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)

This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.

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.

saurabh5/rlvr-code-data-JavaScript dataset hosted on Hugging Face and contributed by the HF Datasets community

The National Tunnel Inventory Elements dataset was compiled on September 02, 2025 and published on August 26, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Tunnel Inventory (NTI) is a collection of information (database) describing the more than 500 of the Nation's tunnels located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible tunnels on Federal lands. The element data present a breakdown of the condition of each structural and civil element for each tunnel on the National Highway System (NHS). A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529051

AutoTrain Dataset for project: javascript-traing-1

  Dataset Description

This dataset has been automatically processed by AutoTrain for project javascript-traing-1.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure





  Data Instances

A sample from this dataset looks as follows: [ { "target": "test/NavbarSpec.js", "feat_repo_name": "aabenoja/react-bootstrap", "text": "import React from 'react'; import… See the full description on the dataset page: https://huggingface.co/datasets/ars-1/autotrain-data-javascript-traing-1.

After scenario questionnaire results. The data contains the results of the After Scenario Questionnaire answered by 14 participants. (CSV 149 kb)

The National Bridge Inventory Elements dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 620,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Specification for the National Bridge Inventory Bridge Elements contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519106. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519106

Laboratory testing tasks. The data contains the task identifier and the instructions given to the participants to complete the task. (CSV 618 kb)

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors. Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 32 data element restricted access dataset. The following apply to the public use datasets and the restricted access dataset: - Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf. - Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. - Some data are suppressed to protect individual privacy. - Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the previously updated datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured. - Datasets are updated monthly. - Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy. - For more information about data collection and reporting, please see wwwn.cdc.gov/nndss/data-collection.html. - For more information about the COVID-19 case surveillance data, please see www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html. Overview The COVID-19 case surveillance database includes patient-level data reported by U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as "immediately notifiable, urgent (within 24 hours)" by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data collected by jurisdictions are shared voluntarily with CDC. For more information, visit: wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/case-definition/2020/08/05/. COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by pu

Results from laboratory testing. The data contains the task identifier, the average time to completion, number of times the task was successfully completed and the total number of errors. (CSV 209 kb)

This dataset contains the predicted prices of the asset JavaScript over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Nielsen’s Heuristic evaluation. The data contains the results form Nielsen’s Heuristic Evaluation conducted by three usability experts. (CSV 116 kb)

A file containing all Min/Max Baseline Reports for 2005-2023 in their original format is available in the Attachments section below. A second file includes a separate set of reports, made available from 2002-2017, that did not include OLDMEDLINE records. MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

These data represent rock samples collected in Idaho that correspond with naturally occurring hydrothermal samples that were collected and analyzed by INL (Idaho Falls, ID). Representative samples of type rocks were selected to best represent the various regions of Idaho in which naturally occurring hydrothermal waters occur. This includes the Snake River Plain (SRP), Basin and Range type structures east of the SRP, and large scale/deep seated orogenic uplift of the Sawtooth Mountains, ID. Analysis includes ICP-OES and ICP-MS methods for Major, Trace, and REE concentrations.

Content of this repository
This is the repository that contains the scripts and dataset for the MSR 2019 mining challenge

Github Repository with the software used : here.
=======

DATASET
The dataset was retrived utilizing google bigquery and dumped to a csv
file for further processing, this original file with no treatment is called jsanswers.csv, here we can find the following information :
1. The Id of the question (PostId)
2. The Content (in this case the code block)
3. the lenght of the code block
4. the line count of the code block
5. The score of the post
6. The title

A quick look at this files, one can notice that a postID can have multiple rows related to it, that's how multiple codeblocks are saved in the database.

Filtered Dataset:

Extracting code from CSV
We used a python script called "ExtractCodeFromCSV.py" to extract the code from the original csv and merge all the codeblocks in their respective javascript file with the postID as name, this resulted in 336 thousand files.

Running ESlint
Due to the single threaded nature of ESlint, we needed to create a script to run ESlint because it took a huge toll on the machine to run it on 336 thousand files, this script is named "ESlintRunnerScript.py", it splits the files in 20 evenly distributed parts and runs 20 processes of esLinter to generate the reports, as such it generates 20 json files.

Number of Violations per Rule
This information was extracted using the script named "parser.py", it generated the file named "NumberofViolationsPerRule.csv" which contains the number of violations per rule used in the linter configuration in the dataset.

Number of violations per Category
As a way to make relevant statistics of the dataset, we generated the number of violations per rule category as defined in the eslinter website, this information was extracted using the same "parser.py" script.

Individual Reports
This information was extracted from the json reports, it's a csv file with PostID and violations per rule.

Rules
The file Rules with categories contains all the rules used and their categories.

The Elemental Data Index provides access to the holdings of NIST Physical Measurement Laboratory (PML) online data organized by element. It is intended to simplify the process of retrieving online scientific data for a specific element from various online databases, including atomic spectroscopy, atomic data, x-ray absorption, and nuclear data. For some of the databases, the data are immediately retrieved; for others, the retrieval form is provided with the element entered in the form, but additional options must be selected in order to retrieve the data. Each of the databases can be individually accessed from the PML's Physical Reference Data page (http://pml.nist.gov/data).

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

This repository contains the data and analysis from an empirical study investigating the adoption trends of modern JavaScript features introduced with ECMAScript 6 (ES6) and beyond. By mining the source code history of 158 open-source JavaScript projects, the study identifies efforts to rejuvenate legacy code by replacing outdated constructs with modern ones. The findings highlight the extensive use of modern features, their widespread adoption within one to two years after ES6's release, and ongoing trends in the rejuvenation of JavaScript codebases.

• scripts.zip: Contains Python scripts used to analyze data and generate the graphs presented in the study's results.

• scripts-threats-analysis.zip: Contains the Python scripts used to analyze the projects without applying the study's filtering criteria and to generate the table presented in the Threats to Validity section.

• jsminer-tool.zip: Includes the tool developed to analyze GitHub repository history and collect metrics on the adoption of modern JavaScript features.

• jsminer_database_backup.zip: Provides a PostgreSQL database dump containing all code review comments from the repositories analyzed in the study.