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334 results for “python”
Supplementary material 2 from: Harrington LA, Green J, Muinde P, Macdonald DW, Auliya M, D'Cruze N (2020) Snakes and ladders: A review of ball python production in West Africa for the global pet market. Nature Conservation 41: 1-24. https://doi.org/10.3897/natureconservation.41.51270
Figure S2
Phylogenomics, biogeography and taxonomic revision of New Guinean pythons (Pythonidae, Leiopython) harvested for international trade
<p>The large and enigmatic New Guinean pythons in the genus <i>Leiopython</i> are harvested from the wild to supply the international trade in pets. Six species are currently recognized (<i>albertisii</i>, <i>biakensis</i>, <i>fredparkeri</i>, <i>huonensis</i>, <i>meridionalis</i>, <i>montanus</i>) but the taxonomy of this group has been controversial. We combined analysis of 421 nuclear loci and complete mitochondrial genomes with morphological data to construct a detailed phylogeny of this group, understand their biogeographic patterns and establish the systematic diversity of this genus. Our molecular genetic data support two major clades, corresponding to <i>L. albertisii</i> and <i>L. meridionalis</i>, but offer no support for the other four species. Our morphological data also only support two species. We therefore recognize <i>L. albertisii</i> and <i>L. meridionalis</i> as valid species and place <i>L. biakensis, L. fredparkeri, L. huonensis </i>and<i> L. montanus </i>into synonymy. We found that <i>L. albertisii</i>and <i>L. meridionalis</i> are sympatric in western New Guinea; an atypical pattern compared to other Papuan species complexes in which the distributions of sister taxa are partitioned to the north and south of the island's central mountain range. For the purpose of conservation management, overestimation of species diversity within <i>Leiopython</i> has resulted in the unnecessary allocation of resources that could have been expended elsewhere. We strongly caution against revising the taxonomy of geographically widespread species groups when little or no molecular genetic data and only small morphological samples are available.</p>
Supplementary material 1 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 S1 – Training, test, and validation data sets used in model training and analysis
Correctness tests for python package matchingproblems
<p>Correctness data and scripts for python package matchingproblems.</p>
ArchPython: architecture conformance checking for Python systems
<p>Dynamically typed languages provide several resources for developers, such as dynamic invocations and constructs. However, such resources combined with short deadlines, conflicts in requirements, or technical difficulties may increase the number of code decisions that go against the planned architecture, leading to the phenomenon known asarchitectural erosion. Even though Python is the 3rd most used programming language, there is no tool that allows developers to monitor the architecture of their systems. This is possibly justified by the complexity to infer types since the same variable can assume different types at run time. Facing such challenge, this article proposes ArchPython, the first complete architectural conformance and visualization tool for Python systems. In a nutshell, developers specify the architecture of their systems in a simple and natural way using JSON files and ArchPython takes care of the rest. Automatically, the tool infers types ( Jedi + propagation heuristics) and detects architectural violations (divergences, absences, and even alerts). In addition to a JSON-based textual report, the tool also provides two ways of visualizing architectural violations (graph and DSM).</p>
Testing Python Import
<p>A repository that is used to test the EESD-Stonemasonry dashboard</p>
Python code and experimental data of three-color dynein stepping
<p>This project is described in: Three-color single-molecule imaging reveals conformational dynamics of dynein undergoing motility (2020) and can be found on BioRxiv: (link to follow)</p> <p>Here, we provide the experimental data of the three-color dynein stepping experiments together with the custom python code used for data analysis. Details on how the code works are given in the script itself.</p>
Data from: Evolution of extreme ontogenetic allometric diversity and heterochrony in pythons, a clade of giant and dwarf snakes
Ontogenetic allometry, how species change with size through their lives, and heterochony, a decoupling between shape, size and age, are major contributors to biological diversity. However, macro-evolutionary allometric and heterochronic trends remain poorly understood because previous studies have focused on small groups of closely related species. Here we focus on testing hypotheses about the evolution of allometry and how allometry and heterochrony drive morphological diversification at the level of an entire species-rich and diverse clade. Pythons are a useful system due to their remarkably diverse and well-adapted phenotypes and extreme size disparity. We collected detailed phenotype data on 40 of the 44 species of python from 1,191 specimens. We used a suite of analyses to test for shifts in trajectories that modify morphological diversity. Heterochrony is the main driver of initial divergence within python clades, and shifts in the slopes of allometric trajectories make exploration of novel phenotypes possible later in divergence history. We found that allometric coefficients are highly evolvable and there is an association between ontogenetic allometry and ecology, suggesting that allometry is both labile and adaptive rather than a constraint on possible phenotypes.
Dataset for "MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning"
<div></div> <div><a href="https://zenodo.org/api/records/17093439/draft/files/micrographCleaner_dataset.tar.gz/content" target="_blank" rel="noopener noreferrer">micrographCleaner_dataset.tar.gz</a>: </div> <p>The dataset used for the Publicaton "MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning" <a href="https://doi.org/10.1016/j.jsb.2020.107498">https://doi.org/10.1016/j.jsb.2020.107498</a></p> <div> </div> <div></div> <div><a href="https://zenodo.org/api/records/17093439/draft/files/deepMicrographCleaner.tgz/content" target="_blank" rel="noopener noreferrer">deepMicrographCleaner.tgz</a>: the tensorflow-2-compatible model checkpoint</div> <p> </p>
Python repositories with tutorials
<p>Email me at justine.gehring <at> gmail <.com> if you have any questions</p>
Unveiling Challenges in Python Library: Insights from Stack Overflow
<p>A dataset of the SANER 2024 ERA track submission "Unveiling Challenges in Python Library: Insights from Stack Overflow" containing all the comments in Stack Overflow that were classified.</p>
Computer codes (in Python) for 'The reproduction number and its probability distribution for stochastic viral dynamics'
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Python files from representing data graphically
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SOAPy: a Python package to dissect spatial architecture, dynamics and communication
<p>Datasets used in the SOAPy article and tutorial could be referred in <strong>data sources.xlsx</strong>. You could download the source data by accessing the download links provided in the file. </p>
Artefact to the paper "Combining Type Inference and Automated Unit Test Generation for Python" submitted to ISSTA 2024
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RQI code python
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Python A star -IWOA_1.7z
<p>Dataset for submission to peerj</p>
Error Identification Strategies for Python Jupyter Notebooks
<p>Replication package for the paper "<strong>Error Identification Strategies for Python Jupyter Notebooks</strong>".</p>
The observational stationary dataset and the Python scripts for the non-stationary data concocting.
<p>dataset and script</p>
Woma Python (Aspidites ramsayi)
SciDraw upload
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.