Dataset

This dataset was created by Towhidul.Tonmoy

Contents

A list of different projects selected to analyze class comments (available in the source code) of various languages such as Java, Python, and Pharo. The projects vary in terms of size, contributors, and domain.

Structure

Projects/
  Java_projects/
    eclipse.zip
    guava.zip
    guice.zip
    hadoop.zip
    spark.zip
    vaadin.zip

  Pharo_projects/
    images/
      GToolkit.zip
      Moose.zip
      PetitParser.zip
      Pillar.zip
      PolyMath.zip
      Roassal2.zip
      Seaside.zip

    vm/
      70-x64/Pharo

    Scripts/
      ClassCommentExtraction.st
      SampleSelectionScript.st    

  Python_projects/
    django.zip
    ipython.zip
    Mailpile.zip
    pandas.zip
    pipenv.zip
    pytorch.zip   
    requests.zip 
  

Contents of the Replication Package

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 available on GitHub Eclipse. - guava.zip - Guava project downloaded from the GitHub. More detail about the project is available on GitHub Guava. - guice.zip - Guice project downloaded from the GitHub. More detail about the project is available on GitHub Guice - hadoop.zip - Apache Hadoop project downloaded from the GitHub. More detail about the project is available on GitHub Apache Hadoop - spark.zip - Apache Spark project downloaded from the GitHub. More detail about the project is available on GitHub Apache Spark - vaadin.zip - Vaadin project downloaded from the GitHub. More detail about the project is available on GitHub Vaadin

• Pharo_projects/

  • images/ -

    • GToolkit.zip - Gtoolkit project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Moose.zip - Moose project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • PetitParser.zip - Petit Parser project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Pillar.zip - Pillar project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • PolyMath.zip - PolyMath project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Roassal2.zip - Roassal2 project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Seaside.zip - Seaside project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.

  • vm/ -

  • 70-x64/Pharo - Pharo7 (version 7 of Pharo) virtual machine to instantiate the Pharo images given in the images/ folder. The user can run the vm on macOS and select any of the Pharo image.

  • Scripts/ - It contains the sample Smalltalk scripts to extract class comments from various projects.

  • ClassCommentExtraction.st - A Smalltalk script to show how class comments are extracted from various Pharo projects. This script is already provided in the respective project image.

  • SampleSelectionScript.st - A Smalltalk script to show sample class comments of Pharo projects are selected. This script can be run in any of the Pharo images given in the images/ folder.

• Python_projects/

  • django.zip - Django project downloaded from the GitHub. More detail about the project is available on GitHub Django
  • ipython.zip - IPython project downloaded from the GitHub. More detail about the project is available on GitHub on IPython
  • Mailpile.zip - Mailpile project downloaded from the GitHub. More detail about the project is available on GitHub on Mailpile
  • pandas.zip - pandas project downloaded from the GitHub. More detail about the project is available on GitHub on pandas
  • pipenv.zip - Pipenv project downloaded from the GitHub. More detail about the project is available on GitHub on Pipenv
  • pytorch.zip - PyTorch project downloaded from the GitHub. More detail about the project is available on GitHub on PyTorch
  • requests.zip - Requests project downloaded from the GitHub. More detail about the project is available on GitHub on Requests

Dataset

This dataset was created by YuanKefan

Released under Apache 2.0

Contents

Dataset

This dataset was created by Hưng Duy

Contents

Dataset

This dataset was created by Nikhil raman K

Released under CC0: Public Domain

Contents

Detailed Token holders metrics and analytics for Spark, including historical data and trends.

Financial overview and grant giving statistics of Spark Project Inc.

# 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.
---

Detailed Code commits metrics and analytics for Spark, including historical data and trends.

Spark Detector

## Overview

Spark Detector is a dataset for object detection tasks - it contains Sparks annotations for 8,212 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Super SPARK An Com

## Overview

Super SPARK An Com is a dataset for instance segmentation tasks - it contains Objects annotations for 885 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Fire Spark

## Overview

Fire Spark is a dataset for object detection tasks - it contains Fire Spark annotations for 337 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Detailed Active users (monthly) metrics and analytics for Spark, including historical data and trends.

The data contain synthetic and measured (spark discharge) emission spectra in order to test and validate the results of the so called multi-element combinatory Boltzmann plot method. This is an OES-based approach to deduce the number concentration ratio of two elements present in a spark discharge plasma employed for binary NP generation in the gas phase. It is aimed to provide a tool for investigating the evolution of the concentration ratio corresponding to the ablated electrode materials in spark-based NP generators under real operational conditions. The method is based on the construction of a Boltzmann plot for the spectral line intensity ratios at every combination. The produced plots (the so-called multi-element combinatory Boltzmann plots, MEC-BPs) are directly related to the LTE plasma temperature and the number concentration ratio of the neutral atoms. The total concentration ratio – including ions – is calculated from a simple plasma model, without requiring further measurements.

The python project in which the method is implemented can be found here: https://pypi.org/project/spark-mec-bp/0.1.0/

Comprehensive open source project metrics including contributor activity, popularity trends, development velocity, and security assessments for RoaringBitmap Java: Advanced Bitset for Apache Spark and Netflix.

OCS Spark

## Overview

OCS Spark is a dataset for object detection tasks - it contains Spark annotations for 372 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Spark plug Market size was valued at USD 1.76 USD Billion in 2023 and is projected to reach USD 2.11 USD Billion by 2032, exhibiting a CAGR of 2.6 % during the forecast period. Recent developments include: July 2023, MAN Engines started offering pre-chamber spark plugs adapted to the respective combustion chamber to all turbocharged natural gas engines of the current MAN E3262, E3268, E0836, and E0834 series as an option, as well as MAN E3872 as obligatory. According to the company, pre-chamber plug for turbocharged natural gas engines will increase the efficiency of hydrogen admixture by up to 20% by volume., July 2023, NGK initiated the acquisition of Denso ceramic products such as spark plugs, O2 sensors, and air-fuel ratio sensors to NGK. Among Denso's internal combustion engine products, it will acquire the development, manufacturing, and sales functions of spark plugs and exhaust sensors in Japan and overseas., June 2023, Niterra introduced the final addition to their product lineup, the groundbreaking ILKFR7A8 reference spark plug, featuring a unique double precious metal design. This innovative spark plug is designed to meet the needs of various Stellantis models, including the durable Jeep Compass, the stylish Alfa Romeo Tonale, and the versatile Fiat 500X crossover., February 2023, Mazda devised a method to further develop the proficiency of the Skyactiv-X engine technology by incorporating an additional spark plug. The company filed a patent outlining the new technology to bolster the control of spark-controlled compression ignition (SCCI). This advancement enables Mazda to regulate timing more precisely, minimizing irregular combustion effectively. Consequently, the engine generates more power while consuming less fuel, potentially integrating into models such as the Mazda 3., March 2021, The European Commission initiated the LASIG-TWIN project to explore more effective methods for igniting fuels within internal combustion engines commonly used in automobiles. The project team sought to assess the potential of alternative ignition systems, such as laser ignition, in enhancing the efficiency of combustion engines. Various techniques were examined and tested within the project network, encompassing approaches for assembling and connecting optical and metallic materials to develop laser spark plugs.. Key drivers for this market are: Increasing Demand for Forged Products in Power, Agriculture, Aerospace, and Defense to Drive Industry Expansion. Potential restraints include: Emergence of Megatrend of Automotive Electrification May Impede the Industry Expansion . Notable trends are: Rising Adoption of Automation in Manufacturing to Drive Market Growth.

SPARK is an AGC kinase found specifically in apicomplexan parasites. We have previously shown that SPARK is essential for parasite calcium mobilization, egress, and invasion. Here we have performed immunoprecipitation followed by mass spectrometry to determine interaction partners of SPARK. We identify a hypothethical protein that interacts with SPARK, which we term SPARKEL.

Detailed Total value locked metrics and analytics for Spark, including historical data and trends.

SPARK is an AGC kinase found in apicomplexan parasites. In previous work, we showed that SPARK knockdown leads to inefficient T. gondii calcium mobilization, egress, and invasion of host cells. We performed immunoprecipitation and mass spectrometry to determine the interaction partners of SPARK-mNG-mAID isolated from T. gondii lysates. We identified a strong interaction partner, which we term SPARKEL.