Research Data Management and Electronic Lab Notebooks
Modern scientific research, particularly in materials science and engineering, generates large and complex datasets through advanced characterization techniques such as electron microscopy, nanoindentation, and high-throughput mechanical testing. Though the datasets are large, they are also sparse in the sense that the metadata is often incomplete. Managing this data effectively is essential not only for ensuring scientific reproducibility but also for enabling meaningful collaboration and long-term knowledge retention. Traditional paper-based lab notebooks are insufficient for handling the scale, complexity, and metadata requirements of such data-rich environments. This has motivated the adoption of Electronic Lab Notebooks (ELNs) and structured Research Data Management (RDM) systems.
ELNs provide a digital platform for documenting experiments, data, and analysis workflows in a standardized, searchable, and shareable format. When integrated with proper RDM strategies, they support the FAIR data principles—making data Findable, Accessible, Interoperable, and Reusable. FAIR-compliant data not only facilitates transparency and reproducibility but also enhances the long-term value of research outputs by making datasets machine-readable and suitable for automated analysis.
Structuring data is particularly critical when employing machine learning (ML) techniques for materials discovery or property prediction. ML algorithms rely on well-labeled, high-quality datasets with consistent formats, meaningful metadata, and provenance tracking. Through ELNs and FAIR-aligned data repositories, researchers can curate datasets that are not only ready for ML training but also easy to integrate with simulation data and other multimodal sources.
Furthermore, standardized RDM enables interoperability between research groups, institutions, and software platforms, reducing redundancy and accelerating scientific discovery. In this context, ELNs become more than digital notebooks—they act as gateways to intelligent data ecosystems that support advanced analytics, automation, and ultimately, data-driven science.
- Institute of Energy Materials and Devices (IMD)
- Structure and Function of Materials (IMD-1)
Room 113