ALBERT

Description: The data presented is a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland at 3500 m a.s.l. during the time period 2008-2023 by the PermaSense project. This data set constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 231.6 million data points. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. This data set provides primary data products as well as derived data products: GNSS raw data: GNSS observables in the form of daily RINEX 2.11 files GNSS derived data products: Daily positions computed using double-differencing GNSS processing Timelapse images: High-resolution visible light images Timeseries data raw: Per-year and location files or raw sampled data: Weather station, ground temperature, ground resistivity, fracture displacement and inclinometer data Timeseries derived data products: Cleaned and aggregated hourly values of the above Timeseries sanity plots: Standardized plots to obtain a visual overview and check data. All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://git.uibk.ac.at/informatik/neslab/public/permasense/permasense_datamgr from the online PermaSense data repository at http://data.permasense.ch. The version/tag used for the 2024 edition of the Matterhorn data is https://git.uibk.ac.at/informatik/neslab/public/permasense/permasense_datamgr/-/tree/Release_Pangaea_MH_data_2023.

Description: A full-factorial experiment was used to study whether ocean acidification increases mortality in subtidal Mytilus trossulus and subtidal M. galloprovincialis, and intertidal M. trossulus following sub-zero air temperature exposure. We examined physiological processes behind variation in freeze tolerance using 1H NMR metabolomics, analyses of fatty acids, and amino acid composition. This dataset is included in the OA-ICC data compilation maintained in the framework of the IAEA Ocean Acidification International Coordination Centre (see https://oa-icc.ipsl.fr). Original data were downloaded from Zenodo (see Source) by the OA-ICC data curator. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2024) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2024-05-08.

Description: Depth, temperature, and conductivity were recorded during the Bathypelagic cruise (from 24th May to 23rd June 2018,) using a SeaBird SBE 25plus CTD, the dissolved oxygen with a Seabird-43 sensor both attached to an oceanographic rosette from surface to a maximum depth of 2000 m, and the fluorometer with a Seapoint sensor in the upper 200 m depth.

Description: The effect of different nitrogen sources (Experiment 1: N-deplete, NO3-, NH4+, Urea) and different light intensities (Experiment 2: 20, 100 and 200 μmol photons m-2 s-1) on the growth response and toxin content of Alexandrium pseudogonyaulax, isolated in 2020 from the Danish Limfjord, were investigated by means of bottle incubation experiments. In the 2nd experiment, the pigment content and photophysiological response of A. pseudogonyaulax was additionally assessed. The 1st experiment was started on the 18th October 2021 and finished on the 13th December 2021, while the 2nd experiment was started on the 16th March 2022 and finished on the 14th April 2022. This dataset contains all data collected within the two experiments, including cell counts, cell sizes, goniodomin A toxin contents, particular organic carbon and nitrogen, pigment contents (only exp. 2) and photophysiological parameters (only exp. 2).

Description: The data set includes sedimentological data (grain size distribution, facies compositions & radiocarbon ages) and satellite imagery data (shoreline analysis) of the reef island Langkai, SW Sulawesi, Indonesia. The data was collected in order to reconstruct the Holocene and modern (sediment) dynamics over the past ca 6,000 years. Sediment samples were obtained from sediment cores taken by percussion drilling and hand-held (Auger) coring in May 2016 and July 2021. Laboratory analysis of the sediments and shoreline analysis were carried out between 2021 and 2023. The grain size distribution was determined by dry-sieving of the bulk sample into fractions following Wentworth (1922). The subsequent analysis of grain size statistics was done in GRADISTAT v9.1 (Blott & Pye, 2001). The skeletal components of the grain size fractions 〉2 mm, 2-1 mm, 1-0.5 mm and 0.5-0.25 mm were analyzed under a digital microscope by point counting. A total of n=100 skeletal components were identified from each fraction. These results were then used to calculate the relative proportions in each sample. A total of 20 radiocarbon ages from AMS dating were calibrated using OxCal online v4.4 (Bronk Ramsey, 2009), with the MARINE20 curve (Heaton et al., 2020) and a regional reservoir correction of deltaR= 0+-0 as suggested by Southon et al. (2002). The shoreline analysis is based on satellite image data (1999-2023; acquired from multiple sources, see data set for detailed description) and was analyzed with the Digital Shoreline Analysis Tool (Thieler et al., 2009) in ArcGIS.

Description: The shoreline analysis of the reef island Langkai, SW Sulawesi, Indonesia is based on satellite images from 1999-2023. Transects (n=306) were used for shoreline analysis. Net shoreline movement and end point rate at each transect were calculated with the Digital Shoreline Analysis Tool (Thieler et al., 2009) in ArcGIS.