Welcome!

The Ecosystem with No Name, but with a Mission

Faced with the proliferation of formats, data schemas, and digital tools, this documentation presents a software ecosystem aimed at providing a generic layer of interoperability and visualization for scientific data.

The approach is based on three reasonably modest objectives:

  1. to convert any database into any other, regardless of its structure, theme, or implementation;

  2. to visualize data in various ways (graphs, maps, timelines, 2D/3D modeling, sonification, etc.), from virtually anywhere;

  3. to implement, faster and at lower cost (time, funding, personnel), some or all applications for data analysis, exploration, management, and dissemination.

Because universal promises tend to age poorly, this approach prioritizes resilience (interoperability with existing infrastructures and data warehouses, digital frugality, architectural simplicity and flexibility, maintainability) and accessibility (usability, portability, internationalization) in order to encourage system sharing and allow scientific communities to focus on their problems rather than on their software.

This document will outline foundational principles, architecture, and potential of a common framework for the production, transformation, and sharing of research data — which may thus become a little less complicated to use.

Too long; didn’t read

Too many systems, not enough compatibility. Solutions to make it more bearable, without trying to solve the universe.

So what am I reading?

This documentation purpose is twofold.

  1. First, to present best practices related to software development in contexts that are highly constrained in terms of human, material and financial resources. As a result, these considerations may prove particularly useful for the creation and improvement of software by, and for, public research. These could help them achieving a form of sustainability.

  2. Second, to present software projects embodying these good practices, so that the reader can see concrete examples of how these practices manifest in a research ecosystem.