Welcome to my GitHub profile, primarily for work-related projects. For my personal projects, please visit LukasOro.

I'm Lukas Gold, a self-taught programmer, scientist, and advocate for FAIR data principles. My coding journey began in 2016 with Python and MATLAB, supporting my research with digital signal processing, the analysis of large experimental datasets and some modelling during my PhD. My doctoral work focused on ultrasound-aided state estimation of lithium-ion batteries and monitoring their production processes. Please refere to my ORCID for scientific publications.

Currently, I work as a Senior Scientist in the Department for Digital Transformation at Fraunhofer ISC, where I specialize in automating analytical data processing and designing data infrastructures that enable sustainable and reproducible scientific workflows.

I’m committed to contributing to the ongoing transformation in how scientific organizations acquire, exchange, and preserve metadata, experimental data, and institutional knowledge — supporting a broader vision of building more interoperable, sustainable, and intelligent data infrastructures that benefit both the scientific community and society at large.

Semantic technologies — around since the 1990s — offer powerful solutions to challenges like data heterogeneity and machine readability. However, their complexity has limited their adoption to large-scale applications, leaving everyday scientific data handling behind.

With the rise of Large Language Models (LLMs), we now have tools that can simplify semantic annotation and contextualization during data entry. This opens the door to broader adoption and more intuitive data management.

I'm inspired by the vision of a future where the concept of the Semantic Web / "Web of Doing Things" becomes a reality — a dynamic, interoperable ecosystem of scientific data and tools. My goal is to contribute by developing practical tools and concepts that bridge the gap between advanced IT solutions and everyday scientific practice.

Battery science and analytics provide an ideal starting point, as they involve repetitive, well-structured processes and complex process chains rich in metadata. These characteristics make them especially suitable for demonstrating the value of semantic technologies and FAIR data practices in real-world scientific workflows.

• Open Semantic Lab (OSL)
  A knowledge platform that merges wiki-style documentation with semantic descriptions of inventory items, organizational structures, and laboratory processes. Designed to preserve institutional knowledge in (materials) science through a reusable and adaptable knowledge graph—applicable to both scientific and non-scientific domains.

• osw-python
  A Python package for interfacing with Open Semantic Lab. It enables data import/export, documentation of scientific workflows, and querying project- or institution-wide knowledge graphs, supporting FAIR data practices and semantic interoperability.

• Object-Oriented Linked Data (OO-LD)
  A conceptual framework that combines object-oriented programming with semantic annotations, lowering the barrier for developers and scientists to adopt Linked Data technologies. It enables machine-readable process descriptions without requiring direct creation of RDF datasets or schemas.

• OpenBattTools
  A modular toolkit for electrochemical testing and analysis of batteries. It aims to establish an open-source pipeline for battery data—from test planning and data acquisition to analysis and FAIR-compliant data sharing.