Combination of multiple linters to run as a GitHub Action or standalone

Qualitis is a one-stop data quality management platform that supports quality verification, notification, and management for various datasource. It is used to solve various data quality problems caused by data processing. https://github.com/WeBankFinTech/Qualitis

BEE (Bug rEport analyzEr), a tool for structuring and analyzing bug reports

CNAqc - Copy Number Alteration (CNA) Quality Check package

A Matlab toolbox for examining the quality of structural (SNR) and functional (tSNR, SFNR) MRI

🖇 Android hodgepodge static code quality check tool gradle plugin.

Dynamic Generation and Quality Checks of Formula Objects

Sistema (lib + REST APIs) para validação de boas práticas na construção de contratos de API em OpenAPI Specification.

BURT: A task-oriented dialogue system for bug reporting

yalsa is Yet Another Live Stream Analyzer running in console.

The package RNAseqAnalysis does the complete analysis of RNA seq data starting from raw reads. It provides the user with differnt functions like generation of qc report, filtering, assembly and GO-term annotation, differential expression analysis and heatmap generation, and Alternative splicing-site prediction

This module provides an interface to a set of Pester tests that enable the enforcement of quality control rules for PowerShell scripts and modules.

Simple and fast linter for Git commit messages

NPM package quality monitoring tool

Estimate the quality of your diffusion-weighted data to map human connectomes.

CSV Data Validator is a tool to validate csv file. It parse csv and validate the data with .hdr(csv meta data) before ingestion to Data Lake. It checks data file availability for every day load and validate data with respective meta data like File Size, Checksum, Delimiter, Record count etc. It ensure landed data conformity before give go ahead …

Data quality monitoring for high energy physics. Doc available at

Brake pad quality inspection

A repository template for a markdown ebook generated with pandoc

Python scripts for transforming specific metocean datasets from text-based to binary formats and checking them for data quality issues