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334 results for “python”
WRF-Chem model results for 2019-2020 Australian wildfires and Python scripts
<p>WRF-Chem model simulation results in the control experiment and Python scripts</p>
Accelerating Python Applications with Dask and ProxyStore
<p>Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions for data flow management, which can restrict performance and scalability. ProxyStore, a middleware layer that optimizes data flow via an advanced pass-by-reference paradigm, has shown to be an effective mechanism for addressing these limitations. Here, we investigate integrating ProxyStore with Dask Distributed, one of the most popular libraries for distributed computing in Python, with the goal of supporting scalable and portable scientific workflows. Dask provides a easy-to-use and flexible framework, but is less optimized for scaling certain data-intensive workflows. We investigate these limitations and detail the technical contributions necessary to develop a robust solution for distributed applications and demonstrate improved performance on synthetic benchmarks and real applications.</p>
Russo-Ukrainian War discussion Zhihu Python data
<p><span>The Russo-Ukrainian conflict is a global issue involving not only Russia and Ukraine, but also the other countries and organizations such as the United States, and NATO. It underscores the intricate connections between local conflicts and global politics in our world today. Many nations are involved or urged to share their viewpoints. The United Nations is voting to suspend Russia's Human Rights Council, and the Chinese Ministry of Foreign Affairs faces ongoing queries regarding their stance on the Russo-Ukrainian conflict in press conferences. The Chinese internet is divided in its discussions on the Russo-Ukrainian conflict. Zhihu, as the largest Chinese knowledge community, provides valuable insights into the perspectives of educated Chinese intellectuals. This study uses content analysis and historical context to examine 585 Zhihu responses, exploring attitudes, arguments, and key factors regarding the conflict in Chinese knowledge community. Drawing on social identification, the research also investigates respondents' views on dissidents</span></p>
GNSS data and Python code for data selection
<p>The Python code in the folder describes a simple algorithm for selection of GNSS data by spatial resolution indexing. An example shown in the code is set up for 2 degrees latitude by 2 degrees longitude. </p>
Data and Python script for the paper
Open the record for dataset details and reuse information.
Replication package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python"
<pre><code class="language-markdown"># RP-comment-convention-adherence-Java-Python Replication Package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python". It uses the dataset provided by Rani et.al.'s work [How to identify class comment types? A multi-language approach for class comment classification](https://github.com/poojaruhal/RP-class-comment-classification). ## Structure ``` RQ1/ RQ1_Java_Rules.xlsx RQ1_Python_Rules.xlsx RQ2/ RQ1_Java_Comments_Validated.xlsx RQ1_Python_Comments_Validated.xlsx Raw-projects/ Java_projects/ eclipse.zip guava.zip guice.zip hadoop.zip spark.zip vaadin.zip Python_projects/ django.zip ipython.zip Mailpile.zip pandas.zip pipenv.zip pytorch.zip requests.zip Style-guides ``` ## Contents of the Replication Package --- - **RQ1/** - contains the data used to answer RQ1 - `RQ1_Java_Rules.xlsx` - contains comment-related rules extracted from various Java style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines. Oracle and Google are the standard guidelines, and the remaining are specific to the projects. - `RQ1_Python_Rules.xlsx` - contains comment-related rules extracted from various Python style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines. PEP, Numpy, and Google are the standard guidelines and the remaining are specific to the projects. - **RQ2/** - contains the data used to answer RQ2 - `RQ2_Java_Comments_Validated.xlsx` - contains Java comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns. - `RQ2_Python_Comments_Validated.xlsx` - contains Python comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns. - **Raw-projects/** contains the raw projects of each language that are used to analyze class comments. - **Java_projects/** - `eclipse.zip` - Eclipse project downloaded from the GitHub. More detail about the project is on https://github.com/eclipse - `guava.zip` - Guava project downloaded from the GitHub. More detail about the project is on https://github.com/google/guava - `guice.zip` - Guice project downloaded from the GitHub. More detail about the project is on https://github.com/google/guice - `hadoop.zip` - Apache Hadoop project downloaded from the GitHub. More detail about the project is on https://github.com/apache/hadoop - `spark.zip` - Apache Hadoop project downloaded from the GitHub. More detail about the project is on https://github.com/apache/spark - `vaadin.zip` - Vaadin project downloaded from the GitHub. More detail about the project is on https://github.com/vaadin/framework - **Python_projects/** - `django.zip` - Django project downloaded from the GitHub. More detail about the project is on https://github.com/django. - `ipython.zip` - IPython project downloaded from the GitHub. More detail about the project is on https://github.com/ipython/ipython - `Mailpile.zip` - Mailpile project downloaded from the GitHub. More detail about the project is on https://github.com/mailpile/Mailpile - `pandas.zip` - pandas project downloaded from the GitHub. More detail about the project is on https://github.com/pandas-dev/pandas - `pipenv.zip` - Pipenv project downloaded from the GitHub. More detail about the project is on https://github.com/pypa/pipenv - `pytorch.zip` - PyTorch project downloaded from the GitHub. More detail about the project is on https://github.com/pytorch/pytorch - `requests.zip` - Requests project downloaded from the GitHub. More detail about the project is on https://github.com/psf/requests/ - **Style-guides/**- contains the style guidelines used for the selected projects. ---</code></pre> <p> </p>
Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material (https://rdcu.be/cAPUh)
<p>Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material<br> Link to the paper: <a href="https://rdcu.be/cAPUh">https://rdcu.be/cAPUh</a></p>
Dataset and tool - A Static Analysis for Discovering Type-Related Coding Idioms in Python Programs
<p>The archive file contains the following materials:</p> <ul> <li>`repos.tar.gz`: dataset of the collected Python repositories</li> <li>`tool.tar.gz`: programs used in the case studies</li> </ul>
projet_Python_BSG
<p>Le code génétique pour Homo sapiens et E.Coli</p>
A computational workflow for binding free energies in Python
<p>Dataset of distances between a host and six different ligands. The host was beta-cyclodextrin (bCD), while the ligands were phenol, benzene, aspirin, toluene, chlorobenzene and 1,3-dichlorobenzene. No bonds were frozen. </p> <p>The ligand were set to move with a step of 0.25 angstrom from -26 to 26 relative to the bCD (a total of 208 distances). At each distance, a energy biasing potential <span class="math-tex">\(E_{bias}\)</span> was applied the keep two molecules in place. </p> <p><span class="math-tex">\(E_{bias} = \frac{1}{2}\cdot K \cdot (R - R_0)^2\)</span></p> <p>The parameters of the ligands were taken from OpenFF while GLYCAM were used for the host bCD. All of it were applied in Python and the OpenMM framework. Starting parameters, pdb-, and sdf-files can be found in the start folder.</p>
How do developers use Code Snippets in README files? Analyzing between Python Software Development and Scientific Libraries
<p>README files contain crucial information for clients to effectively use the software.<br> However, we sometimes found that developers either present insufficient content or leave the README files empty.<br> Recent works suggest that, in popular repositories, developers tend to present more code snippets in their README files.<br> In this study, we investigate how developers present code snippets in 10,784 README files of PyPI libraries in terms of (i) the types of code snippets and (ii) the sections where code snippets are presented.<br> Our results indicate the prevalence of types of code snippets in various sections across different types of libraries.</p>
Artefact for "Combining Type Inference and Automated Unit Test Generation for Python"
<p>Contains the artefact for our ASE 2023 submission “Combining Type Inference and Automated Unit Test Generation for Python”.</p>
Supplementary information for GIL: A python package for designing custom indexing primers
<p>Supplementary information for GIL: A python package for designing custom indexing primers. Final_Indexes.zip contains the output from GIL run with default parameters. The first plate of generated indexing primers were ordered and used to create 44 Illumina sequencing libraries. BCL files from a MiSeq Nano run of the 44 libraries were successfully demultiplexed with bcl2fastq using the sample sheets generated by GIL. Analysis of sequences that were not demultiplexed successfully to determine deletion frequency in index sequences can be found in demultiplexing_tests.zip, along with relevant fastq and stats files.</p>
Rock pools as a source of environmental DNA for the detection of the threatened Pilbara olive python (Liasis olivaceus barroni)
<p>Environmental DNA (eDNA) research is transforming biomonitoring at a global scale, but applicability to reptiles has been restricted because of their presumed low shedding rate. Consequently, eDNA may have considerable limitations as a biomonitoring tool in Australia where 40% of the terrestrial vertebrates are reptiles. However, there is a need to evaluate if method improvements such as targeting certain substrates, improves the ability to detect reptile eDNA. The Pilbara olive python (<em>Liasis olivaceus barroni</em>) is an uncommon and elusive Australian top predator with a high conservation priority. Like many other snake species, Pilbara olive pythons are challenging to monitor with traditional survey methods, therefore exploring an eDNA-based approach is highly relevant. The pythons are known to occasionally reside in rock pools. Thus, development of a reliable eDNA-based approach to detect the pythons in water would provide a needed alternative method. Here we use a previously developed metabarcoding assay targeting reptiles, to sequence a total of 228 water samples collected from 40 rock pools from six broad locations across the Pilbara region of Western Australia, and we confirm the presence of Pilbara olive python eDNA in 37 samples from 12 of those pools at three of the six broad sampling locations. Other vertebrate taxa, including other reptiles, amphibians, mammals and birds were also detected. Our documented ability to detect Pilbara olive python eDNA from rock pool water samples represents an important step towards eDNA-based precision monitoring of this species.</p>
Ariane outputs and Python scripts used in the GRL publication titled "A Lagrangian estimate of the Mediterranean outflow's origin"
<p>Outputs of quantitative experiments performed with the particle tracking software Ariane, and Python scripts to analyze and plot them. </p>
Assessing Code Readability in Python Programming Courses Using Eye-Tracking - Python Code Snippets
<p>Python code snippets for assessing code readability in Python programming courses using eye-tracking.</p>
Analysis datasets for NEMO_validation workflow Byrne et al 2023 GMD. "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models"
<p>Analysis datasets in support of Byrne et al. (2023) "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models", <em>Geoscientific Model Development</em>.</p> <p> </p> <p>The datasets are from a comparative analysis of two versions of the European shelf sea AMM15 (Atlantic Margin Model at 1.5km horizontal resolution) configuration. These are NEMO ocean model configurations with different code base versions. The configurations are CO7, which is based on NEMOv3.6, and CO9p0 (also referred to as P0.0), which is based on NEMOv4.0.4.</p>
Legacy 2021 UAV REM (python) & histograms
<p>UAV REM generated with Python and histograms for Legacy 2021 DTM, REM (ArcMap), and REM (Python)</p>
Replication package for "An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms"
<p>See the README.md file for more details.</p>
Python codes for "Regional Greening as a 'Positive' Tipping Phenomenon".
<p># Regional Greening as a 'Positive' Tipping Phenomenon</p> <p>## Contents</p> <p>- [Overview](#overview)<br> - [Repo Contents](#repo-contents)<br> - [System Requirements](#system-requirements)<br> - [Installation Guide](#installation-guide)<br> - [Demo](#demo)</p> <p># Overview</p> <p>The Python codes for our paper "Regional Greening as a 'Positive' Tipping Phenomenon".</p> <p># Repo Contents<br> - [Code](./Code): main code for simulation and visualization<br> - [Tibet](Code/Tibet) : site percolation simulation and related analysis for Qinghai-Tibetan Plateau (QTP) area.<br> - [EVI_Percolation](Code/Tibet/EVI_Percolation):<br> fragment-size distribution exponent and<br> fractal dimensions for QTP<br> - [EVI_Percolation_Shuffle](Code/Tibet/EVI_Percolation_Shuffle): null models for QTP<br> - [EVI_Percolation_Snapshots](Code/Tibet/EVI_Percolation_Snapshots): correlation length and susceptibility for QTP<br> - [EVI_Percolation_Protection](Code/Tibet/EVI_Percolation_Protection): Optimal enhancing resilience model for QTP<br> - [EVI_Enhanced](Code/Tibet/EVI_Enhanced): Enhanced EVI Coverage for QTP<br> - [Sahel](Code/Sahel) site percolation simulation and related analysis for Sahel area.<br> - [EVI_Percolation](Code/Sahel/EVI_Percolation):<br> fragment-size distribution exponent and<br> fractal dimensions for Sahel<br> - [EVI_Percolation_Shuffle](Code/Sahel/EVI_Percolation_Shuffle): null models for Sahel<br> - [EVI_Percolation_Snapshots](Code/Sahel/EVI_Percolation_Snapshots): correlation length and susceptibility for Sahel<br> - [EVI_Percolation_Protection](Code/Sahel/EVI_Percolation_Protection): Optimal enhancing resilience model for Sahel<br> - [EVI_Enhanced](Code/Sahel/EVI_Enhanced): Enhanced EVI Coverage for Sahel<br> - [Plot_Figs](Code/Plot_Figs) : Draw the pictures<br> - [GEE_Data_Download_Script](./GEE_Data_Download_Script): scripts to download data from Google Earth Engine (GEE)<br> - [Shapefile](./Shapefile): shapefiles used to select the Sahel and Tibet regions in the study.<br> - [SRCJl](./SRCJl) and [SRCPy](./SRCPy) : custom function libraries.</p> <p><br> # System Requirements</p> <p><br> ## Software Requirements</p> <p>### OS Requirements</p> <p>The package development version is tested on *Linux* operating systems. The developmental version of the package has been tested on the following systems:</p> <p>Linux: Ubuntu 22.04 </p> <p># Installation Guide</p> <p>1. Users should build Python (3.9) and Julia (1.9) environment first.<br> For Python, visit [https://www.anaconda.com/download](https://www.anaconda.com/download) to download and install the Anaconda distribution.<br> For Julia, go to [https://julialang.org/downloads/](https://julialang.org/downloads/) and download the Julia installer for your platform. Follow the installation instructions provided on these websites.</p> <p>2. Compile the libraries.<br> ```<br> cd SRCJl\Flib\sitep<br> bash compile_site_percolation.sh<br> ```</p> <p># Prepare Data<br> 1. Download data from GEE by using the [GEE_Data_Download_Script](./GEE_Data_Download_Script) and [shapefile](./Shapefile).<br> 2. Place the data in the corresponding path:<br> - MODIS EVI V6 (QTP): [Code/Tibet/Data/Tibet_EVI_V6/Summer](Code/Tibet/Data/Tibet_EVI_V6/Summer/)<br> - MODIS EVI V6 (Sahel) : [Code/Sahel/Data/Sahel_EVI_V6/Summer](Code/Sahel/Data/Sahel_EVI_V6/Summer/)<br> - TerraClimate Summer precipitation (QTP) : [Code/Tibet/Data/Tibet_TerraClimate_pr_Summer](Code/Tibet/Data/Tibet_TerraClimate_pr_Summer)<br> - TerraClimate Summer precipitation (Sahel) : [Code/Tibet/Data/Sahel_TerraClimate_pr_Summer](Code/Sahel/Data/Tibet_TerraClimate_pr_Summer)</p> <p># Demo<br> ```<br> cd Code/Sahel/EVI_Percolation<br> bash run_all.sh<br> ```<br> ```<br> cd Code/Tibet/EVI_Percolation_Shuffle<br> bash run_all.sh<br> ```<br> ```<br> cd Code/Tibet/EVI_Percolation_Snapshots<br> bash run_all.sh<br> ```<br> ```<br> cd Code/Tibet/EVI_Percolation_Protection<br> bash run_all.sh<br> ```<br> ```<br> cd ode/Tibet/EVI_Enhanced<br> bash run_all.sh<br> ```</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.