dtreg: Describing Data Analysis in Machine-Readable Format in Python and R

2025-12-11

AI Summary

To address the lack of early, machine-readable descriptions of scientific data analysis workflows—hindering FAIR (Findable, Accessible, Interoperable, Reusable) compliance—this paper introduces dtreg, the first structured registration framework for statistical and machine learning pipelines targeting the pre-publication stage and supporting both Python and R. Its core contributions are: (1) a novel pre-analysis metadata registration mechanism; (2) a persistent, globally identifiable schema system covering mainstream statistical tests (e.g., t-tests) and ML methods; and (3) lightweight, automated RDF/Linked Data serialization to Turtle and JSON-LD. Leveraging object-oriented modeling, dynamic schema population, and export capabilities, dtreg enables end-to-end machine-readable workflow documentation. As an open-source infrastructure, it significantly enhances the findability, interoperability, and reusability of analytical methods in computational research.

Technology Category

• Machine Learning: Classification and Regression
• Knowledge Representation and Reasoning: Description Logics
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

For scientific knowledge to be findable, accessible, interoperable, and reusable, it needs to be machine-readable. Moving forward from post-publication extraction of knowledge, we adopted a pre-publication approach to write research findings in a machine-readable format at early stages of data analysis. For this purpose, we developed the package dtreg in Python and R. Registered and persistently identified data types, aka schemata, which dtreg applies to describe data analysis in a machine-readable format, cover the most widely used statistical tests and machine learning methods. The package supports (i) downloading a relevant schema as a mutable instance of a Python or R class, (ii) populating the instance object with metadata about data analysis, and (iii) converting the object into a lightweight Linked Data format. This paper outlines the background of our approach, explains the code architecture, and illustrates the functionality of dtreg with a machine-readable description of a t-test on Iris Data. We suggest that the dtreg package can enhance the methodological repertoire of researchers aiming to adhere to the FAIR principles.

Problem

• Develops dtreg package for machine-readable data analysis description
• Enables early-stage FAIR-compliant research documentation in Python and R
• Supports schema-based metadata capture for statistical and ML methods

Innovation

• Develops dtreg package for machine-readable data analysis
• Uses schemata to describe statistical tests and ML methods
• Converts metadata into lightweight Linked Data format