Coordinator design & analysis
Dr. A. (Anna) Niehues
Area(s) of expertise:
FAIR implementation
FAIR implementation
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Introduction
At LUMC, I am implementing tools and practices to help make research data and software findable, accessible, interoperable, and reusable (FAIR). My focus is on increasing machine-actionability to help automate tasks across the research data lifecycle. I am currently the FAIR coordinator of the Leiden Health-RI node and actively involved in the LUMC DCC.
I studied biosciences at the University of Münster. During my PhD, I integrated enzymatic, mass spectrometric, and computational methods to investigate structural properties of carbohydrate biopolymers and how enzymes modify these bioactive molecules. After that, I worked as a postdoc and data scientist at Radboudumc, focusing on reproducible and reusable data analysis workflows integrating biomedical multi-omics and clinical data. As Translational Data Program Manager at EATRIS, I have been involved in the development of the Multi-omics Toolbox (MOTBX), collaborating closely with scientists across the translational spectrum.
I studied biosciences at the University of Münster. During my PhD, I integrated enzymatic, mass spectrometric, and computational methods to investigate structural properties of carbohydrate biopolymers and how enzymes modify these bioactive molecules. After that, I worked as a postdoc and data scientist at Radboudumc, focusing on reproducible and reusable data analysis workflows integrating biomedical multi-omics and clinical data. As Translational Data Program Manager at EATRIS, I have been involved in the development of the Multi-omics Toolbox (MOTBX), collaborating closely with scientists across the translational spectrum.
Scientific research
As a researcher in the field of bioinformatics and computational method development, I quickly recognized the importance of data availability, quality, and documentation. To improve the efficiency, transparency, and reproducibility of research, I am interested in the practical implementation of the FAIR principles. I have developed and implemented computational solutions to standardize metadata describing diverse datasets (omics, clinical) to enable their integration and analysis. Beyond data, research software plays a critical role in the research process. Its discoverability and interoperability are therefore essential for the reproducibility of data-driven studies and the realization of open science. My current focus is on enabling cultural change through implementation of tools, sharing of best practices and examples, and strengthening the local community. Ultimately, my goal is to empower researchers to effectively share and reuse data.
