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There are several research infrastructures or other data services running in Europe that cover a multitude of marine-related sciences, providing specific datasets coming from observations collected with different methods. These infrastructures constitute a diverse world, each looking at a piece of the big picture, sometimes hindering collaboration and data sharing. Blue-Cloud aims to overcome fragmentation and build a bridge between thematic science clusters - such as marine, climate, food and agriculture sciences - and EOSC, creating a data federation and providing a common access to a so-called thematic EOSC for marine data. By connecting leading marine data management infrastructures with horizontal e-infrastructures, the project aims to maximise the exploitation of data resources available from different sources. The Blue-Cloud framework consists of two major technical components: (1) a Blue-Cloud Data Discovery and Access service, already presented in a previous EOSC in practice story, to serve federated discovery and access to blue data infrastructures, and (2) a Blue-Cloud Virtual Research Environment (VRE) to provide computing platforms and analytical services facilitating the collaboration between researchers, which is detailed hereafter. The Blue-Cloud VRE is powered by the D4Science Infrastructure. [M. Assante et al. (2019) Enacting open science by D4Science. Future Gener. Comput. Syst. 101: 555-563 10.1016/j.future.2019.05.063 ] The full list of EOSC in practice stories is available here

Momarsat 2022 cruise report: summary of dives and operations, and position of moorings and observation infrastructures and sampling locations

This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).

These data consist of both underway and station echosounder observations collected during the 2020 SUMMER (Sustainable Management of Mesopelagic Resources) Mediterranean cruise (30 September 2020 to 18 October 2020) on the RV Sarmiento de Gamboa. Narrowband (18, 38, 70, 120, 200 kHz) underway acoustic data were collected continuously using hull-mounted Simrad EK80 echosounders. The recording depths for the 5 frequencies were 1000, 1000, 750, 500, and 200 m respectively. Calibrations were carried out on the 1st of October 2020 using a 33 mm tungsten sphere,and the calibration results were applied to the instruments. During the survey, a Simrad wideband autonomous receiver (WBAT) was deployed down to a depth of 500 m whilst on station. In total, 31 drops were carried out at 5 stations. Four transducers (central frequencies were 45, 120, 200, 333 kHz) were operated using the WBAT in frequency modulated (FM) mode (bandwidth ranging from 45 to 445 kHz). The WBAT calibration data were collected using a 33 mm tungsten sphere. During the deployment of the WBAT, the hull-mounted EK80 was switched to FM mode to record broadband measurements. Raw power (W), number of transducer segments and transceiver impedance (Ohm) were stored in raw proprietary Simrad format (.raw, .idx).

We provide two instrumental records of air pressure and temperature for the Alpine cities of Rovereto and Bolzano/Bozen, covering the periods 1800-1839 and 1842-1849, respectively. They were measured by two physics teachers and digitized at the University of Bern from a handwritten weather diary and a local newspaper. In addition to the raw (sub-daily) data, we provide daily and monthly means together with a quantitative estimation of their uncertainty. The data were converted to modern units, quality controlled, and homogenized.

These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are split into 20 size bins: 0.5 - 0.6 μm, 0.6 - 0.72 μm, 0.72 - 0.87 μm, 0.87 - 1.05 μm, 1.05 - 1.26 μm, 1.26 - 1.51 μm, 1.51 - 1.82 μm, 1.82 - 2.19 μm, 2.19 - 2.63 μm, 2.63 - 3.16 μm, 3.16 - 3.8 μm, 3.8 - 4.57 μm, 4.57 - 5.5 μm, 5.5 - 6.61 μm, 6.61 - 7.95 μm, 7.95 - 9.56 μm, 9.56 - 11.50 μm, 11.5 - 13.83 μm, 13.83- 16.63 μm and 16.63 - 20 μm. The data were measured by a WIBS-NEO (Wideband Integrated Bioaerosol Sensor, model New Electronics option) by droplet measurement techniques ltd. The data were processed using the IGOR WIBS toolkit V1.36 (DMT) and python version 3.9.7. These datasets have been averaged to 1 hour time resolution. The datasets were cleaned from local pollution sources by applying a pollution flag developed by Beck et al. (2022a,b), which is based on the rate of change in particle number concentration with 1 min time resolution. Data points with more than 10 polluted minutes within an hour were removed from the WIBS datasets. Time periods with zero filter measurements and time periods with unstable flow that affected number concentrations have been removed from the dataset. The WIBS measures the size, asymmetry and fluorescence of particles with an optical diameter of 0.5 – 20 µm. Detected particles are excited by two UV flashlamps at wavelengths of 280 and 370 nm and their emitted fluorescence is measured by two photomultipliers with bandwidths of 310 - 400 nm, and 420 - 650 nm. The WIBS counts excited particles at a maximum frequency of 125 Hz, which corresponds to a maximum concentration of 2.5*104 particles/L with a sample flow of 0.3 L/min. Excited particles were classified as fluorescent if their fluorescent intensity exceeded the background intensity by three standard deviations (3σ) and as hyper-fluorescent if the fluorescent intensity exceeded the background intensity by 9σ. Excited particles with a lower fluorescent intensity were considered to be non-fluorescent. The background fluorescence was determined by measuring the fluorescent signal in the measurement chamber in absence of particles. Background measurements were performed every 26 h. The combination of two excitation wavelengths and two detector wavebands allows the classification of fluorescent particles into seven types: A, B, C, AB, AC, BC, and ABC (Perring et al. (2015); Savage et al. (2017)). For further information about the instrumental setup, refer to Heutte et al. (Submitted).

This data set includes hydrographic and pore water and core incubation silicic acid concentration and isotope measurements, sediment Si-HCl and Si-Alk contents and isotope measurements, and pore water nutrient, major, and trace element concentrations measured in the fjords Ameralik Fjord and Nuup Kangerlua (Godhäbsfjord) in southwest Greenland. Data was collected during a research expedition, R/V Tulu 2019, in September 2019, as part of ERC funded (678371) project ICY-LAB (Isotope CYcling in the LABrador Sea) and Royal Society funded (RGF\EA\181036) project Biogeochemical Cycling in Greenlandic Fjords. Temperature and salinity data derived from CTD rosette casts were recorded at station AM10 in Ameralik Fjord and station GF-inlet in Nuup Kangerlua. Fjord water sampling was carried out at 2 stations (AM10 and AM12) in Ameralik Fjord and station GF-inlet in Nuup Kangerlua using Towfish and Niskin bottles for near surface and sub surface samples, respectively. For bottle samples, temperature and salinity were measured using an EXO3 Multiparameter Water Quality Sonde. Fjord sediments were collected by a large bore sediment corer (Aquatic Research Instruments) at station AM10a in Ameralik Fjord and station GF-inlet in Nuup Kangerlua. Pore waters were extracted from the sediment cores using Rhizon samplers and core incubation experiments were carried out following the methodology of Hammond et al. (2004, doi:10.4319/lom.2004.2.146). Sediment reactive silica was leached using a sequential extraction method from Michalopoulos and Aller (2004, doi:10.1016/j.gca.2003.07.018) and Pickering et al. (2020, doi:10.1029/2020GL087877).

This dataset presents temperature and precipitation reconstruction from pollen records of the Northern Hemisphere. Most of the pollen proxy data where retrieved from the Neotoma Paleoecology Database (https://www.neotomadb.org/), with additional data from Cao et al. (2020; https://doi.org/10.5194/essd-12-119-2020), Cao et al. (2013, https://doi.org/10.1016/j.revpalbo.2013.02.003) and our own collection. Mean July temperature, annual mean temperature and annual precipitation were reconstructed for 2593 sites (1030 sites in North America, 1075 sites in Europe and 488 sites in Asia) using the full modern temperature and precipitation range, as well as using a "tailored" version, where we restricted a climate variable range to reconstruct the respective other climate variable in order to minimize the impact of co-correlation. Statistics on the used calibration sets (i.e. all samples within 2000 km distance to the fossil pollen records) are also provided, as well as results from the significance test of the reconstructions sensu Telford & Birks (2011). The data collection is subdivided in 4 geographical regions: The European, the Asian and for North America the Western North American and the Eastern North American sector. We complement the data publication by providing the source information on the references (most data are related to Neotoma) as a table linked to each Dataset ID, The Dataset- and Site-IDs are from Neotoma if the data sets are derived from the Neotoma repository. In case of our own data collection efforts (Cao et al. (2020), Cao et al. (2013) and our own data) we used the already published PANGAEA event names in case they are related to the data or created own site names with referencing to geographical regions similar to the Neotoma data naming principle.

This bundled publication contains data sets on 1) Polyp size and sex: biometric data of polyps from Dendrophyllia ramea, as well as the sex of each analysed polyp; 2) Oocyte per mesentery: number of oocytes per analysed mesentery; 3) Oocytes per polyp: these data have been used to calculate fecundity; 4) Oocyte developmental stages: oocyte sizes and developmental stage are included; 5. Spermatic cysts: spermatic cysts sizes and developmental stage are included. A total of three polyps per each coral colony have been analysed by histological methods and a total of three coral colonies have been analysed for each sampling month: February 2018, May 2017, July 2018 and October 2017. Samples have been collected in the protected area of Punta La Mona (Granada, Alborán Sea, western Mediterranean) between 30 and 37 meters depth by scuba diving.

The data sets were made during the summer 2021, with samples collected from three cores, at two depths (active and permafrost layers). In total, six samples (3 replicates by samples) were incubated for 67 days at two temperatures (4°C and 20°C). Core sampling were performed during the joint Russian-German LENA 2018 expedition. The data sets were both collected at Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research and GeoForschungsZentrum Helmholtz-Zentrum, Potsdam, Germany. The aim of this study was to understand and quantify how much carbon may be lost during short-term permafrost thaw across different landscape units at the example of study area in the Lena Delta, Siberia. The study measures greenhouse gases (GHG) emissions based on an incubation experiment and focuses on relationships between GHG emissions and microbial abundance shifts during short-term permafrost thaw under anaerobic conditions. The objectives of the study were to: (1) Quantify CH4 and CO2 production during a short-term anaerobic incubation; (2) Establish relationships between CH4 and CO2 production and microbes (methanogens and methanotrophs); (3) and to identify settings and controls that drive gas production rates in thawed permafrost soils.