iinci

iinci is a research project and software framework focused on distributed and reproducible scientific workflows. Its primary goal is to enable researchers to manage, share, and execute complex computational experiments in a transparent and auditable manner. The project emphasizes the importance of capturing not only the code but also the data, environment, and execution parameters that lead to specific results, thereby fostering collaboration and validating scientific findings.

The iinci framework allows users to define workflows using a declarative language, specifying the dependencies between different computational tasks. These tasks can range from data preprocessing and analysis to model training and visualization. iinci aims to abstract away the complexities of underlying computing infrastructure, supporting execution on various platforms, including local machines, clusters, and cloud environments. This portability is a key feature, allowing experiments to be easily replicated across different setups.

A significant aspect of iinci is its focus on provenance tracking. Every step of a workflow's execution, including the inputs used, the commands run, and the outputs generated, is meticulously recorded. This detailed history makes it possible to understand how a particular result was achieved and to reproduce it precisely. The project also facilitates the sharing of these reproducible workflows with others, promoting open science practices and accelerating the pace of discovery. The development of iinci is driven by the need for greater reliability and transparency in computational research.