Interview with Ulrich Kral and Ferdinand Reimer, former researchers in the Research Unit of Waste and Resource Management at the Institute of Water Quality and Resource Management
Data repositories help us to share data, make them citable and encourage others to reuse the data
What is your field of research?
We are interested in understanding the material flows in our society, from source to final disposal. Our latest research is about the construction and demolition waste flows in Vienna over the last 100 years. We explored the spatio-temporal development of the building stocks to understand the drivers of material turnovers in the construction sector. This can help us predicting future waste flows and developing management options for the use of natural resources and environmental protection.
Can you give us examples of how you use data management in your everyday work?
With respect to our latest research, data management affected our work in the project and post-project phase. In the project phase we strictly separated between input, processing and output data. Input data such as external datasets, scanned maps and personal communications were stored without any modifications later on. At the processing stage, we retrieved the input data, converted them into a machine-readable format and performed computations. This stage produced the output data, which are key project results. In the post-project phase, we filed relevant output data to repositories, corresponding articles for peer-review and online codebooks. In the articles, we documented the workflow from input to output data in the method section and supplemented notes for guiding the usage of the data in the future.
Are you using data repositories for data publication?
Yes, we are using data repositories in combination with peer-reviewed articles. Exemplary, we converted an analogous into a machine-readable document, shared the code and dataset on GitHub (https://github.com/ukral/building.schematic/tree/v2.0, opens an external URL in a new window) and pushed the GitHub repository to Zenodo (https://doi.org/10.5281/zenodo.4106173, opens an external URL in a new window). The corresponding article, published in Scientific Data (https://doi.org/10.1038/s41597-021-00822-0, opens an external URL in a new window), describes the background, method, validation and reuse value of the dataset. The article is of key importance in order to provide quality-proven data. In general, data repositories help us to share data, make them citable and encourage others to reuse the data.