1. Data assimilation - the collaborative research centre SFB1294
  2. Research
  3. Research Area Z
  4. Z03

Z03 – Information Infrastructure for data assimilation

The information infrastructure project aims at removing barriers that impede an efficient collaboration between researchers in the CRC, as well as between CRC researchers and external collaboration partners. The CRC addresses several variations of data-assimilation and model building problems. Experimental evaluations involve data from different application areas and algorithms in a multitude of programming languages. Researchers in the CRC publish in different fields, and therefore even published ideas and results are not, in all cases, easily accessible by other researchers. Therefore, the goals of this project are (i) to facilitate the sharing of data, algorithms, and results, (ii) to safeguard the sustainable handling of digital artefacts, and (iii) to ensure good publication practice. To achieve these goals, the project provides information technology infrastructure, establishes research-quality tools, trains young scientists regarding research data and code, and fosters a healthy cultural change towards more FAIRness.

Overview over programming languages used in the CRC:

Information will be available soon.

  • Riedel, C., Hossen Chowdhury, S., Engbert, R., and Lucke, U. (2024): Perceived Barriers to Open Science among Researchers in Mathematics, Natural Sciences, and Cognitive Sciences. In: Klein, M., Krupka, D., Winter, C., Gergeleit, M., & Martin, L. (Hrsg.), INFORMATIK 2024. Gesellschaft für Informatik, Bonn. (S. 2139-2151). doi: 10.18420/INF2024_186

  • Boege, T., Fritze, R., Görgen, C., Hanselman, J., Iglezakis, D., Kastner, L., Koprucki, T., Krause, T. H., Lehrenfeld, C., Polla, S., Reidelbach, M., Riedel, C., Saak, J., Schembera, B., Tabelow, K., & Weber, M. (2023). Research-data management planning in the German mathematical community. European Mathematical Society Magazine. doi: 10.4171/mag/152

  • Riedel, C., Wiepke, A., Jacob, B., Hartmann, N., and Ulrike, L. (2023). Recommendations for Using Data Management Plans in Academic Research Data Management Training. 10. Fachtagung Hochschuldidaktik Informatik (HDI) 2023 - Conference Proceedings, 145–152. doi: 10.5281/zenodo.10255524.

  • Riedel, C., Geßner, H., Seegebrecht, A., Ayon, S. I., Chowdhury, S. H., Engbert, R. and Lucke, U. (2022). Including Data Management in Research Culture Increases the Reproducibility of Scientific Results. In: Demmler, D., Krupka, D. & Federrath, H. (Hrsg.), INFORMATIK 2022. Gesellschaft für Informatik, Bonn. (S. 1341-1352). doi: 10.18420/inf2022_114

  • Geßner, H. (2021). Transparently Safeguarding Good Research Data Management with the Lean Process Assessment Model. In: E-Science-Tage 2021: Share Your Research Data. Heidelberg. DOI: 10.11588/heidok.00029719

  • Geßner, H. & Kiy, A., (2019). A mobile campus application as a sensor node for Personal Learning Environments. In: Pinkwart, N. & Konert, J. (Hrsg.), DELFI 2019. Bonn: Gesellschaft für Informatik e.V.. (S. 187-192). DOI: 10.18420/delfi2019_356