Contexere—Systematic Tracking and Referencing of Digital Artefacts for Postgraduate Students and Early Career Researchers

The efficiency of data-driven research relies not only on high-quality data and sufficient computational resources but also depends sensitively on the personal knowledge management of the researcher. The multitude of digital artefacts created during a researcher’s daily workflow might comprise experimental results, simulation results, literate programming notebooks analysing experiments and simulations, statistical models, machine learning models, figures, tables, and conversations with generative Artificial Intelligence systems. In order to trace and track these interconnected research artefacts over several months of research or even extended research periods and different research projects, these artefacts need to be systematically named so that they can be referenced in note-keeping systems and research outputs. Therefore, the naming and referencing scheme for research artefacts needs to be flexible, consistent, efficient and support the linking of artefacts across different software frameworks and even classical laboratory notebooks. This article introduces a hierarchical naming scheme and the supporting open-source Python package contexere together with best practices for the personal knowledge management for postgraduate students and early career researchers, which provides a clear and linkable structure for data artefacts and thus supports effective personalised research workflows.