axay/javascript-dataset-js dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by sSchumat

Contents

This dataset contains an anonymized list of surveyed developers who provided their expertise level on three popular JavaScript libraries:

ReactJS, a library for building enriched web interfaces

MongoDB, a driver for accessing MongoDB databased

Socket.IO, a library for realtime communication

JavaScript dataset.

Dataset Overview (React Q&A)

Our chatbot will be trained on a specialized Q&A dataset about the React JavaScript library. This React Q&A dataset is provided as a JSON file containing roughly 26,300 question-answer pairs (the exact number may vary slightly). Each entry in the JSON list has a "question" field and a corresponding "answer" field, e.g.:

{"question": "What is React?", "answer": "React is an open-source JavaScript library for building user interfaces..."}

This format (a list of objects with ‘question’ and ‘answer’ strings) is common in QA collections. For comparison, a well-known QA dataset like SQuAD (Stanford Question Answering Dataset) contains on the order of 100,000 question-answer pairs. Our React dataset is smaller but still substantial. It covers many topics relevant to React: definitions (e.g. “What is JSX?”), how-to guides (e.g. “How to install react-datepicker?”), component usage, common patterns, troubleshooting, and performance features.

Because this dataset is domain-specific (about React), it serves as a tailored knowledge base for the chatbot. Using a focused corpus like this is recommended: as noted by experts, “if your QA system focuses on a particular domain (e.g., technical), consider domain-specific corpora” and even curate your own Q&A pairs. This helps the model learn React terminology and concepts deeply. The dataset’s JSON structure (a flat list of QA entries) is simple and ready for loading into typical training pipelines.

angie-chen55/javascript-github-code dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset consists of multiple files which contain bug prediction training data.

The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).

The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.

We used different threshold values from which we considered the edges to be valid. The following threshold values were used:

0.00

0.05

0.20

0.30

The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.

There are four variants for all of these datasets:

Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are:

ID

Name

Longname

Parent ID

Component ID

Path

Line

Column

EndLine

EndColumn

Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)

Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)

Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)

Static source code metrics which are contained in all dataset are the following:

McCC - McCabe Cyclomatic Complexity

NL - Nesting Level

NLE - Nesting Level Else If

CD - Comment Density

CLOC - Comment Lines of Code

DLOC - Documentation Lines of Code

TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)

TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)

LLOC - Logical Lines of Code (Comment and empty lines not counted)

LOC - Lines of Code (Comment and empty lines are counted)

NOS - Number of Statements

NUMPAR - Number of Parameters

TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)

TLOC - Lines of Code (Lines in embedded functions are also counted)

TNOS - Total Number of Statements (Statements in embedded functions are also counted)

Dataset Card for dataset-JavaScript-general-coding

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/dmeldrum6/dataset-JavaScript-general-coding/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/dmeldrum6/dataset-JavaScript-general-coding.

Web UI Elements Dataset

Link repo: https://huggingface.co/datasets/YashJain/UI-Elements-Detection-Dataset

1. Overview A comprehensive dataset of web user interface elements collected from the world's most visited websites. This dataset is specifically curated for training AI models to detect and classify UI components, enabling automated UI testing, accessibility analysis, and interface design studies.

1. Key Features
2. 300+ popular websites sampled
3. 15 essential UI element classes
4. High-resolution screenshots (1920x1080)
5. Rich accessibility metadata
6. YOLO-format annotations
7. Balanced class distribution
8. Element Classes

Interactive Elements: - Buttons - Links - Input fields - Checkboxes - Radio buttons - Dropdowns - Sliders - Toggle switches

Structural Elements: - Labels - Text blocks - Icons - Menu items

Form Elements: - Text areas - Select menus - Clickable regions

This dataset is about books. It has 1 row and is filtered where the book is Reliable JavaScript. It features 7 columns including author, publication date, language, and book publisher.

Dataset Card for "instructional_code-search-net-javacript"

  Dataset Summary

This is an instructional dataset for JavaScript. The dataset contains two different kind of tasks:

Given a piece of code generate a description of what it does. Given a description generate a piece of code that fulfils the description.

  Languages

The dataset is in English.

  Data Splits

There are no splits.

  Dataset Creation

May of 2023

  Curation Rationale… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-javacript.

Tesslate/Next.js-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Logical Element

## Overview

Logical Element is a dataset for computer vision tasks - it contains Logical Elements annotations for 460 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).

This dataset is about book subjects. It has 2 rows and is filtered where the books is Learning JavaScript. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 1 row and is filtered where the book is Eloquent JavaScript : a modern introduction to programming. It features 7 columns including author, publication date, language, and book publisher.

Website Element

## Overview

Website Element is a dataset for object detection tasks - it contains Website annotations for 1,206 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).

Government Code section 65400 requires that each city, county, or city and county, including charter cities, prepare an annual progress report (APR) on the status of the housing element of its general plan and progress in its implementation. This dataset includes information reported to the Department of Housing and Community Development (HCD) by local jurisdictions on their APR form. Additional information about annual progress reports (APR), including the form, instructions, and definition can be found on HCD’s website here: https://www.hcd.ca.gov/planning-and-community-development/annual-progress-reports.

Dataset

This dataset was created by Sheikh Muhammad Abdullah

Contents

Dataset

This dataset was created by Jordan Tantuico

Contents

PCB Element

## Overview

PCB Element is a dataset for object detection tasks - it contains Electronic Elements annotations for 504 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).