A standardized browser-based spreadsheet editor and validator that can be run offline and locally, and which includes templates for SARS-CoV-2 and Monkeypox sampling data. This project, created by the Centre for Infectious Disease Genomics and One Health (CIDGOH), at Simon Fraser University, is now an open-source collaboration with contributions from the National Microbiome Data Collaborative (NMDC), the LinkML development team, and others.

This is the DataHarmonizer spreadsheet web application bundled with pathogen genomics data entry and validation templates

Metadata schemes implemented in LinkML for the DZIF microbial OMICs database plus building scripts for the DZIF DataHarmonizer.

scripts to support the generation of yaml and json files needed to create templates used in DataHarmonizer. The scripts are specifically designed to generate templates for submission of data into ENA.

Generate DataHarmonizer schema to support data entry and generation of manifests for a ENA sample/run submission.

No description available

A catalogue of machine actionable environmental health science related reporting standards for use with DataHarmonizer

Prototype template for use with DataHarmonizer, https://github.com/cidgoh/DataHarmonizer

Bootstrap a DataHarmonizer interface from a LinkML schema

Scaffold a DataHarmonizer project from a LinkML schema.

Nexthikes DataHarmonization project2

Sandbox for testing the content of various minimal schemas vs DataHarmonizer

No description available

Data Harmonizer: Herramienta avanzada de ingeniería de datos para la ingesta, normalización y unión (merge) de datasets heterogéneos (JSON/CSV/Excel) mediante algoritmos heurísticos de detección de Primary Keys y una arquitectura modular Agent-First.

No description available

Software di analisi dati e riformattazione dataset.

Import Checker for DataHarmonizer templates – validates ontology IDs against import files and generates a structured report.

DataHarmonizer is an advanced data integration and management solution designed to streamline the process of consolidating, transforming, and loading data from multiple sources into a centralized relational database system.

A subset of the MIxS specification that's self-documenting and DataHarmonizer compatible. Comes with valid and invalid data examples. Subset = all checklists and all environmental packages, but partial combinations.

A data specification for harmonizing MPox pathogen genomics contextual data. The specification provides standardized (ontology-based) fields and terms which are implemented via a the DataHarmonizer, supported by field and reference guides as well as different curation and new term request SOPs.

A data specification for harmonizing One Health Canadian COVID pathogen genomics contextual data. The specification provides standardized (ontology-based) fields and terms which are implemented via a the DataHarmonizer, supported by field and reference guides as well as different curation and new term request SOPs.

This data specification harmonizes contextual data to support HPAI monitoring with PHA4GE. It provides standardized, ontology-based fields and terms for comprehensive surveillance. Users can implement it via their preferred tools, including DataHarmonizer. Field and reference guides, along with curation and new term request SOPs, support its use.

A One Health data specification harmonizing gold-standard microbial AMR datasets, developed under JPIAMR’s B2B2B AMRDx project. The specification provides standardized ontology-based fields and terms, implemented via the DataHarmonizer tool and supported by detailed reference guides and a new term request SOP.

A data specification for harmonizing pathogen genomics contextual data as part ofthe AMBR project. The specification provides standardized (ontology-based) fields and terms which are implemented via a the DataHarmonizer, supported by field and reference guides as well as different curation and new term request SOPs.

DataHarmonize: Simplify machine learning data prep with an end-to-end ETL pipeline. Integrate data, apply transformations, and load it into SQL Server or PostgreSQL effortlessly.