Software
The SDMX and official statistics community produces a lot of great open source software tools. sdmx.io brings these together to help solve concrete problems, with practical guidance and worked solutions to download and deploy.
Fusion Metadata Registry
FMR structural metadata registry helps official statistics organisations externalise and gain control of their statistical structural metadata with the benefits of maintainability, re-use, standardisation, harmonisation and improved metadata and data governance.
.Stat Suite
The .Stat Suite is a standard-based, componentised, open-source platform for the efficient production and dissemination of high-quality statistical data.
FMR Workbench
The FMR Workbench is a remote registry browser and metadata maintenance tool which brings the strengths and capabilities of the FMR User Interface to any SDMX compliant registry, implementing an SDMX RESTful API.
SDMX Dashboard Generator
The SDMX Dashboard Generator is an open-source application that generates dynamic dashboards
Project LinkageX
Project ‘LinkageX’ is the Swiss Army knife for SDMX. Aimed at data scientists and developers, LinkageX simplifies the use of SDMX data and metadata, enabling seamless integration and full utilization of the SDMX metamodel. This powerful Python-based toolkit streamlines SDMX-related workflows, enhancing data synchronization and harmonization efficiency and effectiveness.
SDMX Test Compatibility Kit
The SDMX TCK is a tool for measuring the compliance and coverage of an SDMX RESTful endpoint against the available SDMX REST API versions.
FusionXL
FusionXL (FXL) is an Excel interface for FMR that complements and provides an alternative to the web user interface.
Fusion Transformer
Fusion Transformer (FT) is a command line application for converting SDMX data between transmission formats.
pysdmx, a LinkageX resource
pysdmx is a pragmatic and opinionated library written in Python. It focuses on simplicity, providing a subset of SDMX functionalities without requiring advanced knowledge of SDMX.
gingado, a LinkageX resource
gingado is a machine learning library focused on economics and finance use cases. This package aims to be suitable for beginners and advanced users alike. Use cases may range from programmatic data retrievals using SDMX to experimentation with machine learning-based econometric estimators to more complex forecasting pipelines used in production.
VTL Engine & VTL Suite
The VTL Engine is a Python library implementing a Validation and Transformation Language (VTL). It provides a simple interface to run VTL on data. It has a full implementation of VTL 2.1.
VTL Engine & Editor
VTL Engine & Ediotor (VTL E&E) is an interpreter engine and an editor for the Validation and Transformation Language, a declarative, metadata-driven language developed in the context of the SDMX standard, with the goal of standardizing data validations and transformations.
Trevas
Trevas is a Java engine for the Validation and Transformation Language (VTL), an SDMX standard that allows the formal definition of algorithms to validate statistical data and calculate derived data. VTL is user oriented and provides a technology-neutral and standard view of statistical processes at the business level.
SDMX Connectors
The SDMX Connectors is a set of plugins that can be installed in the most popular data tools, providing functions for easily and seamlessly downloading data from SDMX providers implementing the SDMX REST API.
SDMX lab
The SDMX lab is the ideal starting point for anyone looking to explore and work hands-on with SDMX. Whether you’re just getting started with the standards or diving into practical implementation, the lab gives you a personal, isolated SDMX environment of key tools from the sdmx.io ecosystem – ready to go, with no setup or installation required: Hands-on SDMX. Instantly.
SDMX Metadata AI Assistant (MAIA): enhancing statistical metadata
The SDMX Metadata AI Assistant (MAIA) is an open-source tool specifically designed to address the challenges of managing and editing statistical metadata. Leveraging AI, SDMX MAIA simplifies metadata workflows by automating key tasks such as checking syntax, consistency checks and formatting adjustments.
