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334 results for “python”
Figure 1 in Pythons in the Eocene of Europe reveal a much older divergence of the group in sympatry with boas
Figure 1. Hypodigm of Messelopython freyi gen. et sp. nov. (a) and (b) Photograph and drawing of skull of holotype, SMNK-PAL 461a. (c) Photograph of holotype. (d) and (e) Photograph and drawing of skull of paratype, SMF-ME 2784a. (f) Drawing of left splenial and angular in medial view in counterpart of paratype. (g) Drawing of right ectopterygoid in dorsal view in main part of paratype. (h) and (i) Photograph and drawing of skull of paratype, HLMD-Be 165. Abbreviations: an, angular; boc, basioccipital; bpt-pr, basipterygoid process; bs, basisphenoid; d, dentary; ec, ectopterygoid; fr, frontal; mx, maxilla; n, nasal; ot, otooccipital; pa, parietal; par, prearticular part of compound bone; pl, palatine; pf, postfrontal; prf, prefrontal; pro, prootic; pt, pterygoid; q, quadrate; sa, surangular part of compound bone; smx, septomaxilla; so, supraorbital; soc, supraoccipital; spl, splenial; st, supratemporal.
Data from: Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes
Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21–36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities.
Data from: Pythons, parasites and pests: anthropogenic impacts on Sarcocystis (Sarcocystidae) transmission in a multi-host system
Parasites are essential components of ecosystems and can be instrumental in maintaining host diversity and populations; however, their role in trophic interactions has often been overlooked. Three apicomplexan parasite species of Sarcocystis (S. singaporensis, S. zamani, and S. villivillosi) use the reticulated python as their definitive hosts and several species within the Rattus genus as intermediate hosts, and they form a system useful for studying interactions between host–parasite and predator–prey relationships, as well as anthropogenic impacts on parasite transmission. Based on predictions from a 1998 survey, which detected an inverse relationship between urban development and Sarcocystis infection in Rattus, we tested the hypothesis that Sarcocystis transmission in Singapore will decrease over time due to anthropogenic activities. Despite a large proportion of the reticulated python diet consisting of Rattus species at all sizes of pythons, Sarcocystis infection rates decreased from 1998 to 2010. Pythons found in industrial areas had lower Sarcocystis infection rates, particularly in the western industrial area of Singapore Island. Average python size also decreased, with implications that we predict may disrupt host–parasite relationships. Anthropogenic activities such as habitat modification, fragmentation, and systematic removal and translocation of pythons have negative impacts on Sarcocystis transmission in Singapore, which in turn may augment pest rat populations. Trends observed may ultimately have negative impacts on human health and biodiversity in the region.
Stellar model grid data for isochrones Python package
<p>These are the data files that get downloaded by the "isochrones" Python package. "mist.tgz" and "dartmouth.tgz" contain stellar model grid data for the MIST and Dartmouth stellar models (http://waps.cfa.harvard.edu/MIST/ and http://stellar.dartmouth.edu/models/). "dartmouth.tri" is the precomputed Delaunay triangulation for the Dartmouth models (the MIST models are too big to use the triangulation-based interpolation method).</p> <p>***Note***</p> <p>For the dartmouth grids here, this zenodo repository should now be used: https://zenodo.org/record/1002927.</p>
Test dataset for python Lagarangian particle package seaduck
<p>Test datasets for seaduck. Including:</p> <p> </p> <p>1. C-grid lat-lon surface velocity derived from AVISO sea-surface height product.</p> <p>2. 3-Month subset from monthly mean ECCOv4r4 product.</p> <p>3. Two small datasets from MITgcm runs with rectilinear and curvilinear grid, inherited from test dataset of python package oceanspy</p> <p>4. One snapshot from the ASTE reanalysis product.</p>
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Artifact for "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"
<p>This is the artifact for the paper entitled "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"</p>
test data for the 'domutils' Python package
<p>This dataset allow to perform tests and demonstrations with the domutils Python package.</p> <p>Source code is available at:</p> <p>https://github.com/dja001/domutils</p>
Maximizing Data Utility for HPC Python Workflow Execution
<p>Large-scale HPC workflows are increasingly implemented in dynamic languages such as Python, which allow for more rapid development than traditional techniques. However, the cost of executing Python applications at scale is often dominated by the distribution of common datasets and complex software dependencies. As the application scales up, data distribution becomes a limiting factor that prevents scaling beyond a few hundred nodes. To address this problem, we present the integration of Parsl (a Python-native parallel programming library) with TaskVine (a data-intensive workflow execution engine). Instead of relying on a shared filesystem to provide data to tasks on demand, Parsl is able to express advance data needs to TaskVine, which then performs efficient data distribution at runtime. This combination provides a performance speedup of 1.48x over the typical method of on-demand paging from the shared filesystem, while also providing an average task speedup of 1.79x with 2048 tasks and 256 nodes.</p>
WisdomWombat: A polyglot dataflow CFD code using Python and Dragon
<p>We present a prototype solution for a polyglot computational fluid dynamics code using the Python Multiprocessing API and Dragon. The code uses an actor-based dataflow architecture with a directed graph to explicitly express program execution including parallelization and asynchronous communication. Computation-heavy parts are covered with individual Fortran executables, the shared state description is written in C with Fortran and Cython wrappers. Our code demonstrates data flow programming in Python for a classical tightly coupled HPC problem, combining cloud-native programming paradigms with HPC communication techniques like RDMA through the Dragon runtime. We demonstrate how a scalable software architecture for classical HPC, AI/ML and HPC workflow applications could look like in the future.</p>
Towards Identifying Python Proficiency to Foster Software Maintenance and Evolution
Open the record for dataset details and reuse information.
RNAglib: a python package for RNA 2.5 D graphs
<p>RNA 3D annotations for version 1.0.0 are generated by x3dna-dssr and RNAglib for all RNA in the RCSB-PDB Databank. These are converted to network files by RNAglib.</p>
Python Annotations MySQL files
<p>This is the database is the basis for a bachelor thesis at Universität Freiburg, Germany, Chair for Programming Languages.</p>
GPlates files, python code, and crustal thickness estimates used to build all of the deformable plate reconstructions presented in King and Welford 2022a and 2022b.
Open the record for dataset details and reuse information.
ROS2 Python code repository from Github
<p>The dataset comprises a curated assemblage of GitHub repositories housing ROS2 source code scripted in Python. Acquired in November 2023, this compilation is organized within a compressed file. Included alongside the repositories is a CSV file mapping each directory within the archive to its respective repository on GitHub, facilitating streamlined navigation and utilization.</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>
Time Series Analysis with Python datasets
<p>Datasets used in the course <a href="https://github.com/FilippoMB/python-time-series-handbook">Time Series Analysis with Python</a>.</p>
Python Notebook and Dataset for Multi-Elves Analysis at the Pierre Auger Observatory
<p>In this directory, you will find the Python notebook <code>multi-elves.ipynb</code> for analyzing the photo traces of the multiple elves detected in the Fluorescence Detector of the Pierre Auger Observatory. Instructions for running the notebook are included in the README file.</p>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py
<p>All data relevant for the paper "Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py". To use with the analysis scripts, clone the github repository https://github.com/UIUC-PPL/charm4py-mpi4py-compare, and check out the "espm2_2021" branch. Unzip this file into the cloned repository, the analysis scripts will access it from there.</p>
ScienceDex guides
Understand access before you commit
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.