Here we present the surface snow samples collected along the international EAIIST project traverse, which took place in 2019-2020 Antarctic Campaign. We report the number of surface samples (upper 10 cm and integrated 1m samples) collected and their geographic information.

We will collect stool samples from the volunteers at the Mario Zucchelli Station at different time points. The samples will be analysed by shotgun metagenomic sequencing, considering only the microbial component.

We will collect oral samples from the volunteers at the Mario Zucchelli Station at different time points, using safe and not harmfull kits. Samples will be analysed by shotgun metagenomic sequencing, considering only the microbial component.

Here we present the snow pits collected along the international EAIIST project traverse, which took place in 2019-2020 Antarctic Campaign. We report the number of snow pits collected, the depth of the samplings and their geographic information.

This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf attached to the dataset. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output. Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)

Here we present the firn cores collected along the international EAIIST project traverse, which took place in 2019-2020 Antarctic Campaign. We report the number of firn cores collected, the depth of the samplings and their geographic information.

We will collect skin samples from the volunteers at the Mario Zucchelli Station at different time points, using safe and not harmfull kits. Samples will be analysed by shotgun metagenomic sequencing, considering only the microbial component.