Based on the Connector of the Eclipse Dataspace Components (EDC), the INESData Dataspace Connector implements additional features such as the management and configuration of vocabularies, the implementation of new policies, the local persistence of datasets in a simple way, etc. Check the new features in the latest versions of the connector.
Originally based on the EDC Data Dashboard, the INESData Dataspace Interface Connector is a refactoring of the original project with some good practices and conventions and implementing a complete end-to-end negotiation and transfer process between participants through an interface.
The INESData Dataspace Local Environment allows users to become familiar with the INESData connector and the main components of the Data Space. By running the local environment fully dockerized for easy execution, users can interact with a Data Space between two organizations, their connectors, catalogs, etc.
This repository contains the code for the automated deployment of elements of the INESData platform. The deployment of a working platform is divided in 3 different steps:
Any data space requires a public portal that is a comprehensive resource for any organization interested in understanding the fundamentals of the Data Space. Typically, interested participants will find detailed information about the ecosystem's objectives, governance structure, and the various participants involved. This project allows promoters and participants to have a simplified way to configure and extend their public portal.
A vocabulary for the description of machine learning models, including LLMs, developed in the INESData project. The objective is to have a light vocabulary with a vision focused on machine learning models, extending standards such as Schema.org and Codemeta. The vocabulary is intended to be compatible with the efforts of the FAIR4ML RDA community that are under discussion.
It is a Python package whose purpose is to obtain information from data sources related to mobility, and process it in such a way that it can be stored within a data space. The sources of information available for data extraction are the following: EMT (Madrid Municipal Transport Company), AEMET (State Meteorological Agency) and Informo (Madrid Mobility Information).
Mapping tool designed to facilitate the creation and management of knowledge graphs using declarative rules. It includes a web application to define, store, and manage mapping rules that efficiently establish relationships between data. The solution includes an optimized engine capable of interpreting these rules and generating graphs from large volumes of data in various formats.
GenAI is a Python package designed to use generative AI to automatically generate a mapping from the INESDATA-MAP mapper web application.
KG_generation is a Python package designed to generate a knowledge graph from a mapping file in RML format, along with the associated data sources and ontologies.
Centralized service designed to manage and federate the participant's catalog. This service facilitates participant management via a RESTful API, providing specific endpoints to retrieve and modify participant information. The endpoints are structured with varying access levels to ensure that critical operations can only be performed by users with the Admin role.
Linguistic data space for sharing language resources in Spanish and co-official languages.