lines, and are remarkably more diverse in phenotype and ecology east of Wallace's line in Australo-Papua. We used an anchored hybrid enrichment approach, with near complete taxon sampling, to extract mitochondrial genomes and 376 nuclear loci to resolve and date their phylogenetic history. Biogeographic reconstruction demonstrates that they originated in Asia around 38-45 Ma and then invaded Australo-Papua around 23 Ma. Australo-Papuan pythons display a sizeable expansion in morphological space, with shifts towards numerous new adaptive optima in head and body shape, coupled with the evolution of new micro-habitat preferences. We provide an updated taxonomy of pythons and our study also demonstrates how ecological opportunity following colonization of novel environments can promotemorphological diversification in a formerly ecomorphologically conservative group.</p>
Python Annotated Code Search (PACS) Datasets & Pretrained Models
<p>This upload contains datasets and pre-trained models used for the paper <em>Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent. </em>The code for easily loading these datasets and models will be made available here: <a href="http://github.com/nokia/codesearch">http://github.com/nokia/codesearch</a> </p> <p><strong>Datasets</strong><br> There are three types of datasets:</p> <ul> <li>snippet collections (code snippets + natural language descriptions): so-ds-feb20, staqc-py-cleaned, conala-curated</li> <li>code search evaluation data (queries linked to relevant snippets of one of the snippet collections): so-ds-feb20-{valid|test}, staqc-py-raw-{valid|test}, conala-curated-0.5-test</li> <li>training data (datasets used to train code retrieval models): so-duplicates-pacs-train, so-python-question-titles-feb20</li> </ul> <p>The staqc-py-cleaned snippet collection, and the conala-curated datasets were derived from existing corpora:</p> <ul> <li>staqc-py-cleaned was derived from the Python StaQC snippet collection. See <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset">https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset</a>, <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset/blob/master/LICENSE.txt">LICENSE</a>. </li> <li>conala-curated was derived from the conala corpus. See <a href="https://conala-corpus.github.io/">https://conala-corpus.github.io/</a> , <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a></li> </ul> <p>The other datasets were mined directly from a recent Stack Overflow dump (https://archive.org/details/stackexchange, <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a>). <br> <br> <strong>Pre-trained models</strong><br> Each model can embed queries and (annotated) code snippets in the same space. The models are released under a BSD 3-Clause License.</p> <ul> <li>ncs-embedder-so-ds-feb20</li> <li>ncs-embedder-staqc-py</li> <li>tnbow-embedder-so-ds-feb20</li> <li>use-embedder-pacs</li> <li>ensemble-embedder-pacs</li> </ul>
Supplementary material 2 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S2 – Python script for custom grid search of hyperparameters for optimization of the neural network
Supplementary material 3 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S3 – The parameters utilized in the grid search for each of the five machine learning algorithms tested in the design of the Alfie package
Supplementary material 4 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S4 – Jupyter notebook with tutorial demonstrating how to apply the Alfie classifier in the Python programming language, and how to train custom alignment-free classifiers using the Alfie training module
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>
Search-based Test Data Generation for Mutation Testing: a tool for Python programs
<p>Test data generation for mutation testing consists of identifying a set of inputs that maximizes the number of mutants killed. Mutation Testing is an excellent test criterion for detecting faults and measuring the effectiveness of test data sets. However, it is not widely used in practice due to the cost and complexity to perform some activities as generating test data. Although test suites can be produced and selected manually by a tester this practice is susceptible to errors and tools are needed to facilitate it. Several tools have been developed to automate mutation testing, but, only a few address the test data generation. The present paper proposes an automated test data generation tool based on weak mutation for Python programming language using the Hill Climbing algorithm. For evaluation, we performed an experiment concerning the effectiveness and cost computational of the tool in a database composed of 348 mutants and we compare it with random generation. Overall, the experiment achieved an average mutation score of 86% for our proposed tool and random testing 64% on average.</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>
Figure 2 in Pythons in the Eocene of Europe reveal a much older divergence of the group in sympatry with boas
Figure 2. (a) Phylogenetic relationships of Messelopython freyi gen. et sp. nov. Support values for nodes are Jackknife/Bremer, values of 100% (Jackknife) or greater than 20 (Bremer) are indicated with an asterisk. (b) Palaeogeographic map of continental distributions at 48 Mya based on Gplates model [38] with location (palaeocoordinates for 48 Mya) of Palaeogene records of the total clades of Boidae, Loxocemidae and Pythonidae. Large arrows indicate hypothesized dispersal directions for the total clade of Pythonidae.
ScienceDex guides
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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.