Methods and Data
WHIRLS follows an open data policy for model and observational data. However, this comes with certain responsibilities. The WHIRLS partners and contributors are committed to handle generated scientific data according to the WHIRLS Research Data Policy1
Research data will be collected or generated in all work packages of the project. A compilation of the observations and data generating activities is given in the Data Management Plan (DMP)2, with more details on specific parameters to be collected as well as file formats and expected volumes.
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The WHIRLS partners and contributors are committed to handle generated scientific data according to the FAIR Data Principles3 (Findable – Accessible – Interoperable – Reusable) and the Guidelines for Safeguarding Good Research Practice4.
Newly collected data within the framework of WHIRLS will be shared transparently and published in long-term repositories for international re-use. Quality control of data as well as workflows for delivery of metadata and data from observation/collection to archiving and publishing will be handled within the partner institutions. These workflows and procedures are documented in the DMP and supported by the data management staff. These basic rules together with more details on research data handling can be found in the project’s Data Policy.
- Getzlaff, K., & WHIRLS Operational Management Committee. (2024). WHIRLS Research Data Policy. Zenodo. https://doi.org/10.5281/zenodo.14193289
- Getzlaff, K., & WHIRLS Operational Management Committee. (2024). ERC Data Management Plan – WHIRLS. Zenodo. https://doi.org/10.5281/zenodo.14359661
- Wilkinson, M.D., et al. (2016): The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18
- Deutsche Forschungsgemeinschaft (2019). Guidelines for Safeguarding Good Research Practice. Code of Conduct. https://doi.org/10.5281/zenodo.3923602
Simulations
forced and coupled mode, and with biogeochemistry (Visualisation showing daily averaged surface normalised relative vorticity in eINALT100 (Schwarzkopf, 2025, DOI:10.3289/eINALT100.Z120-KFS002_surfacevort_2021).
Observations
