From data to knowledge: IOER publishes innovative training materials for transparent spatial analyses

How can the data regularly published by the IOER Research Data Centre be put to practical use? The latest publication from the IOER RDC now provides interested parties with an important working aid. The materials recently published by the team go far beyond traditional publications of research results. They are presented in the form of a so-called “Jupyter Book”, which allows users to follow each step of the process from the data basis to the results live in their browser and to experiment with the code. The use case is the investigation of the relationship between the occurrence of the house sparrow (Passer domesticus) and settlement density in Saxony. The data for this is retrieved directly via the application programming interfaces (APIs) of the global biodiversity database GBIF and IOER's Monitor of Settlement and Open Space Development.

“Our goal is to bridge the gap between research practice and the high demands for transparency and reproducibility,” explains Alexander Dunkel, one of the authors of the training materials. “We not only document our methods, but also provide the entire technical infrastructure and best practices. The basis for this is our IOER FDZ Carto-Lab – a fully equipped, versioned working environment that ensures that our analyses can be accurately reproduced by anyone. This strengthens trust in science and enables more efficient and higher-quality research.”

The materials were developed in the context of the NFDI4Biodiversity consortium, which is part of the National Research Data Infrastructure (NFDI). “Funding from the NFDI4Biodiversity Flexfund made it possible to create the training materials in the first place,” explains Claudia Dworczyk from the IOER, who helped develop the materials and coordinated the collaboration with NFDI4Biodiversity. The training data and accompanying material are in line with the FAIR principles, meaning they are findable, accessible, interoperable and reusable, and they demonstrate how modern tools can be used to promote open science. The training materials, including code, documentation and sample data, are permanently published and citable as a "replication package" via the IOER’s Research Data Repository, ioerDATA.

The materials are aimed at students, doctoral candidates and researchers who want to expand their skills in spatial data analysis with Python and learn modern standards of research data management.

Further information
Training materials
Data publication
Technical infrastructure (IOER Carto-Lab)

Contact at the IOER
Dr Alexander Dunkel, e-mail: a.dunkelioer@ioer.de
Dr Maria Nieswand (head of IOER FDZ), e-mail: m.nieswandioer@ioer.de