PASTA-ELN: Simplifying Research Data Management for Experimental Materials Science

RWTH Publications (RWTH Aachen)

Abstract

Research data management remains a persistent challenge across scientific disciplines, as many electronic lab notebooks (ELNs) impose steep learning curves, rigid workflows, or network dependencies that hinder everyday use.We present PASTA-ELN, an opensource, cross-platform ELN designed from the scientist's perspective to simplify documentation while supporting the transition toward FAIR and machine-actionable data practices.PASTA-ELN employs a local-first-then-sync architecture, ensuring full offline functionality, user control over data, and seamless, optional synchronization with server-based ELNs such as elabFTW.Its hierarchical folder structure mirrors natural laboratory organization, while modular Python add-ons enable customizable data extraction, visualization, semantic annotation, and project reporting without altering the core system.All information is stored in open, standards-based formats facilitating long-term preservation and interoperability via RO-Crate and the .elnfile profile.By combining intuitive desktop usability with robust research data management, PASTA-ELN provides an accessible, extensible framework that facilitates the transition from basic file organization to digital workflows with user-defined levels of FAIR compliance. | IntroductionThe management, documentation, and reproducibility of scientific data remain central challenges across modern research disciplines, including materials science and engineering (MSE).As experimental complexity increases and laboratories routinely generate heterogeneous datasets-from microscopy images and diffraction patterns to mechanical-testing curves and proprietary instrument formats-researchers are increasingly expected to maintain workflows that are transparent, well structured, and aligned with the FAIR principles (Findable, Accessible, Interoperable, and Reusable) [1].Yet, despite the growing availability of digital research-data-management (RDM) tools, many laboratories still rely on ad hoc folder structures, instrument-specific conventions, or personal note-taking practices that impede long-term preservation, reproducibility, and cross-instrument data integration.A wide range of open-source ELNs-such as elabFTW, Chemotion, Kadi4Mat, and SampleDB, OpenSemanticlab, and OpenBIS-have advanced the structuring, preservation, and sharing of research data.However, ELNs [2, 3] often fail to attract users.Many systems remain difficult to adopt in everyday laboratory practice because they require substantial configuration, depend on stable network access, or impose rigid, server-centric workflows that do not align naturally with how experimentalists organize and handle data.elabFTW [4], for example, provides a robust self-hosted platform for managing experiments and other resources, as well as file attachments, templates, and collaborative features including versioning, timestamps, and access control.SampleDB [5] provides a web-based environment for capturing and managing metadata for samples, measurements, and simulations, offering customizable schemas and integrations such as Jupyter notebooks.OpenSemanticLab (OSL) [6] emphasizes ontologydriven interoperability by combining structured and unstructured data through JSON-Schema and JSON-LD, enabling

Authors 3

1. Steffen Brinckmann
2. G. Winkens
3. Ruth Schwaiger Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen